Push the Limitations of Crystal Structure Determination by 3D Electron

Jingjing Zhao Push the limitations of crystal structure determination by 3D

Push limitationsthe structure determination of crystal by 3D electron diffraction electron diffraction

From inorganic porous materials to biomolecules

Jingjing Zhao

Jingjing Zhao was born in China. She received Master degree in Material Science and Engineering from University of Science and Technology of China. She started her PhD study at Stockholm University in 2017. She has expertise in electron crystallography.

ISBN 978-91-7911-448-0

Department of Materials and Environmental Chemistry

Doctoral Thesis in Physical Chemistry at Stockholm University, Sweden 2021

Push the limitations of crystal structure determination by 3D electron diffraction From inorganic porous materials to biomolecules Jingjing Zhao Academic dissertation for the Degree of Doctor of Philosophy in Physical Chemistry at Stockholm University to be publicly defended on Friday 11 June 2021 at 09.00 in Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B.

Abstract Structure elucidation is fundamental to understanding the chemical and physical properties of a material. Three-dimensional electron diffraction (3D ED) has shown great power for structure determination of nanometer- or submicrometer-sized crystals that are either too small or too complex for X-ray diffraction. 3D ED can be applied to a wide range of crystalline materials from inorganic materials, small organic molecules, to macromolecules. In this thesis, continuous rotation electron diffraction (cRED), also known as micro-crystal electron diffraction (MicroED) in macromolecular crystallography, has been applied for the determination of interesting novel crystal structures. New methods and protocols have been developed to push the current limitations of crystal structure determination by 3D ED. The structure of silicate zeolite PST-24 is highly disordered. A combination of cRED with high-resolution transmission electron microscopy (HRTEM) revealed its unique channel system with varying dimensionality from 2D to 3D. The aluminum metal-organic framework CAU-23 nanocrystals form aggregates and are very beam sensitive. Its structure, as determined by cRED, is built by twisted helical Al-O chains connected by TDC2- linkers, forming a chiral structure with square channels. The unique structure of CAU-23 provides high stability and high water adsorption capacity, making it an ideal material for ultra-low temperature adsorption driven chillers. A simple pressure-assisted specimen preparation method, denoted Preassis, has been developed to overcome the challenges in the application of MicroED on biological samples with high viscosity and low crystal concentration. It has been successfully applied for the specimen preparation of several bio-molecular crystals including a novel R2lox metalloenzyme, which was crucial for its structure determination. Furthermore, an investigation of the influence of radiation damage on lysozyme crystals was performed to improve the data quality and final structural model. Finally, the crystal structure of acetylated amyloid-β fragment Ac-Aβ16-20, related to Alzheimer’s disease, has been studied. The crystal has an active optical wave-guiding property with an excitation wavenumber of 488 nm due to its unique packing of Ac-KLVFF β–sheets.

Keywords: electron crystallography, 3D electron diffraction, cryo-EM specimen preparation, structure determination, porous materials, biomolecules.

Stockholm 2021 http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-192517

ISBN 978-91-7911-448-0 ISBN 978-91-7911-449-7

Department of Materials and Environmental Chemistry (MMK)

Stockholm University, 106 91 Stockholm

PUSH THE LIMITATIONS OF CRYSTAL STRUCTURE DETERMINATION BY 3D ELECTRON DIFFRACTION

Jingjing Zhao

Push the limitations of crystal structure determination by 3D electron diffraction

From inorganic porous materials to biomolecules

Jingjing Zhao ©Jingjing Zhao, Stockholm University 2021

ISBN print 978-91-7911-448-0 ISBN PDF 978-91-7911-449-7

Printed in Sweden by Universitetsservice US-AB, Stockholm 2021 To my family.

Doctoral Thesis 2021 Department of Materials and Environmental Chemistry Stockholm University, Sweden

Faculty opponent: Dr. Tim Grüne Centre for X-ray Structure Analysis, Faculty of Chemistry University of Vienna, Austria

Evaluation committee: Prof. Eva Olsson Department of Physics Chalmers University of Technology, Sweden

Assoc. Prof. Alexey Amunts Department of Biochemistry and Biophysics Stockholm University, Sweden

Dr. Philippe Boullay Laboratoire de Cristallographie et Sciences des Materiaux Normandie Universite, France

Substitute: Prof. Gunnar Svensson Department of Materials and Environmental Chemistry Stockholm University, Sweden

Abstract

Structure elucidation is fundamental to understanding the chemical and physical properties of a material. Three-dimensional electron diffraction (3D ED) has shown great power for structure determination of nanometer- or submicrometer-sized crystals that are either too small or too complex for X- ray diffraction. 3D ED can be applied to a wide range of crystalline materials from inorganic materials, small organic molecules, to macromolecules. In this thesis, continuous rotation electron diffraction (cRED), also known as microcrystal electron diffraction (MicroED) in macromolecular crystallography, has been applied for the determination of interesting novel crystal structures. New methods and protocols have been developed to push the current limitations of crystal structure determination by 3D ED. The structure of silicate zeolite PST-24 is highly disordered. A combination of cRED with high-resolution transmission electron microscopy (HRTEM) revealed its unique channel system with varying dimensionality from 2D to 3D. The aluminum metal-organic framework CAU-23 nanocrystals form aggregates and are very beam sensitive. Its structure, as determined by cRED, 2- is built by twisted helical Al-O chains connected by TDC linkers, forming a chiral structure with square channels. The unique structure of CAU-23 provides high stability and high water adsorption capacity, making it an ideal material for ultra-low temperature adsorption driven chillers. A simple pressure-assisted specimen preparation method, denoted Preassis, has been developed to overcome the challenges in the application of MicroED on biological samples with high viscosity and low crystal concentration. It has been successfully applied for the specimen preparation of several bio- molecular crystals including a novel R2lox metalloenzyme, which was crucial for its structure determination. Furthermore, an investigation of the influence of radiation damage on lysozyme crystals was performed to improve the data quality and final structural model. Finally, the crystal structure of acetylated amyloid- fragment Ac-A16-20, related to Alzheimer’s disease, has been studied. The crystal has an active optical wave-guiding property with an excitation wavenumber of 488 nm due to its unique packing of Ac-KLVFF β– sheets.

Keywords: electron crystallography, 3D electron diffraction, cryo-EM specimen preparation, porous structure materials, determination, biomolecules. i

List of publications

This thesis is based on the following papers: Paper I: PST-24: A zeolite with varying intracrystalline channel dimensionality D. Jo,† J. Zhao,† J. Cho,† J.H. Lee, Y. Liu, C.J. Liu, X. Zou, and S.B. Hong Angew. Chew. 2020 , Int. Ed.59, 17691– 17696. DOI: 10.1002/anie.202007804 Scientific contributions: I collected the cRED data and HRTEM images. I did the data processing, structure determination, image processing, and structure analysis of the disorder. I contributed to the ED pattern simulation, proposed three polytypes. I contributed to the main figure making and the manuscript writing, especially the structure-related part. Paper II: A metal-organic framework for efficient water-based ultra-low- temperature-driven cooling D. Lenzen, J. Zhao, S.J. Ernst, M. Wahiduzzaman, A.K. Inge, D. Fröhlich, H. Xu, H.J. Bart, C. Janiak, S. Henninger, G. Maurin, X. Zou, and N. Stock Nat. Commun. 2019, 10:3025. DOI: 10.1038/s41467-019-10960-0 Scientific contributions: I contributed to the cRED data collection, processing, and structure determination. I also contributed to the topology analysis. I made figures and wrote the part of manuscript related to cRED experiments and structure discussions. Paper III: A simple pressure-assisted method for MicroED specimen preparation J. Zhao, H. Xu, H. Lebrette, M. Carroni, H. Taberman, M. Högbom, and X. Zou Nat. Commun. 2021, under review Scientifc contributions: I contributed to the setup building, modifications, and also the design of experiments. I performed major part of the experiments, including specimen preparation, diffraction and image collection, and also analysis. I wrote the initial manuscript of this work. ii

Paper IV:

Solving a new R2lox protein structure by microcrystal electron diffraction H. Xu, H. Lebrette, M.T.B. Clabbers, J. Zhao, J.J. Griese, X. Zou, and M. Högbom Sci. Adv. 2019, 5:eaax4621. DOI: 10.1126/sciadv.aax4621 Scientific contributions: I contributed to the MicroED specimen preparation and data collection. Paper V: Limiting the effects of radiation damage in MicroED through dose selection during data processing H. C. Bwanika, J. Zhao, G. Hofer, U. Sauer, X. Zou, H. Xu J. Synchrotron Radia. 2021, under review Scientific contributions: I was responsible for the study of the radiation- induced displacement of the soaked metal cation in a lysozyme protein structure. I did the related experimental and analysis work, including sample preparation, data collection, processing, structure determination, and analysis. I made a figure and table for this manuscript, and also wrote a related description and analysis. Paper VI: Atomic structure of amyloid crystals C. Bortolini,† J. Zhao,† L. Ciccone, S. M. Sønderskov, C. Sandt, F. Borondics, P. Legrand, T. Tuccinardi, X. Zou, H. Xu, and M. Dong In manuscript Scientific contributions: I contributed to the MicroED data collection, processing, and structure determination. I did the determination of the fast crystal growing direction by combining crystal images and diffraction patterns. I also contributed to the structure discussion related to fast self-assembly and enhanced stability. I made figures and wrote the part of manuscript related to MicroED experiments and structure discussions.

†Authors contributed equally to the paper. iii

Preprints were made with permission from the publishers.

Papers not included in this thesis: Paper VII:

MyD88 TIR domain higher-order assembly interactions revealed by microcrystal electron diffraction and serial femtosecond crystallography M.T.B. Clabbers, S. Holmes, T.W. Muusse, P. Vajjhala, S.J. Thygesen, A. K. Malde , D. J.B. Hunter, T. I. Croll, L. Flueckiger, J. D. Nanson, M. H. Rahaman, A. Aquila, M. Hunter, M. Liang, C.H. Yoon, J. Zhao, N.A. Zatsepin, B. Abbey, E. Sierecki, Y. Gambin, K. J. Stacey, C. Darmanin, B. Kobe, H. Xu, and T. Ve Nat. Commun. 2021, accepted Scientific contributions: I contributed to the sample screening for MicroED data collection. Paper VIII: Breathing metal−organic framework based on flexible inorganic building units E.S. Grape, H. Xu, O. Cheung, M. Calmels, J. Zhao, C. Dejoie, D. M. Proserpio, X. Zou, and A.K. Inge

Cryst. Growth Des. 2020, 20, 1, 320–329. DOI: 10.1021/acs.cgd.9b01266 Scientific contributions: I contributed to the cRED data collection and structure determination of one MOF phase SU-100. Patent IX Cryo-EM specimen preparation X. Zou, J. Zhao, H. Xu Publication number: WO/2020/171764 Scientific contributions: I made the initial design and optimization of the setup of the novel cryo-EM specimen preparation method, Preassis. I demonstrated the advantages of Preassis for MicroED specimen preparation, and I also extended this method for specimen preparation of single-particle cryo-EM. I contributed to some new ideas and designs for the future development and optimization of this method in the patent. I contributed to the patent preparation and writing. iv

Contents

Abstract ...... i List of publications ...... ii Abbreviations ...... viii 1. Introduction (aim of this thesis) ...... 1 2. Introduction to electron crystallography ...... 4 2.1 Basics of electron crystallography ...... 4 2.1.1 Transmission electron microscopy ...... 4 2.1.2 Crystallography ...... 6 2.1.3 Atomic scattering factor and structure factor ...... 7 2.1.4 Electrostatic potential ...... 9 2.1.5 Electron diffraction ...... 9 2.1.6 High-resolution electron microscopy ...... 10 2.2 3D electron diffraction (3D ED) ...... 11 2.2.1 The development of 3D ED ...... 11 2.2.2 The cRED/MicroED method ...... 12 3. Experimental Methods ...... 14 3.1 Specimen preparation for 3D ED experiments ...... 14 3.1.1 Specimen preparation for inorganic crystalline samples ...... 14 3.1.2 Specimen preparation for bio-molecular crystals ...... 15 3.2 cRED/MicroED data collection ...... 16 3.3 cRED/MicroED data processing and structure determination ...... 17 3.3.1 XDS for cRED/MicroED data processing ...... 17 3.3.2 SHELX for structure determination of inorganic and organic crystals ...... 19 3.3.3 Software suites for macromolecular structure determination ...... 20 4. Structural studies of porous materials by 3D ED (cRED) ...... 22 4.1 Introduction to porous materials ...... 22 4.2 Structural studies of a disordered zeolite PST-24 ...... 23 4.2.1 Introduction to zeolite PST-24 ...... 23

4.2.2 cRED data processing and analysis ...... 23 4.2.3 Structure elucidation of zeolite PST-24 ...... 25 4.2.4 Structure confirmation by HRTEM and diffraction pattern simulation ...... 29 4.2.5 Prediction of new polytypes ...... 32 4.2.6 Conclusions ...... 33 4.3 Structure determination of a metal-organic framework CAU-23 ...... 34 4.3.1 Introduction to MOF CAU-23 ...... 34 4.3.2 Specimen preparation and cRED data collection ...... 34 4.3.3 Structure determination and analysis ...... 36 4.3.4 Conclusions ...... 38 5. Method development and applications of 3D ED (MicroED) for structural studies of biomolecules ...... 39 5.1 Introduction ...... 39 5.2 A simple pressure-assisted method for MicroED specimen preparation ...... 40 5.2.1 Introduction to MicroED specimen preparation ...... 40 5.2.2 The Preassis method and setup ...... 41 5.2.3 Advantages of Preassis ...... 44 5.2.4 Parameters for the adjustment of vitrified ice thickness ...... 49 5.2.5 Features of a successful specimen prepared by Preassis ...... 52 5.2.6 Conclusions ...... 53 5.3 Radiation damage induced atomic displacement of metal cations ...... 54 5.3.1 Introduction to electron radiation damage ...... 54 5.3.2 Effects of radiation damage on the atomic displacement of Gd3+ cations ...... 54 5.3.3 Conclusions ...... 57 5.4 Structural studies of amyloid Ac-KLVFF ...... 58 5.4.1 Introduction to Ac-KLVFF ...... 58 5.4.2 Structure determination using MicroED data ...... 58 5.4.3 Crystal growth discussion ...... 62 5.4.4 Conclusions ...... 63 vi

6. Summary ...... 64 7. Future perspectives ...... 65 Sammanfattning ...... 67 Acknowledgments...... 69 References ...... 72

vii

Abbreviations

TEM Transmission electron microscopy ED Electron diffraction 3D ED Three-dimensional electron diffraction RED Rotation electron diffraction cRED Continuous rotation electron diffraction MicroED Microcrystal electron diffraction ADT Automated diffraction tomography EDT Electron diffraction tomography SCED Single-crystal electron diffraction PEDT Precession electron diffraction tomography SerialED Serial electron diffraction SerialRED Serial rotation electron diffraction SCXRD Single crystal X-ray diffraction PXRD Powder X-ray diffraction CMOS Complementary metal-oxide-semiconductor XFEL X-ray free-electron laser PEG Polyethylene glycol Cryo-FIB Cryo-focused ion beam MR Molecular replacement MOFs Metal-organic frameworks PDB Protein data bank d5r Double 5-ring cas-zz cas-zigzag TDC Thiophene dicarboxylate

viii

1. Introduction (aim of this thesis)

Structure determination is crucial to understanding material properties and exploring their applications. X-ray crystallography has been established as the most popular and advanced method for the structure determination of crystalline materials since 19131,2. However, this method requires crystals of sufficient sizes (> 5 × 5 × 5 μm3 for traditional single crystal X-ray diffraction (SCXRD))3 and quality, while many crystals are only available in nano- or submicron sizes. Although powder X-ray diffraction (PXRD) and X- ray free-electron laser (XFEL) can handle micron-sized crystals, they are limited by the problems of indexing and intensity measurements of reflections owing to heavy peak overlap4 and large sample consumption as well as low accessibility of dedicated facilities3, respectively. Electron crystallography becomes a powerful complementary method. This is due to the strong Coulomb interaction between electrons and matter which allows data collection from nano- or submicro-sized crystals with relatively high data quality. Both the amplitude and phase information of structure factors can be obtained from the same sample, using electron diffraction with high- resolution imaging5. Electron diffraction (ED) with slow electrons (accelerated by several hundred volts) was discovered in 1927 by Davisson and Germer from a nickel single crystal, and the first experiment with fast electrons (accelerated by several tens of kilo-volts) was carried out by G.P. Thomson from polycrystalline films of gold and other metals6. Between 1932-1953, a group of Soviet scientists made important contributions to electron diffraction in many fields, such as the basic design of the current electron diffraction camera, incorporating a magnetic lens, and applying electron diffraction to solve crucial problems in the allied fields6. However, the crystal thickness made it difficult to explain the relationship between the diffraction intensities and structures. In 1957, Cowley and Moodie proposed an n-beam dynamical diffraction theory and pointed out that already diffracted beams can be diffracted multiple times depending on the sample thickness, thereby changing the measured diffraction intensities. Regardless of the multiple scattering, in the 70s - 90s, Henderson7, Dorset8,9, Hovmöller10,11, and their co-workers further demonstrated that ED data can be used for structural characterization of both organic and inorganic nanocrystalline samples. At that time, ED patterns were often recorded along low-index zone axes which suffer from strong dynamical effects because of the simultaneous excitation of many geometrically related reflections5. This was one of the main bottlenecks that hindered the developments and applications of electron crystallography.

