A Simple Model of GC x GC Separations John V. Seeley Oakland University 3/6/07.

Slides:



Advertisements
Similar presentations
Section 8.6. You probably know that a lighter tree climber can crawl farther out on a branch than a heavier climber can, before the branch is in danger.
Advertisements

Yinyin Yuan and Chang-Tsun Li Computer Science Department
Introduction to Chromatography
Vertical Jump Margaria Kalamen Wingate
Gas Chromatography.
Mass Spectrometry.
Example 2.2 Estimating the Relationship between Price and Demand.
Multidimensional Parallel Column Gas Chromatography P. M. Owens and D. W. Loehle Center for Molecular Sciences United States Military Academy West Point,
Fast Algorithms For Hierarchical Range Histogram Constructions
GC x GC With Valve-Based Modulation John Seeley Oakland University Department of Chemistry Rochester, MI 48309
Mathematical Modeling Overview on Mathematical Modeling in Chemical Engineering By Wiratni, PhD Chemical Engineering Gadjah Mada University Yogyakarta.
1 HPLC Lecture Mobile Phase Selection in Partition Chromatography Optimization of the mobile phase composition and polarity is vital for obtaining.
Regression Analysis Module 3. Regression Regression is the attempt to explain the variation in a dependent variable using the variation in independent.
The structure and evolution of stars
EARS1160 – Numerical Methods notes by G. Houseman
The geometry of capillary columns is fairly simple, consisting of length, internal diameter, and stationary phase thickness. Nevertheless, there are endless.
Chem. 31 – 4/8 Lecture. Announcements I Exam 2 – Monday –Covering Ch. 6 (topics since exam 1), 7, 8-1, 17, and parts of 22 (up to and including retention.
Chem. 133 – 5/5 Lecture. Announcements Lab Report 2.4 due Thursday – can turn in today for reduction of late penalties Term Project Progress Report –
Factor Analysis There are two main types of factor analysis:
Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-1 Chapter 6 The Normal Distribution and Other Continuous Distributions.
Chapter 5 Continuous Random Variables and Probability Distributions
Testing Bridge Lengths The Gadsden Group. Goals and Objectives Collect and express data in the form of tables and graphs Look for patterns to make predictions.
Designing a Separations Process Without VLE Data by Thomas Schafer - Koch Modular Process Systems, LLC This presentation utilizes as it’s example a problem.
Slides 13b: Time-Series Models; Measuring Forecast Error
CHAPTER 29 Supercritical Fluid Chromatography The mobile phase is a supercritical fluid (a fluid above its critical T and critical pressure) Supercritical.
Chapter 4 Continuous Random Variables and Probability Distributions
Mathematics for Economics and Business Jean Soper chapter two Equations in Economics 1.
ECON 6012 Cost Benefit Analysis Memorial University of Newfoundland
Lecture 1 Signals in the Time and Frequency Domains
NSW Curriculum and Learning Innovation Centre Tinker with Tinker Plots Elaine Watkins, Senior Curriculum Officer, Numeracy.
Wilkes University - CHM D Gas Chromatography (or 3D with MS Detector) A powerful separations tool for complex volatile mixtures of heat-stable samples.
Chap 6-1 Copyright ©2013 Pearson Education, Inc. publishing as Prentice Hall Chapter 6 The Normal Distribution Business Statistics: A First Course 6 th.
Quantitative Methods Heteroskedasticity.
1.1 General description - Sample dissolved in and transported by a mobile phase - Some components in sample interact more strongly with stationary phase.
