Super-Resolution Imaging with Graphene

biosensors

Review Super-Resolution Imaging with Graphene

Xiaoxiao Jiang 1, Lu Kong 1, Yu Ying 2, Qiongchan Gu 1, Jiangtao Lv 1, Zhigao Dai 3 and Guangyuan Si 4,*

1 College of Information Science and Engineering, Northeastern University, Shenyang 110004, China; [email protected] (X.J.); [email protected] (L.K.); [email protected] (Q.G.); [email protected] (J.L.) 2 College of Information and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China; [email protected] 3 Engineering Research Center of Nano-Geomaterials of Ministry of Education, Faculty of Materials Science and Chemistry, China University of Geosciences, 388 Lumo Road, Wuhan 430074, China; [email protected] 4 Melbourne Centre for Nanofabrication, Victorian Node of the Australian National Fabrication Facility, Clayton, VIC 3168, Australia * Correspondence: [email protected]

Abstract: Super-resolution optical imaging is a consistent research hotspot for promoting studies in nanotechnology and biotechnology due to its capability of overcoming the diffraction limit, which is an intrinsic obstacle in pursuing higher resolution for conventional microscopy techniques. In the past few decades, a great number of techniques in this research domain have been theoretically proposed and experimentally demonstrated. Graphene, a special two-dimensional material, has become the most meritorious candidate and attracted incredible attention in high-resolution imaging domain due to its distinctive properties. In this article, the working principle of graphene-assisted imaging devices is summarized, and recent advances of super-resolution optical imaging based on graphene are reviewed for both near-ﬁeld and far-ﬁeld applications.

Keywords: super-resolution imaging; graphene plasmonics; evanescent-ﬁeld enhancement

Citation: Jiang, X.; Kong, L.; Ying, Y.; Gu, Q.; Lv, J.; Dai, Z.; Si, G. Super-Resolution Imaging with 1. Introduction Graphene. Biosensors 2021, 11, 307. Graphene is a two-dimensional material made up of sp2-hybridized carbon arranged https://doi.org/10.3390/bios in a honeycomb crystal lattice with one-atom thickness [1]. Since single-layer graphene 11090307 flakes were experimentally isolated by Geim and Novoselov in 2004 [2], it has drawn remarkable attention owing to its perfect structural [3], optical [4], electric [5], and ther- Received: 31 July 2021 mal [6,7] properties. In the past few years, the research on graphene has made significant Accepted: 28 August 2021 progress because many new effective synthesis methods of graphene in different types Published: 30 August 2021 have been explored and accomplished including micromechanical exfoliation [8], growth on various substrates [9], deposition [10–12], and so on [13–15]. Publisher’s Note: MDPI stays neutral One of the most important properties of graphene is that the complex conductivity with regard to jurisdictional claims in can be dynamically tuned by external parameters [16–18] such as electric field, magnetic published maps and institutional affil- field, and gate voltage [19,20], which makes graphene behave like thin metallic materials iations. that possess a negative permittivity at low frequencies [21]. In addition, the surface plasmon polariton in graphene is different from conventional plasmons in both metals and two-dimensional electron gases [22,23]. For instance, graphene plasmons show high confinement and relatively low loss with more flexible features. All these unique features Copyright: © 2021 by the authors. have made graphene a promising candidate for a variety of crucial applications [24,25], Licensee MDPI, Basel, Switzerland. such as super-resolution imaging and optical biosensing [26–28]. This article is an open access article After the first microscope was invented and named as well as applied to observe distributed under the terms and cells successfully in the 17th century, microscopic techniques remain the most widespread conditions of the Creative Commons imaging method and always play an irreplaceable role in the research field of nanotech- Attribution (CC BY) license (https:// nology and biotechnology, especially in bioscience [29], owing to its numerous advan- creativecommons.org/licenses/by/ 4.0/). tages (e.g., noninvasive, reliable, suitable for various samples, and so on). However,

Biosensors 2021, 11, 307. https://doi.org/10.3390/bios11090307 https://www.mdpi.com/journal/biosensors Biosensors 2021, 11, 307 2 of 15

the resolution of traditional fluorescence microscopy is fundamentally limited to λ⁄2NA (λ is the wavelength of the incident light and NA is the numerical aperture). This intrinsic limit, also known as diffraction limit [29,30], has become the main obstacle to high-resolution optical imaging. Thus far, a great number of novel methods for achieving super-resolution imaging have been proposed and demonstrated experimentally in both near field and far field, such as near-field scanning optical microscopy (NSOM) [31–33]; far-field superlens (FSL) [34–36], hyperlens [37–39], and metalens [40–43]; stimulated emission depletion microscopy (STED) [44–47]; stochastic optical reconstruction microscopy (STORM) [48–52]; structured illumination microscopy (SIM) [53–55]; plasmonic structured illumination microscopy derived from SIM [56–58], and so on [59,60]. It is worth mention- ing that graphene-related materials exhibit different properties according to their lateral size, number of layers and oxidation degree. For specific applications, graphene of different lateral sizes shows different performance expressions even if they are similar in terms of defects and number of layers. As the size increases, it becomes more difficult to disperse and composite with graphene. These controllable properties will also affect the device performance of graphene-assisted imaging components when graphene is integrated into a super-resolution imaging system. However, remarkable improvements are achievable. There are several super-resolution technologies which have been cooperated with graphene such as NSOM, hyperlens, superlens, STORM, and SIM. This review presents a comprehensive summary of the research on super-resolution optical imaging based on graphene, including both experimental and theoretical studies. The article first introduces the principle of super-resolution imaging with graphene and the following sections will focus on different imaging methods with graphene found in the bibliography and categorized by application regions (near field and far field).

2. Working Principle of a Super-Resolution Imaging System with Graphene Achieving super-resolution imaging means to overcome the diffraction limit origi- nated from the exponential decay of the evanescent waves which carry the high spatial frequency information of the objects. In essence, overcoming the fundamental limit means magnifying the evanescent waves directly or converting evanescent waves to propagating ones and further providing magnification. Graphene can significantly enhance evanescent fields due to the fact that its conductivity can be tuned in the infrared and terahertz (THz) regions [61,62]. Among all these amazing properties of graphene, the one that is most worthy of mention is that the surface conductivity of graphene can be tuned via external parameters [19,63]. The surface conductivity of graphene can be calculated by the Kubo for- mula [64–66]:

2 µc 2 ie kBT µc − ie 2µc − }(w + i/τ) = + kBT + + σg 2 2 ln e 1 ln (1) π} (w + i/τ) kBT 4π} 2µc + }(w + i/τ)

−19 −23 where e = −1.6 × 10 C is the electron charge, kB = 1.3806505 × 10 J/K is Boltzmann − constant, and } = 1.05 × 10 34 J·s is the reduced Planck constant. The surface conductivity of graphene depends on Kelvin temperature T, the radian frequency w, the momentum relaxation time τ, and the chemical potential µc. The chemical potential µc depends on the carrier density and can be controlled by gate voltage, electric ﬁeld, magnetic ﬁeld, and doping [67,68]. The effective optical permittivity of graphene can be written as:

iσgη0 εg = 1 + (2) k0∆

where η0 ≈ 377 Ω is the impedance of air and ∆ is the thickness of graphene. It should be noted that the permittivity of graphene depends on the surface conductivity. At low frequencies such as infrared and THz range, graphene behaves like a thin metal layer with negative permittivity because the imaginary part of conductivity can be tuned to be Biosensors 2021, 11, x FOR PEER REVIEW 3 of 15

Biosensors 2021, 11, 307 3 of 15 negative permittivity because the imaginary part of conductivity can be tuned to be posi- tive via an external parameter. Due to its capability of enhancing the evanescent field, graphene has become a promising candidate for imaging applications [69–71]. positiveMoreover, via an external since surface parameter. plasmons Due to (SPs) its capability have been of experimentally enhancing the evanescentdemonstrated field, in graphenegraphene has [28], become graphene a promising plasmons candidate (the coup forledimaging state between applications photons [69 and–71 ].collective Di- rac electronsMoreover, in since graphene) surface have plasmons found extensive (SPs) have practical been experimentally applications [72,73]. demonstrated Compared in grapheneto the SPs [28 excited], graphene on metallic plasmons surfaces, (the coupled the field state of SPs between supported photons by andgraphene collective possesses Dirac electronssignificant in graphene)advantages. have Graphene found extensive plasmons practical are more applications tightly confined [72,73]. Comparedon the surface to the of SPsgraphene excited with on metallic an effective surfaces, index the capable field of of SPs reaching supported 70 in by the graphene far-infrared possesses region, signifi- com- cantpared advantages. to the index Graphene value of plasmons1.03 for SPs are on more metal tightly surfaces confined [4]. Besides, on the surface the damping of graphene loss of withgraphene an effective plasmons index is capablerelatively of low reaching and the 70 propagation in the far-infrared distance region, could compared reach dozens to the of indexwavelengths value of of 1.03 SPs for [74]. SPs It is on also metal important surfaces th [at4]. the Besides, SPs excited the damping on graphene loss can of graphene be simply plasmonsmanipulated is relatively by external low and parameters the propagation [75–77] distance. The abovementioned could reach dozens advantages of wavelengths of gra- ofphene SPs [ 74plasmons]. It is also have important made graphene that the SPs a mome excitedntous on graphene candidate can for be a simply variety manipulated of practical byapplications external parameters [78–80], especially [75–77]. Thein the abovementioned super-resolution advantages imaging offield graphene [81–83]. plasmons Most re- havecently, made real-space graphene imaging a momentous of acoustic candidate plasmons for a in variety large-area of practical graphene applications was experimen- [78–80], especially in the super-resolution imaging field [81–83]. Most recently, real-space imaging tally demonstrated, enabling a new platform for strong light–matter interaction [84]. In of acoustic plasmons in large-area graphene was experimentally demonstrated, enabling a addition, more graphene quantum dot based materials have been widely used for sensing new platform for strong light–matter interaction [84]. In addition, more graphene quantum and bio-imaging applications [85]. dot based materials have been widely used for sensing and bio-imaging applications [85].

