CHEM 3420 /7420G Instrumental Analysis

Transcription

1 CHEM 3420 /7420G Instrumental Analysis Prof. Brian Gibney 2411 Ingersoll Course Introduction Prof. Brian R. Gibney B.S. Chemistry (ACS Certified) Ph.D. Chemistry Brooklyn College, Associate Professor - former undergraduate advisor CUNY Ph.D. Program in Chemistry, Executive Officer American Chemical Society - Member, ACS Committee on Science - Chair, NY Local Section 1

2 Course Introduction Text : Principles of Instrumental Analysis Sixth Edition Skoog-Holler-Crouch Brooks/Cole Lecture: T/Th 6:30-7:20 pm Laboratory: T/Th 7:30-10:20 pm Office Hours: Th 5:00 6:00 pm Course Introduction Structure: Quiz 10% Exam I 15% Exam II 15% Final Exam 20% Lab Reports 40% You must pass the lecture portion of the course in order to pass the course. 2

3 Course Introduction Important Dates: September 19 Quiz on Measurement Basics Electronics Lab Reports Due September 28 Potentiometry Lab Report Due October 10 - Exam I October 22 ISE, CV, ASV Lab Reports Due November 16 Exam II December 5 All remaining Lab Reports Due December 12 Final Exam Course Rules No food or drink No open toed shoes No cell phones No programmable calculators Safety Goggles must be worn - indirect ventilation President Obama 3

4 Why Instrumental Analysis Matters Nassau County, NY Crime Lab - concerns raised in put on probation Dec closed in Feb Pipettor issue in Testing costs to date $2.4 million - All new testing being done elsewhere - 3,000 retests ordered ($500,000) - $20.7 million for new crime lab (to open in 2016) - How many cases need to be retried? Chapter 1: Introduction Classification of Analytical Methods - Classical methods gravimetric titrimetric (Chem 3415W) - Instrumental methods electroanalytical spectroscopic chromatographic 4

5 Chapter 1: Instrumental Methods Chapter 1: Generic Instrument Block Diagram Energy Source Analytical Sample Information Sorter Analytical information Input transducer (detector) Data Domain of Transduced Information Readout Output signal Signal processor 5

6 Chapter 1: Instrument Components Chapter 1: Instrument Components Basic Components of an Analytical Instrument - Signal generator - Detector input transducer - Signal processor circuits computer - Readout device digital readout strip-chart recorder computer file 6

7 Chapter 1: Data Domains Data Domains def. physical or chemical characteristics encoded in electrical signals transducers encode analytical information into data domains Types - analog magnitude of some electrical quantity - time time relationship of signal fluctuations - digital information stores as one of two conditions Chapter 1: Instrument Components How does a ph meter work? - Signal generator - Detector - Signal processor - Readout device 7

8 Chapter 1: Instrument Components Calibration of Instrumental Methods - Direct comparison - Titration - External standards - Least squares methods - mathematical fit - Standard addition methods - Internal standards Chapter 1: Calibration Methods Direct Comparison -compare sample with known standard -dilute known standard to achieve properties of sample - null comparison - isomation method 8

9 Chapter 1: Calibration Methods Titration (Chem 3415W) - react analyte with standardized reagent with known stoichiometry - vary titrant until equivalence is reached - chemical comparison method - very accurate Chapter 1: Calibration Methods Titration (Chem 3415W) -Equivalence point quantity of added titrant is the exact amount necessary for the stoichiometric reaction with the analyte -Indicator measures end point -End point point measured by experiment, marked by sudden change in a physical property -Difference is titration error a form of bias 9

10 Chapter 1: Calibration Methods External standards (Most common) - Measure external standards to generate a calibration curve -Determine mathematical expression that reproduces the curve - least squares curve fit -Sources of error - blanks (ideal vs. solvent or reagent) - matrix effects - systematic errors in standard preparation/measuremen Least Squares Linear least squares analysis (linear regression) Method to find mathematical function which reproduces dataset This is what Excel does when you select Add Trendline For a linear function, y = mx + b Minimizes variance between points and line 10

11 Least Squares Least squares analysis Chapter 1: Calibration Methods External standard calibration method Most common method used Convenient when large numbers of similar samples are analyze Facilitates calculation of Figures of Merit Assumptions std. dev. is constant over calibration range uncertainty in signal is less than uncert. in standard co Uncertainty in measured conc. lowest at centroid of calibration 11

12 Chapter 1: Calibration Methods Constructing a calibration curve best practices -Calibrate over conc. in instrument linear range -Carefully prepare standard concentrations -At least seven calibration points -Evenly spaced (avoid leverage effects) -Unknown should be near center of calibration range -Where confidence interval is narrowest -Regression analysis to get calibration curve -R-value is not a good indication of error -View residuals to check model Chapter 1: Calibration Methods External standards - Measure external standards to generate a calibration curve -Determine mathematical expression that reproduces the curve - least squares curve fit -Sources of error - blanks (ideal vs. solvent or reagent) - matrix effects - systematic errors in standard preparation/measuremen 12