Analyzing Reliability and Validity in Outcomes Assessment (Part 1) Robert W. Lingard and Deborah K. van Alphen California State University, Northridge.
Copyright © by Holt, Rinehart and Winston. All rights reserved. ResourcesChapter menu Organic Compounds All organic compounds contain carbon atoms, but.
BPS - 3rd Ed. Chapter 211 Inference for Regression.
Quantitative Skills 1: Graphing
MODULE 8 APPROXIMATION METHODS I Once we move past the two particle systems, the Schrödinger equation cannot be solved exactly. The electronic inter-repulsion.
Module 1: Statistical Issues in Micro simulation Paul Sousa.
Copyright © 2006 The McGraw-Hill Companies, Inc. Permission required for reproduction or display. 1 Chapter 2.
GC Separations are a Function of: 1. Temperature 2. Selectivity of the Stationary Phase 3. Mobile Phase Flow Rate 4. Amount of Stationary Phase Present.
CHROMATOGRAPHY Chromatography basically involves the separation of mixtures due to differences in the distribution coefficient.
Identifying Applicability Domains for Quantitative Structure Property Relationships Mordechai Shacham a, Neima Brauner b Georgi St. Cholakov c and Roumiana.
CS 782 – Machine Learning Lecture 4 Linear Models for Classification  Probabilistic generative models  Probabilistic discriminative models.
COLUMN CHROMATOGRAPHY
Real Gas Relationships
A "Reference Series" Method for Prediction of Properties of Long-Chain Substances Inga Paster and Mordechai Shacham Dept. Chem. Eng. Ben-Gurion University.
EXCEL DECISION MAKING TOOLS BASIC FORMULAE - REGRESSION - GOAL SEEK - SOLVER.
The Chromatogram  Terms:  Retention time  Peak area  Peak width (at half height, at base)  Peak height  Void time/volume  Adjusted retention time.
Advanced Engineering Mathematics, 7 th Edition Peter V. O’Neil © 2012 Cengage Learning Engineering. All Rights Reserved. CHAPTER 4 Series Solutions.
How does Science Work? Presented by : Sabar Nurohman, M.Pd.
EXCEL DECISION MAKING TOOLS AND CHARTS BASIC FORMULAE - REGRESSION - GOAL SEEK - SOLVER.
Chem. 133 – 5/3 Lecture. Announcements Lab – Term Project Progress Report Due Today – Last Assignments: Term Project Poster and Peer Review Grading (Friday,
1 A latent information function to extend domain attributes to improve the accuracy of small-data-set forecasting Reporter : Zhao-Wei Luo Che-Jung Chang,Der-Chiang.
BPS - 5th Ed. Chapter 231 Inference for Regression.
Chapter 15 Forecasting. Forecasting Methods n Forecasting methods can be classified as qualitative or quantitative. n Such methods are appropriate when.
Instrumental Analysis (I)  HPLC Tutorial #7 PHCMt561 – 5 th Sem. Pharm.
A Study of Smoothing Methods for Language Models Applied to Ad Hoc Information Retrieval Chengxiang Zhai, John Lafferty School of Computer Science Carnegie.
Fundamentals of Data Analysis Lecture 11 Methods of parametric estimation.
Hirophysics.com The Genetic Algorithm vs. Simulated Annealing Charles Barnes PHY 327.
Welcome to MM305 Unit 5 Seminar Dr. Bob Forecasting.
Lecture 5 Non-ideal conditions.
Chapter 7. Classification and Prediction
Exercises 5.
Chapter 22 Organic Compounds
Basic Practice of Statistics - 3rd Edition Inference for Regression
1/18 Objective: Explain the purpose and process of chromatography.
CHROMATOGRAPHY Chromatography : It is a physical method of separation in which the components to be separated are distributed between two phases, one.
The structure and evolution of stars
Presentation transcript:

