Teaching Chromatography Using Virtual ... - ACS Publications

In the Laboratory

Teaching Chromatography Using Virtual Laboratory Exercises

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David C. Stone Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada; [email protected]

Chromatography is a deceptively simple subject to teach. While the basic concepts are readily demonstrated using filter paper, beaker, solvent, and marker pens, there are many complex nuances to both theory and practice that must be addressed in a thorough presentation. This is particularly evident when addressing such topics as method selection, development, and optimization, especially given the degree of sophistication required by contemporary analytical problems. The challenge for the instructor, then, is to present chromatographic theory and practice so that students can grasp the significance of these details with the same clarity imparted by the familiar demonstration mentioned above. Indeed, the ubiquitous nature of chromatography in research and industry suggests that the effective teaching of chromatography is an important part of undergraduate chemistry, a view backed up by the emphasis on chromatography in the proposed European curriculum (1) and U.S. initiatives (2, 3). Arguably, one of the best ways to teach chromatography is simply to have the students do it. Indeed, many excellent undergraduate chromatography experiments have already been published (4). Unfortunately, class size, available resources, and run-times set practical limits on the number of experiments that can be performed. It also has to be admitted that this is hardly a stimulating experience, especially when run-times can be 15 minutes or more. In short, a rigorous experimental approach to teaching chromatographic theory and practice is unfeasible and, in some respects, potentially undesirable. A common alternative is to supplement actual experiments with specimen chromatograms in lectures that illustrate chromatographic evolution under different experimental conditions. In fact, column manufacturers have made over 4700 specimen chromatograms available through a searchable Web site (5), allowing students to be exposed to everything from fast microbore HPLC separations eluting in under 5 minutes to thermal gradient GC–MS studies with run-times of well over an hour. Indeed, it is possible to illustrate many different aspects of separation and optimization through the judicious choice of specimen chromatograms. Although selfevident, it is worth noting that such an approach should include extensive interactive class discussion of the specimen material, lest the experience be less than stimulating from the students’ perspective! While the “case study” approach outlined above can be effective, it suffers from the same principal drawback as the experimental approach; namely, that there is a finite limit to how many chromatograms can be shown and discussed. Further, specimen chromatograms are static objects, so students gain neither a sense of the dynamic nature of processes occurring, nor of the subtle effects that can accompany even minor changes in experimental parameters. It is here that simulation and animation can play an important role. This in and of itself is hardly a new observation—an article from 1488

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1972, for example, discusses the relative merits of slide–tape programs, videos, and 8- and 16-mm movies (6). Computer simulation also found an early role, as evidenced by the simple GC simulation implemented by Yuan (7). Advances in both computer technology and chromatography theory, however, provide new opportunities for teaching chromatography through the appropriate use of technology. Recently, a strong emphasis on the development of simulation software for method development and optimization in both HPLC and GC since the pioneering work of Snyder and co-workers has culminated in at least two commercial method development packages (8, 9) and various educational programs (10–16). Clearly, a variety of tools are available to offer virtual chromatography exercises as supplements for existing laboratory and classroom activities. The issue confronted by the instructor, therefore, is not so much what is available as what to actually do with these tools. In other words, the goal is not the incorporation of technology into the classroom per se, but achieving increased teaching effectiveness through the use of such tools. In this respect, there is relatively little information in the literature. For example, Grob et al. (17) described a series of GC simulations using method development software that could be used to supplement laboratory experiments, but did not discuss how this might be presented. A similar set of experiments for ion chromatography described recently by Haddad et al. (18) contains more detailed learning goals, although these are obviously specific to IC. This article describes the design, implementation, and evaluation of a set of virtual chromatography exercises in a fourth-year undergraduate course in separation science. These were developed for both GC and HPLC, with the goal of achieving increased teaching effectiveness through the use of available chromatographic method development software. The pedagogical goals and design criteria employed are clearly defined, and the resulting modules described in terms of their content, structure, and learning objectives. Student performance and survey results are also described. It is believed that the self-paced nature of these exercises, combined with the unique visual and interactive nature of the software, provide a valuable supplement to the learning experience. Finally, issues surrounding the practical implementation of such experiments will be addressed. Background The undergraduate analytical chemistry curriculum at this university consists of introductory and advanced lecture– laboratory courses in second and third year, followed by specialist lecture courses in fourth year. Students are taught basic GC and HPLC in third year, with two or three four-hour practical sessions on each technique. A more thorough discussion of chromatographic theory, current practice, and