3.3. Super-ResolutionSuper-Resolution Imaging Imaging Cooperated Cooperated with with Graphene Graphene 3.1.3.1. Graphene-AssistedGraphene-Assisted Super-ResolutionSuper-Resolution Imaging Imaging in in Near Near Field Field ForFor near-fieldnear-field applications,applications, graphenegraphene hashas beenbeen integratedintegrated withwith NSOM,NSOM, superlens,superlens, andand wirewire medium, medium, and and therefore therefore can can provide provide magnification magnification of of the the evanescent evanescent waves waves for for achievingachieving super-resolution super-resolution imaging imaging [ 86[86–88]–88] via via tunable tunable conductivity conductivity and and the the coupling coupling of of graphenegraphene plasmons plasmons [ 89[89–91].–91]. MonolayerMonolayer graphenegraphene was was demonstrated demonstrated to to offer offer a a sevenfold sevenfold enhancementenhancement ofof evanescentevanescent informationinformation andand successfullysuccessfully resolveresolve buriedburied structuresstructures atat a a 500500 nmnm depth depth with withλ λ⁄11-resolution⁄11-resolution via via graphene-enhanced graphene-enhanced NSOMNSOM inin 2014 2014 [ 70[70].]. A A sharp sharp probe,probe, the the key key component component of of NSOM NSOM imaging imaging systems, systems, was was used used to to pick pick up up the the evanescent evanescent signals,signals, butbut it it was was incapable incapable of of imaging imaging buried buried structures. structures. However, However, when when a a monolayer monolayer graphenegraphene waswas coated on on the the top top of of a apolymethyl polymethyl methacrylate methacrylate (PMMA) (PMMA) sample, sample, the eva- the evanescentnescent information information could could be enhanced be enhanced and and detected detected by the by probe the probe due dueto the to surface the surface plas- plasmonmon polaritons polaritons of ofgraphene graphene wi withth ultrasmall ultrasmall plasmon plasmon wavelengths wavelengths as as shown in FigureFigure1 1.. InIn this this case, case, the the resolution resolution of of this this imaging imaging system system is is mainly mainly determined determined by by the the wavelength wavelength ofof graphene graphene plasmon plasmon rather rather than than the the free-space free-space wavelength. wavelength. With With the the assist assist of of a a graphene graphene layer,layer, a a buried buried hole hole can can be be resolved resolved clearly clearly compared compared to theto the bare bare PMMA PMMA sample. sample. Moreover, Moreo- thever, configuration the configuration is more is convenientmore convenient and feasible and feasible than the than superlens–NSOM the superlens–NSOM combination, combi- whichnation, needs which a perfectneeds a superlens perfect supe forrlens practical for practical applications. applications.

FigureFigure 1. 1.Graphene-assisted Graphene-assisted imaging. imaging. ( a(a)) Schematic Schematic drawing drawing of of the the graphene–PMMA graphene–PMMA hybrid hybrid system system with a 1.5 μm diameter buried hole for near field imaging. (b) Near-field amplitude images collected with a 1.5 µm diameter buried hole for near ﬁeld imaging. (b) Near-ﬁeld amplitude images collected with a bare PMMA layer, graphene–PMMA combination, and PMMA–graphene combination, re- with a bare PMMA layer, graphene–PMMA combination, and PMMA–graphene combination, respectively. In both graphene-assisted hybrid systems, the subwavelength hole can be clearly resolved. All images are 4 µm × 4 µm. Reprinted with permission from [70]. Copyright 2014 American Chemical Society. Biosensors 2021, 11, x FOR PEER REVIEW 4 of 15

spectively. In both graphene-assisted hybrid systems, the subwavelength hole can be clearly resolved. All images are 4 μm × 4 μm. Reprinted with permission from [70]. Copyright 2014 American Biosensors 2021, 11, 307 4 of 15 Chemical Society.

In 2015, Forouzmand and co-workers proposed two kinds of novel graphene-loaded wireIn medium 2015, Forouzmand (WM) slab structures and co-workers that were proposed suitable two for kindsdual-band of novel or tunable graphene-loaded broadband wiresuper-resolution medium (WM) imaging slab structures in near field that were[81,82]. suitable Figure for 2a dual-bandschematically or tunable illustrates broadband the pro- super-resolutionposed devices of imaging WM slab in loaded near field graphene [81,82 ].nanopatch Figure2a metasurfaces schematically (GNMs). illustrates The the princi- pro- posedple of devices the aforementioned of WM slab loaded structures graphene relies nanopatch on the enhancement metasurfaces of (GNMs). the evanescent The principle waves ofdue the to aforementioned the coupling of structures the SPs between relies on the enhancementlower and upper of the graphene evanescent as well waves as duethe tore- themarkable coupling waveguiding of the SPs between of the evanescent the lower waves and upper of the graphene WM slab as [87]. well In as addition, the remarkable the per- waveguidingformance of the of the structures evanescent was waves analyzed of the in WMthe presence slab [87]. of In a addition,magnetic the line performance source (Figure of the2b). structures The resolution was analyzed was quantified in the presence by using of the a magnetic half power line beam source width (Figure (HPBW)2 b). The [85] reso- and lutionthe Rayleigh was quantified criterion. by Super-resolution using the half power imaging beam effects width may (HPBW) enable [ more85] and extensive the Rayleigh appli- criterion.cations with Super-resolution practical potential imaging and possess effects may the capability enable more of resolving extensive closely applications spaced with light practicalsources, potentialas indicated and possessin Figure the 2c capability using the of Rayleigh resolving criterion. closely spaced The working light sources, mechanism as in- dicatedof this superlens in Figure2 crelies using on the gr Rayleighaphene plasmon criterion. resonances The working and mechanism can significantly of this amplify superlens the reliesevanescent on graphene waves plasmonthat include resonances the high and spatia canl significantlyfrequency information amplify the and evanescent restore them waves at thatthe includeimage plane. the high Graphene spatial plasmons frequency excited information at both and lower restore and themupper at GNMs the image can be plane. cou- Graphenepled and further plasmons used excited to help at amplify both lower the andevanescent upper GNMswaves. can As beexpected, coupled the and hybridiza- further usedtion-enhanced to help amplify bilayer the design evanescent could achieve waves. higher As expected, resolution the because hybridization-enhanced of stronger interac- bilayertions than design the couldmonolayer achieve graphene higher design. resolution Note because that the of number stronger of interactions layers will thanaffect the the monolayerimaging performance graphene design. since Note multilayer that the desi numbergns ofcan layers involve will affectthe interactions the imaging between perfor- manceneighboring since multilayer layers and designstherefore can impact involve optical the interactionsproperties compared between neighboringwith the monolayer layers andlayout. therefore impact optical properties compared with the monolayer layout.

Figure 2. (a) Diagram of WM slab-loaded GNMs. (b) Sketch of the WM slab-loaded GNMs using a magnetic line source. (c) Normalized intensity distribution calculated at the image plane for 22.8 and 25.9 THz. Reprinted with permission from [81]. Copyright 2015 American Institute of Physics. Biosensors 2021, 11, x FOR PEER REVIEW 5 of 15

Biosensors 2021, 11, 307 Figure 2. (a) Diagram of WM slab-loaded GNMs. (b) Sketch of the WM slab-loaded GNMs using5 of 15 a magnetic line source. (c) Normalized intensity distribution calculated at the image plane for 22.8 and 25.9 THz. Reprinted with permission from [81]. Copyright 2015 American Institute of Physics.

AnotherAnother graphene-assistedgraphene-assisted subwavelengthsubwavelength imagingimaging devicedevice waswas reportedreported in 2017 [[71]71] whichwhich reliedrelied onon thethe Fabry–PerotFabry–Perot resonanceresonance ofof graphenegraphene edgeedge plasmonplasmon waves [[88,89]88,89] for breakingbreaking thethe diffractiondiffraction limitlimit inin thethe THzTHz frequencyfrequency range.range. TheThe superlenssuperlens was constituted byby aa singlesingle sheetsheet ofof graphenegraphene andand aa metallicmetallic gratinggrating voltagevoltage gate.gate. Figure3 3aa showsshows thethe perspectiveperspective ofof thethe proposedproposed superlenssuperlens andand FigureFigure3 3bb demonstrates demonstrates the the equivalent equivalent modelmodel forfor aa certaincertain frequency.frequency. TheThe mostmost noteworthynoteworthy advantageadvantage ofof thisthis superlenssuperlens is that it can be easilyeasily manipulatedmanipulated inin aa widewide rangerange fromfrom 4.34.3 THz to 9 THz by adjusting the gate voltage. InIn addition, addition, oneone cancan readilyreadily obtainobtain subwavelengthsubwavelength targetstargets magnifiedmagnified images by replacing thethe grating grating gate gate with with a a radial radial shape. shape. The The best best resolution resolution achieved achieved was was 400 400 nm nm as as shown shown in Figurein Figure3c (top-view) 3c (top-view) and Figureand Figure3d (cross 3d (cross section), section), leading leading to great to potential great potential applications applica- in THztions near in THz field near imaging fieldsystems. imaging Insystems. addition, In oneaddition, should one note should that the note impact that the of lateral impact size of onlateral device size performance on device performance is significant is sincesignific grapheneant since edge graphene plasmons edge are plasmons highly dimensionare highly dependent.dimension dependent. For instance, For by instan reducingce, by the reducing size of the the graphene size of the structures, graphene onestructures, can readily one manipulatecan readily themanipulate working the frequency working of frequency graphene plasmonsof graphene from plasmons longer wavelengthsfrom longer wave- to the visiblelengths and to the near-infrared visible and range. near-infrared range.