A Simple Model of GC x GC Separations John V. Seeley Oakland University 3/6/07

Model Goals Generation of a “Simplified Chromatogram” from: –1-D retention times –Linear free energy relationship parameters –Retention indices Utility of the “Simplified Chromatogram” –Demonstrates the underlying mechanisms of a GC x GC separation –Approximate representation of relative peak position –Quick screening new column sets –Demonstrates the influence of stationary phase order on chromatogram structure –Demonstrates the concept of “orthogonality” in GC x GC

Model Goals The model does not attempt to: –Predict absolute retention times (just relative retention position) –Predict peak widths –Find optimal flow, temperature, modulation conditions, and/or column dimensions –Generate accuracy at the cost of convenience

A 3-Step Solvation Model

Conclusions Based on the 3-Step Solvation Model Retention Order  –  G o Retention Order  (Solvent Cohes. – Constant) (Solute Size) + (Solvent Polarity) (Solute Polarity) Retention Order  (Solute Size) + [(Solvent Polarity)/(Solvent Cohes. – Constant)] (Solute Polarity) Big Conclusions: Solute Size should have a “universal” impact on retention order Solute Polarity will have an impact that is separable from Solute Size The impact of Solute Polarity will depend on Solvent Polarity and Solvent Cohes.

The “Logic” Behind a 2-D Chromatogram GC x GC Chromatograms generate separations in two dimensions one dimension is primarily a “size” separation one dimension is primarily a “polarity” separation Mixtures of monofunctional homologous organic compounds of the type Z – (CH 2 ) n – H are the simplest samples to demonstrate the nature of GC x GC separations. Size determined by n and Z Polarity determined by Z

Z–(CH 2 ) n –H Homologous Groups

A GC x GC Chromatogram of Several Series of Homologous Compounds DB-624 x DB-Wax

A GC x GC Chromatogram of Several Series of Homologous Compounds Fairly flat bands Uniform vertical structure for different values of n Increasing n

Observations of Chromatogram Structure Each homolgous group (i.e., each Z) has a different starting primary retention time. Changing the value of n leads to a shift in primary retention time that is independent of Z. This suggests the use of a retention index, r, that is linearly related to n and has a Z-dependent offset, r z. r = n + r z r Z is a unique constant for each functional class and each column 1 t R = f (r) f = monotonically increasing function Compounds of the same functional class generate peaks in a horizontal band. This means secondary retention time is independent of n and most likely determined by the r z factors on the primary and secondary column.

Determination of r Z We would like to be able to determine the values of r z for a wide variety of functional groups on a wide range of columns. There are many possible sources of data that can be used to determine r z, but temperature-programmed 1-D GC data is probably the most plentiful. For this study we primarily use 1-D GC-MS Data –DB-624 (30m x 250  m x 1.4  m) –DB-Wax (30m x 250  m x 0.25  m) –DB-210 (30m x 250  m x 0.5  m) Experimental Conditions: –Constant flow = 1 mL/min He –Temp. Program: 35 o C for 4 min; 5 o C/min to 200 o C; Hold for 10 min.

Determination of r Z r z provides information on the significance of dispersive and polar interactions between the stationary phase and the functional group Z. We define r Z = 0 for n-alkanes. Plot t R vs. n for several homologous sets including alkanes and horizontally shift the homologous sets achieve maximum alignment. The value of the shift is defined to be r z. Once r z is determined. The value of the retention index r is known for each member of the homologous set using r = n + r z. The retention index r is essentially a nonparametric, diversely defined, divided by 100, temperature-programmed Kovats retention index.

Determination of r Z Classr Z Alkanes0 Alkenes0 Cyclohexanes0 2-ketones0 Aromatics0 Acetates0 Aldehydes0 1-chloros0 1-alcohols0 2-alcohols0 tert-alcohols0

Determination of r Z Classr Z Alkanes0 Alkenes1.97 Cyclohexanes ketones4.35 Aromatics6.85 Acetates4.45 Aldehydes chloros alcohols alcohols4.46 tert-alcohols4.81

Determination of r Z ClassesDB-624 r Z DB-Wax r Z DB-210 r Z Alkanes0.00 Alkenes Cyclohexanes ketones Aromatics Acetates Aldehydes chloros alcohols alcohols Tert-alcohols

Determination of r Z Alignment analysis was repeated with data from: –catalog retention times –columns with different dimensions (same stationary phase) –diverse temperature programs variability r z values was on the order of +/- 0.1 alignment analysis generates comparable fits for other commonly used stationary phases including DB- 1, DB-1701, HP-5, and HP-50+

r Z & n – Relationship to 2D Chromatogram Initial study focused on determining the r z values of 11 different compound classes.

r Z & n – Relationship to 2D Chromatogram The primary retention time is essentially linearly related to n + r Z. 1 t R  (r Z + n)

r Z & n – Relationship to 2D Chromatogram Examine the secondary retention of a small region of the 2D chromatogram

r Z & n – Relationship to 2D Chromatogram The secondary retention time is exponentially related to  r Z. 2 t R  (exp  r Z ) where  r z = 2 r z – 1 r z

Definition of the Simplified Chromatogram Our goal is to generate a 2D retention time plot with “structure” that is similar to the real GC x GC chromatogram. 1 t R proxy: 1 r = 1 r z + n 2 t R proxy: A  rz  r z = 2 r z - 1 r z A is a constant between 1.5 and 1.8 Thus, the simplified chromatogram is generated from 1-D retention indices and a single, narrowly defined constant (A).