13 Chapter 1: Calibration Methods External standard calibration method Types of blanks solvent: contains solvent used analyze sample reagent: solvent, plus all reagents used to prepare sample ideal blank: identical to sample, but without analyte Assumption matrix does not change the analytical signal of the analyte Chapter 1: Calibration Methods Matix Effects What is the matrix? components in the sample aside from analyte How does it effect my analyte? loss or gain of signal constant (bias) or proportional Can I mask it s effect or correct for it? Bias can be corrected The matrix may be complex unable to be reproduced. - External standard calibration will not work - Use the Method of Standard Addition 13

14 Chapter 1: Calibration Methods Standard Addition methods - analyze unknown sample - add aliquots of a known concentration of analyte to sample - spiking - because matrix remains the same, matrix effect error are minimized - also used when a small number of samples are to be analyzed - Use for Anodic Striping Voltammetry Lab Chapter 1: Calibration Methods Standard Addition methods Signal = mv s + b - slope m = kc s / V t - y-intercept = b = kv x c x / V t Where k = proportionality constant c s = standard solution concentration c x = unknown solution concentration V x = unknown sample aliquot volume V s = volume of standard solution added V t = total volume 14

15 Chapter 1: Calibration Methods Standard Addition methods Use linear least squares to fit data Obtain slope (m) and y-intercept (b) to find the concentration of the unknown sample, c x c x = bc s / mv x Chapter 1: Calibration Methods Internal standard method - add known compound to all samples all blanks all calibration standards - use ratio of sample to internal standard - compensates for random and systematic errors - difficult to find ideal internal standard - signal similar to analyte, but distinguishable - not present in sample matrix - does not interfere with analyte 15

16 Define the Analytical Problem to be Solved - What accuracy is required? - How much sample is available? - What is the concentration range of the analyte? - What components in the sample might cause interference? - What are the physical/chemical properties of the sample matrix? - How many samples are to be analyzed? Select the right tool for the right job! Some practical concerns - Speed of analysis - Ease and convenience - Cost/availability of the instrument - Per-sample cost 16

17 -Quantitative Instrument Performance eria and associated figures of merit - Precision absolute/relative standard deviation variance, coefficient of variation - Bias absolute/relative systematic error - Sensitivity calibration/analytical sensitivity - Detection limits blank signal + 3x std. dev. of blank signal - Dynamic range limit of quantitation/linearity - Selectivity coefficient of selectivity Precision mutual agreement of replicate measurements random errors lower precision std. dev. and variance are common measures Repeatability - same person/same day - how good is the analyst Reproducibility - different people/ different days - how robust is the method Accuracy agreement between data and true value 17

18 Precision vs. Accuracy Precision vs. Accuracy 18

19 Precision vs. Accuracy Precision vs. Accuracy 19

20 Bias (D) measures of systemic error of a method D = µ - t where µ = population mean or average of a distribution t = true concentration Test standard reference material times to get a measure of bias Biased method 20

21 Bias (D) can be corrected for Recall: the equivalence point and the end point of a titration are different, thus titations have bias Bias (D) can be corrected for Recall: the equivalence point and the end point of a titration are different, thus titations have bias In Chem 3415W how did you correct for this titration error? 21

22 Sensitivity Two factors limit sensitivity slope of calibration curve -steeper slope/greater sensitivity reproducibility of measurements - equal slope, better reproducibility, greater sensitivity Calibration sensitivity def. by IUPAC as S = mc + S bl where S = measured signal c = concentration of the analyte S bl = instrumental signal for blank m = slope of calibration line ignores precision 22

23 Analytical sensitivity def. as g = m/s s where s s = std. dev. of the signals m = slope of calibration line advantages: - relatively insensitive to amplification factors - independent of units disadvantage: - std. dev. of signal can vary with concentration Chapter 1: Sensitivity A Here are two calibration curves. Instrument response B Which is more sensitive? Concentration 23

24 Detection Limit, c m def. minimum conc., or weight of analyte that can be detected at a known confidence level Minimum distinguishable analytical signal S m S m = S bl + ks bl where S bl = average (mean) blank signal k = some multiple (normally 3) s bl = absolute standard deviation of the blank determine S bl and s bl by measuring blanks over extended period of time detection limit = c m = (S m - S bl )/m (m = slope of calibration curve) Chapter 1: Dynamic Range Instrument response Dynamic range c m = detection limit LOQ = limit of quantitative measurement LOL = limit of linear response 5% deviation c m LOQ Concentration LOL 24

25 Chapter 1: Dynamic Range Selectivity def. degree to which method is free from interference by other species contained in the matrix S = m A c A + m B c B + m C c C + S bl where S = analytical signal c A, c B, c C = concentrations of A, B, and C, m A, m B, m C = calibration sensitivities of A, B, and C, S bl = instrumental signal of blank Chapter 1: Dynamic Range Selectivity coefficient k B,A = m B /m A where yields and k C,A = m C /m A k B,A = selectivity coefficient for B with respect to A k C,A = selectivity coefficient for C with respect to A S = m A (c A + k B,A c B + k C,A c C ) + S bl 25

26 Chapter 1: Calibration Curves Assigned Reading on Calibration Curves - on website - discuss at beginning of Tuesday s class 26