Figure 3. Voltage-gate-assisted hybrid superlens. (a) Schematic layout of the proposed device. (b) Figure 3. Voltage-gate-assisted hybrid superlens. (a) Schematic layout of the proposed device. (b) The The equivalent model for a certain wavelength. (c) Top-view and (d) cross-sectional view of the equivalent model for a certain wavelength. (c) Top-view and (d) cross-sectional view of the electric electric field distribution when the two sources are 400 nm spaced. Note that the frequency is 5 THz fieldwith distributionλ/150 resolution. when Reprinted the two sources with permission are 400 nm from spaced. [71]. NoteCopyright thatthe 2017 frequency Springer isNature. 5 THz with λ/150 resolution. Reprinted with permission from [71]. Copyright 2017 Springer Nature. A fast-paced graphene-based near-field optical microscopy (GNOM) for overcoming A fast-paced graphene-based near-field optical microscopy (GNOM) for overcoming the diffraction limit was proposed by Inampudi and coworkers who utilized the electronic the diffraction limit was proposed by Inampudi and coworkers who utilized the electronic scanning property of graphene gratings (different from the mechanical scanning of a scanning property of graphene gratings (different from the mechanical scanning of a sharp sharp tip in NSOM) [72]. Figure 4 schematically shows the proposed GNOM. Based on tip in NSOM) [72]. Figure4 schematically shows the proposed GNOM. Based on the fact the fact that the graphene’s surface conductivity is reconfigurable, grating scattered light that the graphene’s surface conductivity is reconfigurable, grating scattered light can be can be collected and then processed by the rigorous coupled-wave analysis. In this work, collected and then processed by the rigorous coupled-wave analysis. In this work, the the authors demonstrated the highest resolution of λ⁄16 (λ = 10 μm) theoretically. In addi- authors demonstrated the highest resolution of λ⁄16 (λ = 10 µm) theoretically. In addition, tion, numerical optimization based on the genetic algorithm was also demonstrated to numerical optimization based on the genetic algorithm was also demonstrated to design design an optimum set of diffraction grating and minimize the artifacts in the image, an optimum set of diffraction grating and minimize the artifacts in the image, which was which was an extremely challenging task [90]. an extremely challenging task [90]. In 2018, Liu and co-workers introduced a graphene sheet as an ultrathin nonlinear negative reflection lens for achieving super-resolution imaging based on four wave mixing (FWM) process in the terahertz regime [73] thanks to the fact that graphene possesses strong nonlinear electromagnetic response. This is totally different from traditional materials which reduce the field strength necessary for the nonlinear process, such as FWM [89]. Figure5a schematically demonstrates the working principles. It has been theoretically predicted and experimentally validated that the FWM wave can be focused on a point in the image plane via modulating the incident angle. Figure5b shows the electric field of the signal waves without the graphene lens (dotted line). As a comparison, FWM waves are plotted using the solid line, and one can see that the full width at half-maximum obtained is 3.28 µm (λ⁄5), which indicates great potential applications in THz microscopy. Biosensors 2021, 11, x FOR PEER REVIEW 6 of 15

Biosensors 2021, 11, 307 6 of 15 Biosensors 2021, 11, x FOR PEER REVIEW 6 of 15

Figure 4. Working mechanism of GNOM. The width of the graphene strips placed side-by-side (in the x-y plane) is represented by d and Λ is the periodicity. Reprinted with permission from [72]. Copyright 2017 The Optical Society.

In 2018, Liu and co-workers introduced a graphene sheet as an ultrathin nonlinear negative reflection lens for achieving super-resolution imaging based on four wave mixing (FWM) process in the terahertz regime [73] thanks to the fact that graphene possesses strong nonlinear electromagnetic response. This is totally different from traditional materials which reduce the field strength necessary for the nonlinear process, such as FWM [89]. Figure 5a schematically demonstrates the working principles. It has been theoretically predicted and experimentally validated that the FWM wave can be focused on a

point in the image plane via modulating the incident angle. Figure 5b shows the electric Figurefield of 4. the Working signal mechanismmechanism waves without ofof GNOM.GNOM. the graphe TheThe widthwidthne lens ofof thethe (dotted graphenegraphene line). stripsstrips As a placedplaced comparison, side-by-sideside-by-side FWM (in(in thethewaves x-yx-y plane)plane)are plotted isis representedrepresented using the byby solid dd andand line, ΛΛ andisis thethe one periodicity.periodicity. can see that ReprintedReprinted the full withwidth permission at half-maximum from [[72].72 ]. Copyright 2017 The Optical Society. Copyrightobtained 2017is 3.28 The μm Optical (λ⁄5), Society.which indicates great potential applications in THz microscopy. In 2018, Liu and co-workers introduced a graphene sheet as an ultrathin nonlinear negative reflection lens for achieving super-resolution imaging based on four wave mixing (FWM) process in the terahertz regime [73] thanks to the fact that graphene possesses strong nonlinear electromagnetic response. This is totally different from traditional materials which reduce the field strength necessary for the nonlinear process, such as FWM [89]. Figure 5a schematically demonstrates the working principles. It has been theoretically predicted and experimentally validated that the FWM wave can be focused on a point in the image plane via modulating the incident angle. Figure 5b shows the electric field of the signal waves without the graphene lens (dotted line). As a comparison, FWM waves are plotted using the solid line, and one can see that the full width at half-maximum obtained is 3.28 μm (λ⁄5), which indicates great potential applications in THz microscopy.

FigureFigure 5.5. ((aa)) SchematicSchematic drawingdrawing of the negative reflection reﬂection lens lens based based on on FWM FWM process. process. (b (b) )Electric Electric ﬁeldfield forfor thethe signalsignal andand FWMFWM waveswaves at the imagingimaging plane. Reprinted Reprinted with with permission permission from from [73]. [73 ]. Copyright 2018 The Optical Society. Copyright 2018 The Optical Society.

3.2.3.2. Graphene-AssistedGraphene-Assisted Super-Resolution Imaging in in Far Far Field Field InIn farfar ﬁeld, field, graphene graphene has has normally normally been been integrated integrated with with a hyperlens. a hyperlens. Surface Surface plasmons plas- canmons help can convert help convert the evanescent the evanescent waves waves to the to propagatingthe propagating ones ones and and therefore therefore provide pro- magniﬁcationvide magnification for achieving for achieving super-resolution super-resolution imaging imaging via the via tunable the tunable conductivity conductivity and the couplingand the coupling of graphene of graphene plasmons. plasmons. In 2013, In Zhang 2013, and Zhang coworkers and coworkers designed designed two kinds two of differentkinds of hyperlensesdifferent hyperlenses [69] composed [69] composed by alternating by alternating graphene/dielectric graphene/dielectric layered structures layered forstructures achieving for achieving super-resolution super-resolution optical imaging optical imaging in the mid-infrared in the mid-infrared range. range. They They were triangle-shaped and cylindrical hyperlenses, respectively. Figure6 schematically shows theFigure cross-section 5. (a) Schematic view drawing of the proposed of the negative novel reflection layered structures.lens based on FWM process. (b) Electric field Thefor the working signal and mechanism FWM waves is identical at the imagin for theseg plane. two Reprinted kinds of hyperlenses,with permission which from relies [73]. onCopyright the fact 2018 that The the Optical hyperbolic Society. dispersion curve can amplify and support the propagation of the evanescent waves straightly along the normal direction of the layered structures 3.2. Graphene-Assisted Super-Resolution Imaging in Far Field and form two imaging spots at the output plane under the condition of Re εk > 0, In far field, graphene has normally been integrated with a hyperlens. Surface plas- ( ) < → Remonsε⊥ can 0help, and convert Re εk the0 evanescent (εk , ε⊥ are waves the permittivity to the propagating of the structure ones and along therefore tangential pro- andvide radialmagnification directions, for respectively). achieving super-resolution The distances betweenimaging twovia the imaging tunable spots conductivity are d⁄cosθ and d(rthe + coupling t))⁄r for the of triangle-shapedgraphene plasmons. and cylindrical In 2013, Zhang hyperlenses, and coworkers respectively. designed In addition, two itkinds is worth of different noting thathyperlenses the layered [69] structurescomposed can by achievealternating propagation graphene/dielectric of the evanescent layered structures for achieving super-resolution optical imaging in the mid-infrared range. They Biosensors 2021, 11, x FOR PEER REVIEW 7 of 15

were triangle-shaped and cylindrical hyperlenses, respectively. Figure 6 schematically shows the cross-section view of the proposed novel layered structures.

Biosensors 2021, 11, 307 7 of 15

Biosensors 2021, 11, x FOR PEER REVIEW 7 of 15

waves for a ﬁxed wavelength by manipulating µ , as shown in Figure7, enabling extensive were triangle-shaped and cylindrical hyperlenses, respectively.c Figure 6 schematically potentialshows the cross-section applications view in of broadband the proposed super-resolution novel layered structures. imaging.

Figure 6. (a) Triangle-shaped hyperlens. The layered structures are covered with a silver coating (100 nm thick) with two slits (10 nm width) separated by d = 3.3 μm. (b) Cylindrical hyperlens is coated with a silver layer (100 nm thick) with two slits (50 nm wide) separated by d = 1 μm in the inner surface whose radius is defined as r = 1 μm. Reprinted with permission from [69]. Copyright 2013 The Optical Society.

The working mechanism is identical for these two kinds of hyperlenses, which relies on the fact that the hyperbolic dispersion curve can amplify and support the propagation of the evanescent waves straightly along the normal direction of the layered structures andFigure form 6. ( atwo) Triangle-shaped imaging spots hyperlens. at the outputThe layered plane structures under arethe coveredcondition with of a silverRe(𝜀∥ )>0coating, Figure 6. a Re(𝜀(100 nm)<0 thick)(, and) with Triangle-shaped Re two(𝜀∥ )slits→0 (10(𝜀∥ nm, 𝜀hyperlens. width) are the separated permittivity The by layered d =of 3.3 the μ structuresm. structure (b) Cylindrical along are covered hyperlenstangential with is a silver coating (100andcoated radial nm with thick) directions, a silver with layer respectively). two (100 slitsnm thick) (10 Th nmwieth distances width) two slitsseparated (50between nm wide) two by separated imaging d = 3.3 by spotsµ m.d = 1 (are bμ)m Cylindricald in⁄cos theθ hyperlens is coatedandinner d(r surface + with t))⁄r whosefor a silver the radius triangle-shaped layer is defined (100 nmas and r = thick) 1cylindrical μm. withReprinted hyperlenses, two with slits permissi (50 respectively. nmon wide)from [69]. In separated addition,Copyright by d = 1 µm in the it2013 is worth The Optical noting Society. that the layered structures can achieve propagation of the evanescent inner surface whose radius is deﬁned as r = 1 µm. Reprinted with permission from [69]. Copyright waves for a fixed wavelength by manipulating μc, as shown in Figure 7, enabling extensive 2013potential The applicationsworking Optical mechanism Society. in broadband is identical super-resolution for these two imaging. kinds of hyperlenses, which relies on the fact that the hyperbolic dispersion curve can amplify and support the propagation of the evanescent waves straightly along the normal direction of the layered structures and form two imaging spots at the output plane under the condition of Re(𝜀∥)>0, Re(𝜀)<0, and Re(𝜀∥)→0 (𝜀∥ , 𝜀 are the permittivity of the structure along tangential and radial directions, respectively). The distances between two imaging spots are d⁄cosθ and d(r + t))⁄r for the triangle-shaped and cylindrical hyperlenses, respectively. In addition, it is worth noting that the layered structures can achieve propagation of the evanescent waves for a fixed wavelength by manipulating μc, as shown in Figure 7, enabling extensive potential applications in broadband super-resolution imaging.

