Method for Determining the Absolute Umber Concentration Of
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Using Serial Dilution to Understand Ppm/Ppb
Using Serial Dilution to Understand ppm/ppb Adapted from Investigating Groundwater: The Fruitvale Story, Science Education for Public Understanding Program (SEPUP), Lawrence Hall of Science (1996) by Dana Haine, UNC Superfund Research Program. Overview: Students will perform a serial dilution of food coloring to create highly diluted solutions to learn about the units, parts per million (ppm) and parts per billion (ppb), that scientists often use to describe chemical contamination of water or soil. Objectives: At the end of this activity, students will be able to: • Define serial dilution; • Define ppm and ppb; • Observe that a contaminant can be present in water even if it isn’t visible. Alignment to North Carolina Essential Standards for Science This lesson addresses components of the specific learning objectives: 8th Grade Science 8.E.1.3 Predict the safety and potability of water supplies in North Carolina based on physical and biological factors, including temperature, dissolved oxygen, pH, nitrates and phosphates, turbidity, bio-indicators 8.E.1.4 Conclude that the good health of humans requires: monitoring of the hydrosphere, water quality standards, methods of water treatment, maintaining safe water quality, stewardship Earth and Environmental Science EEn.2.4.1 Evaluate human influences on freshwater availability. EEn.2.4.2 Evaluate human influences on water quality in North Carolina’s river basins, wetlands and tidal environments. Physical Science PSc.2.1.2 Explain the phases of matter and the physical changes that matter undergoes. Materials: Student Data Collection Sheet, one per student Plastic tray with at least 9 wells or 9 test tubes, one tray/set of tubes per group White scrap paper to place under trays/tubes to observe results, one per group Red Food Coloring Two small cups of water, one labeled “rinse” Medicine dropper, one per group Paper towels Red colored pencils (optional) Duration 20-30 minutes Procedure: 1. -
Particle Characterisation In
LIFE SCIENCE I TECHNICAL BULLETIN ISSUE N°11 /JULY 2008 PARTICLE CHARACTERISATION IN EXCIPIENTS, DRUG PRODUCTS AND DRUG SUBSTANCES AUTHOR: HILDEGARD BRÜMMER, PhD, CUSTOMER SERVICE MANAGER, SGS LIFE SCIENCE SERVICES, GERMANY Particle characterization has significance in many industries. For the pharmaceutical industry, particle size impacts products in two ways: as an influence on drug performance and as an indicator of contamination. This article will briefly examine how particle size impacts both, and review the arsenal of methods for measuring and tracking particle size. Furthermore, examples of compromised product quality observed within our laboratories illustrate why characterization is so important. INDICATOR OF CONTAMINATION Controlling the limits of contaminating • Adverse indirect reactions: particles Particle characterisation of drug particles is critical for injectable are identified by the immune system as substances, drug products and excipients (parenteral) solutions. Particle foreign material and immune reaction is an important factor in R&D, production contamination of solutions can potentially might impose secondary effects. and quality control of pharmaceuticals. have the following results: It is becoming increasingly important for In order to protect a patient and to compliance with requirements of FDA and • Adverse direct reactions: e.g. particles guarantee a high quality product, several European Health Authorities. are distributed via the blood in the body chapters in the compendia (USP, EP, JP) and cause toxicity to specific tissues or describe techniques for characterisation organs, or particles of a given size can of limits. Some of the most relevant cause a physiological effect blocking blood chapters are listed in Table 1. flow e.g. in the lungs. -
Chapter 3 Bose-Einstein Condensation of an Ideal