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In the Laboratory

method development and optimization then takes place within the broader context of separation science in fourth year. It was found that, even though students acquired some practical exposure in third year, chromatography was still very much a “black box” technique and not well understood. The lack of a fourth-year laboratory session also made it harder for students to gain an intuitive understanding of the effects of different operating parameters on run-time and resolution, and the robustness of chromatographic separations. When, therefore, an opportunity arose to explore the use of a chromatographic simulation software package (9) in teaching, it was enthusiastically taken. The first virtual laboratory exercises were offered as an alternative to conventional assignments in a fourth-year course. Student surveys were included with the exercises and, based on this information, the exercises were revised and fully incorporated into the course the following year. A third round of revisions and surveys have since been completed, the results of which are presented below. Before discussing the exercises themselves, it is useful to describe some specific features of the software that are attractive from an educational perspective. First, the software provides a searchable database containing more than 6000 chromatograms, derived from column manufacturers and development partners. (This is a larger set than that available publicly through the Web; ref 5). Second, the simulation tools allow the user to dynamically and continuously vary an operational parameter (e.g., temperature, solvent ratio) while directly observing the effect on the chromatogram. Although similar results can be achieved with a spreadsheet, the result is typically more granular and static; values must be entered sequentially into a cell. The ability to click and drag the endpoint on a graphical representation of a temperature gradient (for example) and see the run-time and resolution vary continuously with the final temperature, duration, and heating rate is a powerful tool for exploring retention behavior (Figure 1). Third, the software incorporates chemical structure drawing and physical constant calculations, so students can rapidly associate structure, properties, and chromatographic behavior. Finally, the software can use user-entered structures to predict the retention behavior of compounds not in the

Figure 1. Screen-capture showing the GC simulator module with the temperature program displayed directly above the chromatogram.

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specimen chromatogram. This helps reinforce the underlying physical mechanism involved in the separation process and provides a context for discussing method robustness. Pedagogical Goals and Design Criteria The primary goal in developing these exercises was to take advantage of current technology to enhance and extend the existing course content and delivery. At the same time, it was recognized that the use of the software should not be an obstacle or distraction to the actual performance of the exercises. Some considerable time was therefore invested in determining how best to present the tools to the students so that they could focus on interacting with the data rather than with the software. It was decided to provide the students with files that could be opened directly from within the software rather than having them create or import data. Step-by-step instructions were compiled and illustrated with numerous screen-captures. Each exercise was further broken down into discrete parts, so that students did not have to complete an entire exercise in one go. The exercise subjects and topics were ordered such that those requiring the fewest functions in the software were completed first. Thus the GC module was presented first since optimization involves fewer parameters than HPLC. This allowed the students to become familiar with the software before tackling more complex tasks, effectively reducing the learning curve. Finally, the exercises were released to the students once the related course material had been introduced in lectures. The necessary software and files were installed on computers that could be made available during open-access times. This allowed the students to complete the exercises in their own time over a two-week period. A second fundamental goal was that each exercise should complement and illustrate the material covered in lectures. Close attention was therefore paid to the order of lecture material and the sequence of virtual laboratory topics. Specimen chromatograms for the different exercises were chosen to reflect those used in lectures and to draw attention to no more than two key concepts at a time. Different categories of questions were also distributed throughout each exercise. These included factual (Is this a normal or reverse-phase separation?), computational (What is the resolution between peaks 2 and 3?), deductive (How does retention vary for linear organic acids?), inductive (What explains the different retentions for branched versus linear organic acids?), and predictive (When would hexanoic acid elute?) Students were required to record their answers before continuing to the next step, and to explain any discrepancies between their answers and the predictions obtained from the computer software. These questions serve a variety of purposes. Obviously, they provide an immediate means for student assessment and feedback. More importantly, however, they encourage the students to think and make connections between topics covered in this and other courses. In part, this is because the students have to respond to and interpret the data in front of them, rather than reproduce formulaic principles from a textbook. Finally, it was intended that students should not only gain academically from these exercises, but feel that the exercises were a worthwhile investment of their time and effort. This was important as the exercises were added to existing lecture material; even though they replaced some existing assignments,


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In the Laboratory Table 1. Integration of the GC Virtual Lab with Lecture Content Lecture Topic

Virtual Lab Component

Highlighted Concepts

Parts of a GC

—

—

Common detectors

—

—

Stationary and mobile phases

—

—

General elution in GC

—

Boiling point, carbon number, functional group, and structure

Linear and branched alcohols (wax & C18 columns)

Part 1: Linear and branched organic acids

Isothermal elution, homologous series, structural isomers (linear vs branched), tR prediction

Packed vs capillary columns, film thickness

Part 2: Retention, resolution, and column length

Use of retention relationships, confirmation of elution order, comparison of packed and capillary columns

Injection techniques and temperature programming

Part 3: Holds and gradients

Effect of initial temperature and ramp rate; analysis time vs resolution

this still represented an increase in workload. To this end, it was essential to monitor student progress and solicit feedback on the exercises. In the following sections, task- and goal-oriented descriptions of the modules are provided, together with the student survey results. Instructions specific to the particular

software package used are not included here, since the aim of the present article is to describe the design, implementation, and evaluation of the exercises rather than a particular software program. Interested readers may, however, find copies of the actual instructions in the Supplemental Material.W The GC Virtual Laboratory

Figure 2. Flow diagram summarizing tasks and goals for the GC exercise.