FigureFigure 7. 7.Far-fieldFar-ﬁeld sub-diffraction sub-diffraction imaging results imaging under results differentunder conditions: different (a) λ = 9.2 conditions: μm, μc = (a) λ = 9.2 µm, 0.0965 eV, ε|| = 0.1419 + 0.2933 i, ε┴ = −159.8 + 378.6 i; (b) λ = 10.2 μm, μc = 0.085 eV, ε|| = 0.1058 + µ0.3709c = 0.0965i, ε┴ = −74.07 eV, +ε ||341.7= i;0.1419 (c) λ = 11.2 + 0.2933μm, μc = i0.075, ε⊥ eV,= ε−|| 159.8= 0.4481 + + 378.60.4678 i,i ;(ε┴ b= )−122.9λ = + 10.2 152.8µ m, µc = 0.085 eV, i; (d) λ = 12.2 μm, μc = 0.067 eV, ε|| = 0.761 + 0.5831 i, ε┴ = −90.05 + 86.94 i. Reprinted with permission ε|| = 0.1058 + 0.3709 i, ε⊥ = −74.07 + 341.7 i;(c) λ = 11.2 µm, µc = 0.075 eV, ε|| = 0.4481 + 0.4678 i, from [69]. Copyright 2013 The Optical Society. ε⊥ = −122.9 + 152.8 i;(d) λ = 12.2 µm, µc = 0.067 eV, ε|| = 0.761 + 0.5831 i, ε⊥ = −90.05 + 86.94 i. ReprintedIn 2016, with Yang permission and coworkers from proposed [69]. Copyright a graphene 2013 nanocavity The Optical on metasurface Society. structure (GNMS) [83] to excite graphene surface plasmons at mid-infrared waveband and In 2016, Yang and coworkers proposed a graphene nanocavity on metasurface struc-

tureFigure (GNMS) 7. Far-field [sub-diffraction83] to excite imaging graphene results under surface different plasmons conditions: at (a) mid-infraredλ = 9.2 μm, μc = waveband and achieved0.0965 eV, ε super-resolution|| = 0.1419 + 0.2933 i, ε┴ optical = −159.8 imaging+ 378.6 i; (b by) λ = integrating 10.2 μm, μc = 0.085 the GNMSeV, ε|| = 0.1058 device + with plasmonic 0.3709 i, ε┴ = −74.07 + 341.7 i; (c) λ = 11.2 μm, μc = 0.075 eV, ε|| = 0.4481 + 0.4678 i, ε┴ = −122.9 + 152.8 structuredi; (d) λ = 12.2 μ illuminationm, μc = 0.067 eV, ε microscopy|| = 0.761 + 0.5831 (PSIM). i, ε┴ = −90.05 Figure + 86.948 ia. Reprinted shows thewith schematicpermission of GNMS and onefrom can[69]. Copyright see that 2013 two The layers Optical ofSociety. graphene are involved to form a cavity ﬁlled with water. Figure8b illustrates the cross section of GNMS. In 2016, Yang and coworkers proposed a graphene nanocavity on metasurface structure (GNMS) [83] to excite graphene surface plasmons at mid-infrared waveband and Biosensors 2021, 11, x FOR PEER REVIEW 8 8of of 15 15

achieved super-resolution opticaloptical imagingimaging byby integratingintegrating thethe GNMSGNMS devicedevice with with plas- plas- monic structured illumination microscopy (PSIM). Figure 8a shows the schematic of Biosensors 2021, 11, 307 monic structured illumination microscopy (PSIM). Figure 8a shows the schematic8 of of 15 GNMS and oneone cancan seesee thatthat twotwo layerslayers ofof grapgraphenehene areare involvedinvolved to to form form a a cavity cavity filled filled with water. FigureFigure 8b8b illustratesillustrates thethe crosscross sectionsection of of GNMS. GNMS.

FigureFigure 8. 8.( (a(a))) Layout LayoutLayout and andand working workingworking principleprincipleprinciple ofof GNMS.GNMS. Two Two layers layers of of graphene graphene formform form the thethe nanocavity, nanocavity,nanocavity, which is filled withwith water.water. ((bb)) CrossCross sectionsection ofof thethe GNMS.GNMS. ReprintedReprinted with with permission permission from from [83]. [83]. which is ﬁlled with water. (b) Cross section of the GNMS. Reprinted with permission from [83]. Copyright 2016 SpringerSpringer Nature.Nature. Copyright 2016 Springer Nature.

ToTo further further study studystudy the thethe property propertyproperty of ofof the thethe GNMS GNMSGNMS device, device,device, the thethe functionfunction function ofof of thethe the gratinggrating grating and and thethe effecteffect of ofof the thethe structural structuralstructural parameters parametersparameters on onon the thethe graphene graphenegraphene plasmonic plasmonicplasmonic interference interferenceinterference patternpattern pattern werewere discussed. discussed. AccordingAccordingAccording tototo thethethe results,results,results, oneononee cancancan learnlearnlearn that thatthat the thethe periodperiod period ofof of thethe the graphenegraphene graphene plasmonicplasmonic interference interference pattern patternpattern is isis 52 5252 nm nmnm whenwhen when thethe the wavelength wavelength of of the the incident incident light light isis is 77 7 μµ μm.m.m. WhenWhen thethe GNMSGNMS devicedevice is is integratedintegrated withwith with PSIM,PSIM, PSIM, anan an imagingimaging imaging resolution resolution resolution of of of26 26 26nm nm nm can can can be be beachieved achieved because because ofof of thethe the graphenegraphene graphene plasmonsplasmons plasmons withwith deep deepdeep sub-wavelength.sub-wavelength. Figure Figure 9 9a–ca–c9a–c showsshows the the simulation simulationsimulation results resultsresults of ofof the thethe electric electricelectric distribution distributiondistribution of grapheneofof graphegraphe plasmonicnene plasmonic plasmonic interference interfer- interfer- patterns.ence patterns. Figure FigureFigure9d presents 9d9d presentspresents the performance thethe perforperformance ofmance GNMS ofof GNMS GNMS quantiﬁed quantified quantified by using by by the using using full the widththe full full atwidth half maximumat half maximummaximum (FWHM) (FWHM)(FWHM) of the point ofof thethe spread pointpoint spread functionspread functionfunction (PSF). Since(PSF). (PSF). mid-infrared Since Since mid-infrared mid-infrared is safe foris safe biological forfor biologicalbiological cells, this cells,cells, work thisthis may workwork pave maymay a new pavepave way aa for newnew optical wayway for super-resolutionfor opticaloptical super-resolutionsuper-resolution imaging at mid-infraredimaging atat mid-infraredmid-infrared waveband for wavebandwaveband biological forfor research. biologicalbiological research. research.

Figure 9. Imaging performance of the nanocavity-enhanced metasurface device. (a) Side view of FigureFigure 9. 9.Imaging Imaging performance performance of of the the nanocavity-enhanced nanocavity-enhanced metasurface metasurface device. device. (a(a)) Side Side view view of of electric field mapping. (b) Top view mapping. (c) Intensity plot of the white dashed line shown in electricelectric ﬁeld field mapping. mapping. (b(b)) Top Top view view mapping. mapping. ( c(c)) Intensity Intensity plot plot of of the the white white dashed dashed line line shown shown in in (a). (d) Normalized intensity of normal epiﬂuorescence microscopy (red) and GNMS-assisted PSIM (green). Reprinted with permission from [83]. Copyright 2016 Springer Nature.

Moreover, the same group proposed another elegant design to realize wide-ﬁeld optical imaging based on a hybrid graphene on metasurface structure (GMS) model [86]. Biosensors 2021, 11, x FOR PEER REVIEW 9 of 15

Biosensors 2021, 11, 307 9 of 15 (a). (d) Normalized intensity of normal epifluorescence microscopy (red) and GNMS-assisted PSIM (green). Reprinted with permission from [83]. Copyright 2016 Springer Nature.

Moreover, the same group proposed another elegant design to realize wide-field op- Figuretical imaging 10a based is the on schematica hybrid graphene view on of metasurface GMS, including structure (GMS) a monolayer model [86]. graphene deposited Figure 10a is the schematic view of GMS, including a monolayer graphene deposited on on a SiO2/Ag/SiO2 multilayer design. From Figure 10b, one can see more clearly the a SiO2/Ag/SiO2 multilayer design. From Figure 10b, one can see more clearly the crosscross-sectionalsectional view of a unite view cell of of a the unite GMS. cell of the GMS.