In 2007, Kolb’s group proposed to reduce the dynamical effects by collecting off-zone ED patterns by tilting a sample in small mechanical steps (automated diffraction tomography (ADT)) and then reconstructing the 3D reciprocal space12,13. In 2008, Hovmöller, Zou, and co-workers developed a rotation electron diffraction (RED) method, where 3D ED data are collected by a step- wise goniometer tilt combined with fine beam tilt14–16. The concept of collecting ED data in 3D led to a resurgence of ED, promoting further modifications and developments, such as electron diffraction tomography (EDT)17, precession electron diffraction tomography (PEDT)18, continuous rotation electron diffraction (cRED)19, and microcrystal electron diffraction (MicroED)20,21. Owing to the developments of complementary metal-oxide- semiconductor (CMOS) technologies and later hybrid detectors, 3D ED data could be collected within a few minutes in movie mode21–23. This made 3D ED applicable for beam-sensitive materials, including porous materials4, small- organic molecules24–26, and protein crystals21,27–29. Although the dynamical scattering is decreased somehow by collecting off-zone diffraction patterns, it is still not appropriate to use a pure kinematical approximation during structure refinement. This makes the figures of merit obtained from the structure refinement against 3D ED data (usually 10%-30%) much higher than that obtained from X-ray diffraction data (< 5%). In 2015, a dynamical refinement approach based on Bloch-wave theory was developed by Palatinus and co-workers for PEDT30,31, which makes the figures of merit obtained from 3D ED comparable with those from X-ray diffraction. It has been shown that hydrogen positions can be located in both organic and inorganic samples32 and the absolute configuration of organic molecules in pharmaceutical nanocrystals could be determined unambiguously by applying dynamical refinement33. Porous materials, including metal-organic frameworks (MOFs) and zeolites, attract great scientific interest for their wide applications in catalysis, gas separation, adsorption, etc. The structure determination has been one of the main challenges of these materials since they are usually crystallized in nano- sizes, which are either too small or too complex to be studied by SCXRD and PXRD. 3D ED is a complementary method that can be applied for the structure determination of nano-sized crystals. Furthermore, such nano-sized porous materials are often unstable under high vacuum and electron radiation and easily form disorders, which challenge the structure determination of porous materials using 3D ED. Since 2013, 3D ED/MicroED has also been applied to structure determination of protein crystals. However, the total number of reported protein structures solved by 3D ED/MicroED is below 20 according to the PDB database in 2020. One of the major bottlenecks for applying MicroED to macromolecular 2 crystals has been specimen preparation, the most delicate and time-consuming step. For the MicroED specimen, nano- or submicro-meter thick crystals are required to be embedded in a thin layer of vitrified ice, to keep the protein crystal hydrated while allowing the electron beam to penetrate through the specimen with minimized multiple scattering and sufficient signal-to-noise ratio. High molecular weight polymers (such as polyethylene glycols (PEG)) are commonly used to introduce volume-exclusion effects for successful protein crystallization34–36. However, these increase the viscosity of crystal buffers and make it difficult to get MicroED specimens with thin enough ice using the conventional method (pipetting-blotting-plunging routine)37,38. Manual back-side blotting38 and cryo-focused ion beam (cryo-FIB)39,40 could be possible approaches to deal with the viscous samples. However, there are no detailed studies on the first approach and it is time-consuming of using cryo-FIB. There is an urgent need to develop a simple and universal method for specimen preparation to widen the applications of MicroED for macromolecular crystallography. This thesis aims to push the limitations and further develop 3D ED for applications on a wide range of crystal systems, from inorganics to biomolecules. The thesis is organized into seven chapters. The first three chapters will provide the research background, theories, and experimental methods used in this thesis, including the basics of electron crystallography, 3D ED methods, specimen preparation, data processing, and structure determination software. The fourth chapter will discuss structure elucidations of two porous materials, a zeolite with unique disorders and channel dimensionalities and a metal-organic framework with interesting topology and applications. The fifth chapter will discuss a new specimen preparation method for macromolecular crystals and small biomolecules, electron radiation damage induced atomic displacement of metal cations, and the structure study of a bio-functionalized peptide. In the last two chapters, a summary of the results in this thesis and future perspectives of 3D ED will be presented. In conclusion, 3D ED has been demonstrated as a powerful method for exploring structural details of inorganic materials, organic small-molecular crystals, and protein crystals. The developments of automated data collection, high throughput, as well as the data processing and refinement software dedicated for 3D ED will further widen its applications and possibilities.

2. Introduction to electron crystallography

2.1 Basics of electron crystallography 2.1.1 Transmission electron microscopy The basic principle of transmission electron microscopy (TEM) is to use magnetic lenses to manipulate accelerated electrons to interact with a specimen and thereby to form an image onto a detector. The first electron microscope was built by Max Knoll and Ernst Ruska in 193141. Because of the short de Broglie wavelength of electrons, TEM is adequate to image samples at atomic resolution. It has become a powerful technique and has made large impacts on the research fields of physics, chemistry, and biology.

Figure 2.1 Schematic views of the imaging mode (left) and diffraction mode (right) in TEM.

A TEM instrument is normally built by three basic systems, illumination, image formation, and image recording systems. The illumination system includes an electron gun and condenser lenses. The gun can generate

4 accelerated electrons and the condenser lenses can manipulate the electrons to form a parallel or converged beam onto the sample. The illumination system affects the coherency of the electrons and thereby affects the resulted images. The image formation system includes three types of lenses (objective lens, intermediate lens, and projector lens) and two apertures (objective and selected-area apertures). After the electrons interacted with a sample, they are focused by the objective lens, and a diffraction pattern is formed at the back focal plane and an image of the sample is formed at the image plane of the objective lens. By changing the current of the intermediate lens, either the diffraction pattern or the image can be focused and magnified. The projector lenses can further magnify the image or the diffraction pattern. Finally, the image or the diffraction pattern can be captured by recording media, such as photographic film, CCD, and CMOS detectors. Since the magnetic lenses can manipulate the electron beam and the interactions between the electrons and sample can produce different information, a TEM can be operated in different modes, including conventional imaging, diffraction, scanning TEM imaging, spectroscopy, and their combinations. The ray paths of the imaging and diffraction modes are illustrated in Figure 2.1.

Figure 2.2 Electron-sample interaction in TEM. There are three possibilities for every primary electron: unscattered transmitted electron, elastically scattered electron, and inelastically scattered electron. In elastic scattering, the kinetic energies and momenta of colliding particles are unchanged. In the case of inelastic scattering, some energy of the primary incident beam is lost during the scattering event.

Electrons interact with both the nuclei and electrons of the atoms through Coulomb forces, while X-rays only interact with the electron cloud of the atoms. This makes the interaction between electrons and matter much stronger than that for X-rays, allowing for the study of nano- or submicro-meter sized samples, which are much smaller than those needed for X-rays. For each 5 primary electron, there are three possible interactions, unscattered transmitting, elastic scattering, and inelastic scattering. The latter two types are the interactions that can provide information of the sample. For elastic scattering, the kinetic energies and momenta of colliding particles are preserved. This is an important interaction making electrons possible for Bragg diffraction and imaging experiments. For inelastic scattering, there is an energy loss caused by the scattering event. If the lost energy is transferred to an electron in the core shell of the sample, the electron can be ejected, leaving a hole in the inner shell. An electron from an outer shell can then fill this hole and emit X-ray radiation by doing so. Emission of secondary electrons (Auger) can also occur if the emitted X-ray is reabsorbed by an electron in the sample and the energy is high enough to cause the ejection of this electron. In reality, the interaction between electrons and matter can be more complicated42 if we take into account multiple scattering where the scattered beam can be the incident beam for a new scattering event. 2.1.2 Crystallography Crystallography is a method for elucidating the arrangement of atoms, ions, or molecules in crystals. In real space, crystal structures can be described by a repeating unit which can build the whole structure by simple translation. Symmetry, widely existing in nature and our daily life, is an important property of crystals. The symmetry elements can be summarized as rotation axes, mirror planes, inversion centres, screw axes, glide planes, and their combinations. Combining the unit cell parameters and symmetries, 2D crystals can be classified into four crystal systems (Oblique, Rectangular, Square, and Hexagonal) and 3D crystals can be classified into seven crystal systems (Cubic, Hexagonal, Trigonal (Rhombohedral), Tetragonal, Orthorhombic, Monoclinic, and Triclinic). Taking the translation into account for these 7 crystal systems, there are 14 Bravais lattice types. Overall, in 3D there are 7 crystal systems, 14 Bravais lattice types, 32 point groups, and 230 space groups. Because of the symmetries in the unit cell, atomic positions in the asymmetric unit are enough to describe a crystal structure, instead of all atomic positions in the unit cell. X-ray crystallography started when Max von Laue obtained the first diffraction pattern from the interaction between X-rays and crystals in 191243. A milestone of X-ray crystallography was the discovery of Bragg’s law:

2푑 sin 휃 = 푛휆 (2.1) where 푑 is the space between lattice planes, 휃 is the angle between the incident beam and lattice planes, 푛 is an integer number, and 휆 is the wavelength of the incident beam. This formula describes the interconnections 6 between the crystal structure and diffraction, making it possible for structure determination using X-ray diffraction. This formula was derived by William Henry Bragg and his son William Lawrence Bragg44, for which they were awarded the Nobel Prize in Physics in 1915. This formula applies to other diffraction processes as well, such as electron diffraction and neutron diffraction. Reciprocal space is the Fourier transform of real space. Therefore, the symmetries and structure information of a crystal in real space can be reflected by diffraction in reciprocal space. This is the reason why we can determine the crystal structure based on diffraction data. Three basis vectors of the reciprocal lattice are related to the vectors in real space lattice by the following three equations:

푏⃑⃑ × 푐⃑ 푐⃑ × 푎⃑ 푎⃑ × 푏⃑⃑ 푎⃑∗ = 푏⃑⃑∗ = 푐⃑∗ = (2.2) Ω Ω Ω where 푎⃑∗, 푏⃑⃑∗, and 푐⃑∗ are the basis vectors of a unit cell in reciprocal space, 푎⃑, 푏⃑⃑, and 푐⃑ are the basis vectors in real space, and Ω is the unit cell volume. In diffraction, Friedel’s law (I (hkl) = I (-h-k-l)) is true when the crystal is centrosymmetric but not true for X-ray diffraction due to resonant scattering (or anomalous dispersion), nor for electron diffraction due to dynamical scattering. When Friedel’s law applies, there are six Laue classes (point groups in reciprocal space) in 2D and eleven Laue classes in 3D. We could derive the symmetries of the crystal structure from the Laue symmetries in diffraction patterns. However, due to Friedel’s law, a diffraction pattern will show 6-fold symmetry if it is taken along a 3-fold axis, and it is a problem to distinguish centrosymmetric and non-centrosymmetric point groups based on diffraction patterns. This problem can be solved when resonant scattering in X-ray diffraction or dynamical scattering in electron diffraction is not negligible, breaking Friedel’s law. In addition, for electron crystallography, a combination of other methods, such as real-space imaging and convergent- beam electron diffraction (CBED), is helpful to distinguish centrosymmetric and non-centrosymmetric space groups. 2.1.3 Atomic scattering factor and structure factor The atomic scattering factor, or atomic form factor, is a description of the scattering ability of an isolated atom. It depends on the type of atom and the incident beam, such as X-ray, electron, or neutron. X-ray scattering factor is the Fourier transform of the electron density, and can be described theoretically using wave functions and can be fitted by a sum of multiple Gaussian functions45:

4 2 푥 −푏푖(푠) 푓 (푠) = ∑ 푎푖푒 + 푐 (2.3) 푖=1 where 푥 is the type of the incident beam, s = sin 휃 /휆 (휃 is the scattering angle and 휆 is the wavelength), 푎푖, 푏푖, and 푐 are fitting parameters. For the atomic scattering factor for electrons, it can be presented as the Fourier transform of the electrostatic potential of an atom, and it is related to that of X-ray diffraction by the Mott formula46:

푚 푒2 [푍 − 푓푥(푠)] 푓푒(푠) = 0 0 (2.4) 8휋2ℎ2 푠2 where 푍0 is the atomic number of the atom, and 푚0 is the electron mass. This formula is valid for neutral atoms. The electron scattering factor is also affected by the charge of the atoms, especially at the resolution range < 0.3 Å−1. This is because the divergence of the electron scattering factor of an ion arises from the contribution of the unscreened long-range Coulomb potential of the ionic charge on the nucleus47. Therefore, the formula 2.4 was modified by Doyle and Turner for ions48:

푚 푒2 Δ푍 푓푒(푠) = 푓푒(푠) + 0 (2.5) 0 8휋2ℎ2 푠2 where Δ푍 = 푍 - 푍0 represents the ionic charge. The first term explains the electron scattering from the screened potential field, while the second term describes the divergent contribution from the unscreened Coulomb potential of the ionic charge. The structure factors show how atoms interact with the incident beams and it can be mathematically described as the Fourier series. The amplitudes of the structure factors of electron density are measured in X-ray diffraction, while the amplitudes of the structure factors of electrostatic potential are measured in electron diffraction. The radiation source, atomic types, atomic positions, and the thermal vibration of the atoms will affect the structure factors. The structure factor is defined as49:

푁

2휋푖(ℎ푥푗+푘푦푗+푙푧푗 ) 퐹(ℎ푘푙) = ∑ 푓푗(ℎ푘푙)푒 (2.6) 푗=1 where 푁 is the number of atoms in the unit cell, 푓푗(ℎ푘푙) is the atomic scattering factor of the jth atom, hkl is the index of a reflection, and 푥푗, 푦푗, 푧푗 are the fractional coordinates of the jth atom.