Chapter 3 Bose-Einstein Condensation of An Ideal Gas An ideal gas consisting of non-interacting Bose particles is a ¯ctitious system since every realistic Bose gas shows some level of particle-particle interaction. Nevertheless, such a mathematical model provides the simplest example for the realization of Bose-Einstein condensation. This simple model, ¯rst studied by A. Einstein [1], correctly describes important basic properties of actual non-ideal (interacting) Bose gas. In particular, such basic concepts as BEC critical temperature Tc (or critical particle density nc), condensate fraction N0=N and the dimensionality issue will be obtained. 3.1 The ideal Bose gas in the canonical and grand canonical ensemble Suppose an ideal gas of non-interacting particles with ¯xed particle number N is trapped in a box with a volume V and at equilibrium temperature T . We assume a particle system somehow establishes an equilibrium temperature in spite of the absence of interaction. Such a system can be characterized by the thermodynamic partition function of canonical ensemble X Z = e¡¯ER ; (3.1) R where R stands for a macroscopic state of the gas and is uniquely speci¯ed by the occupa- tion number ni of each single particle state i: fn0; n1; ¢ ¢ ¢ ¢ ¢ ¢g. ¯ = 1=kBT is a temperature parameter. Then, the total energy of a macroscopic state R is given by only the kinetic energy: X ER = "ini; (3.2) i where "i is the eigen-energy of the single particle state i and the occupation number ni satis¯es the normalization condition X N = ni: (3.3) i 1 The probability -
Laboratory Exercises in Microbiology: Discovering the Unseen World Through Hands-On Investigation
City University of New York (CUNY) CUNY Academic Works Open Educational Resources Queensborough Community College 2016 Laboratory Exercises in Microbiology: Discovering the Unseen World Through Hands-On Investigation Joan Petersen CUNY Queensborough Community College Susan McLaughlin CUNY Queensborough Community College How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/qb_oers/16 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] Laboratory Exercises in Microbiology: Discovering the Unseen World through Hands-On Investigation By Dr. Susan McLaughlin & Dr. Joan Petersen Queensborough Community College Laboratory Exercises in Microbiology: Discovering the Unseen World through Hands-On Investigation Table of Contents Preface………………………………………………………………………………………i Acknowledgments…………………………………………………………………………..ii Microbiology Lab Safety Instructions…………………………………………………...... iii Lab 1. Introduction to Microscopy and Diversity of Cell Types……………………......... 1 Lab 2. Introduction to Aseptic Techniques and Growth Media………………………...... 19 Lab 3. Preparation of Bacterial Smears and Introduction to Staining…………………...... 37 Lab 4. Acid fast and Endospore Staining……………………………………………......... 49 Lab 5. Metabolic Activities of Bacteria…………………………………………….…....... 59 Lab 6. Dichotomous Keys……………………………………………………………......... 77 Lab 7. The Effect of Physical Factors on Microbial Growth……………………………... 85 Lab 8. Chemical Control of Microbial Growth—Disinfectants and Antibiotics…………. 99 Lab 9. The Microbiology of Milk and Food………………………………………………. 111 Lab 10. The Eukaryotes………………………………………………………………........ 123 Lab 11. Clinical Microbiology I; Anaerobic pathogens; Vectors of Infectious Disease….. 141 Lab 12. Clinical Microbiology II—Immunology and the Biolog System………………… 153 Lab 13. Putting it all Together: Case Studies in Microbiology…………………………… 163 Appendix I. -
Determination of the Solubility Product of an Ionic Compound
Determination of the Solubility Product of an Ionic Compound Kyle Miller January 11, 2007 1 Data The following data were collected. Dilution Precipitation well Ca2+ #6 OH− #4 2 Calculations 2.1 Calcium Ion Serial Dilution The first well without a precipitate is well number 6. The concentration of calcium ions per well is 0.10 [Ca2+] = M (1) 2n where n is the well number. So, the concentration of calcium ions in well number 6 is 0.10 −3 26 M = 1.6 × 10 M For each of the wells in this series, the concentration of added NaOH was a constant 0.1 M 2 = 0.05 M after being added to each well. 2+ − The Ksp for this ion equilibrium, Ca(OH)2 Ca + 2OH , is 2+ −2 Ksp = Ca OH (2) So, putting in the concentrations for well number 6, which is assumed to have complete −3 2 −6 dissociation of the calcium hydroxide, Ksp = 1.6 × 10 [0.05] = 3.9 × 10 1 2.2 Hydroxide Ion Serial Dilution The first well without precipitate for this series is well number 4. The concentration of hydroxide ions per well is 0.10 [OH−] = M (3) 2n again, with n being the well number. So, the concentration of hydroxide ions in well 0.10 −3 number 4 is 24 M = 6.3 × 10 M For each of the wells in this series, the concentration of added calcium nitrate was a constant 0.1 M 2 = 0.05 M after being added to each well. Using the ion equilibrium equation and these concentrations, we can find that Ksp = [0.05] 6.3 × 10−32 = 2.0 × 10−6 ¯ 3.9×10−6+2.0×10−6 −6 Then, Ksp = 2 = 3.0 × 10 −6 The actual Ksp is 5.02×10 and the percent error from this value to the calculated average |3.0×10−6−5.02×10−6| is 5.02×10−6 = 40.% 3 Discussion 1. -
Experiment 16
FV 2/10/2017 Experiment 16 HYDRONIUM ION CONCENTRATION MATERIALS: 7 centrifuge tubes with 10 mL mark, six 25 x 150 mm large test tubes, 5 mL pipet, 50 mL beaker, 50 mL graduated cylinder, 1.0 M HCl, 1.0 M CH3COOH, CH3COONa, methyl violet and methyl orange indicator solutions, test tube rack, calibrated pH meter (instructor use only). PURPOSE: The purpose of this experiment is to perform serial dilutions and use indicators to estimate the pH of various solutions. LEARNING OBJECTIVES: By the end of this experiment, the students should be able to demonstrate the following proficiencies: 1. Prepare solutions by serial dilution. + 2. Correlate the H3O ion concentration of a solution with its pH value. 3. Use indicators to estimate the pH of solutions of various acid concentrations. 4. Explain the common ion effect. 5. Calculate percent dissociation of a weak acid. PRE-LAB: Read over the experiment and complete the pre-lab questions on OWL before the lab. DISCUSSION: In any aqueous solution, the product of the hydronium ion concentration and the hydroxide ion concentration is equal to a constant, known as Kw. At a temperature of 25°C, this product is: + - -14 Kw = [H3O ]⋅ [OH ] = 1.0 x 10 (25° C) + - If the [H3O ] concentration is altered, the [OH ] concentration changes so that the product of the two terms remains 1.0 x 10-14. In pure, distilled water at 25°C: + - -7 [H3O ] = [OH ] = 1.0 x 10 M (25° C) To describe acidity in a simple way, the pH scale was adopted: + pH = -log[H3O ] Thus, for pure, distilled water at 25°C: -7 = ° pH = -log(1.0 x 10 ) 7.00 (25 C) + The practical range of pH in aqueous solutions at 25°C is from 0 to 14. -
Robust Estimation of Bacterial Cell Count from Optical Density
bioRxiv preprint first posted online Oct. 13, 2019; doi: http://dx.doi.org/10.1101/803239. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Robust Estimation of Bacterial Cell Count from Optical Density Jacob Beal1*, Natalie G. Farny2*, Traci Haddock-Angelli3*, Vinoo Selvarajah3, Geoff S. Baldwin4*, Russell Buckley-Taylor4, Markus Gershater5*, Daisuke Kiga6, John Marken7, Vishal Sanchania5, Abigail Sison3, Christopher T. Workman8*, and the iGEM Interlab Study Contributors 1 Raytheon BBN Technologies, Cambridge, MA, USA 2 Department of Biology and Biotechnology, Worcester Polytechnic Institute, Worcester, MA, USA 3 iGEM Foundation, Cambridge, MA, USA 4 Department of Life Sciences and IC-Centre for Synthetic Biology, Imperial College London, London, UK 5 Synthace, London, UK 6 Faculty of Science and Engineering, School of Advanced Science and Engineering, Waseda University, Tokyo, Japan 7 Department of Bioengineering, California Institute of Technology, Pasadena, CA, USA 8 DTU-Bioengineering, Technical University of Denmark, Kongens Lyngby, Denmark Membership list is provided in Supplementary Note: iGEM Interlab Study Contributors. * [email protected] (J.B.), [email protected] (N.G.F.), [email protected] (T.H-A.), [email protected] (G.S.B.), [email protected] (M.G.), [email protected] (C.T.W.) Abstract Optical density (OD) is a fast, cheap, and high-throughput measurement widely used to estimate the density of cells in liquid culture. These measurements, however, cannot be compared between instruments without a standardized calibration protocol and are challenging to relate to actual cell count. -
2. Classical Gases