Figure 10. The schematic diagram of the GMS design. (a) The perspective view with coordinates Figureand (b) the 10. cross-sectionalThe schematic layout. diagram Vbias is the control of the voltage. GMS design.The thickness (a) Theof the perspective flat SiO2 substrate view with coordinates and is d1 = 200 nm. A thin Ag film with d2 = 50 nm thickness is covered by a 10 nm thick SiO2 film. A (b) the cross-sectional layout. Vbias is the control voltage. The thickness of the flat SiO2 substrate nanoslit with w = 30 nm width in x direction and an infinite length in y direction is filled with SiO2 isin dthe1 =Ag 200 film. nm. The unite A thin cell has Ag a same film periodicity with d2 of= P 50 = 350 nm nm thickness in both x and is y direction. covered Reprinted by a 10 nm thick SiO2 film. A with permission from [86]. Copyright 2017 The Optical Society. nanoslit with w = 30 nm width in x direction and an infinite length in y direction is filled with SiO2 in the AgIn this film. work, The it uniteis crucial cell to has utilize a same the most periodicity significant of featureP = 350of graphene nm inboth plasmons, x and y direction. Reprinted withwhich permission is the ultra-high from wave [86 vector,]. Copyright to combine 2017 theThe model Optical of GMS Society. with the PSIM method and further achieve super-resolution imaging. The authors employed the finite-difference time-domainIn this (FDTD) work, method it is crucial to model to and utilize simulate the the most GMSsignificant structure and featurefound that of graphene plasmons, the standing wave of surface plasmons (SW-SPs) with an 11 nm period can be achieved whichon graphene. is the Furthermore, ultra-high when wave the vector,GMS structure to combine was applied the modelin the PSIM of GMSmethod, with the PSIM method andthey found further thatachieve an imaging super-resolution resolution of 6 nm could imaging. be obtained The forauthors a 980 nm illumination employed the finite-difference time-domainwavelength which (FDTD) was improved method 39.6-fold to modelin comparison and with simulate the conventional the GMS micros- structure and found that copy technique with resolution of 283 nm. Additionally, the resolving capability of GMS– thePSIM standing system was wave acquired of by surface imaging plasmonstwo point objects (SW-SPs) separated with by 6 nm. an 11The nmsimula- period can be achieved ontion graphene. results are shown Furthermore, in Figure 11. when the GMS structure was applied in the PSIM method, they found that an imaging resolution of 6 nm could be obtained for a 980 nm illumination wavelength which was improved 39.6-fold in comparison with the conventional microscopy technique with resolution of 283 nm. Additionally, the resolving capability of GMS–PSIM system was acquired by imaging two point objects separated by 6 nm. The simulation results are shown in Figure 11. In addition to the tunable surface conductivity and graphene plasmons, graphene has been introduced to the applications of electron microscopy to achieve super-resolution imaging of wet cells by utilizing the impermeable and conductive properties. More important bioimaging applications can be enabled using graphene-based nanomaterials (e.g., graphene quantum dots) and their derivatives [92–94]. Wojcik and coworkers utilized graphene which was synthesized by chemical vapor deposition (CVD) as an impermeable and conductive membrane to enable electron microscopy of wet and untreated cells, enabling direct electron microscopy of wet cells via simple sample preparation without demanding special devices and equipment as well as the comparable contrast and resolution with a conventional scanning electron microscope [95]. To summarize the above discussion on progress in super-resolution imaging with graphene-based nanostructures in infrared and THz frequencies for both near field and far field, Table1 lists more detailed information for a more comprehensive and systemic comparison. Note that most of the experimentally demonstrated applications so far have been limited to infrared and THz Biosensors 2021, 11, 307 10 of 15

frequencies and it is extremely challenging to further extend the working ranges due to the intrinsic properties of graphene plasmons. However, more ﬂexible and tunable devices are desired to meet the increasing demands of practical applications. In addition, more Biosensors 2021, 11, x FOR PEER REVIEW 10 of 15 efforts should be made to further reduce the fabrication cost of such devices to enable massive production.

FigureFigure 11.11. ((a)) The The reconstructed reconstructed image image of of a apoint point object object in inthe the x direction. x direction. (b) The (b) Theimage image of the of point the object in the conventional fluorescence microscopy system. (c) FWHF comparison between (a) (red point object in the conventional fluorescence microscopy system. (c) FWHF comparison between line) and (b) (blue line). (d) Illustration resolving capability of GMS–PSIM system of two point ob- (a) (red line) and (b) (blue line). (d) Illustration resolving capability of GMS–PSIM system of two jects separated with different distances of 2, 4, 6, 10, and 20 nm. Reprinted with permission from point[86]. Copyright objects separated 2017 The with Optical different Society. distances of 2, 4, 6, 10, and 20 nm. Reprinted with permission from [86]. Copyright 2017 The Optical Society. In addition to the tunable surface conductivity and graphene plasmons, graphene Table 1. Summary of super-resolution imaging systems integrated with graphene. has been introduced to the applications of electron microscopy to achieve super-resolu- Year Device Typetion Near/Far imaging Field of wet Waveband cells by utilizing Resolution the impermeable Physical and conductive Mechanism properties. Refs. More 2013 Hyperlensimportant Far field bioimaging Mid-infrared applications canλ /10be enabled using Tunable graphene-based conductivity nanomaterials [69] 2014 SNOM(e.g., Near graphene field quantum Infrared dots) and theirλ/11 derivatives [92–94]. Graphene Wojcik plasmonic and coworkers [70] uti- 2015 Superlenslized Near graphene field which 60 was THz synthesized λby/50 chemical vapor Graphene deposition plasmonic (CVD) as an [80 imper-] 22.8 THz λ/10 2015 Wire mediummeable Near fieldand conductive membrane to enable electronGraphene microscopy plasmonic of wet and untreated [81] cells, enabling direct25.9 electron THz microscopy0.14 ofλ wet cells via simple sample preparation with- 2015 Wire mediumout Near demanding field special THz devices and equipmentλ/10 as well Graphene as the plasmoniccomparable contrast [82] and 2016 GNMS Far field Mid infrared 26 nm Graphene plasmonic [83] 2017 Superlensresolution Near field with a conventional 4.3~9 THz scanning 400 nm electron microscope Tunable conductivity [95]. To summarize [71] the 2017 GNOMabove Near fielddiscussion on 30 THzprogress in super-reλ/16solution Tunable imaging conductivity with graphene-based [72] 2017 GMSnanostructures Far field in infr Infraredared and THz frequencies 6 nm for both Graphene near field plasmonic and far field, [ 86Table] 1 2018 FWMlists Near more field detailed information THz for a moreλ/5 comprehensive Tunable and conductivity systemic comparison. [73] Note that most of the experimentally demonstrated applications so far have been limited to 4.infrared Alternative and THz Imaging frequencies Devices and Integrated it is extremely with Graphene challenging Oxide to further extend the working rangesGraphene due oxideto the (GO)intrinsic has properties also found of extensive graphene applications plasmons. However, in integrated more photon- flexible icsand[96 tunable,97] and devices therapy are [ 98desired–101]. to Moreover, meet the increasing it can be used demands to cooperate of practical with applications. quenched stochasticIn addition, optical more reconstruction efforts should microscopybe made to (qSTORM)further reduce to achieve the fabrication the super-resolution cost of such imagingdevices to effect enable of massive self-assembled production. peptide fibrils and Escherichia coli. In 2018, Li et al. experimentally demonstrated that GO coating with qSTORM could increase the imag- Table 1.ing Summary resolution of super-resolution and contrast [imaging102] when systems imaging integrated the peptides with graphene. and bacteria via the strong quenching effect. Due to the same hexagonal lattice and similar electronic properties for Year Device Type Near/Far Field Waveband Resolution Physical Mechanism Ref. both graphene and GO, the quenching effect is advisable with GO. In the experiments 2013 Hyperlens Far field Mid-infrared 𝜆10⁄ Tunable conductivity [69] the authors designed, the stacked films were employed to perform qSTORM with GO ⁄ 2014 SNOM coating Near as field illustrated inInfrared Figure 12 a. In this𝜆11 design, theGraphene GO coating plasmonic played a significant [70] 2015 Superlens Near field 60 THz 𝜆50⁄ Graphene plasmonic [80] 22.8 THz 𝜆10⁄ 2015 Wire medium Near field Graphene plasmonic [81] 25.9 THz 0.14λ 2015 Wire medium Near field THz 𝜆10⁄ Graphene plasmonic [82] 2016 GNMS Far field Mid infrared 26 nm Graphene plasmonic [83] 2017 Superlens Near field 4.3~9 THz 400 nm Tunable conductivity [71] Biosensors 2021, 11, x FOR PEER REVIEW 11 of 15

2017 GNOM Near field 30 THz 𝜆16⁄ Tunable conductivity [72] 2017 GMS Far field Infrared 6 nm Graphene plasmonic [86] 2018 FWM Near field THz 𝜆5⁄ Tunable conductivity [73]

4. Alternative Imaging Devices Integrated with Graphene Oxide Graphene oxide (GO) has also found extensive applications in integrated photonics [96,97] and therapy [98–101]. Moreover, it can be used to cooperate with quenched stochastic optical reconstruction microscopy (qSTORM) to achieve the super-resolution imaging effect of self-assembled peptide fibrils and Escherichia coli. In 2018, Li et al. experimentally demonstrated that GO coating with qSTORM could increase the imaging resolution and contrast [102] when imaging the peptides and bacteria via the strong quenching effect. Due to the same hexagonal lattice and similar electronic properties for both gra- Biosensors 2021, 11, 307 phene and GO, the quenching effect is advisable with GO. In the experiments the authors11 of 15 designed, the stacked films were employed to perform qSTORM with GO coating as illustrated in Figure 12a. In this design, the GO coating played a significant role in removing rolebackground in removing noise background and improving noise the and signal-t improvingo-noise the ratio. signal-to-noise Figure 12b ratio.shows Figure the recon- 12b showsstructed the STORM reconstructed image of STORM peptide image fibers. of In peptide the imaging fibers. performance In the imaging of the performance self-assem- ofbled the peptide self-assembled fibrils, the peptide contrast fibrils, changed the contrast from 13 changed (±47%) fromto 133 13 (±40%) (±47%) with to 133GO ( ±and40%) the withresolution GO and of the the resolution image with of theGO image and without with GO GO and was without 19 nm GO and was 23 19 nm, nm respectively, and 23 nm, respectively,using the Fourier using Ring the Fourier Correlation Ring method Correlation [103,104] method with [103 qSTORM.,104] with In qSTORM.the imaging In theper- imagingformance performance of Escherichia of coli, Escherichia the contrast coli, changed the contrast from changed 24 (±27%) from to 3317 24 ((±37%)±27%) with to 3317 GO. (±The37%) resolution with GO. of The imaging resolution peptide of imaging fibrils and peptide Escherichia fibrils andcoli Escherichiawas 11 nm and coli was24 nm 11 with nm and5 nm 24 GO nm coating with 5 nm by GOusing coating the feature by using of theinterest feature (FOI) of interestmetric, (FOI)leading metric, to a dramatic leading to im- a dramaticprovement. improvement. Note that the Note oxidation that the grade oxidation of GO grade can of affect GO canthe affectimaging the imagingquality since quality the sincecarbon-oxygen the carbon-oxygen ratio can ratiosimply can modulate simply modulatethe photoluminescence the photoluminescence quenching quenchingcapabilities capabilitiesand therefore and further therefore control further the controldevice theperformance. device performance.