2.1.4 Electrostatic potential The electrostatic potential is a combination of the positive nuclei and the negative electrons of the atoms in a crystal. This potential is affected by the type of atoms and their positions within a unit cell. It can be described by49:

푁

휑(풓) = ∑ 휑푗(풓 − 풓푗) (2.7) 푗=1 where 휑(풓) is the electrostatic potential at a position 풓 = (푥푦푧), 푁 is the number of atoms in the unit cell, and 휑푗(풓 − 풓푗) is the potential contributed by the jth atom at position 풓푗. The electrostatic potential 휑(풓) is related to the structure factor by a Fourier transform49:

ℎ2 퐹푇[휑(풓)] = 휙(풖) = 퐹(풖) (2.8) 2휋푚푒Ω where ℎ is the Planck constant, 푚 and 푒 are the relativistic mass and charge of the electron, and Ω is the unit cell volume. The electrostatic potential (2.7) is a continuous function in real space, and the structure factor F(u) is discrete in reciprocal space. Finally, the electrostatic potential 휑(푥푦푧) can be expressed as a summation of cosine waves49:

휆 휑(푥푦푧) = [∑|퐹(ℎ푘푙)| cos[2휋(ℎ푥 + 푘푦 + 푙푧) − 휙(ℎ푘푙)]] (2.9) 휎Ω ℎ푘푙 where 휎 is an interaction constant. |퐹(ℎ푘푙)| and 휙(ℎ푘푙) represent the amplitude and the phase of the structure factor of the reflection hkl. 2.1.5 Electron diffraction Both elastic scattering and inelastic scattering will be recorded on an electron diffraction pattern. For a crystalline sample, the inelastic scattering contributes to the background, while the elastic scattering contributes to the sharp diffraction spots. The positions and the intensities of diffraction spots provide structure information of a crystal. The positions of diffraction spots can be used to derive the unit cell parameters and the lattice type of the corresponding crystal. The intensities of diffraction spots correlate to the square of the structure factors, which relate to the arrangement and the types of atoms in the unit cell. However, the phases of the structure factors are lost in the diffraction. If the crystal is thin enough (e.g. < 200 nm for small organic and protein crystals42, and a few tens of nanometers for inorganic crystals5), electron diffraction can be treated by kinematical approximation, and it is possible to 9 determine the crystal structure based on the diffraction intensities with relatively high accuracy. For thicker crystals, the probability of dynamical scattering is much higher, which will affect the diffraction intensities. Many factors can affect the dynamical scattering including the accelerating voltage, sample composition, crystal thickness, crystal orientation, and solvent in the crystal. On average, multiple scattering or dynamical scattering makes the weak reflections stronger and the strong reflections weaker11,50. Since the dynamical scattering is more severe when many geometrically related reflections are simultaneously excited, off-zone diffraction patterns are less affected by the dynamical effects compared to the patterns taken along the zone-axes5,11. 2.1.6 High-resolution electron microscopy Electron diffraction can only preserve the amplitudes of structure factors, while HRTEM imaging can preserve both amplitudes and phases. However, the image contrast can be simply related to the projected electrostatic potential only when the multiple scattering and non-linear effect (interference of different scattered waves) can be neglected to fulfill the weak-phase object approximation, and when the contrast transfer function is corrected. Under the weak-phase object approximation, the wave function at the exit surface can be described as49:

휓푒푥(푥푦) = 1 − 𝑖휎푁푧휑(푥푦) (2.10) where 휎 is an interaction constant and 푁푧 is the number of periods along the incident beam direction. Under this approximation, the Fourier transform of the exit wave function 휓푒푥(푥푦) can be correlated to the structure factor 퐹(풖) 49: 휆푡 휋 휓 (풖) = exp (−𝑖 ) 퐹(풖) (for 풖 ≠ 0 ) (2.11) 푒푥 Ω 2 Where 푡 is the thickness and Ω is the volume of the crystal. Formula 2.11 shows that the amplitude of 휓푒푥(풖) is proportional to that of structure factor 퐹(풖), while their phases are shifted by 90°. In addition, due to the defects of the lenses in TEM, the phases of the electron waves will be modified. Therefore, under the weak-phase-object approximation, the relationship between the Fourier transform 퐼푖푚(풖) of the intensity of an HRTEM image and the structure factor 퐹(풖) can be described as49: Ω 퐼 (풖) 퐹(풖) = 푖푚 (2.12) 2휆푡 푇(풖) 10 where T(u) is the contrast transfer function (CTF) which describes how the phases of the electron waves are modified by the magnetic lenses. It can be further described by two parts49:

푇(풖) = 퐷(풖) sin 휒(풖) (2.13) where 퐷(풖) is an envelope function which can dampen the amplitudes of the high spatial frequency parts, and sin 휒(풖) is an oscillation function that modifies the contrast of the images based on the defocus value of the objective lens. The defocus value, which can give the same sign of sin 휒(풖) over a large 51 spatial frequency, is called Scherzer defocus Δ푓푠푐ℎ and can be calculated as:

4퐶 휆 Δ푓 = −√ 푠 (2.14) 푠푐ℎ 3 where 퐶푠 is the spherical aberration coefficient of the objective lens. Under this condition, the contrast of an HRTEM image can be simply related to the projected electrostatic potential. 2.2 3D electron diffraction (3D ED) 2.2.1 The development of 3D ED In the beginning, ED patterns, for structure determination, were always taken along individual low-index zone-axes. This caused systematic errors in the measurements of diffraction intensities due to the strong multiple scattering along zone-axes5,52 and also incomplete intensity merging53. Starting from 2007, electron crystallographers realized the importance of collecting ED data off-zone and fine-sampling the reciprocal lattice in 3D. In 2007, Ute Kolb’s group developed a data collection method, automated diffraction tomography (ADT), in which the ED patterns are taken along arbitrary axes by tilting the sample step-wise using a goniometer12,13 and later combined with the precession electron technique54, as shown in Figure 2.3a. This method can sample reciprocal space with high data completeness and made it possible to reconstruct the diffraction spots in the 3D reciprocal lattice. Almost at the same time, Sven Hovmöller and Xiaodong Zou’s group developed a data collection method, rotation electron diffraction (RED), in which the ED patterns are collected by combining step-wise goniometer tilt with fine electron beam tilt14,15, as shown in Figure 2.3b. The development of these data collection methods boosted the applications of 3D ED since the data collection became faster and simpler, and dynamical effects are reduced owing to the off-zone diffraction patterns. The intensities obtained by 3D ED are quasi- kinematical and the data can be processed by the programs originally developed for X-ray diffraction5. Based on the same core concept, sampling 11

3D reciprocal space by tilting the sample along an arbitrary axis5, a number of similar data collection strategies are developed with different acronyms, such as precession electron diffraction tomography (PEDT)18, continuous rotation electron diffraction (cRED)55, and micro-crystal electron diffraction (MicroED)20,21, etc.

Figure 2.3 Data collection sketches of precession electron diffraction tomography (ADT/PEDT) and rotation electron diffraction (RED). a Data acquisition by combining step-wise goniometer tilt with beam precession. b Data acquisition by combining step-wise goniometer tilt with fine beam tilt.

2.2.2 The cRED/MicroED method The developments of highly sensitive and short readout time detectors, such as hybrid (e.g. Timepix23) and CMOS (e.g. CETA-D56) based cameras, enable 3D ED data to be collected in movie mode within a few minutes. As a result, instead of step-wise goniometer tilting, the sample can be continually tilted along one rotation axis while the diffraction patterns can be recorded as a movie (Figure 2.4). This is similar to the X-ray data collection strategy21–23,57,58. Based on these developments, the data collection time has been shortened from several hours to a few minutes and the total electron dose can be reduced to less than 5 푒−/ Å2, which is a great advantage for data collection from beam-sensitive materials, such as porous materials4, organic small-molecular crystals24,25,25, and macromolecular crystals21,29,38,42,59.

Figure 2.4 Data collection sketches of continuous rotation electron diffraction (cRED). It is also known as MicroED in macromolecular crystallography. The crystal is continuously rotated by the goniometer and diffraction patterns are recorded in movie mode by a fast detector. The oscillation angle of each frame is calculated by the ratio of total rotation angle and frame number. Since the camera has a very short readout time, the information lost during readout is minimized (readout blank).

3. Experimental Methods

3.1 Specimen preparation for 3D ED experiments 3.1.1 Specimen preparation for inorganic crystalline samples For 3D ED experiments, crystals should be thin enough to let incident electrons interact with a sample and go through without heavy dynamical effects. Depending on the sample compositions (i.e. light or heavy atoms), the size of the unit cell, and the energy of the incident electron beam, the critical thickness can vary from a few tens of nanometers to several hundred nanometers. In the case of inorganic crystalline samples, physical crushing can be used for producing crystals of suitable thickness/size for 3D ED experiments (Figure 3.1). Once the crystals are within the desired size, they need to be deposited onto a TEM grid. The key point here is to prepare a grid with suitable crystal densities (e.g. 10 to 20 crystals per grid square) and distributions without too much crystal overlap or aggregation. Therefore, it is helpful to suspend the crushed crystals in a solvent to make a homogeneous crystal suspension. The commonly used solvent is ethanol, providing the crystals are stable in it. Moreover, ultrasonic vibrations can be used to reduce the risk of crystal aggregation in the suspension. It is worth noting that the crystallinity of some fragile samples may be decreased by this process. Afterward, several microliters of the crystal suspension are deposited onto a TEM grid. The grid is then dried in the air before inserting it into the microscope. In cases where the sample cannot be suspended in any solvents, the crushed powder can be applied directly onto an EM grid. Subsequently, all big crystal fragments

Figure 3.1 Specimen preparation for inorganic samplea in a powder form. The basic procedures include crushing the crystals into a few hundred nanometers, suspending the sample in a solvent, and applying the sample onto a TEM grid. 14 should be blown away to prevent the microscope column from contamination. It usually takes a few trials to optimize the specimen preparation for a new sample before the specimen can be used for 3D ED data collection. The

method presented here can also be applied to organic compounds. 3.1.2 Specimen preparation for bio-molecular crystals The specimen preparation for sensitive bio-molecular crystals (e.g. peptide and macromolecular crystals) is more complicated and time-consuming. These crystals are usually difficult to handle since they are sensitive to the humidity, temperature, and high vacuum in the microscope. First of all, the methods used to modify the crystal sizes for stable inorganic samples are not suitable for bio-molecular crystals. Segmenting large crystals using mechanical forces (e.g. vigorous pipetting, sonication, vortexing with beads)60 works only to some extent. However, the crystallinity of crystals can be affected. Focused ion beam milling under cryogenic conditions (cryo-FIB) has been demonstrated as a good method for precisely milling the crystals to a target thickness39,56,61,62.

Figure 3.2 MicroED specimen preparation using Vitrobot.

Bio-molecular crystals need to be embedded in a thin vitrified ice layer since the high vacuum in the microscope can damage the crystals. The pipetting- blotting-plunging routine37, originally designed for single-particle cryoEM specimen preparation, is widely applied for 3D ED/MicroED specimen preparations (such as a commercialized prototype Vitrobot, Figure 3.2). Firstly, a few microliters of the sample suspension (usually 3 μl) are deposited onto a glow-discharged EM grid. Secondly, the extra liquid is removed by two-side blotting or back-side blotting using filter paper. Finally, the grid with crystals embedded in a thin liquid layer is plunged into a cryogenic liquid (e.g. liquid ethane) for vitrification. However, one of the drawbacks of this method is that the double-side blotting takes away the majority of crystals (> 90%).

Manual back-side blotting can keep more crystals than that of Vitrobot38. Furthermore, protein crystallization on a TEM grid directly can also improve the number of crystals on the grid63. Another drawback of this blotting routine is that it cannot efficiently remove the viscous buffer. However, viscosity is a common parameter for successful protein crystallization. Cryo-FIB is capable of dealing with crystals grown in high viscous buffers, such as membrane protein crystals grown in the lipid-cubic phase40,64, however, it is time- consuming. 3.2 cRED/MicroED data collection Several software packages have been developed for automating cRED/MicroED data collection in different groups, such as Instamatic55 developed by Smeets in Xiaodong Zou’s group and EPU-D developed by Thermo Fisher Scientific. Instamatic55 has been used for the work included in this thesis. Instamatic can communicate with the TEM and the detector for data collection, and crystal tracking can be achieved by defocusing the diffraction spots to show a shadow image of the crystal at a constant interval defined by the user. This development allows data to be collected over a large rotation range (up to ~ 140° in JEOL 2100 LaB6) with high throughput. The software is user-friendly and open source, which allows it to be installed and used in other TEM labs. It is worth mentioning that several data collection strategies have been implemented in Instamatic, including cRED/MicroED, RED, serial electron diffraction (SerialED)65, and serial rotation electron diffraction (SerialRED)66. This software is currently installed on a JEOL JEM- 2100-LaB6 operating at 200 kV and equipped with a 512 × 512 Timepix hybrid pixel detector (55 × 55 μm pixel size, model QTPX-262k). Furthermore, a DigitalMicrograph script, InsteaDMatic67, was also developed to allow cRED/MicroED data collection using a Gatan detector (i.e. oneView IS camera installed on a ThemisZ microscope).

Figure 3.3 User interfaces of Instamatic and InsteaDMatic.

In this thesis, cRED/MicroED data were collected using Instamatic and InsteaDMatic. The oscillation angle per frame is typically between 0.2° and 1°, which is controlled by both the rotation speed of the goniometer and the exposure time. These parameters are chosen to ensure sufficient sampling of reciprocal space whilst achieving a high signal-to-noise ratio, as well as enough speed to minimize radiation damage. In addition, the choice of rotation speed and exposure time is also a balance between the total electron dose that the sample can withstand and the signal-to-noise ratio. One of the advantages of Instamatic is that it can control the defocus of the intermediate lens to form a shadow image of the crystal at a constant interval. It means that both crystal searching and tracking can be done in diffraction mode which can improve the beam stability and increase the efficiency of the data collection. Furthermore, the contrast of the shadow image is better than that of a normal TEM image, which is very useful when searching for organic crystals, especially protein crystals. Since the diffraction information will be lost when the beam is defocused for forming the shadow image, the image interval shouldn’t be too small (usually every 10 or 20 frames). When data collection is finished, the diffraction patterns are converted into different formats (TIFF, MRC, and SMV) for different data processing software. 3.3 cRED/MicroED data processing and structure determination 3.3.1 XDS for cRED/MicroED data processing Since the data collection strategy of cRED/MicroED is similar to single- crystal X-ray diffraction (SCXRD), data processing software that was originally developed for SCXRD can be used for processing cRED/MicroED data, for example DIALS68, MOSFLM69,70, and XDS71,72. In this thesis, mainly XDS was used for data processing. The XDS package consists of several programs, including XDS (for processing a single diffraction dataset), XSCALE (for scaling and merging of several datasets), and XDSCONV (for converting a reflection output file to other formats). The main data processing protocol is shown as a flow chart in Figure 3.4. Due to the dynamical scattering, space group determination based on statistical analysis may fail in XDS. On the other hand, the dynamical scattering can be used for analyzing the systematic absences from certain zone-axis patterns and, therefore, obtaining the reflection conditions for space group determination. In this thesis, REDp15,16 was used for space group analysis by reconstructing the 3D reciprocal lattice and investigating 2D slices of certain reciprocal lattice planes.