An Application of the Simplified Chromatogram: Changing Stationary Phase Order

Key Results Simplified chromatograms for both column orders (i.e., non-polar x polar and polar x non-polar) are generated with the same sets of r z values. The simplified chromatograms “capture the essence” of the retention positions in both configurations. Thus, switching stationary phase order leads to a simple, predictable change in peak positions: logarithmic warping of the primary retention time inversion of secondary retention time Comparable results are obtained with the DB-1 & HP-50+ column set.

An Application of the Simplified Chromatogram: Predicting the Retention Position of Non-Homologous Compounds The simplified chromatogram concept can be easily extended to non- homologous mixtures provided that the retention indices of the mixture compounds are known. We have fit our plots of t R vs (r z + n) with an asymmetric sigmoid function. This function can then be inverted to calculate the retention index of any compound (homologous or non-homologous) from its retention time. Retention indices on primary and secondary columns can be combined to generate a simplified chromatogram. 1 t R proxy: 1 r 2 t R proxy: A  r

Asymmetric Sigmoid of DB-624 GC-MS Data

Asymmetric Sigmoid of DB-Wax GC-MS Data

Alcohol Mixture r values are calculated from the curve fits. Excellent prediction of peak position

Aromatic Mixture Excellent prediction of relative retention of non- homologous compounds

Aromatic/Alcohol Mixture Great intra-group predictions Poor inter-group predictions. This is due to the extreme structural differences between the two groups.

A Linear Free Energy Model of GC x GC Separations Simple models that predict retention from a linear combination of solute descriptors and corresponding stationary phase descriptors have been the subject of numerous studies over the past 40 years. The linear free energy model originally developed by Abrahams et al. has been adopted by several research groups. Descriptors are available for over 1000 solutes. Poole et al. have published the descriptors of most of the commonly used capillary column stationary phases. Poole et al. are currently revising the solute and stationary phase descriptors for improved accuracy.

r = n + n z + s’S + e’E + a’A Compound size Polarity of Functional Group

Definition of the LFER Simplified Chromatogram 1 t R proxy: 1 r = n + n z + 1 s’ S + 1 e’ E + 1 a’ A 2 t R proxy: A  r  r = 2 r - 1 r =  s’ S +  e’ E +  a’ A A is a constant between 1.5 and 1.8 Thus, the primary dimension is influenced by size and polarity, while the secondary dimension is only influenced by polarity.

Evaluation of LFER Simplified Chromatogram

LFER Studies LFER simplified chromatograms are surprisingly accurate. Comparable results were obtained for HP-5 x DB-Wax, DB-Wax x HP-5, DB-1 x HP-50, and HP-50 x DB-1. The LFER model shows that relative primary retention is dictated by compound size and column specific polarity. The relative secondary retention is dictated by the difference in the column specific polarity between the primary column and the secondary column (compound size does not matter). The notion of a non-polar x polar separation as being “orthogonal” is not entirely accurate. While the secondary dimension is orthogonal to compound size, the primary dimension is not orthogonal to compound polarity (I.e., compound polarity plays a role in the primary retention). Thus, the two dimensions are not orthogonal to one another. Actually, a lack of orthogonality is not a bad thing; especially, when trying to separate compounds with similar size.

Main Conclusions The retention index of a compound can be expressed as a linear combination of a size descriptor and and a column-specific polarity descriptor. The retention indices (and/or the size and polarity descriptors) of compounds can be determined from temperature-programmed, 1-D GC runs. Such retention indices can be combined in a straightforward fashion to generate a simplified chromatogram. The simplified 2-D chromatogram is a surprisingly accurate representation of the structure of the chromatogram. Linear free energy parameters can be incorporated into the simplified chromatogram concept to generate a flexible tool for retention time prediction. The accuracy won’t be great, but it will be useful for screening column sets and stationary phase order. The notion of “orthogonality” in GC x GC has been misused and over-hyped.