FigureFigure 12. 12.( a()a) The The schematic schematic of of the the stacked stacked films films to performto perform qSTORM qSTORM with with GO coating.GO coating. Note Note that thethat the Cy3b fluorophores belong to the Cyanine family. (b) Reconstructed STORM image of peptide Cy3b fluorophores belong to the Cyanine family. (b) Reconstructed STORM image of peptide fibers. fibers. Reprinted with permission from [102]. Copyright 2018 Springer Nature. Reprinted with permission from [102]. Copyright 2018 Springer Nature.

5.5. Conclusions Conclusions and and Outlook Outlook ToTo conclude conclude the the review, review, the the working working principles, principles, implementations, implementations, and and performances performances ofof the the super-resolution super-resolution imaging imaging integrated integrated with with graphene/GO graphene/GO have have been comprehensivelybeen comprehen- summarized.sively summarized. Due to theDue most to the useful most property useful pr thatoperty the surface that the conductivity surface conductivity of graphene of cangra- bephene rapidly can tuned be rapidly by external tuned parameters,by external parame grapheneters, is capablegraphene of is enhancing capable of the enhancing evanescent the waves.evanescent Thepresent waves. review The present strongly review suggests strong thatly graphene suggests has that great graphene advantages has ingreat super- ad- resolutionvantages imagingin super-resolution in the infrared imaging and THz in regionsthe infrared both inand near THz field regions and far both field. in However,near field theand use far of field. the grapheneHowever, forthe super-resolution use of the graphene imaging for super-resolution is still at a very earlyimaging stage, is still which at a meansvery early that westage, still which need ameans lot of timethat we and still efforts need to a explore lot of time its full and potential efforts to experimentally. explore its full Inpotential our opinion, experimentally. thinner, faster, In our and opinion, more reliable thinner, imaging faster, devices and more using reliable graphene-assisted imaging de- components are highly desired for various practical applications and therefore, more efforts should be made to develop related devices. More flexible components are also needed to meet the increasing future demands. Moreover, thorough investigations on structural parameters are necessary for studying their impact and working mechanisms. Given the progress in both the theory and fabrication of the graphene-assisted materials, there is no doubt that more methods and devices on super-resolution imaging with graphene will be developed to meet the increasing demanding of more practical application requirements in the near future.

Author Contributions: Conceptualization, X.J. and L.K.; methodology, X.J.; software, L.K.; validation, Y.Y. and Q.G.; formal analysis, J.L.; investigation, Z.D.; resources, Z.D.; writing—original draft preparation, L.K.; writing—review and editing, X.J. and G.S.; visualization, G.S.; supervision, Z.D. and G.S.; project administration, X.J.; funding acquisition, Y.Y. and J.L. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Natural Science Foundation of Hebei Province (Grant No. F2018501063), National Natural Science Foundation of China (Grant No. 11704263), Fundamental Research Funds for the Central Universities Key Scientiﬁc Research Guidance Project (Grant No. Biosensors 2021, 11, 307 12 of 15

N2023005), and Hebei Province Science and Technology Plan Key Research Development Project Funds (Grant No. 18273902D) for financial support. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Acknowledgments: This work was performed in part at the Melbourne Centre for Nanofabrication (MCN) in the Victorian Node of the Australian National Fabrication Facility (ANFF). Conflicts of Interest: The authors declare no conflict of interest.

References 1. Allen, M.J.; Tung, V.C.; Kaner, R.B. Honeycomb carbon: A review of graphene. Chem. Rev. 2010, 110, 132–145. [CrossRef] 2. Geim, A.K.; Novoselov, K.S. The rise of graphene. Nat. Mater. 2007, 6, 183–191. [CrossRef][PubMed] 3. Lee, C.; Wei, X.D.; Kysar, J.W.; Hone, J. Measurement of the elastic properties and intrinsic strength of monolayer graphene. Science 2008, 321, 385–388. [CrossRef][PubMed] 4. Vakil, A.; Engheta, N. Transformation optics using graphene. Science 2011, 332, 1291–1294. [CrossRef][PubMed] 5. Bonaccorso, F.; Sun, Z.; Hasan, T.; Ferrari, A.C. Graphene photonics and optoelectronics. Nat. Photonics 2010, 4, 611–622. [CrossRef] 6. Sang, M.; Shin, J.; Kim, K.; Yu, K.J. Electronic and thermal properties of graphene and recent advances in graphene based electronics applications. Nanomaterials 2019, 9, 374. [CrossRef] 7. Balandin, A.A. Thermal properties of graphene and nanostructured carbon materials. Nat. Mater. 2011, 10, 569–581. [CrossRef] [PubMed] 8. Novoselov, K.S.; Geim, A.K.; Morozov, S.V.; Jiang, D.; Zhang, Y.; Dubonos, S.V.; Grigorieva, I.V.; Firsov, A.A. Electric field effect in atomically thin carbon films. Science 2004, 306, 666–669. [CrossRef] 9. Mishra, N.; Boeckl, J.; Motta, N.; Iacopi, F. Graphene growth on silicon carbide: A review. Phys. Status Solidi A 2016, 213, 2277–2289. [CrossRef] 10. Zhang, Y.; Zhang, L.Y.; Zhou, C.W. Review of chemical vapor deposition of graphene and related applications. Acc. Chem. Res. 2013, 46, 2329–2339. [CrossRef] 11. Othman, M.; Ritikos, R.; Rahman, S.A. Growth of plasma-enhanced chemical vapour deposition and hot filament plasma- enhanced chemical vapour deposition transfer-free graphene using a nickel catalyst. Thin Solid Films 2019, 685, 335–342. [CrossRef] 12. Yang, X.H.; Zhang, G.X.; Prakash, J.; Chen, Z.S.; Gauthier, M.; Sun, S.H. Chemical vapour deposition of graphene: Layer control, the transfer process, characterisation, and related applications. Int. Rev. Phys. Chem. 2019, 38, 149–199. [CrossRef] 13. Fukumori, M.; Pandey, R.R.; Fujiwara, T.; TermehYousefi, A.; Negishi, R.; Kobayashi, Y.; Tanaka, H.; Ogawa, T. Diameter dependence of longitudinal unzipping of single-walled carbon nanotube to obtain graphene nanoribbon. Jpn. J. Appl. Phys. 2017, 56, 06GG12. [CrossRef] 14. Dimiev, A.M.; Khannanov, A.; Vakhitov, I.; Kiiamov, A.; Shukhina, K.; Tour, J.M. Revisiting the mechanism of oxidative unzipping of multiwall carbon nanotubes to graphene nanoribbons. ACS Nano 2018, 12, 3985–3993. [CrossRef][PubMed] 15. Du, W.C.; Jiang, X.Q.; Zhu, L.H. From graphite to graphene: Direct liquid-phase exfoliation of graphite to produce single-and few-layered pristine graphene. J. Mater. Chem. A 2013, 1, 10592–10606. [CrossRef] 16. Lotya, M.; Hernandez, Y.; King, P.J.; Smith, R.J.; Nicolosi, V.; Karlsson, L.S.; Blighe, F.M.; De, S.; Wang, Z.M.; McGovern, I.T.; et al. Liquid phase production of graphene by exfoliation of graphite in surfactant/water solutions. J. Am. Chem. Soc. 2009, 131, 3611–3620. [CrossRef] 17. Stankovich, S.; Dikin, D.A.; Piner, R.D.; Kohlhaas, K.A.; Kleinhammes, A.; Jia, Y.; Wu, Y.; Nguyen, S.T.; Ruoff, R.S. Synthesis of graphene-based nanosheets via chemical reduction of exfoliated graphite oxide. Carbon 2007, 45, 1558–1565. [CrossRef] 18. De Silva, K.K.H.; Huang, H.H.; Joshi, R.K.; Yoshimura, M. Chemical reduction of graphene oxide using green reductants. Carbon 2017, 119, 190–199. [CrossRef] 19. Jiang, L.W.; Zheng, Y.S. Remarkable enhancement of terahertz conductivity of graphene tuned by periodic gate voltage. Phys. Lett. A 2010, 375, 203–207. [CrossRef] 20. Chang, Y.C.; Liu, C.H.; Liu, C.H.; Zhong, Z.H.; Norris, T.B. Extracting the complex optical conductivity of mono- and bilayer graphene by ellipsometry. Appl. Phys. Lett. 2014, 104, 261909. [CrossRef] 21. Wang, B.; Zhang, X.; Garcia-Vidal, F.J.; Yuan, X.C.; Teng, J. Strong coupling of surface plasmon polaritons in monolayer graphene sheet arrays. Phys. Rev. Lett. 2012, 109, 73901. [CrossRef][PubMed] 22. Chen, J.N.; Badioli, M.; Alonso-Gonzalez, P.; Thongrattanasiri, S.; Huth, F.; Osmond, J.; Spasenovic, M.; Centeno, A.; Pesquera, A.; Godignon, P.; et al. Optical nano-imaging of gate-tunable graphene plasmons. Nature 2012, 487, 77–81. [CrossRef][PubMed] 23. Grigorenko, A.N.; Polini, M.; Novoselov, K.S. Graphene plasmonics. Nat. Photonics 2012, 6, 749–758. [CrossRef] 24. Koppens, F.H.L.; Chang, D.E.; de Abajo, F.J.G. Graphene plasmonics: A platform for strong light-matter interactions. Nano Lett. 2011, 11, 3370–3377. [CrossRef][PubMed] 25. De Abajo, F.J.G. Graphene plasmonics: Challenges and opportunities. ACS Photonics 2014, 1, 135–152. [CrossRef] Biosensors 2021, 11, 307 13 of 15