Data processing by XDS contains several steps to estimate background, search peaks, determine unit cell, index, scale, and integrate intensities71, as shown in the flow chart of Figure 3.4. Before starting to process data by XDS, there is a need to set up parameters in the file XDS.INP correctly, including input data range, detector distance, detector origin (position of the direct beam), rotation axis, wavelength, oscillation range, etc. After finishing the initial setup and the first run, it is necessary to check the statistic table of data processing in the file CORRECT.LP and to update or modify some parameters in the file XDS.INP accordingly. Generally, the data resolution can be cut to where CC(1/2) (percentage of correlation between intensities from random half-datasets) is marked by an asterisk (correlation is significant). The parameters of STRONG_PIXEL and SIGNAL_PIXEL can be modified to improve the accuracy of unit cell determination and the intensity measurements, and BEAM-DIVERGENCE can be updated to have a better estimation of the diffraction mosaicity. XDS can be rerun several times to improve the statistics of data processing, and a proxy for good data and data processing is Isa (a measure of systematic error arising from beamline, crystal, and data processing). Finally, the cRED/MicroED data need to be re-indexed

Figure 3.4 Flow chart for cRED/MicroED data processing by REDp and XDS. 18 with the correct space group obtained from REDp, and HKL files will be

output. The final integrated intensities are saved in the file XDS_ASCII.HKL. 3.3.2 SHELX for structure determination of inorganic and organic crystals In diffraction experiments, phases of structure factors are lost. Several methods have been developed to solve the crystallographic phase problem of inorganic or organic crystals, such as the standard direct method (SHELXS73, SIR201474), dual-space method (Charge flipping75,76, SHELXT77), and simulated annealing (SIR201474). SHELX is a well-established software package that includes SHELXS, SHELXD, and SHELXT for structure solution and SHELXL (least-square refinement) for structure refinement. In this thesis, it was used for the structure determination of two crystalline inorganic porous materials and one crystalline peptide. The structure solution procedures of SHELXT are illustrated in Figure 3.5. It uses the dual-space methods in which the phases of structure factors are retrieved by iteratively modifying the structure factors in reciprocal space and the electron density in real space77. The dual-space recycling (Figure 3.5) can start with random phases or a Patterson superposition minimum function. Typically, for

Figure 3.5 Flow chart of the SHELXT procedures. Structure solution is performed by a dual-space algorithm. Space-group assignment and isotropic

refinement are performed in parallel. 퐺표 , 퐺푐 , and FFT are modified observed structure factors, calculated structure factors, and Fast Fourier transform,

respectively. 휑푐 is the phase of 퐺푐. 19 structure determination by SHELXT, the resolution of the data should be better than 1.2 Å. After an initial structural model has been obtained from SHELXT, the model needs to be checked by a structure visualization software (e.g. VESTA78). For cRED/MicroED data, the assignment of atoms in the asymmetric unit may need to be modified manually before running SHELXL for refinement. The electron scattering factor and wavelength also need to be updated in the input file .ins. In some cases, constraints or restraints for some bond angles and distance may need to be introduced during refinement. To conclude whether the structure determination is correct or not, two rules of thumb are that the structure needs to be chemically reasonable and the refinement is converged. The 푅 (figure of merit) values should be low. Typically, the 푅1 value for the refinement against electron diffraction data is ranging from 10% to 30% for 3D ED data, which is quite high compared with those obtained with X-ray data (< 5%). The possible reasons can be: 1) dynamical effects; 2) data processing software is not optimized for electron diffraction; 3) errors in data collection (crystal moving out from the beam and the changes in crystal height). The 푅1 value can now be reduced by applying dynamical refinement32,33. 3.3.3 Software suites for macromolecular structure determination Macromolecular crystals are more complex and diffract to relatively lower resolutions compared with inorganic or organic crystals. Direct methods or dual-space methods are usually not feasible for phase determination. Therefore, molecular replacement (MR)79,80, single/multiple isomorphous replacements (SIR/MIR)81–83, single/multiple-wavelength anomalous diffraction (SAD/MAD)84–86, and radiation-induced phasing87,88 have been developed for solving the phase problem of macromolecular X-ray crystallography. Among them, MR is the most widely used method for phasing. In MR, the initial phases of the target structure are obtained from a related or homologous protein structure. MR has been implemented in the

CCP4 suite89 as well as Phenix90. The flow chart of procedures for structure determination is shown in Figure 3.6.

Figure 3.6 Procedures of macromolecular structure determination based on MR.

The basic procedures, as described in Figure 3.6, are the same for both X-ray data and ED data. For MR in Phaser, log-likelihood gain (LLG) and Z-scores for rotation and translation functions (RFZ/TFZ) are used for the assessment of MR solutions. Normally, a single solution, LLG > 120, TFZ > 8 are good indications of successful placement of the search model. For the refinement by REFMAC in CCP4, two additional keywords are needed for ED data: ‘SOURCE ELECTRON’ and ‘MAPC FREE EXCLUDE’. The first keyword is to instruct REFMAC to use the electron scattering factors. The second one is to prevent REFMAC from generating missing reflections when calculating the electron density or electrostatic potential map. Currently, the dynamical effects are ignored in REFMAC for ED data.

4. Structural studies of porous materials by 3D ED (cRED)

4.1 Introduction to porous materials Porous materials, such as zeolites, metal-organic frameworks (MOFs), and covalent organic frameworks (COFs), are a group of functional materials with intrinsic periodic porosity in either the micro-porous (pore size < 2 nm) or meso-porous ranges (pore size between 2 and 50 nm). They have wide applications owing to their excellent adsorption, separation, ion exchange, and catalytic properties. Zeolites are normally built by tetrahedrally coordinated silicon/aluminum/germanium/gallium/boron atoms (T-atoms) that are linked by oxygen atoms. The first zeolite was discovered in nature by the Swedish mineralogist Axel Fredrik Cronstedt in 175691. Starting from the mid 19th century, a large number of zeolites were synthesized in labs by hydrothermal synthesis with the assistance of the structure-directing agent (SDA), for example, ZSM-592 and zeolite Beta93. The properties of zeolites fundamentally depend on the pore size and shape, as well as the chemical composition. The types of T-atoms can affect the acidity and active sites, making zeolites important for catalysis. It can also affect the stability of the framework. For example, pure silicates normally have higher thermal and chemical stability, while zeolites with a high ratio of aluminum or germanium have significantly lower stability. In comparison to zeolites, MOFs usually have larger pores. They can be one-, two-, or three-dimensional structures. They are built by metal ions or clusters coordinated to organic ligands. The metal ions/clusters can have several impacts, including the oxidation state, redox activity, possibilities of metal- cation exchange, and solvent removal for unsaturation, while the types of linkers can affect the acidity, redox properties, and possibilities of functionalization94. Moreover, MOFs have ultra-high surface areas (up to 10000 m2 g-1 according to Brunauer-Emmett-Teller theory)95, and their structures are flexible and can be tuned by changing the metal cations, linkers, and by post-synthesis modification96. Their structural and chemical properties provide them with promising and wide applications, including gas storage, drug delivery, catalysis, chemical sensing, electronic devices, etc. Various synthetic methods, including microwave, electrochemical, mechanochemical, and ultrasonic syntheses have been used to explore new MOFs with specific properties and functions. More than 20 000 MOF structures have been reported and some of them can be produced in large amounts and are

22 commercially available at Sigma-Aldrich, Strem Chemical, and MOF technologies94. To have a better understanding of the properties and explore new porous materials with designed applications, it is crucial to study their atomic structures. Since these materials are usually in powder form with submicron- or nano-meter sizes, there are difficulties in using SCXRD and PXRD owing to the crystal size and the peak overlap, respectively. 3D ED, as a complementary diffraction method, has shown great advantages in the structural characterization of porous materials4. This chapter will show the studies of using 3D ED (cRED) for structure determination of a disordered zeolite PST-24 and a MOF CAU-23. 4.2 Structural studies of a disordered zeolite PST-24 4.2.1 Introduction to zeolite PST-24 Since zeolites are built from basic building units that can connect in different ways without significantly increasing the energy of the structure, disorder is common in zeolite strucures97. Most disordered zeolites can be built by the same building layers but with different stacking sequences, leading to peak broadening in PXRD patterns and diffuse scattering in electron diffraction patterns98. In this complex case, a combination of 3D ED and HRTEM is necessary to study the structure with disorders. The different stacking sequences of building layers can generate many different polytypes, while their channel dimensionalities are generally the same97–100. Moreover, the channel dimensionality can affect the diffusion of the guest molecules and ions and, therefore, affect the catalytic and adsorption applications. In this thesis, a disordered zeolite PST-24 with varying intracrystalline channel dimensionality has been studied. This zeolite was synthesized using pentamethylimidazolium (PMI+) ions as an organic structure-directing agent (OSDA) by a excess fluoride (F−) approach. 4.2.2 cRED data processing and analysis PST-24 crystals were physically crushed in a mortar to obtain submicron- or nano-meter-sized crystals, and then dispersed in ethanol and treated by ultrasonication for ~ 30 s to obtain crystal suspensions with well-separated crystals. Afterward, a few microliter crystal suspensions were applied onto a lacey carbon TEM grid (Okenshoji). The dried specimen was then loaded onto a single-tilt tomography holder (± 75°). cRED data were collected at room temperature on a JEOL JEM-2100 LaB6 TEM at 200 kV equipped with a high- speed hybrid camera (Timepix Quad). Instamatic55 was used to control the data collection with a rotation speed of ~ 0.5° s-1 and exposure time of 0.5 s. 23

The data were initially processed and analyzed by REDp15,16 for unit cell and space group determination, as well as disorder analysis. The 3D projection of reciprocal space lattice and 2D slices of different reciprocal lattice planes in Figure 4.1 were obtained from REDp. The cRED data were then processed by XDS to extract the intensities of the sharp diffraction spots.

Figure 4.1 Crystal morphology of PST-24 and projections of reciprocal lattice planes reconstructed from the cRED data. a SEM image of PST-24 crystals. b Projection of 3D reciprocal lattice viewed along the c*-axis. c-f 2D slices of h0l, 0kl, hk0, and h±3l reciprocal lattice planes. (Reproduced with permission from Angew. Chem. Int. Ed. 2020, 59, 17691 –17696. Copyright (1999-2021) John Wiley & Sons, Inc.) Rectangular plate-shaped PST-24 crystals (Figure 4.1a) were synthesized in highly excess F− conditions with PMI+ cation as the OSDA. These crystals are heavily disordered and the resulted diffuse scattering appears in every two diffraction planes along the b*-axis as shown in Figure 4.1b-e. The streaks caused by diffuse scattering appear both a* and c* axes in the reciprocal lattice planes of h0l and 0kl (Figure 4.1d-e), while no streaks are observed in the hk0 plane (Figure 4.1c). Furthermore, the diffuse scattering is quite random without any specific rules in the planes where k = 2n + 1(b = 10.4 Å, Figure 4.1f). This suggests that the disorder happens in the planes parallel to the b- axis and a shift between these planes may exist. As it is difficult to take the diffuse scattering into account during the data processing and structure determination, a feasible way is to use the intensities from the sharp diffraction spots to obtain an average structure and then to derive the disordered structure98,100. Using the sharp diffraction spots, the unit cell parameters of the 24 average structure of calcined PST-24 was determined to be a = 24.140(44) Å, b = 5.211(18) Å, c = 21.761(30) Å, α = 90°, β = 111.389(180)°, γ = 90°. Based on this unit cell setting and the corresponding reflection conditions (hkl: h + k = 2n, 0kl: k = 2n, h0l: h = 2n, hk0: h + k = 2n), three possible space groups for the average structure were obtained: C2 (no. 5), Cm (no. 8), and C2/m (no. 12). 4.2.3 Structure elucidation of zeolite PST-24 An initial structure solution of the average structure was obtained by SHELXT77 in both space groups C2 and C2/m, while the further structure refinement demonstrated that the framework model obtained in space group C2/m has a more reasonable bond geometry. There are 11 symmetry- independent tetrahedral sites (T-sites) in the asymmetric unit of the average structure of PST-24 (Figure 4.2a). The whole framework consists of three typical building units: [54.62] (cas), zigzag chains, and [45.52] (double 5-ring; d5r) units, while the cas units and zigzag chains are connected forming a composite cas-zigzag (cas-zz) chain along b-axis. The cas-zz chains are orderly and periodically distributed in the whole framework (Figure 4.2b-c, shown in yellow). On the contrary, the d5r columns (Figure 4.2a-b, shown in blue) show a chemically unreasonable stacking: the two most adjacent d5r units along the b-axis are too close to existing simultaneously. To keep a reasonable bond geometry, only one of these two d5r units can exist in the column. Therefore, the atoms on the d5r unit (Si21-Si25 and O21-O30) are refined with an occupancy of 0.5. The basic experimental parameters for cRED data collection and crystallographic details are shown in Table 4.1.

Figure 4.2 Derivation of the disordered structure based on the average structural model. a Average structural model of PST-24 showing both the ordered cas-zz chains (in yellow) and chemically unreasonable stacked d5r units (in blue). Bridging O atoms have been omitted for clarity. b One example of the real structure of PST-24 projected along the b-axis. The ordered cas-zz chains are highlighted in yellow. The dark blue and light blue colors present the pair of most adjacent d5r columns with the same (S) and different (D) heights, respectively. c Composite cas-zz chain corresponding to the area highlighted by a red dashed box in image b. d Arrangements S (same height) and D (different height) of the two most adjacent d5r columns, as indicated by black dashed boxes in image b, projected along the c-axis. e Arrangements S and D projected along the [101] direction. Arrangement S creates the 10-ring apertures perpendicular to the b-axis, whereas the opposites holds for arrangement D. f Arrangement D in the third direction showing pockets instead of the additional channels. g One example of the channel system in the real structures of PST-24. The light blue 10-ring channels along [101] are generated by the random distribution of D arrangements. (Reprinted with permission from Angew. Chem. Int. Ed. 2020, 59, 17691 –17696. Copyright (1999-2021) John Wiley & Sons, Inc.)

To study the disorder, the b-axis has to be doubled to ~10.4 Å, and only every second d5r unit in each d5r column can exist to keep the four-connected framework with a reasonable bond geometry. Furthermore, the diffuse scattering appears in every two layers in the reciprocal lattice and they are perpendicular to the b*-axis (Figure 4.1). These observations indicate that the disorder is related to a 1/2b shift (~ 5.2 Å) between the planes parallel to the b-axis. Based on the features of diffuse scattering and the understanding of the average structural model, each d5r column has two arrangements that are related by a 1/2b shift, while the choice is independent of each other. In 26 summary, the 3D framework of PST-24 is built up by two distinct units, orderly distributed cas-zz chains and d5r columns with a random distribution of the two arrangements (Figure 4.2b). Despite the presence of disorder, PST-24 has at least a 2D channel system, including straight 10-ring (5.8 × 5.4 Å) and 8-ring (4.8 × 3.1 Å) channels along the b-axis and sinusoidal 10-ring channels along the c-axis (Figure 4.2g, grey channels). Furthermore, the randomly distributed two choices of d5r columns can also modulate the channel system from 2D to 3D. This is because the two d5r columns connected by a 6-ring (marked in Figure 4.2b) can have two height relations: either have the same b height or have different heights related by a 1/2b shift. These two relations are defined as arrangements S (same) and D (different), respectively (Figure 4.2e). The arrangement S creates 10-ring apertures (6.1 × 3.5 Å) that connect the channels running along the c-axis (Figure 4.2e, left). In contrast, arrangement D creates pockets (Figure 4.2f) that block the connections between channels running along the c-axis (Figure 4.2e, right). Consequently, the arrangements S and D, like a two-way ‘valve’, can open and close the connections between the channels running along the c- axis. In reality, these two arrangements are distributed randomly in the crystal, as illustrated in Figure 4.2b. Therefore, the channel dimensionality of PST-24 can vary from 2D to 3D randomly (Figure 4.2g), which affects the intracrystalline molecular diffusion. Table 4.1. cRED data collection and crystallographic details for the structure refinement of the as-made and calcined forms of pure-silica PST-24.[a]

Sample As-made PST-24 Calcined PST-24

Wavelength (Å) 0.0251 0.0251

Rotation range (°) 70-115 70-110

Rotation speed (°/s) 0.23 0.23

Exposure time/frame (s) 0.5 0.5

No. of data sets 7 3

Crystal width (nm) 100-200 100-200

Resolution (Å) 0.90 0.90

Crystal system Monoclinic Monoclinic

Space group C2/m (No. 12) C2/m (No. 12)

Unit cell parameters (cRED)

a (Å) 24.313(44) 24.140(44)

b (Å) 5.384(2) 5.211(18)

c (Å) 22.016(39) 21.761(30)

β (°) 111.422(190) 111.389(180)

Completeness 0.846 0.918

Total reflections 12752 7374

Rint 0.2583 0.1797

No. unique reflections I > 2 1391/1642 1469/1788 (I)/all

No. of parameters 113 111

No. of restrains 57 87

R1(F > 4(F))/R1(all) 0.2148/0.2387 0.3387/0.3684

GOF 2.102 3.072

[a] The unit cell parameters, obtained by the Pawley and Rietveld fits of the as-made (lab) and calcined (synchrotron) PXRD patterns, respectively, were used for the structure refinement. The unit cell parameters from the cRED data are slightly larger than those from the PXRD data, presumably due to lens distortions and the sample height changing during the data collection. (Reprinted with permission from Angew. Chem. Int. Ed. 2020, 59, 17691 –17696. Copyright (1999-2021) John Wiley & Sons, Inc.)

4.2.4 Structure confirmation by HRTEM and diffraction pattern simulation HRTEM images were collected on a JEOL-2100F TEM at 200 kV equipped with a Gatan 2k × 2k charge-coupled camera. They are recorded as a through- focus series with a focus step of -53.3 Å and an exposure time of 5 s per image. The reconstruction of the structure projection was conducted by QFocus101. The starting defocus, twofold astigmatism, and azimuth angle were determined to be -183 Å, 188 Å, and 117°, respectively. As shown in the reconstructed structure potential image (Figure 4.3a), the image contrast of the columns along the b-axis is periodic, while the contrast between different columns varies randomly. When a majority of the d5r columns are at the same b-height and ordered along the c-axis, the openings of the sinusoidal 10-ring channels are visible as the features with the brightest contrast on the reconstructed image, as shown in the middle of the image in Figure 4.3b. These brightest features can have different orientations depending on the b- height relationship between the neighboring arrays of d5r columns along the a-axis. When the d5r columns are randomly arranged at different heights along either the c-axis, a-axis or both axes, the 10-ring channels become more sinusoidal and, therefore, cannot show clear contrast in the image. In conclusion, the feature of the image contrast is consistent with the disordered structural model of PST-24.