2. Classical Gases Our goal in this section is to use the techniques of statistical mechanics to describe the dynamics of the simplest system: a gas. This means a bunch of particles, flying around in a box. Although much of the last section was formulated in the language of quantum mechanics, here we will revert back to classical mechanics. Nonetheless, a recurrent theme will be that the quantum world is never far behind: we’ll see several puzzles, both theoretical and experimental, which can only truly be resolved by turning on ~. 2.1 The Classical Partition Function For most of this section we will work in the canonical ensemble. We start by reformu- lating the idea of a partition function in classical mechanics. We’ll consider a simple system – a single particle of mass m moving in three dimensions in a potential V (~q ). The classical Hamiltonian of the system3 is the sum of kinetic and potential energy, p~ 2 H = + V (~q ) 2m We earlier defined the partition function (1.21) to be the sum over all quantum states of the system. Here we want to do something similar. In classical mechanics, the state of a system is determined by a point in phase space.Wemustspecifyboththeposition and momentum of each of the particles — only then do we have enough information to figure out what the system will do for all times in the future. This motivates the definition of the partition function for a single classical particle as the integration over phase space, 1 3 3 βH(p,q) Z = d qd pe− (2.1) 1 h3 Z The only slightly odd thing is the factor of 1/h3 that sits out front. -
Particle Number Concentrations and Size Distribution in a Polluted Megacity
https://doi.org/10.5194/acp-2020-6 Preprint. Discussion started: 17 February 2020 c Author(s) 2020. CC BY 4.0 License. Particle number concentrations and size distribution in a polluted megacity: The Delhi Aerosol Supersite study Shahzad Gani1, Sahil Bhandari2, Kanan Patel2, Sarah Seraj1, Prashant Soni3, Zainab Arub3, Gazala Habib3, Lea Hildebrandt Ruiz2, and Joshua S. Apte1 1Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, Texas, USA 2McKetta Department of Chemical Engineering, The University of Texas at Austin, Texas, USA 3Department of Civil Engineering, Indian Institute of Technology Delhi, New Delhi, India Correspondence: Joshua S. Apte ([email protected]), Lea Hildebrandt Ruiz ([email protected]) Abstract. The Indian national capital, Delhi, routinely experiences some of the world's highest urban particulate matter concentrations. While fine particulate matter (PM2.5) mass concentrations in Delhi are at least an order of magnitude higher than in many western cities, the particle number (PN) concentrations are not similarly elevated. Here we report on 1.25 years of highly 5 time resolved particle size distributions (PSD) data in the size range of 12–560 nm. We observed that the large number of accumulation mode particles—that constitute most of the PM2.5 mass—also contributed substantially to the PN concentrations. The ultrafine particles (UFP, Dp <100 nm) fraction of PN was higher during the traffic rush hours and for daytimes of warmer seasons—consistent with traffic and nucleation events being major sources of urban UFP. UFP concentrations were found to be relatively lower during periods with some of the highest mass concentrations. -
Statistical Physics 06/07
STATISTICAL PHYSICS 06/07 Quantum Statistical Mechanics Tutorial Sheet 3 The questions that follow on this and succeeding sheets are an integral part of this course. The code beside each question has the following significance: • K: key question – explores core material • R: review question – an invitation to consolidate • C: challenge question – going beyond the basic framework of the course • S: standard question – general fitness training! 