26. Low, T.; Avouris, P. Graphene plasmonics for terahertz to mid-infrared applications. ACS Nano 2014, 8, 1086–1101. [CrossRef] [PubMed] 27. Xiao, S.S.; Zhu, X.L.; Li, B.H.; Mortensen, N.A. Graphene-plasmon polaritons: From fundamental properties to potential applications. Front. Phys. 2016, 11, 117801. [CrossRef] 28. Huang, S.Y.; Song, C.Y.; Zhang, G.W.; Yan, H.G. Graphene plasmonics: Physics and potential applications. Nanophotonics 2017, 6, 1191–1204. [CrossRef] 29. Heilemann, M. Fluorescence microscopy beyond the diffraction limit. J. Biotechnol. 2010, 149, 243–251. [CrossRef] 30. Cricenti, A.; Colonna, S.; Girasole, M.; Gori, P.; Ronci, F.; Longo, G.; Dinarelli, S.; Luce, M.; Rinaldi, M.; Ortenzi, M. Scanning probe microscopy in material science and biology. J. Phys. D Appl. Phys. 2011, 44, 464008. [CrossRef] 31. Bulat, K.; Rygula, A.; Szafraniec, E.; Ozaki, Y.; Baranska, M. Live endothelial cells imaged by scanning near-field optical microscopy (SNOM): Capabilities and challenges. J. Biophotonics 2017, 10, 928–938. [CrossRef][PubMed] 32. Kazantsev, D.V.; Kuznetsov, E.V.; Timofeev, S.V.; Shelaev, A.V.; Kazantseva, E.A. Apertureless near-field optical microscopy. Phys. Uspekhi 2017, 60, 259–275. [CrossRef] 33. Bazylewski, P.; Ezugwu, S.; Fanchini, G. A review of three-dimensional scanning near-field optical microscopy (3D-SNOM) and its applications in nanoscale light management. Appl. Sci. 2017, 7, 973. [CrossRef] 34. Durant, S.; Liu, Z.W.; Steele, J.A.; Zhang, X. Theory of the transmission properties of an optical far-field superlens for imaging beyond the diffraction limit. J. Opt. Soc. Am. B 2006, 23, 2383–2392. [CrossRef] 35. Du, Y.J.; Zhang, Z.Y.; Xin, W.J. Study on the imaging characteristics of far-field superlens. Optik 2014, 125, 208–211. [CrossRef] 36. Li, W.; Chen, S.Q.; Deng, H. Potential application of far-field superlens in optical critical dimension metrology: A simulation study. Opt. Eng. 2017, 56, 53109. [CrossRef] 37. Lee, H.; Liu, Z.; Xiong, Y.; Sun, C.; Zhang, X. Development of optical hyperlens for imaging below the diffraction limit. Opt. Express 2007, 15, 15886–15891. [CrossRef][PubMed] 38. Rho, J.; Ye, Z.L.; Xiong, Y.; Yin, X.B.; Liu, Z.W.; Choi, H.; Bartal, G.; Zhang, X.A. Spherical hyperlens for two-dimensional sub-diffractional imaging at visible frequencies. Nat. Commun. 2010, 1, 143. [CrossRef] 39. Hayashi, J.G.; Stefani, A.; Antipov, S.; Lwin, R.; Jackson, S.D.; Hudson, D.D.; Fleming, S.; Argyros, A.; Kuhlmey, B.T. Towards subdiffraction imaging with wire array metamaterial hyperlenses at MIR frequencies. Opt. Express 2019, 27, 21420–21434. [CrossRef] 40. Lu, D.L.; Liu, Z.W. Hyperlenses and metalenses for far-field super-resolution imaging. Nat. Commun. 2012, 3, 1205. [CrossRef] 41. Lemoult, F.; Lerosey, G.; de Rosny, J.; Fink, M. Resonant metalenses for breaking the diffraction barrier. Phys. Rev. Lett. 2010, 104, 203901. [CrossRef] 42. Zuo, R.Z.; Liu, W.W.; Cheng, H.; Chen, S.Q.; Tian, J.G. Breaking the diffraction limit with radially polarized light based on dielectric metalenses. Adv. Opt. Mater. 2018, 6, 1800795. [CrossRef] 43. Zhuang, Z.P.; Chen, R.; Fan, Z.B.; Pang, X.N.; Dong, J.W. High focusing efficiency in subdiffraction focusing metalens. Nanophotonics 2019, 8, 1279–1289. [CrossRef] 44. Sarmento, M.J.; Oneto, M.; Pelicci, S.; Pesce, L.; Scipioni, L.; Faretta, M.; Furia, L.; Dellino, G.I.; Pelicci, P.G.; Bianchini, P.; et al. Exploiting the tunability of stimulated emission depletion microscopy for super-resolution imaging of nuclear structures. Nat. Commun. 2018, 9, 3415. [CrossRef] 45. Wang, L.W.; Chen, B.L.; Yan, W.; Yang, Z.G.; Peng, X.; Lin, D.Y.; Weng, X.Y.; Ye, T.; Qu, J.L. Resolution improvement in STED super-resolution microscopy at low power using a phasor plot approach. Nanoscale 2018, 10, 16252–16260. [CrossRef][PubMed] 46. Vicidomini, G.; Bianchini, P.; Diaspro, A. STED super-resolved microscopy. Nat. Methods 2018, 15, 173–182. [CrossRef] 47. Zhang, W.C.; Noa, A.; Nienhaus, K.; Hilbert, L.; Nienhaus, G.U. Super-resolution imaging of densely packed DNA in nuclei of zebrafish embryos using stimulated emission double depletion microscopy. J. Phys. D Appl. Phys. 2019, 52, 414001. [CrossRef] 48. Cheng, T.; Chen, D.N.; Yu, B.; Niu, H.B. Reconstruction of super-resolution STORM images using compressed sensing based on low-resolution raw images and interpolation. Biomed. Opt. Express 2017, 8, 2445–2457. [CrossRef] 49. Hainsworth, A.H.; Lee, S.; Foot, P.; Patel, A.; Poon, W.W.; Knight, A.E. Super-resolution imaging of subcortical white matter using stochastic optical reconstruction microscopy (STORM) and super-resolution optical fluctuation imaging (SOFI). Neuropathol. Appl. Neurobiol. 2018, 44, 417–426. [CrossRef] 50. Lin, D.Y.; Gagnon, L.A.; Howard, M.D.; Halpern, A.R.; Vaughan, J.C. Extended-depth 3D super-resolution imaging using probe-refresh STORM. Biophys. J. 2018, 114, 1980–1987. [CrossRef] 51. Nehme, E.; Weiss, L.E.; Michaeli, T.; Shechtman, Y. Deep-STORM: Super-resolution single-molecule microscopy by deep learning. Optica 2018, 5, 458–464. [CrossRef] 52. Samanta, S.; Gong, W.J.; Li, W.; Sharma, A.; Shim, I.; Zhang, W.; Das, P.; Pan, W.H.; Liu, L.W.; Yang, Z.G.; et al. Organic fluorescent probes for stochastic optical reconstruction microscopy (STORM): Recent highlights and future possibilities. Coord. Chem. Rev. 2019, 380, 17–34. [CrossRef] 53. Karras, C.; Smedh, M.; Forster, R.; Deschout, H.; Fernandez-Rodriguez, J.; Heintzmann, R. Successful optimization of reconstruction parameters in structured illumination microscopy—A practical guide. Opt. Commun. 2019, 436, 69–75. [CrossRef] 54. Huttunen, M.J.; Abbas, A.; Upham, J.; Boyd, R.W. Label-free super-resolution with coherent nonlinear structured-illumination microscopy. J. Opt. 2017, 19, 85504. [CrossRef] Biosensors 2021, 11, 307 14 of 15