Figure 4.3 Structure confirmation by HRTEM. a Structure projection of calcinaed PST-24 reconstructed from 17 through-focus HRTEM images taken along the c-axis. b Enlarged image of the area selected in the red box in the reconstructed image a. A disordered structural model was superimposed in the image, which confirmed the disorder behaviour. (Reprinted with permission from Angew. Chem. Int. Ed. 2020, 59, 17691 –17696. Copyright (1999-2021) John Wiley & Sons, Inc.) 29

The disordered PST-24 structure was further confirmed by simulations of both ED (Figure 4.4) and PXRD patterns (Figure 4.5). A supercell (10a × 1b ×10c, b = 10. 1696 Å) model with a random distribution of arrangements S and D was generated by an in-house script (GitHub, chojy8300). The atomic coordinates of this supercell were further optimized by DLS76. The simulation of kinematical ED and PXRD patterns was conducted using CrystDiff102 and Materials Studio103, respectively. The simulated diffraction patterns agree well with the experimental patterns although there is a small difference at high diffraction angles (Figure 4.4b) which may result from the size limitation of the supercell. In principle, the supercell should be infinite to describe the disorder in the real crystal. However, it is time-consuming for simulation.

Figure 4.4 Simulation of ED patterns. a Experimental ED pattern taken along [001] projection. b Simulated ED pattern along the same projection from a 10a  1b  10c supercell with a random distribution of the arrangements S and D. (Reproduced with permission from Angew. Chem. Int. Ed. 2020, 59, 17691 – 17696. Copyright (1999-2021) John Wiley & Sons, Inc.)

Figure 4.5 The experimental (black) and simulated (red) PXRD patterns of calcined, pure-silica PST-24 (λ = 1.5175 Å). The simulation was performed on the same 10a  1b  10c supercell by Materials Studio. (Reproduced with permission from Angew. Chem. Int. Ed. 2020, 59, 17691 –17696. Copyright (1999-2021) John Wiley & Sons, Inc.)

4.2.5 Prediction of new polytypes Based on the structure building units and the possibilities of different arrangements of S and D, infinite numbers of ordered structures can be predicted. If we limit the possible structures by the given unit cell parameters, three ordered polytypes can be constructed: PST-24A (P21/c) with the arrangements of DDDD· · ·, PST-24B (P 1̅ ) with the arrangements of SDSD · · ·, and PST-24C (P2/c) with the arrangements of SSSS· · ·, as shown in Figure 4.6. Since the arrangements S and D can work as a ‘valve’ to open or close the connection between the channels running along the c-axis, the channel dimensionalities of these three polytypes are different. PST-24A has the closed valve only, leading to a 2D channel system (Figure 4.6d). PST-24B has an alternating arrangement of the opened and closed valves, leading to a bi-level 2D channel system (Figure 4.6e). PST-24C has the opened valves only, leading to a 3D channel system (Figure 4.6f). Interestingly, PST-24 is the first example where the polytypes have similar pore structures while their channel dimensionalities are different. The framework energies (relative to α- quartz) of these three polytypes with pure-silica were calculated to be 11.6, 11.8, and 11.7 kJ·(mol Si)-1, respectively, using the Sanders-Leslie-Catlow potential with P1 symmetry. Their framework energies are very close to each other and they have the same framework density (18.7 T-atoms per 1000 Å3),

Figure 4.6 Three polytypes based on the different arrangements of S and D. a-c Polytypes PST-24A, PST-24B, PST-24C with the arrangements of DDDD · · ·, SDSD· · ·, and SSSS· · · along a-axis, respectively. d-f Channel systems in these three polytypes, showing the changes of channel dimensionality from 2D, bi- level 2D, to 3D. The cas-zz chains are colored in yellow, and the d5r units in blue. Only the Si-Si connections are shown for clarity. (Reprinted with permission from Angew. Chem. Int. Ed. 2020, 59, 17691 –17696. Copyright (1999- 2021) John Wiley & Sons, Inc.) 32 illustrating that there is no preference for any particular polytype in the real crystallization. 4.2.6 Conclusions A clear picture of a disordered zeolite PST-24 was revealed by a combination of 3D ED and HRTEM. The average structural model was obtained based on the intensities from the sharp diffraction spots, and the disorder in the real structure was analyzed by the features of the diffuse scattering as well as the chemical knowledge of zeolites. The 3D framework of PST-24 is built by cas- zz chains and d5r columns. The cas-zz chains are periodically distributed and isolated to each other throughout the entire crystal, while the d5r column pairs with two arrangements are randomly distributed. Since the two randomly distributed arrangements can close or open the connections between the channels running along the c-axis, the channel system in the actual PST-24 crystal is unique which can vary locally from 2D to 3D. The structure with the unique disorder was further confirmed by HRTEM images and the simulation of diffraction patterns. It was found that the high-silica (Si/Al = 200) PST-24 has high activity and selectivity for the dehydration of 1,3-butanediol to butadiene, which may result from the varying channel dimensionality that affects the intracrystalline diffusivity of guest molecules. Finally, three ordered PST-24 polytypes have been proposed. Their channel dimensionalities change consecutively from 2D, bi-level 2D, to 3D.

4.3 Structure determination of a metal-organic framework CAU-23 4.3.1 Introduction to MOF CAU-23 Exploring materials for efficient use of energy for cooling applications is very important. Adsorption-driven chillers (ADCs) with ultra-low driving temperature (푇푑푟푖푣푖푛𝑔 < 80 °C), which only need low driving energy and have a wide range of usable energy sources, are one environment-friendly option. The water sorption property of MOFs is attractive for this application. Several such MOFs have been reported, such as MIL-160 (90 °C)104,105, CAU-10-BDC (70 °C)106,107, and Al-fum (90 °C)108–110. However, it is challenging to obtain MOFs with high stability. Furthermore, structure determination is essential for understanding the properties of MOFs. In this work, 3D ED (cRED) was used to determine the structure of a highly stable Al-MOF, denoted as CAU-23. CAU-23 shows a low driving temperature (< 60 °C), which is mainly due to its unique crystal structure. 4.3.2 Specimen preparation and cRED data collection

CAU-23 ([Al(C6H2O4S)(OH) · xH2O]) was synthesized by mixing AlCl3 and NaAlO2 as the metal sources, and sodium thiophenedicarboxylate (Na2TDC) as the linker sources, under reflux conditions at ambient pressure. After several washing steps, nan-sized crystalline particles were obtained. The size of these nanoparticles is between 100 – 200 nm (Figure 4.7). Due to the serious peak-overlap of the PXRD pattern, 3D ED was used for the structure determination of CAU-23. Specimen preparation is the first and most important step to collect high-quality cRED data. Generally, the crystallinity of the crystals needs to be preserved and the crystals need to be evenly distributed on an EM grid with a distance of 5 to 10 μm to avoid overlap during rotation. In the beginning, the CAU-23 particles were deposited onto an EM grid by the following steps: crushing in a mortar, dispersing in ethanol,

Figure 4.7 Specimen optimization for the aggregation problem of nanoparticles. a-c SEM images taken from the grids prepared from different crystal suspensions. These suspensions were made by mixing 5 ml MOF suspensions with 1 drop, 2 drops, and 3 drops of HCl (0.1 mol/L), respectively. 34 applying a few drops onto the EM grid, and then drying in the air. However, this procedure was not successful due to the heavy aggregation of nanoparticles as shown in Figure 4.7a. The aggregation may be due to the surface charges of these nano-sized particles. Therefore, hydrogen chloride (HCl) was chosen to neutralize the surface charges. It was successful as shown in Figure 4.7. The final EM grid for cRED data collection was prepared under the condition of Figure 4.7c, plus an additional ultrasonication treatment (~ 30 s). cRED datasets were collected on a JEOL JEM-2100 LaB6 microscope (200 kV) at room temperature equipped with a Timepix camera (QTPX-262). The rotation speed of the goniometer, exposure time, spot size, and camera length were 0.45°s−1, 2 s, 2, and 40 cm, respectively. Four datasets with rotation angles between 70° to 90° were collected. The raw datasets were processed by REDp15,16 for the unit cell and space group determination and Dials111 for intensity integration. The reflection conditions were be deduced to be h00: h = 2n, 0k0: k = 2n based on the h0l, hk0, and 0kl reciprocal lattice planes (as shown in Figure 4.8), which is consistent with the space group P21212. The

Figure 4.8 Initial space-group determination by cRED. a Projection of 3D reciprocal lattice reconstructed from cRED data. The inset shows the crystal from which the data was collected. b-d 2D slices of h0l, hk0, and 0kl reciprocal lattice planes. (Reproduced with permission from Nat Commun. 2019, 10, 3025. Copyright (2019) Springer Nature) 35 unit cell parameters were determined to be a = 15.8 (4), b = 24.1 (5), and c = 14.1(3) in the orthorhombic lattice system. 4.3.3 Structure determination and analysis The structure of CAU-23 was solved and refined using SHELX. Basic crystallographic results were given in Table 4.2. All the atoms in the asymmetric unit were located with reasonable agreement factors (Figure 4.9, Table 4.2). Each Al connects to six O, forming an AlO6 polyhedron. These polyhedrons connect to each other forming rod-shaped inorganic building units (Al-O chain) running along the b-axis. These Al-O chains are built by alternating consecutive trans and cis corner-sharing AlO6 polyhedrons, corresponding to the straight and helical sections, respectively (Figure 4.10b). Furthermore, square channels are formed by the connection of these Al-O chains and organic linkers (TDC2-) and they are running along the b-axis with a side opening of 7.6 Å (Figure 4.10). These channels are essential for the high water sorption capacity. Table 4.2 cRED data processing and structure determination details of CAU- 23.

Number of data sets 4 Wavelength (Å) 0.0251 Resolution (Å) 1.13 Crystal system Orthorhombic Space group P21212 (No. 18) Unit cell a, b, c (Å) 15.8 (4), 24.1 (5), 14.1(3) Total No. reflections 19496 No. unique reflections (F > 4(F)/all) 2038/3665 MeanI/sigma(I) 3.89 Completeness 0.959 Parameters 210 R1 (F > 4(F))/ R1 (all) 0.206/0.263 GOF 1.466

Figure 4.9 Atomic structure of CAU-23 determined from cRED data. a Asymmetric structure unit and electrostatic potential map. The map is -3 -3 represented by Fobs (blue, 1.2 Å ) and Fo-Fc (green (positive value at 0.63 Å ), red (negative value at -0.63 Å-3)). b Atomic structural model in the full unit cell of CAU-23. (Reprinted with permission from Nat Commun. 2019, 10, 3025. Copyright (2019) Springer Nature)

Figure 4.10 Polyhedral structural model of CAU-23. a-b Structural model projected along the b- and c-axes, respectively. The waved square channels are propagating along the b-axis. c View of the Al-chain showing a clear helical twist feature. (Reproduced with permission from Nat Commun. 2019, 10, 3025. Copyright (2019) Springer Nature)

Topological analysis can simplify the structure description of complex structures, such as MOFs, and thus help to better understand the structure features. The net topology of MOFs normally composes of nodes (vertices) and linkers (edges), in which the metal ions (or metal clusters), organic linkers are regarded as secondary building units (SBUs)112,113. Carboxylate carbon atoms in the H2TDC linkers are simplified as the nodes in the 3-periodic net of CAU-23 (Figure 4.11). The whole topological net of CAU-23 is built by 8 unique nodes and 17 unique edges (transitivity 8 17). These nodes are 4- 37 connected and can be grouped into two categories, green and red, based on their vertex symbols. The green nodes are on the straight segment and the red nodes are on the helical twist segment. Furthermore, for each green node, there are two quadrilaterals, two hexagons, and two octagons {426282}; for each red node, there are two quadrilaterals, three hexagons, and one octagon {42638}. Therefore, the vortex symbol of the topological net of CAU-23 is {426282}{42638}.

Figure 4.11 Topological representation of the CAU-23 structrue, viewed along [001]. (Reproduced with permission from Nat Commun. 2019, 10, 3025. Copyright (2019) Springer Nature)

4.3.4 Conclusions In this work, the atomic structure of CAU-23 was determined using cRED data. It has a chiral structure in a non-centrosymmetric space group P21212. The AlO6 polyhedrons are connected by alternating units of four consecutive trans and cis, forming a helical twist Al-O chain. In addition, a channel system with a side opening of 7.6 Å is formed by interconnection between Al-O chains and TDC2- linkers. The channels are essential for molecular adsorption. Additional studies showed that CAU-23 is an ideal material for ultra-low temperature adsorption driven chillers, because of its low driving temperature of 60 °C, high uptake capacities of 0.37 gH2O/gsorbent, and excellent stability. These outstanding properties are mainly due to the unique structural features of CAU-23.

5. Method development and applications of 3D ED (MicroED) for structural studies of biomolecules

5.1 Introduction Biological molecules, including peptides (2-50 amino acids) and proteins (> 50 amino acids), exist in all living organisms and play a key role in all biological activities. Proteins are constructed from twenty types of amino acids, with functions of enzymatic catalysis, transport and storage, coordinated motion, mechanical support, generation and transmission of nerve impulses, and control of growth and differentiation. Peptides can exist in natural sources or be prepared by chemical synthesis, while the majority of them are encrypted in the proteins and can be released by enzymatic processes114. Both peptides and proteins can affect human health. They perform hormone and drug-like activities. Based on their mode of action, they can be classifiedobial, opioid,as antimicr antihypertensive, immunomodulatory, mineral binding, anti-thrombotic, and antioxidative114. Their functions or activities highly depend on their structures, such as amino acid compositions, hydrophobic/hydrophilic characteristics of amino acid chains, charge characters of amino acids, sizes of the molecules, and folding. Therefore, structural studies are essential to understanding and exploring their functions. Many experimental methods have been developed for structure determination of biological samples, such as single-particle cryo-EM, nuclear magnetic resonance, cryo-tomography, and crystallography-based methods (SCXRD, XFEL, and 3D ED/MicroED). These methods are complementary to each other depending on their advantages and limitations. For example, single- particle cryo-EM is powerful, however, has difficulties to study small proteins (< 60 kDa); cryo-tomography can study proteins in situ, however, has relatively low data resolution; SCXRD with a synchrotron source can solve structures fast and accurately, but needs large crystals (> 5 × 5 × 5 μm3); XFEL can work with micro-sized crystals and can perform time-resolved experiments, however, the sample consumption is huge. MicroED, as a relatively new method, has shown great potential for structure determination of nano- or submicron-sized protein crystals20,21,29,42,59,115. Moreover, it has been successfully applied for structure determination of peptide crystals116–119 and pharmaceutical compounds24–26. MicroED has potential to become a standard method similar to SCXRD, since TEM instruments are widely available in almost all universities and institutes. However, some limitations 39 prevent this method from wide applications in macromolecular crystallography. The first issue is the specimen preparation for protein samples, which is the most time-consuming and delicate step115. Furthermore, the data processing and structure determination software packages, initially developed for SCXRD, are not modified and optimized for MicroED. Dynamical effects also need to be considered to reveal more structural details. In this chapter, a pressure-assisted method for MicroED specimen preparation, denoted Preassis, is presented. Parameters that can adjust the vitrified ice thickness are studied. Results of specimen preparation from non-viscous to highly-viscous crystal suspensions and the features of a successful specimen are analyzed and discussed. A successful example of applying Preassis for preparing MicroED specimens and thereby high-quality data collection and structure determination are shown. Furthermore, radiation damage-induced atomic displacements of metal cations are discussed and a data processing strategy is proposed. Finally, MicroED data collected from an amyloid peptide specimen prepared by Preassis are analyzed, processed, and used for structure determination. The molecule packing and interactions are discussed to understand the self-assembly mechanism of peptide crystals. 5.2 A simple pressure-assisted method for MicroED specimen preparation 5.2.1 Introduction to MicroED specimen preparation For MicroED experiments, a specimen with submicron- or nano-meter sized protein crystals embedded in thin vitrified ice is required. Although it has been shown that the optimal crystal thickness for proteinase K is around 200 nm61, this value can vary depending on the unit cell dimensions, compositions, the energy of the incident electrons, crystal orientations, etc. Ideally, crystals should be located 5 to 10 μm away from each other on the grid. If they are too dense, the crystals will overlap during rotating and the vitrified ice will be thick since crystals can bring liquids; if they are too sparse, it will be time- consuming to find crystals to collect data from. Furthermore, the vitrified ice layer needs to be thin enough to maximize the signal-to-noise ratio and, at the same time, protect the crystal from dehydration. Protein crystallographers normally aim to obtain large crystals for X-ray experiments instead of sub-micron sized crystals. Some additional efforts are needed to obtain crystals with suitable sizes for MicroED experiments. It is possible to adjust the crystal size by changing some crystallization parameters, such as PEG concentration, pH value, protein concentration, temperature, as well as salt concentration120,121. Physical fragmentation has also been shown