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The GC module is divided into three parts. Table 1 shows how these relate to the lecture material, while Figure 2 provides an overview of the complete exercise. When this section is introduced, students have already encountered basic chromatographic theory (the van Deemter equation and variants, plate number, resolution, basic calculations, etc.) After a short review of earlier material, a more detailed investigation of retention in GC is undertaken. The lectures consider the particular case of C1–C5 linear and branched alcohols separated on both polar (glycol) and nonpolar (methyl) columns. Students are also provided with a more comprehensive set of retention–boiling point and retention–carbon number data than found in the standard instrumental analysis texts. One of the primary aims in this is to emphasize the connections between molecular structure, intermolecular forces (e.g., hydrogen bonding and van der Waals interactions), and physical properties such as vapor pressure and solubility. This is further reinforced in the GC exercise by investigating the retention behavior of a similar set of carboxylic acids on a nonpolar column (Table 2.) Since this is the students’ first exposure to the software environment, the required tasks are restricted to simple navigation through the chromatographic data. The structures, molar masses, and normal boiling points of all compounds are provided, either through the specimen data or written instructions. Questions in this part of the exercise were designed to test how well the students had assimilated earlier material, including the analysis of the aliphatic alcohol chromatograms. In addition, the prediction questions use the wellknown relationships between retention and carbon number or boiling point. At this point, students have the option to either continue with the exercise or quit and resume later. This flexibility is important when considering situation-specific implementation of similar exercises, which will be considered in detail later.


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In the Laboratory Table 2. Specimen Compound Sets for the GC Virtual Lab Peak 1

Part 1 and 2

Part 3

Acetic acid

Ethylbenzene

2

Propanoic acid

p-Xylene

3

Isobutyric acid (2-methyl propanoic acid)

o-Xylene

4

Butyric acid (butanoic acid)

i-Propylbenzene

5

Isovaleric acid (2-methyl butanoic acid)

1,2,4-Trimethylbenzene

6

Valeric acid (pentanoic acid)

1,2,4,5-Tetramethylbenzene

7

–

Naphthalene

a

Hexanoic acid

Benzene

b

Pivalic acid (2,2-dimethyl propanoic acid)

Toluene

c

–

n-Propylbenzene

NOTE: Compounds a–c are added to the original data during the virtual lab exercises.

The second part of the module has students compare their predictions with those made by the software and actual chromatographic data. For the software used here, this is as simple as entering the chemical structures of the new compounds and clicking a button. Predictions are based on structure fragment boiling point or vapor pressure and octanol–water partition constant (log P ) calculations. To save time, the structures are provided to the students in a file readable by the software. Once the prediction is obtained, students are asked to compare the predicted retention times, and explain any differences. This latter step was found to be important since, even though it had been emphasized in lectures and handout materials, not all students used an appropriate calculation to estimate retention times for the compounds. Some, for example, performed linear interpolations between related compounds, while others simply guessed a “more than” or “less than” value relative to a comparable peak in the specimen data. The database component of the software also provided an opportunity for students to examine and compare the same separation on both packed and capillary columns. Such data could have been imported from real experiments conducted locally; it is planned to incorporate this in future versions. Finally, the students were asked to calculate the length of column required to achieve full baseline resolution of butyric and isobutyric acids, which are unresolved in the specimen data. The third part of this module explores the use of thermal gradients in GC separations, using a set of aromatic compounds (Table 2) similar in nature to that used in the third-year instrumental laboratory course. It should be noted that the third-year laboratory experiments all involve isothermal elution owing to time constraints; this is the first time that the students will gain any firsthand experience with temperature programming. This is where chromatographic simulation software can make a strong visual impact: students can continuously vary any point on the temperature program and follow the effect on the resulting chromatogram. By changwww.JCE.DivCHED.org