55. Wu, Y.C.; Shroff, H. Faster, sharper, and deeper: Structured illumination microscopy for biological imaging. Nat. Methods 2018, 15, 1011–1019. [CrossRef] 56. Turcottea, R.; Liang, Y.J.; Tanimoto, M.; Zhang, Q.R.; Li, Z.W.; Koyama, M.; Betzig, E.; Ji, N. Dynamic super-resolution structured illumination imaging in the living brain. Proc. Natl. Acad. Sci. USA 2019, 116, 9586–9591. [CrossRef][PubMed] 57. Wei, F.F.; Lu, D.L.; Shen, H.; Wan, W.W.; Ponsetto, J.L.; Huang, E.; Liu, Z.W. Wide field super-resolution surface imaging through plasmonic structured illumination microscopy. Nano Lett. 2014, 14, 4634–4639. [CrossRef] 58. Bezryadina, A.; Li, J.X.; Zhao, J.X.; Kothambawala, A.; Ponsetto, J.; Huang, E.; Wang, J.; Liu, Z.W. Localized plasmonic structured illumination microscopy with an optically trapped microlens. Nanoscale 2017, 9, 14907–14912. [CrossRef][PubMed] 59. Ponsetto, J.L.; Bezryadina, A.; Wei, F.F.; Onishi, K.; Shen, H.; Huang, E.; Ferrari, L.; Ma, Q.; Zou, Y.M.; Liu, Z.W. Experimental demonstration of localized plasmonic structured illumination microscopy. ACS Nano 2017, 11, 5344–5350. [CrossRef][PubMed] 60. Bezryadina, A.; Zhao, J.X.; Xia, Y.; Zhang, X.; Liu, Z.W. High spatiotemporal resolution imaging with localized plasmonic structured illumination microscopy. ACS Nano 2018, 12, 8248–8254. [CrossRef][PubMed] 61. Andryieuski, A.; Lavrinenko, A.V.; Chigrin, D.N. Graphene hyperlens for terahertz radiation. Phys. Rev. B 2012, 86, 121108. [CrossRef] 62. Li, P.N.; Taubner, T. Broadband subwavelength imaging using a tunable graphene-lens. ACS Nano 2012, 6, 10107–10114. [CrossRef] [PubMed] 63. Houmad, M.; Zaari, H.; Benyoussef, A.; El Kenz, A.; Ez-Zahraouy, H. Optical conductivity enhancement and band gap opening with silicon doped graphene. Carbon 2015, 94, 1021–1027. [CrossRef] 64. Hanson, G.W. Dyadic Green’s functions and guided surface waves for a surface conductivity model of graphene. J. Appl. Phys. 2008, 103, 64302. [CrossRef] 65. Falkovsky, L.A.; Pershoguba, S.S. Optical far-infrared properties of a graphene monolayer and multilayer. Phys. Rev. B 2007, 76, 153410. [CrossRef] 66. Chen, P.Y.; Alu, A. Atomically thin surface cloak using graphene monolayers. ACS Nano 2011, 5, 5855–5863. [CrossRef] 67. Li, Z.Q.; Henriksen, E.A.; Jiang, Z.; Hao, Z.; Martin, M.C.; Kim, P.; Stormer, H.L.; Basov, D.N. Dirac charge dynamics in graphene by infrared spectroscopy. Nat. Phys. 2008, 4, 532–535. [CrossRef] 68. Gusynin, V.P.; Sharapov, S.G.; Carbotte, J.P. Magneto-optical conductivity in graphene. J. Phys. Condens. Matter 2007, 19, 26222. [CrossRef] 69. Zhang, T.; Chen, L.; Li, X. Graphene-based tunable broadband hyperlens for far-field subdiffraction imaging at mid-infrared frequencies. Opt. Express 2013, 21, 20888–20899. [CrossRef] 70. Li, P.N.; Wang, T.; Bockmann, H.; Taubner, T. Graphene-enhanced infrared near-field microscopy. Nano Lett. 2014, 14, 4400–4405. [CrossRef][PubMed] 71. Tang, H.H.; Huang, T.J.; Liu, J.Y.; Tan, Y.H.; Liu, P.K. Tunable terahertz deep subwavelength imaging based on a graphene monolayer. Sci. Rep. 2017, 7, 46283. [CrossRef] 72. Inampudi, S.; Cheng, J.R.; Mosallaei, H. Graphene-based near-field optical microscopy: High-resolution imaging using reconfigurable gratings. Appl. Opt. 2017, 56, 3132–3141. [CrossRef] 73. Liu, J.Y.; Huang, T.J.; Liu, P.K. Terahertz super-resolution imaging using four-wave mixing in graphene. Opt. Lett. 2018, 43, 2102–2105. [CrossRef][PubMed] 74. Purkayastha, A.; Srivastava, T.; Jha, R. Ultrasensitive THz—Plasmonics gaseous sensor using doped graphene. Sens. Actuators B 2016, 227, 291–295. [CrossRef] 75. Liu, M.; Yin, X.B.; Ulin-Avila, E.; Geng, B.S.; Zentgraf, T.; Ju, L.; Wang, F.; Zhang, X. A graphene-based broadband optical modulator. Nature 2011, 474, 64–67. [CrossRef][PubMed] 76. Bao, Q.L.; Zhang, H.; Wang, B.; Ni, Z.H.; Lim, C.H.Y.X.; Wang, Y.; Tang, D.Y.; Loh, K.P. Broadband graphene polarizer. Nat. Photonics 2011, 5, 411–415. [CrossRef] 77. Liu, M.; Yin, X.B.; Zhang, X. Double-layer graphene optical modulator. Nano Lett. 2012, 12, 1482–1485. [CrossRef] 78. Pirruccio, G.; Moreno, L.M.; Lozano, G.; Rivas, J.G. Coherent and broadband enhanced optical absorption in graphene. ACS Nano 2013, 7, 4810–4817. [CrossRef][PubMed] 79. Yao, Y.; Kats, M.A.; Genevet, P.; Yu, N.F.; Song, Y.; Kong, J.; Capasso, F. Broad electrical tuning of graphene-loaded plasmonic antennas. Nano Lett. 2013, 13, 1257–1264. [CrossRef][PubMed] 80. Wang, P.W.; Tang, C.J.; Yan, Z.D.; Wang, Q.G.; Liu, F.X.; Chen, J.; Xu, Z.J.; Sui, C.H. Graphene-based superlens for subwavelength optical imaging by graphene plasmon resonances. Plasmonics 2016, 11, 515–522. [CrossRef] 81. Forouzmand, A.; Yakovlev, A.B. Tunable dual-band subwavelength imaging with a wire medium slab loaded with nanostructured graphene metasurfaces. AIP Adv. 2015, 5, 77108. [CrossRef] 82. Forouzmand, A.; Bernety, H.M.; Yakovlev, A.B. Graphene-loaded wire medium for tunable broadband subwavelength imaging. Phys. Rev. B 2015, 92, 85402. [CrossRef] 83. Yang, J.Z.; Wang, T.S.; Chen, Z.L.; Hu, B.L.; Yu, W.X. Super-resolution imaging at mid-infrared waveband in graphene-nanocavity formed on meta-surface. Sci. Rep. 2016, 6, 37898. [CrossRef] 84. Menabde, S.G.; Lee, I.H.; Lee, S.; Ha, H.; Heiden, J.T.; Yoo, D.; Kim, T.T.; Low, T.; Lee, Y.H.; Oh, S.H.; et al. Real-space imaging of acoustic plasmons in large-area graphene grown by chemical vapor deposition. Nat. Commun. 2021, 12, 938. [CrossRef] Biosensors 2021, 11, 307 15 of 15

85. Kumar, Y.R.; Deshmukh, K.; Sadasivuni, K.K.; Pasha, S.K.K. Graphene quantum dot based materials for sensing, bio-imaging and energy storage applications: A review. RSC Adv. 2020, 10, 23861–23898. [CrossRef] 86. Cao, S.; Wang, T.S.; Sun, Q.; Hu, B.L.; Levy, U.; Yu, W.X. Graphene on meta-surface for super-resolution optical imaging with a sub-10 nm resolution. Opt. Express 2017, 25, 14494–14503. [CrossRef][PubMed] 87. Kaipa, C.S.R.; Yakovlev, A.B.; Maslovski, S.I.; Silveirinha, M.G. Near-field imaging with a loaded wire medium. Phys. Rev. B 2012, 86, 155103. [CrossRef] 88. Nikitin, A.Y.; Alonso-Gonzalez, P.; Velez, S.; Mastel, S.; Centeno, A.; Pesquera, A.; Zurutuza, A.; Casanova, F.; Hueso, L.E.; Koppens, F.H.L.; et al. Real-space mapping of tailored sheet and edge plasmons in graphene nanoresonators. Nat. Photonics 2016, 10, 239–243. [CrossRef] 89. Fei, Z.; Goldflam, M.D.; Wu, J.S.; Dai, S.; Wagner, M.; McLeod, A.S.; Liu, M.K.; Post, K.W.; Zhu, S.; Janssen, G.C.A.M.; et al. Edge and surface plasmons in graphene nanoribbons. Nano Lett. 2015, 15, 8271–8276. [CrossRef] 90. Ghanekar, A.; Ricci, M.; Tian, Y.P.; Gregory, O.; Zheng, Y. Strain-induced modulation of near-field radiative transfer. Appl. Phys. Lett. 2018, 112, 241104. [CrossRef][PubMed] 91. Hipolito, F.; Taghizadeh, A.; Pedersen, T.G. Nonlinear optical response of doped monolayer and bilayer graphene: Length gauge tight-binding model. Phys. Rev. B 2018, 98, 205420. [CrossRef] 92. Younis, M.R.; He, G.; Lin, J.; Huang, P. Graphene-semiconductor nanocomposites for cancer phototherapy. Biomed. Mater. 2021, 16, 22007. [CrossRef] 93. Lin, J.; Chen, X.; Huang, P. Graphene-based nanomaterials for bioimaging. Adv. Drug Deliv. Rev. 2016, 105, 242–254. [CrossRef] [PubMed] 94. Younis, M.R.; He, G.; Lin, J.; Huang, P. Recent advances on graphene quantum dots for bioimaging applications. Front. Chem. 2020, 8, 424. [CrossRef] 95. Wojcik, M.; Hauser, M.; Li, W.; Moon, S.; Xu, K. Graphene-enabled electron microscopy and correlated super-resolution microscopy of wet cells. Nat. Commun. 2015, 6, 7384. [CrossRef][PubMed] 96. Zheng, X.; Xu, B.; Li, S.; Lin, H.; Qiu, L.; Li, D.; Jia, B. Free-standing graphene oxide mid-infrared polarizers. Nanoscale 2020, 12, 11480–11488. [CrossRef] 97. Wu, J.; Jia, L.; Zhang, Y.; Qu, Y.; Jia, B.; Moss, D.J. Graphene oxide for integrated photonics and flat optics. Adv. Mater. 2020, 33, 2006415. [CrossRef] 98. Li, R.; Wang, Y.; Du, J.; Wang, X.; Duan, A.; Gao, R.; Liu, J.; Li, B. Graphene oxide loaded with tumor-targeted peptide and anti-cancer drugs for cancer target therapy. Sci. Rep. 2021, 11, 1725. [CrossRef] 99. Chang, X.; Zhang, M.; Wang, C.; Zhang, J.; Wu, H.; Yang, S. Graphene oxide/BaHoF5/PEG nanocomposite for dual-modal imaging and heat shock protein inhibitor-sensitized tumor photothermal therapy. Carbon 2020, 158, 382–385. [CrossRef] 100. Sun, X.; Liu, Z.; Welsher, K.; Robinson, J.T.; Goodwin, A.; Zaric, S.; Dai, H. Nano-Graphene Oxide for Cellular Imaging and Drug Delivery. Nano Res. 2008, 1, 203–212. [CrossRef] 101. Liu, X.; Yan, B.; Li, Y.; Ma, X.; Jiao, W.; Shi, K.; Zhang, T.; Chen, S.; He, Y.; Liang, X.J.; et al. Graphene oxide-grafted magnetic nanorings mediated magnetothermodynamic therapy favoring reactive oxygen species-related immune response for enhanced antitumor efficacy. ACS Nano 2020, 14, 1936–1950. [CrossRef] 102. Li, R.H.; Georgiades, P.; Cox, H.; Phanphak, S.; Roberts, I.S.; Waigh, T.A.; Lu, J.R. Quenched stochastic optical reconstruction microscopy (qSTORM) with graphene oxide. Sci. Rep. 2018, 8, 16928. [CrossRef] 103. Chen, Z.Y.; Berciaud, S.; Nuckolls, C.; Heinz, T.F.; Brus, L.E. Energy transfer from individual semiconductor nanocrystals to graphene. ACS Nano 2010, 4, 2964–2968. [CrossRef] 104. Nieuwenhuizen, R.P.J.; Lidke, K.A.; Bates, M.; Puig, D.L.; Grunwald, D.; Stallinga, S.; Rieger, B. Measuring image resolution in optical nanoscopy. Nat. Methods 2013, 10, 557–562. [CrossRef]