40 as a possible way to obtain sub-micron-sized crystals and also improve the diffraction quality in some cases. The fragmentation can be vigorous pipetting, sonication or vortexing with beads60. In addition, cryo-FIB has been demonstrated as a more controllable way to obtain sub-micron crystals with target thickness39,56,62,63. However, none of these methods is universal and efficient. Once the protein crystals with suitable sizes are obtained, embedding the crystals in thin vitrified ice is another critical step. Too thick ice will not make the specimen beam transparent or will decrease the signal-to-noise ratio, and consequently, decrease the resolution of diffraction data. On the other hand, too thin ice may make the crystal dehydrated. To produce volume-exclusion effects for successful protein crystallization, some high molecular weight polymers (such as polyethylene glycols (PEG)) are commonly used. These polymers can make the crystallization buffer viscous and cause problems for MicroED specimen preparation38,39,115. An extreme case is the membrane protein crystals grown in lipid-cubic-phase (LCP), which is as viscous as toothpaste. Nowadays, the pipetting-blotting-plunging routine37 is also applied for MicroED specimen preparation, which was originally designed for specimen preparation of single-particle cryoEM. This method is not ideal for MicroED specimen preparation for two reasons; 1) loss of a majority of crystals onto the blotting paper and 2) insufficient removal of viscous liquids38. Back-side manual blotting38 has been claimed that it can keep more crystals on the grid and handle crystals in slightly viscous buffers. However, a detailed study is missing. Cryo-FIB can also deal with the viscosity problem39,62, especially for the LCP case40,64, but it is time-consuming. Nevertheless, there is an urgent need to develop a simple and universal method for MicroED specimen preparation and, therefore, to apply MicroED to structural studies of a wide range of biomolecular crystals. 5.2.2 The Preassis method and setup The core idea of Preassis is to remove the excess liquid through an EM grid with the assistance of suction/pressure. A simple setup of Preassis is shown in Figure 5.1. Requiring only a pump connecting to a flask, this setup can be easily realized in all cryo-EM labs. In this work, the key components include a Büchner flask (GLASSCO 500 mL), a pump machine (PC 3001 VARIO), and filter papers (Munktell FiltrakTM Grade3). It consists of the following specimen preparation procedures: i, Condense liquid ethane in a coolant container. ii, Glow discharge an EM grid to make it hydrophilic. The parameters are usually 20 mA current and 60 s, and they can be changed if required. The grid 41 can be any holey carbon EM grid. If the crystal density is relatively low and the crystal buffer is not very viscous, a grid with holes slightly smaller than the crystal size is suggested. However, grids with large hole sizes are recommended if the sample suspension is viscous. iii, Rest the filter paper (or glass-fiber filter, or any other support with high water sorption capacity) on top of the flask. iv, Put the glow-discharged grid onto the centre of the filter paper with the copper side facing down. v, Turn on the pump to produce suction underneath the filter paper. Using the same type of pump, flask, and filter paper as described above, a typical pressure of around 20 mbar is recommended for non-viscous samples if a Quantifoil R1.2/1.3 is used; while, for viscous samples, it requires a higher pressure (> 70 mbar) together with a grid with a larger hole size, e.g. Quantifoil R3.5/1. vi, Apply a drop of sample suspension onto the EM grid and keep the grid with suction for ~ 5 s (can be modified). The volume of the drop is typically 3 μl, but can be modified if necessary. vii, Pick up the grid and plunge freeze it into liquid ethane immediately after step vi. viii, Transfer the grid from the liquid ethane to a cryo-grid box stored in liquid nitrogen.

Figure 5.1 Schematic drawing of the Preassis setup. A drop (~ 3 μl) of crystal suspension is applied onto an EM grid resting on a support which has suction underneath produced by a pump. The extra liquid is removed through the support with the assistance of pressure. After a few seconds (usually ~ 5 s), the grid is manually picked up and plunged into liquid ethane for vitrification. Figure reproduced from Paper III.

Parameters, such as pressure, volume of the drop, type of EM grids, and suction time, should be modified or fine-tuned for a new sample or when the setup has been modified. Since currently the sample deposition, grid picking, and plunge-freezing need to be done manually, the specimens prepared under the same conditions can have small deviations, as shown in Figure 5.2. Every specimen condition was repeated three to five times in this thesis. The results show that the deviation can be minimized and will not affect the overall trend if we handle the process properly. It is highly recommended to make this method automated and implement a temperature and humidity controller, to improve the throughput of specimen preparation with high reproducibility. For example, a vertical setup of Preassis can be designed and implemented into a commercialized cryo specimen preparation instrument, such as Vitrobot (Mark IV).

Figure 5.2 Reproducibility of specimen preparation by Preassis. a-f TEM images taken from three MicroED speciemns prepared by Preassis using the same experimental parameters: 37.2 mbar pressure, 3 μl droplet, Quantifoil grid R 1.2/1.3, suction time ca 5 s, room temperature (~ 20 °C) and humidity (~35%) . Low magnification images (a-c) are distorted as a result of geometrical distortion of lenses. Tetragonal lysozyme crystal sample was used in this experiment. Figure reproduced from Paper III. The distribution of ice thickness of the specimen prepared by Preassis is inhomogeneous resulting from the non-uniform contact of the grid and filter paper. There are always some grid squares with thin ice and some grid squares with thick ice on the same grid, as shown in Figure 5.3. This suggests that we

43 could have a relatively large window to play the parameters to have enough grid squares with suitable ice thickness.

Figure 5.3 Inhomogeneous ice thickness distribution. a Overall ice thickness distribution on the grid. b-c Images taken from the area with relatively thick ice. d-e Images taken from the area with suitable ice thickness. The specimen was prepared by Preassis under 37.2 mbar using tetragonal lysozyme crystals. Figure reproduced from Paper III.

5.2.3 Advantages of Preassis Compared to Vitrobot, there are two major advantages of Preassis. Firstly, Preassis can preserve up to two orders of the number of crystals on the EM grid than that of Vitrobot, as shown in Figure 5.4. This is mainly because Preassis uses a kind of back-side blotting and the holey carbon film functions as a sieve to keep crystals, while Vitrobot is performed by double-side blotting and the filter paper facing the sample drop directly can take away a major portion of crystals. This also suggests that Preassis can use grids with different hole sizes to control the density of crystals on the EM grid. In the case when

44 the crystal density in the initial crystallization drop is low, it is recommended to use a grid with a hole size smaller than the crystal size.

Figure 5.4 Comparison of the density of tetragonal lysozyme crystals obtained from grids prepared by Preassis and Vitrobot. a-c Typical TEM images taken from the grid prepared by Preassis using 500  diluted sample. d-f Typical TEM images taken from the grid prepared by Vitrobot using 500  diluted sample. g-i Typical TEM images taken from the grid prepared by Vitrobot using 2 diluted sample.These images were collected on a ThemisZ microscope (300 kV) equipped with a Gatan OneView camera. Figure reproduced from Paper III.

Figure 5.5 Comparison of EM grids prepared by Preassis and Vitrobot under different humidity using a mixture of ZSM-5 microcrystals and 40% PEG 400. a- c Low magnification images of MicroED specimens prepared by Vitrobot (Mark IV) with blotting force 5, bloting time 5 s, and humidity of 35%, 80%, and 100%, respectively. d-e Low magnification images of MicroED specimens prepared by Preassis with pressure of 180 mbar, suction time 5 s, and humidity of 35% and 80%, respectively. The EM grids (Quantifoil R 3.5/1) were prepared at room temperature (~ 20 °C). The images were collected on a JEOL JEM-2100LaB6 microscope equipped with an Orius detector. We note that the images are distorted at low magnification especially at the edges, resulted from the geometrical distortion of lenses. Each experiment condition was repeated more than 4 times. Figure reproduced from Paper III.

Secondly, Preassis can take away the excess liquid more efficiently compared to Vitrobot, especially when the crystal suspension is viscous. A quantitative 46 study was performed by analyzing the total transparent areas of the TEM images of the specimens prepared by Preassis and Vitrobot at different humidity, as shown in Figures 5.5 and 5.6. The comparison between the specimens prepared at 35%, 80%, and 100% humidity by Vitrobot shows that high humidity can decrease the liquid adsorption ability of the filter paper (Figure 5.5a-c and Figure 5.6). In contrast, there is no obvious change in the ice thickness of the specimens prepared by Preassis at 35% humidity and 80% humidity. This could be because the pressure provides extra assistance for removing liquids and, therefore, the humidity has less influence on the ice thickness for the case of Preassis. The grid prepared at 80% humidity by Preassis has even thinner ice compared to that prepared at 35% humidity which may be due to the changes of the contact angle between the grid and filter paper in each specimen preparation experiment.

Figure 5.6 Quantitative study of the vitrified ice thickness of specimens prepared by Preassis and Vitrobot under different humidity using a mixture of ZSM-5 microcrystals and 40% PEG 400. This analysis is based on the TEM images of the specimens shown in Figure 5.5. The transparent areas (shown as no. pixels) of each grid were extracted using DigitalMicrograph and used to describe the overall ice thickness of each grid, as shown in image a. The sum of the transparent area of each grid is used to compare the overall ice thickness of the grid. The transparent areas of grids prepared under different specimen preparation conditions are shown in image b. Figure reproduced from Paper III. This advantage is significant, as shown in Figure 5.7. The specimen preparation of Sulfolobus acidocaldarius R2-like ligand-binding oxidase (R2lox) was difficult using Vitrobot even under extreme blotting conditions, as shown in Figure 5.7a-c. Since the ice was too thick and crystals were too sparse to collect MicroED data, this project was withheld for more than one- year until a specimen was successfully prepared by Preassis. As shown in Figure 5.7c and f, the diffraction resolution was improved significantly from 9.0 Å to 3.0 Å. Owing to the development of Preassis, high-quality MicroED 47 data of R2lox were collected and its structure was determined (Figure 5.8), which is the first new protein structure solved by MicroED with a searching model of 35% sequence identity59.

Figure 5.7 Improved data resolution by reducing the ice thickness using Preassis for viscous R2lox crystal suspension. a-b TEM images of a grid square and a crystal taken from a specimen prepared by Vitrobot using 100% humidity, 4 °C, two layers of blotting papers on each pad, 16 blotting force, and 10 s blotting time with a Quantifoil grid R 3.5/1. A typical ED pattern is shown in image c. d-e TEM images of a grid square and a R2lox crystal taken from a specimen prepared by Preassis using 30.7 mbar pressure and 5 s suction time with a Quantifoil grid R 3.5/1. A typical ED pattern is shown in image f. R2lox crystals were crystallized in a buffer containing 44% PEG 400. Figure reproduced from Paper III.

Figure 5.8 High-quality electrostatic potential maps allowing accurate model interpretation. a SaR2lox structure solved by MicroED with three colored selections as examples to show electrostatic potential maps in images b-e.

Electrostatic scattering potential maps 2Fo - Fc (contoured at 1σ; colored blue) and Fo - Fc (contoured at ±3σ; colored green and red for positive and negative peaks, respectively) and simulated annealing composite omit maps (contoured at 1σ; colored magenta) are shown for residues 164 - 177 (orange) and 235 - 249 (yellow) in images b and c, respectively, and for residues 202 to 217 (cyan) in images d and e, respectively. Simulated annealing composite omit electrostatic potential maps are calculated with sequential 5% fractions of the structure omitted. Only observed reflections were used for map calculations, i.e., no missing F(obs) were restored using a weighted F(calc). Oxygen and nitrogen atoms are colored red and blue, respectively, and carbons are colored according to the selections mentioned previously. Fo and Fc represent the observed structure factor and calculated structure factor, respectively. (Reprinted with permission from Sci Adv. 2019, 5, eaax4621. Copyright (2019) American Association for the Advancement of Science)

5.2.4 Parameters for the adjustment of vitrified ice thickness Many parameters can adjust the vitrified ice thickness on the grid, including type of support (filter paper, or glass fiber filter, or something else with water adsorption capability), contact angle between the support and grid, hole size of the grid, pressure applied onto the grid, and also the suction time. Here we studied the influence of hole sizes and pressure on the ice thickness (Figure 5.9). Orthorhombic lysozyme crystal suspension was used for this study. The results show that when a grid with a small hole size (R 1.2 μm) is used,

49 relatively high pressure should be applied to avoid too thick ice (Figure 5.9a and c); when a grid with a large hole size is used, relatively low pressure should be applied to prevent the crystals from dehydration (Figure 5.9b and d). In summary, increasing the pressure or hole size can produce thinner ice. On the contrary, the ice thickness increases with the decrease of the pressure or hole size. There is a balance between these two parameters.

Figure 5.9 Parameters for adjusting the vitrified ice thickness. a-d TEM images and diffraction patterns taken form the specimens prepared under different grid hole sizes and pressures. Diffraction patterns are taken from the crystals in the inserted displays (right bottom corner of the diffraction patterns). Orthorhombic lysozyme crystal suspension was used for this experiment. The TEM images shown here are the most representative situation of ice thickness of the specimens prepared under different conditions. Figure reproduced from Paper III.

Pressure is not critical for crystals grown in non-viscous media but can be used to fine-tune the ice thickness. When the crystal suspension is viscous, the pressure will be important to obtain thin ice as shown in Figure 5.10. This is significant especially when the suspension is highly viscous, such as containing 35% PEG 6000 as shown in Figure 5.10 d and h. Furthermore, the hole size of the grid also plays an important role when the crystal suspension is viscous. The ice thickness can be optimized significantly when a grid with a large hole size is used, as shown in Figure 5.10a-b and e-f. In reality, the pressure should be further increased to make the ice layer thinner in the very viscous case as shown in Figure 5.10d. Consequently, we recommend using grids with large hole sizes and high pressure for viscous crystal suspensions. In the case when crystals are small and the concentration of the crystal suspension is low, grids with small hole sizes should be used to keep more

50 crystals on the grid but high pressure (such as > 180 mbar) also needs to be applied.

Figure 5.10 Influence of pressure on the ice thickness of specimens prepared from crystal suspensions with different viscosities. a-h Low magnification images taken from the specimens prepared by Preassis using crystal suspensions with different viscosities, different hole sizes, and different pressures. The crystal suspensions were prepared by mixing ZSM-5 microcrystals and PEG 6000 with concentration of 15%, 25%, and 35%. Figure reproduced from Paper III.

