Advanced Analytical Chemistry

Lecture 1

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Chemistry 5570 Fall 2017

Lecture: TTh 8:00 – 9:20 p.m. Room 253Chemistry.

Instructor: Dr. Teresa D. Golden. Chemistry279, 565-2888, tgolden@unt.edu.

Office hours: MW 3:30 - 4:30 p.m. Room207B Chemistry.

Course Material: Quality Assurance in AnalyticalChemistry, by Prichard andBarwick.

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Chemistry 5570 Fall 2010

Topics: QC/QA, Basic electronics, signals and noise, vacuum technology, optical systems, instrument design, sensors and detectors, spectroscopy and interpretation, data analysis, surface analysis, and selected instruments.

Homework: Homework will be assigned in class throughout the semester.

Exams: There will be 2-3 exams and a comprehensive final exam. Dates for exams will be announced one week ahead of time. The final is scheduled for 12/12/2017 from 8:00-10:00 a.m. in Room 253.

*Absolutely no make-up exams will be given without a signed physician’s note.

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Research Paper Chem 5570Advances in Analytical Instrumentation

(1) Research the chemistry literature on new or improved analytical instrumentation techniques. Paper must follow ACS guidelines

(2) Paper covering student’s research project for publication in journal. Must follow journal guidelines + expanded sections.

(3) Paper for 6010 proposal. Must follow funding agency guidelines.

(4) Introduction chapter for thesis or dissertation. Must follow UNT guidelines.

Good places to start:Chemical Abstracts –Sci ScholarACS Journals - Analytical Chemistry JournalCritical Reviews in Analytical ChemistryElsevier Journals under Science Direct

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Research Paper Chem 5570Advances in Analytical Instrumentation

Write a short abstract over the topic with a short reference list for

approval – Due 09/14/17 (10pts)

Write an outline of your research paper with detailed reference list– Due

10/10/17 (30 pts)

Complete Research Paper – Due 11/30/17 (100 pts)

Your Research Paper will count as one Test grade for the course (140 points)

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Advances in Analytical Instrumentation - Format of Paper

I. Title Page

Name, semester, course, professor, title of paper, which format paper fits.

II. Abstract

100 - 200 words

III. Introduction

General overview of instrument, where does it fit? - spectroscopy, chromatography, electrochemistry, etc...

IV. Experimental

Instrumentation, what are the components, how are they arranged, what is their purpose, show diagrams, specs

V. Theory

How does the instrument work, what are the fundamental principles, equations (mathematical and/or chemical).

VI. Applications

For what applications is the instrument used, table, how is the analysis done, what existing problem does the instrument help solve, how is it an improvement over previous techniques or instruments for this application.

VII. References

ACS format, Numbered with Complete bibliography

*Any plagiarism in the report will result in a failing grade. Use your own words; if you are not sure about any section or part of your writing come see me for help.

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Chemistry 5570 Fall 2017

1. Attend Class

2. Reread Notes Each Week

3. Write and Draw to Study (Practice Test)

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Quality Assurance

A set of activities that ensures that development and/or maintenance processes are adequate in order for a system to meet its objectives.

Quality Control

A set of activities designed to evaluate the developed product.

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Quality Assurance Examples Develop and validate methods for analyzing and producing products to ensure

they meet specifications and document those methods in standard operating procedures (SOPs) and production records

Audit quality control, production and sales functions, and other records to ensure compliance with SOPs

Assist quality control staff with troubleshooting and resolving unusual problems

Develop quality metrics and assess trends for product or process implications

Develop and implement improvements to existing sample and data analysis techniques

Critically evaluate current processes and continually identify ways to save time and money without sacrificing product quality

Investigate product, and process deviations, identify root cause, and develop corrective and preventative actions

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Quality Control Examples Set up, troubleshoot, and maintain equipment, including calibration

logs and training records

Prepare samples in accordance with standard operating procedures

Conduct analytical tests on starting materials, ingredients in-process samples and finished products, following specific protocols

Provide timely and high-quality data reports that document test results

Recheck out-of-specification results, using alternative methods if necessary

Test products for long-term stability or stability under various conditions

Interface, maintain, and populate a Laboratory Information Management System

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Why QA/QC matters

Large percentage of a nations GNP (>5%)

Need reliable data for political debates and agencies (i.e, global warming, fracking, contaminated water, forensic evidence)

Wrong forensic analysis – wrongful conviction

Undetected water contamination (Flint, Michigan, files false reports)

Local pharmaceutical laboratories (raided FBI)

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Chemist must choose the correct analytical method or instrument to solve a problem.

In order to do this, the chemist must understand a wide variety of methods and instruments and the limitations of each one.

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To correctly select the method or instrument, the problem must be clearly defined.

1. What accuracy is required?

2. How much sample is available?

3. What is the concentration range of the analyte?

4. What components of the sample will cause interference?

5. What are the physical and chemical properties of the sample matrix?

6. How many samples are to be analyzed?

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Measurements made in one laboratory must be repeatable in another laboratory.

How do we accomplish this?

Need standards or Certified Reference Materials (CRMs).

Need known calibration procedures

Need common agreed upon procedures

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Standards have been developed by a variety of national, international, and businesses.

Standards can refer to physical components or operating processes.

Physical:

An Fe solution standard for AAS

LaB6 powder for XRD

A calibrated 1 gr weight for a balance

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Standards have been developed by a variety of national, international, and businesses.

Standards can refer to physical components or operating processes.

Process:

International Standard, ISO 90001:2000

Quality Management Systems - Requirements

Good Laboratory Practice (GLP)

Am National Accreditation Board (ANAB)(ASCLD)

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International Standard, ISO 90001:2000

Gives the requirements for a quality management system

Organization for Economic Co-operation and Development (OECD)

Developed GLP principles to promote development of quality test data

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Quality assurance of analytical measurements results in a system comprising 5 independent elements.

Assurance of measuring traceability of the obtained results

Evaluation of uncertainty of the results

Use of certified reference materials

Participation in various interlaboratory comparisons

Validation of the applied analytical procedures

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To excel at QA/QC must understand and use statistical analysis.

Correct equations must be used for the particular circumstance

Review basic statistical analysis

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Distribution of measurements

Normal distribution (Gaussian distribution)

Characterized by an expected value, a median, and a variance

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Since we never have the entire distribution but a sample of the population, statistical parameters are used to describe the sample, these parameters are divided into basic groups:

Measure of location

Measure of statistical dispersion

Measure of asymmetry

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Measure of location

One value to represent the values of the population

Can be mean, mode, median, etc

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Measure of dispersion

To determine differences between individual observations and mean value

Can be range, variance, standard deviation, average deviation, coefficient of variance, etc

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Measure of asymmetry

An absolute value expressed as the difference between an arithmetic mean and a mode.

Different instrumentation methods may have own unique approach, i.e., XRD (pearson VII), XPS, chromatography

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Bias = m – xt

m – population mean

xt – true concentration

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Sample – finite number of experimental observations (all the replicate).

The sample is a fraction of the infinite number of observations possible. (i.e. 50 measurements).

This infinite number of observations (measurements) is called the populationor universe of data.

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Instruments or methods ability to discriminate between small differences in analyte concentration.

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Degree to which the method is free from interference by other species contained in the sample matrix.

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Minimum concentration or mass of analyte that can be detected at a known confidence level.

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Review Statistics

Begin Paper Abstract (Topic)

Read Chapters 1 and 2

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