3.1 Particle Number Fluctuations for Fermions [s] (a) For a single fermion state in the grand canonical ensemble, show that 2 h(∆nj) i =n ¯j(1 − n¯j) wheren ¯j is the mean occupancy. Hint: You only need to use the exclusion principle not the explict form ofn ¯j. 2 How is the fact that h(∆nj) i is not in general small compared ton ¯j to be reconciled with the sharp values of macroscopic observables? (b) For a gas of noninteracting particles in the grand canonical ensemble, show that 2 X 2 h(∆N) i = h(∆nj) i j (you will need to invoke that nj and nk are uncorrelated in the GCE for j 6= k). Hence show that for noninteracting Fermions Z h(∆N)2i = g() f(1 − f) d follows from (a) where f(, µ) is the F-D distribution and g() is the density of states. (c) Show that for low temperatures f(1 − f) is sharply peaked at = µ, and hence that 2 h∆N i ' kBT g(F ) where F = µ(T = 0) R ∞ ex dx [You may use without proof the result that −∞ (ex+1)2 = 1.] 3.2 Entropy of the Ideal Fermi Gas [C] The Grand Potential for an ideal Fermi is given by X Φ = −kT ln [1 + exp β(µ − j)] j Show that for Fermions X Φ = kT ln(1 − pj) , j where pj = f(j) is the probability of occupation of the state j. -
Appendix a Review of Probability Distribution
Appendix A Review of Probability Distribution In this appendix we review some fundamental concepts of probability theory. After deriving in Sect. A.1 the binomial probability distribution, in Sect. A.2 we show that for systems described by a large number of random variables this distribution tends to a Gaussian function. Finally, in Sect. A.3 we define moments and cumulants of a generic probability distribution, showing how the central limit theorem can be easily derived. A.1 Binomial Distribution Consider an ideal gas at equilibrium, composed of N particles, contained in a box of volume V . Isolating within V a subsystem of volume v, as the gas is macroscop- ically homogeneous, the probability to find a particle within v equals p = v/V , while the probability to find it in a volume V − v is q = 1 − p. Accordingly, the probability to find any one given configuration (i.e. assuming that particles could all be distinguished from each other), with n molecules in v and the remaining (N − n) within (V − v), would be equal to pnqN−n, i.e. the product of the respective prob- abilities.1 However, as the particles are identical to each other, we have to multiply this probability by the Tartaglia pre-factor, that us the number of possibilities of 1Here, we assume that finding a particle within v is not correlated with finding another, which is true in ideal gases, that are composed of point-like molecules. In real fluids, however, since molecules do occupy an effective volume, we should consider an excluded volume effect. -
Standard Methods for the Examination of Water and Wastewater
Standard Methods for the Examination of Water and Wastewater Part 1000 INTRODUCTION 1010 INTRODUCTION 1010 A. Scope and Application of Methods The procedures described in these standards are intended for the examination of waters of a wide range of quality, including water suitable for domestic or industrial supplies, surface water, ground water, cooling or circulating water, boiler water, boiler feed water, treated and untreated municipal or industrial wastewater, and saline water. The unity of the fields of water supply, receiving water quality, and wastewater treatment and disposal is recognized by presenting methods of analysis for each constituent in a single section for all types of waters. An effort has been made to present methods that apply generally. Where alternative methods are necessary for samples of different composition, the basis for selecting the most appropriate method is presented as clearly as possible. However, samples with extreme concentrations or otherwise unusual compositions or characteristics may present difficulties that preclude the direct use of these methods. Hence, some modification of a procedure may be necessary in specific instances. Whenever a procedure is modified, the analyst should state plainly the nature of modification in the report of results. Certain procedures are intended for use with sludges and sediments. Here again, the effort has been to present methods of the widest possible application, but when chemical sludges or slurries or other samples of highly unusual composition are encountered, the methods of this manual may require modification or may be inappropriate. Most of the methods included here have been endorsed by regulatory agencies. Procedural modification without formal approval may be unacceptable to a regulatory body.