5.2.5 Features of a successful specimen prepared by Preassis Since the ice thickness distribution across the grid is not homogeneous, it is important to identify the good grid squares and crystals for MicroED data collection. When the crystal suspension is not viscous, such as the case for tetragonal lysozyme, the grid squares with crystals embedded in a thin ice layer are normally those with straight edges and right angles, in which most of the carbon holes are empty except the holes covered by crystals (Figure 5.11a). Meanwhile, the area of the crystals on top of the carbon film shows blurred edges, while the area hanging over the hole shows relatively clear

Figure 5.11 Features of a successful specimen prepared by Preassis from non- viscous and viscous crystal suspensions. a TEM images and diffraction patterns taken from a specimen prepared from tetragonal lysozyme crystal suspension which is non-viscous. Quantifoil grid R 1.2/1.3 and pressure of 37.2 mbar were used in this experiment. b-c TEM images and diffraction patterns taken from specimens prepared from R2lox and GTPase crystal suspensions with 44% PEG 400 and 30% PEG 4000, respectively. Quantifoil R 3.5/1 grids were used for both experiments. 30.7 mbar and 181 mbar pressures were used for R2lox and GTPase, respectively. Figure reproduced from Paper III. 52 edges (as shown in the red-circled area in Figure 5.11a), indicating the ice thickness is optimized and ideal for MicroED data collection. When the crystal suspension is viscous, such as the cases for R2lox (44% PEG 400) and GTPase (30% PEG 4000), the good grid squares are those with smooth edges and round corners, in which the holes near the crystals are normally still covered by ice (Figure 5.11b and c). Inside those squares, the crystals are always embedded in the vitrified ice showing blurred edges (Figure 5.11b and c). When the above conditions are achieved, the good crystals or areas for MicroED data collection are always those near or hanging over the holes, where the ice thickness is minimized but the crystals are still hydrated. This ensures the MicroED data will have a high signal-to-noise ratio and resolution. The discussion here is important for future automated data collection and also useful for spreading the MicroED techniques. 5.2.6 Conclusions In this work, a pressure-assisted method, Preassis, was developed for MicroED specimen preparation. Preassis is a simple specimen preparation method for preparing MicroED grids from both non-viscous and viscous crystal suspensions. Compared to Vitrobot, Preassis can preserve more than two orders of the number of crystals on the grid and has higher efficiency of removing excess liquid, especially for viscous liquid. These advantages are significant since crystal density in the original crystallization drop is often low, and viscous additives are normally used for successful crystallization. Furthermore, the pressure and hole size of the grid were studied to adjust the vitrified ice thickness. Generally, to get optimized ice thickness for non- viscous sample suspensions, the grid with a large hole size may need to be combined with low pressure, while the grid with a small hole size may need to be combined with high pressure. For relatively high viscous buffer conditions, using a grid with a large hole size (e.g. R 3.5/1) and high pressure (e.g. >180 mbar) is recommended. Finally, the features of grid squares and crystals with optimized ice thickness were discussed for both non-viscous and viscous buffer conditions. Preassis is simple and widely feasible for MicroED specimen preparation and, therefore, will broaden the MicroED applications. In the future, an automated setup with an environmental chamber is necessary to improve reproducibility and also throughput.

5.3 Radiation damage induced atomic displacement of metal cations 5.3.1 Introduction to electron radiation damage The interaction between electrons and atoms can result in unscattered transmitting, elastic scattering, and inelastic scattering. The latter two types of scattering introduce radiation damage, limiting the spatial resolution of electron-beam imaging. Elastic scattering can cause knock-on displacement, while inelastic scattering can introduce noise, ionization, specimen heating, and mass loss122,123. For organic and biomolecular samples, the probability of knock-on displacement is smaller compared with the damage arising from the inelastic scattering. For biological samples, radiation damage starts to be apparent at exposures as low as 1 e-Å-2, and atomic resolution information (defined as better than 3 Å) is lost after 3 e-Å-2 exposure123. In this work, the effect of electron radiation damage on the atomic displacement of a soaked metal cation Gd3+ in tetragonal lysozyme crystals has been studied by refining the same lysozyme structural model against the data with different dose cut-offs. 5.3.2 Effects of radiation damage on the atomic displacement of Gd3+ cations A MicroED specimen was prepared from tetragonal lysozyme crystals soaked by Gd3+ (lysozyme: Gd3+). Preassis was used to take away excess liquid and vitrify samples. MicroED data were collected on a Themis Z microscope (300 keV) equipped with a Gatan OneView camera. The dose rate for data collection was 0.09 e-Å-2 s-1. Since the radiation dose increases with the number of diffraction frames, the dose cut-offs were set to 0-1.8 e-Å-2, 1.8-3.6 e-Å-2, 3.6-6.3 e-Å-2, and 0-6.3 e-Å-2, which correspond to 1-10 frames (1-10F), 11-20 frames (11-20F), and 21-35 frames (21-35F), and all data frames (1- 35F), respectively, as shown in Figure 5.12. The data processing and refinement statistics are given in Table 5.1. The difference electrostatic potential maps were analyzed to study the atomic displacements of the soaked Gd3+ cation with increasing electron radiation dose. The Gd3+ can interact with the oxygen atoms on the side-chain of amino acids Asp52 and Asn46. The position of Gd3+ is located by the strongest difference potential peak as shown in Figure 5.12. There is a weak peak nearby but too far away from neighbouring amino acids to form interactions, which could be a Gd3+ cation sharing a full occupancy with the Gd3+ at the strongest peak, or a Cl− ion. A more detailed study is needed to address the second peak, and here the focus is the atomic displacement of the Gd3+ at the strongest peak. 54

3+ Figure 5.12 a-d Difference electrostatic potential maps (mFo−DFc ) of the Gd position obtained from the refinement against MicroED data with different radiation dose. The positive mFo−DFc maps are shown in green, blue, yellow and pink in the images a-b, respectively. The negative maps are shown in red. All the difference maps are contoured at ±3.0σ. A carve of 2 Å around atoms is assigned for all densities. The position of Gd3+ ion in these images is obtained from the structure refinement against the data with 1.8 e-Å−2 dose. It is used to mark the changes of the difference potential maps. Figure reproduced from Paper V.

After a few refinement cycles against the data with different dose cut-offs, the atomic displacement of Gd3+ is represented by atomic B-factors. The overall atomic B-factor of the lysozyme molecule increased from 25.65 Å2, 32.22 Å2, to 48.56 Å2 when the dose was increased from 0-1.8 e-Å-2, 1.8-3.6 e-Å-2, to 3.6-6.3 e-Å-2, which is consistent with the previously published result123. Similarly, with the increase of electron dose, the difference map for Gd3+ changed from round and isolated spheres to an elongated blob as shown in Figure 5.12, and the B-factor of Gd3+ increased from 70.83 Å2, 73.75 Å2, to 76. 98 Å2. Such a high B-factor of the Gd3+ is mainly due to the weak bonding environment, and the further increase of the mobility is related to the increase in radiation dose. Note that the difference between the maps obtained from datasets with dose cut-offs of 0-1.8 e-Å-2 and 1.8-3.6 e-/Å2 is small, which 55 could be because other effects, such as dynamical scattering, contribute more to the final map quality than that from the radiation damage. Furthermore, when the structure was refined against all data ranges (Figure 5.12d), the overall B-factor of the structure is lower than that from data in the range of 21-35F, while the B-factor of Gd3+ increases significantly. This study suggests that, by processing and utilizing the data frames with a low dose cut- off, the atomic displacement induced by radiation damage can be minimized and, therefore, can help to locate the atomic positions. This data processing strategy can be used for detailed structural studies when the dose limitation is unknown. Table 5.1 Data processing and structure refinement statistics for lysozyme: GdCl3

Dataset 1-10F 11-20F All 21-35 Space group P43212 P43212 P43212 P43212 Unit cell a, b, c (Å) 80.43, 80.43, 80.43, 80.43,80.43, 80.43,80.43, 80.43, 37.73 37.73 37.73 37.73 α, β, γ (°) 90, 90, 90 90, 90, 90 90, 90, 90 90, 90, 90 Resolution (Å) 35.97-3.00 26.04-3.00 35.97-3.00 35.97-3.00 (3.11-3.00) (3.11-3.00) (3.11-3.00) (3.11-3.00) Rmerge 0.481 (1.410) 0.349 (1.546) 0.504 (1.964) 0.534 (3.466) Rpim 0.161 (0.466) 0.109 (0.467) 0.097 (0.376) 0.137 (0.867) I/σ 4.2 (1.8) 5.6 (2.0) 5.9 (2.2) 4.7 (1.3) CC1/2 (%) 87.1 (26.8) 96.4 (63.4) 93.3 (55.8 95.2 (44.6) Completeness (%) 82.5 (82.8) 79.3 (75.8) 93.6 (91.6) 76.4 (74.8) Wilson B-factor 41.4 48.7 52.2 64.4 (Å2) Refinement No. of reflections 2246 (217) 2156 (197) 2551 (240) 2081 (196) Rwork 0.2157(0.2529) 0.2018 (0.2708) 0.2211 (0.2326) 0.2231 (0.2822) Rfree 0.2603 0.2373 (0.3267) 0.2397 (0.2719) 0.2469 (0.2978) (0.3000) No. of atoms Non-hydrogen 993 993 993 993 Protein 992 992 992 992 Gd3+ ion 1 1 1 1 B factor (Å2) Protein 25.65 32.22 36.48 48.56 Gd3+ Ion 70.83 73.75 91.99 76.98 R.m.s. deviations Bond lengths (Å) 0.007 0.009 0.008 0.008 Bond angles (°) 1.548 1.862 1.780 1.649 Ramachandran Favored (%) 95.24 94.44 94.44 92.86 Allowed (%) 4.76 5.56 5.56 7.44 Outliers (%) 0.00 0.00 0.00 0.00 Rotamer 0.00 0.00 0.00 0.00 outliers(%) Clashscore 8.24 5.15 11.85 8.24

Highest-resolution shell values are shown in parentheses A total of 9 crystals were merged.

*Table reproduced from Paper V.

5.3.3 Conclusions In this work, the effect of radiation damage-induced atomic displacement has been studied. The mobility of Gd3+ increases with the increase of electron radiation dose. Processing MicroED data with a certain cut-off of radiation dose could be a strategy to limit the radiation damage and reveal more structural details. Furthermore, since the final structural model can be improved and more structural details can be revealed by limiting the dose during data processing, it will also benefit structure-based drug discovery. Different samples will have different limits of electron radiation dose, therefore, an automated data processing software would be beneficial for providing the best cut-off of radiation dose immediately after the data collection.

5.4 Structural studies of amyloid Ac-KLVFF 5.4.1 Introduction to Ac-KLVFF Since the energy of the native state of proteins is only slightly lower than that of the misfolded states, improperly folded proteins can occur124, consequently causing some severe neurodegenerative diseases, such as Alzheimer’s, Huntington’s, and Parkinson’s125. Amyloid-β (Aβ) is related to the development of Alzheimer’s disease, where the short Aβ fragment, KLVFF (Aβ16-20), was known as a strong inhibitor to prevent Aβ from toxic aggregations126,127. The understanding of KLVFF’s folding is therefore important. Acetylated KLVFF (Ac-KLVFF) was successfully crystallized via rapid self-assembly128, however, its atomic structure was unsolved. It is crucial to obtain the structure to understand its folding mechanism as well as the biological importance of amyloid crystals. In this work, the atomic structure of Ac-KLVFF has been revealed by MicroED, and a detailed study of the driven forces for self-assembly is performed. 5.4.2 Structure determination using MicroED data A MicroED specimen of Ac-KLVFF crystals was prepared by Preassis with 25 mbar pressure, 5 s, and Quantifoil grid R 1.2/1.3. Eighteen MicroED datasets were collected with a rotation speed of 0.23°s-1 and exposure time of 0.5 s/frame. The dose rate was estimated to be 0.1 e-Å-2s-1. Because of the preferred orientation of crystals, eighteen datasets were collected with different starting rotation angles and merged by XSCALE to improve the data completeness. MicroED data (Figure 5.13) show that Ac-KLVFF was crystallized in a monoclinic crystal system. Combining the ED pattern and the corresponding crystal image, the fastest crystal growth direction has been determined to be along the b-axis, as shown in Figure 5.13f.

Figure 5.13 a TEM image of Ac-KLVFF crystals. b-e Projection and 2D slices of the 3D reconstructed reciprocal lattice from MicroED data. Reflection conditions can be deduced to be hkl: h + k = 2n; 0kl: k = 2n; h0l: h = 2n; hk0: h + k = 2n. f Diffraction pattern used to determine the crystal growth direction. The inset is the corresponding crystal image collected at defocused diffraction mode. Figure reproduced from Paper VI.

The atomic structure of Ac-KLVFF was determined ab-initio by SHELX129, as shown in Figure 5.14. Details of data processing and refinement are givenin Table 5.2. Ac-KLVFF crystal structure is built by antiparallel β-sheets stacking along both the a- and b-axes. The antiparallel β-sheets along the b- axis interact by hydrogen bonding as shown in Figure 5.14a, while along the direction of the a- and c-axes, the β-sheets interact by hydrogen bonding, as well as T-shaped 휋 − 휋 stacking. These interactions are known as non- specific interactions which contribute to the molecular assembly, protein folding, and stability. Furthermore, close-range electrostatic interactions (salt bridges), defined by spatially proximal pairs (within 4 Å) of oppositely

59 charged residues, can also stabilize the protein structure130. In the molecule of Ac-KLVFF, there is one positively charged nitrogen on Lys16 terminus and one negatively charged carbonyl oxygen on the Phe20 terminus, as shown by the electrostatic potential map in Figure 5.14a. These charged terminals meet together in the ac-plane and form parallelogram nets of salt bridges as shown in Figure 5.15a. The electrostatic potential distribution for the peptide packing is illustrated in Figure 5.14a and b. These salt bridge nets further improve the stability of the crystals.

Figure 5.14 a Structure projection along the a-axis, showing hydrogen bonding between the antiparallel β-sheets. b Structure projection along the b- axis, showing hydrogen bonding and salt bridges between the molecular layers along the a-direction, and the T-shaped 휋 − 휋 stacking in the molecular layers. The pink arrows illustrate the antiparallel stacking of Ac- KLVFF peptides. Figure reproduced from Paper VI.

Figure 5.15 a Parallelogram nets of salt bridges and electrostatic potential map projected along the b-axis. b Electrostatic potential map projected along a-axis. The electrostatic potential map was generated by Pymol. Red and blue coloured maps represent the negative potential and positive potential, respectively. Figure reproduced from Paper VI.

Table 5.2 Crystallographic parameters of the structure determination of Ac-KLVFF. cRED Crystal size in diameter (μm) 0.2 – 0.8 No. of data sets merged 18 Wavelength (Å) 0.0251 Resolution (Å) 0.90 (0.95-0.90) Crystal system Monoclinic Space group C2 (No. 5) Unit cell a, b, c (Å) 21.545(24), 9.673(24), 21.756(23) β (°) 100.362(31) No. unique reflections (F > 4(F)/all) 4420/6256 Mean I/sigma(I) 5.83 (1.95) Redundancy 4.52 (2.06) Completeness 0.911 (0.930) CC1/2 0.979 (0.539) No. of atoms (non-hydrogen) 200 Parameters 405 Restraints 71 R1 (F > 4(F))/R1(all) 0.178/0.200 GOF 1.322 R.m.s. bond length deviation (Å) 0.0232 R.m.s. bond angle deviation (°) 2.51 Flack x No 1. Numbers reported in parentheses correspond to values in the highest-resolution shell (0.95 - 0.90 Å). 2. The unit cell parameters obtained from X-ray diffraction was also used for the structure refinement against MicroED data (The error of the unit cell parameters from the electron diffraction data is slightly larger than that from the X-

61 ray diffraction data because of the lens distortion and the height changing during the data collection).

* Table reproduced from Paper VI. 5.4.3 Crystal growth discussion The fastest-growing direction of the Ac-KLVFF crystal is along the b-axis. It was determined based on the diffraction pattern and the corresponding crystal image, as shown in Figure 5.13f. Furthermore, based on the AFM images and measurements, each crystal has a flat surface on the top and side-surfaces with a step height of ~ 2.3 nm (Figure 5.16a). This suggests that the crystals are built by nano-thick lamellae. Since the measured thickness is close to the calculated d-spacing between the ab-planes (2.1 nm), we could deduce that the individual lamella is a 2D crystal with only one unit along the c-axis, as illustrated in Figure 5.16b. The difference between the measured and calculated thicknesses may result from the experimental conditions during AFM measurements. A perspective view (Figure 5.16c) of the structural model along the c-axis explained the second AFM image in Figure 5.16a. Previous study128 proposed that the growth of this amyloid crystal may start with the nuclei aggregation of nano-lamella and then follow an Ostwald ripening process with the fast accreting of monomers. This hypothesis was

Figure 5.16 a AFM images of an individual Ac-KLVFF crystal revealing the underlying lamellar sub-structure with a lamellar depth of ~ 2.3 nm. b-c Atomic structural model projected along the b-axis and the corresponding perspective view along the c-axis. Figure reproduced from Paper VI.

62 confirmed here by the atomic structure of the crystals. Furthermore, the salt bridges and hydrogen bonding between these lamellae contributed to the assembly and stability of the Ac-KLVFF crystal. 5.4.4 Conclusions In this work, the atomic structure of the Ac-KLVFF crystal has been determined by MicroED. The structure was solved in space group C2 with unit cell parameters of a = 21.545 (24), b = 9.673 (24), c = 21.756 (23), and β = 100.362 (31)°. It has been revealed that the Ac-KLVFF β–sheets are antiparallel stacked along both the b- and a-axes, building a 2D lamella. In each lamella, the intermolecular interactions, including hydrogen bonding, T- shaped 휋 − 휋 stacking between neighboring phenylalanines (19FF20), as well as the salt bridges between the positively charged nitrogen on the Lys16 terminus and the negatively charged carbonyl oxygen on the Phe20 terminus, contribute to the assembly and stability of individual lamella. Furthermore, salt bridge and hydrogen bonding nets are also formed between the 2D lamella, which drives the assembly of the lamella, and consequently, forming 3D crystals. The close-range electrostatic interaction (salt bridges) and hydrogen bonding are the main driving forces for the assembly of this amyloid crystal. This is further confirmed by experimental observations of the assembly/disassembly crystallization process with the change of pH. It is known that pH can modulate the charges of the molecules. Therefore, it can affect the electrostatic interactions and hydrogen bonding between molecules and further change the molecule packing during crystallization131. Finally, this work also confirmed the crystal growth mechanism proposed before128. Because of the unique structure of the Ac-KLVFF crystals, it has been shown that these crystals have an active optical waveguiding property with an excitation wavenumber of 488 nm. This property provides a potential application in photonic devices.

6. Summary

In summary, this thesis has shown that 3D ED is a powerful crystallographic method to study and explore the structural details of crystalline samples, ranging from inorganic porous materials to biomolecular crystals. In this thesis, 3D ED (cRED/MicroED) has been applied for the determination of three interesting and novel crystal structures: a silicate zeolite PST-24 with a unique channel system, an aluminium MOF CAU-23 with exceptional water adsorption property, and a peptide fragment Ac-A16-20 of amyloid- related to Alzheimer’s disease. Furthermore, new methods and protocols have been developed to push the limitations of crystal structure determination of biomolecules by 3D ED. Firstly, 3D ED has been applied for structure determination of two porous materials, a disordered zeolite PST-24 and a MOF CAU-23. The structure of the disordered PST-24 was determined by cRED combined with HRTEM. It shows a unique channel system with locally varying dimensionality from 2D to 3D. Moreover, the structure of MOF CAU-23 is built by helically twisted Al-O chains and TDC2- linkers, forming a chiral structure with square channels. The unique structure of CAU-23 provides high stability and excellent water adsorption capacity, making it an ideal material for ultra-low temperature adsorption-driven chillers. These works show the power of 3D ED for crystal structure determination of beam-sensitive and complex porous materials. Secondly, to push the limitations of crystal structure determination of biomolecules by 3D ED, a specimen preparation method, denoted Preasis, has been developed and an investigation of the effects of radiation damage on protein structures has been performed. Preassis is a simple pressure-assisted specimen preparation method that can overcome the challenges in the application of MicroED on biological samples with high viscosity and low crystal concentration. This method has been successfully applied for the specimen preparation of several biomolecular crystals including a novel R2lox metalloenzyme, which was crucial for its structure determination. The study of the radiation damage-induced atomic displacement of 퐺푑3+ in the tetragonal lysozyme structure shows that the data quality and final structural model can be improved by processing MicroED data with a certain low-dose cut-off. Finally, a crystal structure of Ac-A16-20 has been studied by MicroED.

Ac-A16-20 crystals exhibit an active optical waveguiding property, showing a potential application in photonic devices.

7. Future perspectives

This thesis has shown that 3D ED is a powerful method for the structural study of crystalline samples, including inorganic porous materials and biomolecules. One specimen preparation method has been developed which could further extend the 3D ED for structure determination of biomolecules. Despite the great success of 3D ED, there are still some questions and problems that need to be answered and solved to make it a more widely accessible method. First of all, obtaining protein crystals with sub-micron sizes is still a challenge for applying 3D ED for the structure determination of protein crystals. Most of the protein crystals are grown for X-ray experiments with several micrometers or even millimeters in size. They are too big for 3D ED experiments. Although several methods, such as cryo-FIB39,40,56,62 and physical fragmentation60, have been tried to get suitable crystal sizes, these methods are either unsuitable for high-throughput specimen preparation or often damage the crystallinity. It has been shown that crystallization parameters can be used to control the crystal size120,121 for XFEL experiments. This may be also a future direction for controlling crystal sizes for 3D ED experiments. A detailed study will be needed to address the crystal size problem for 3D ED experiments. Secondly, many software packages have been developed for 3D ED data collection, while the data processing software packages, originally designed for X-ray data, have not yet been optimized for 3D ED data. For 3D ED data, there is a need to correct the lens distortions, the changes of the sample height during goniometer rotating, the mosaicity model, etc. Furthermore, to explore more details of the structure, such as hydrogen positions, charge states, and chirality of molecules, several things need to first be addressed. In terms of data collection, the radiation dose needs to be carefully chosen to limit global radiation damage as well as site-specific damage. Moreover, an energy filter can be used to filter out the inelastic scattering and to improve the accuracy of the measurement of diffraction intensities, especially for diffraction at the low-resolution range which plays an important role in determining the ionization states. In terms of data processing and structure determination, not only the data processing software needs to be optimized, but also the structure determination software needs to be further developed to take the dynamical scattering and electron scattering factors for charged atoms into account. Last but not least, MR has also been widely used for solving the phase problem in 3D ED for macromolecules, while no experimental phasing methods have 65 been developed. Theoretically, SIR or MIR should work for 3D ED data as well. Based on the study of Christoph Burmester and Rasmus R. Schröder, in the ideal case, the isomorphous information in 3D ED data is about 3 times less than that in X-ray data132. This indicates that it will be practically more difficult to apply isomorphous phasing for 3D ED data. In addition, due to the radiation damage and the difficulties of determining a common origin of the phases in different projections, so far no 3D protein crystals have been phased by imaging. There are 10% of activities, such as SAD and MAD phasing, focused on phase determination based on the X-ray data from the last ten years at Diamond3. This suggests that it is still meaningful to explore experimental phasing methods for protein structure determination using 3D ED data.

Sammanfattning

Det första och mest avgörande steget för att förstå och utforska materials egenskaper och tillämpningar är att bestämma deras atomära strukturer. Röntgenkristallografi har varit den mest etablerade metoden för strukturbestämning av kristallina material. Denna metod har emellertid problem med kristaller i nano- eller submikronstorlek eftersom det krävs stora kristaller (> 5 × 5 × 5 μm3) för enkristallröntgendiffraktion (SCXRD), toppöverlappning i pulverröntgendiffraktion (PXRD) och stor provförbrukning samt låg tillgänglighet hos frielektronlaser med röntgenstrålning (XFEL). Elektronkristallografi, inklusive elektrondiffraktion (ED) och högupplöst transmissionselektronmikroskopi (HRTEM), blir en kraftfull kompletterande metod som kan hantera kristaller i nano- eller submikronstorlek med relativt god datakvalitet där både diffraktion och fasinformation kan erhållas från samma prov. Vidare har tredimensionell elektrondiffraktion (3D ED) visat stor potential för att klarlägga strukturen i oorganiska och organiska prover i nano- eller submikronstorlek, såsom porösa material, farmaceutiska föreningar och makromolekylära kristaller. I denna avhandling genomfördes strukturbestämning av porösa material och biomolekyler och metodutveckling provpreparering baserat på 3D ED, specifikt kontinuerlig tionselektrondiffraktion rota (cRED) / mikrokristallelektrondiffraktion (MicroED). Zeoliter och metallorganiska ramverk (MOF) är porösa material som lockar stort vetenskapligt intresse för sina breda tillämpningar inom katalys, gasseparation, adsorption etc. Strukturbestämningen är en av utmaningarna för dessa material eftersom de normalt är av nanostorlek, har relativt låg stabilitet och lätt bildas med oordning. Därför applicerades 3D ED (cRED) i denna avhandling först för strukturbestämning av dessa porösa material. Inledningsvis bestämdes strukturen hos en oordnad zeolit PST-24 genom cRED kombinerat med HRTEM. En fullständig studie av oordningen och varierande intrakristallin kanaldimensionalitet genomfördes baserat på den klarlagda strukturen. Vidare avslöjades den kirala strukturen hos en nanokristallin MOF CAU-23 med cRED. Dess unika strukturella och topologiska egenskaper gör den idealisk för tillämpning i adsorptionsdrivna kylaggregat med extremt låga temperaturer. Det är mer utmanande att tillämpa 3D ED (microED) på organiska molekyler, särskilt makromolekyler. En av de främsta orsakerna är svårigheterna med preparering av prover för makromolekylära / proteinkristaller, vilket är det mest tidskrävande och känsliga steget. I allmänhet måste kristaller vara nanometer- eller högst submikrometer tjocka för att undvika för mycket 67 multipel spridning, och utspridda med separerade kristaller. Dessutom behöver proteinkristaller inbäddas i tunn amorf is för att skydda dem från uttorkning och skador under högvakuum och elektroner med hög energi. Att få ett tunt isskikt kan emellertid vara svårt när kristallsuspensionen är viskös. Viskositet är en vanlig och populär parameter för framgångsrik kristallisering. Följaktligen utvecklades i denna avhandling en tryckassisterad metod, benämnd Preassis, för preparering av MicroED-prover. Denna metod kan hålla fler kristaller på ett TEM-rutnät jämfört med en generell tvåsidig pappersblottingmetod. Ännu viktigare är förmågan att hantera kristaller som odlas under olika buffertförhållanden, särskilt de som odlas i högviskösa buffertar. Denna metod kan hjälpa till att utvidga tillämpningarna av MicroED. Dessutom kan Preassis appliceras på andra organiska små molekyler, såsom farmaceutiska föreningar och peptider. Dessutom har effekten av elektronstrålningsskador på atomförskjutningen av metallkatjoner tillsammans med en behandlingsstrategi med en viss dosavgränsning studerats. Slutligen bestämdes strukturen av amyloidpeptid från början av MicroED- data, som byggs av de alternerande antiparallella β-arkmotiven av Ac-KLVFF.

Acknowledgments

First of all, I would like to appreciate Prof. Xiaodong Zou for her great supervision during my Ph.D. study. You are not only a fantastic scientist but also an excellent teacher. You taught me how to grasp the key concept during research, the way of thinking when facing difficulties, how to find the positive points from negative experimental results, how to build collaborations, and how to be a real scientist with rigorous logic and objective attitudes. These skills will undoubtedly benefit my future career and life. Your great passion, diligence, and persistence in science encouraged me greatly throughout my research. I am very appreciative that you have built such a supportive and warm research group which created a great working environment for me. Thank you for your unlimited support and encouragement. It has been my great pleasure and honor to be your student. I also sincerely appreciate my co-supervisor Dr. Hongyi Xu. Thank you for leading me to the MicroED research field and providing me with opportunities to work with proteins which makes me feel that my work is meaningful to human life. Thank you for teaching me crystallography knowledge and experimental skills, including specimen preparation, microscope operation, data collection, and processing. This knowledge and skills were critical for my Ph.D. study. You are a scientist always with passion, new ideas, and confidence, which inspired and encouraged me considerably. I am always inspired by your excellent skills in presentation, communication, and building collaborations which have taught me a lot and will be very useful for my future career. I also appreciate all of your great discussions, suggestions, revisions, and modifications of my scientific writing. Furthermore, I would like to thank you for your great patience, kindness, and support during my Ph.D. study. I want to express my gratitude to Prof. Sven Hovmöller. Thank you for your introduction to electron crystallography and also the introduction to Sweden. You shared a lot of interesting scientific stories with us, which are always very inspiring. I also want to thank you for helping me revise my thesis. I also appreciate Dr. Wei Wan. Thank you for explaining the Swedish culture, introducing the working environment at MMK, and also providing TEM training to me. These were very helpful at the beginning of my Ph.D. I would like to give many thanks to Dr. Tom Willhammar, Dr. Andrew Ken Inge, and Dr. Thomas Thersleff. Thank you for your teaching, support, and help during my Ph.D. study. I also want to thank Dr. Zhehao Huang, Dr. Maria Roslova, and Dr. Yi Luo. I appreciate your valuable discussions and help. You were always kind and available whenever I had questions or needed help. I

69 also would like to give many thanks to Dr. Bin Wang and Dr. Taimin Yang for their help when I had computer or program issues. I want to express my heartfelt thanks to my officemate, Molly Lightowler. Thank you for helping me correct my English writing, especially for this thesis. I also would like to give my great appreciation to Dr. Cheuk-Wai Tai and Dr. Anumol Ashok for TEM training and explanations of the basic principles of TEM. I want to give my great appreciation to my collaborators: Prof. Suk Bong Hong, Dr. Donghui Jo, Dr. Dirk Lenzen, Prof. Norbert Stock, Dr. Hugo Lebrette, Dr. Marta Carroni, Dr. Helena Taberman, Prof. Martin Högbom, Dr. Kristine Grave, Dr. Karin Walldén, Dr. Rei Matsuoka, Dr. Julian Conrad, Dr. Sorbi Rathore, Agnes Moe, Dr. Christian Bortolini, and Prof. Mingdong Dong. Thank you for your wonderful contributions to the collaborated projects and fruitful discussions. I sincerely appreciate my other lovely and helpful group members: Laura Samperisi, Victor Bengtsson, Elina Kapaca, Meng Ge, Erik Svensson Grape, Jungyoun Cho, Laura Calmanovici Pacoste, Jiaoyan Xu, Dr. Gerhard Hofer. I want to express my thanks to our previous group members: Dr. Stef Smeets, Dr. Magda Cichokka, Dr. Yunchen Wang, Dr. Ning Yuan, Dr. Jonas Ångström, Dr. Aditya Dharanipragada, Dr. Max Clabbers, and Dr. Li Jian. I thank you all for providing a great work environment for me and your help and valuable discussions. Without your help and support, I wouldn’t have had such a nice Ph.D. study experience over the last four years. I would like to many thanks to my colleagues in MMK: Prof. Lars Eriksson, Dr. Anne Ertan, Shihui Feng, Alisa Gordeeva, Dr. Jekabs Grins, Prof. Ulrich Häussermann, Dr. Kjell Jansson, Prof. Mats Johnsson, Dr. Hui Wang, Stefanie Siebeneichler, Dr. Zoltan Bacsik, Dr. Tamara Church, Prof. Niklas Hedin, Xia Wang, Atefen Khorsand Kheirabad, Vahid Saadattalab, Prof. Gunnar Svensson, Jakob Paulin, Rolf Eriksson, Camilla Berg, Christer Degerstedt, Tatiana Bulavina, Helmi Frejman, and so on. Thank you to all the colleagues I couldn’t list here for creating such a nice work environment at MMK and for your help over the last four years. I also want to express my gratitude to my previous supervisor Prof. Jinping Zhang and his wife Dr. Dejie Li at Suzhou Institute of Nano-Tech and Nano- bionics, Chinese Academy of Science. Thank you for your encouragement and help towards my studies and life. I have learned a lot from your optimistic attitude and open minds towards life and work. I would like to give my appreciation to Prof. Qibin Yang at Xiangtan University. Thank you for providing me a short-time internship to learn crystallography. This knowledge has been very important for my PhD study. I also sincerely appreciate Prof.

Jixue Li at Zhejiang University. Thank you for your support and encouragement during my PhD application. In the end, I want to give my greatest thanks to my parents for their support and understanding. Thank you for your endless love and effort in providing me a good family environment and giving me enough freedom to choose what I want. I also want to thank my brother, my uncles, and my friends for helping me to solve some daily life problems and sharing their life experiences with me. Finally, I want to thank my partner, Rei Matsuoka. Thank you for your love that makes me feel peaceful and happy.

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