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ing the gradient from one that is essentially isothermal to one that is extremely steep, students see firsthand the variation of peak width and height with changing retention time, and the dramatic impact that gradient techniques can have on run-time. To the best of the author’s knowledge, none of the existing educational simulation packages have such a direct link between operating parameters and the resulting chromatogram; rather, one must vary the conditions and then wait for the chromatogram to “run” before the result can be seen. Once students have had a chance to interact with this aspect of the software, they are asked to set up a specific temperature program and are led through a systematic investigation of initial temperature and hold time, ramp rate, and final temperature. Throughout this process, students are asked to predict what the effect of a change will be, then make the change and test their prediction. This serves to demonstrate that changing either the initial temperature or hold time alone will affect the steepness of the ensuing temperature ramp and that there are practical limits beyond which the retention behavior of the solvent and first sample peaks are unaffected. This relates to concurrent lecture material on injection techniques, since the initial temperature and hold time can be manipulated to improve separation between the solvent and the early-eluting sample components (e.g., split-less and large volume injection techniques). The students are asked to systematically vary the temperature program in an attempt to optimize run-time and resolution while not exceeding a column temperature of 250 ⬚C. They are then required to predict elution times for benzene, toluene, and n-propylbenzene under their derived conditions, and test their prediction by adding these compounds from a predefined file as described earlier. This requires them to distinguish between compounds that elute during the initial hold and those that elute during the thermal gradient, since only the former can be calculated using the relationships exploited in the first part of this module. The students modify their temperature program as required to ensure full resolution of all peaks. Finally, the students are asked to summarize what they have learned about the relationship between molecular structure and elution order in gas chromatography. The HPLC Virtual Laboratory An overview of the HPLC virtual laboratory is provided in Table 3 and Figure 3. This module examines two key parameters for HPLC optimization: pH and solvent ratio. It also introduces the use of resolution maps and the concept of robustness, topics not covered by typical undergraduate texts. The order of pH then solvent optimization was deliberately chosen to highlight the complexities of method development in contemporary chromatography. Historically, pH would be a secondary consideration for optimization. An inappropriate choice of initial pH, however, can lead to an extremely narrow range over which resolution is retained. This in turn contributes to poor robustness in the final method, which is a significant issue for quality control–quality assurance applications in the pharmaceutical industry. Advances in method development tools have also made it significantly easier to combine pH and solvent (along with column temperature) optimization in a single process. Having said that, there is no particular reason why the order of the exercises


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In the Laboratory Table 3. Integration of the HPLC Virtual Lab with Lecture Content Lecture Topic

Virtual Lab Component

Highlighted Concepts

Parts of an HPLC

—

—

Common detectors, etc.

—

—

Stationary and mobile phases

—

Adsorption and partition, mass transfer term

Method development

Part 1: Effect of pH

Acidic and basic groups, log D dependence

Optimization: varying k´, α

Part 2: Solvent strength and gradient

Structure and solubility, solvent strength parameters, log P dependence

Figure 4. Structures of the antiepileptic drugs used in the LC exercise, showing calculated pKa values.

Figure 3. Flow diagram summarizing tasks and goals for the LC exercise. (%B is the volume fraction of organic modifier in the mobile phase.)

Figure 5. Screen-capture showing the pH-based resolution map in the LC simulator module.

should not be reversed to better fit a particular instructional pedagogy. Different examples covering ternary and quaternary solvent optimization, which are not discussed here, could also be included. As with the GC module, the first part of the exercise is intended to familiarize students with the way the software presents chromatographic information. The first specimen chromatogram shows the isocratic reverse-phase elution of a series of antiepileptic drugs (AEDs, Figure 4). These provide

an interesting example for two reasons. First, therapeutic and toxic levels for these drugs vary significantly between individuals. Frequent determination of AEDs and metabolites in blood and urine is therefore critical, making a powerful connection between chromatography and everyday life. Second, these compounds contain primary and secondary amide groups with a range of pKa values, resulting in a strong pH dependence for both run-time and elution order. In fact, many contemporary HPLC separations require pH optimi-

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zation during method development. Figure 5 shows a screencapture of the LC simulation window as seen by the student during the first part of the HPLC module. The software was pre-configured to use isocratic elution and optimize for pH; this is reflected in the upper panel, which shows the resolution map (predicted resolution between the closest-eluting peaks) as a function of pH. Notice that the graph (Figure 5) shows a relatively flat profile up to about pH 6, followed by a series of sharp minima and maxima between pH 7–11. The prediction is based on experimental data and calculated log D values (the pH-dependent octanol–water partition coefficient; ref 19) for the individual compounds. Numerous methods have been described for predicting log P, pKa, and log D using substituent constants (20, 21). As with the GC simulation software, it is possible to drag the marker on the resolution map and watch the effect of mobile-phase pH on the resulting chromatogram directly below. When this is done, the peaks for primidone and carbamazepine show hardly any variation in retention time, whereas the other peaks shift dramatically. Students are asked to consider the structure of these compounds and explain why this might be so. This reinforces the connection between molecular structure and solubility and makes explicit connections to acid–base behavior, structural organic chemistry, liquid–liquid extraction, and qualitative structure–activity relationships. This exercise also introduces students to the concept of method robustness and reinforces the practical limits of real chromatographic systems. The students are asked to systematically vary pH in an attempt to reduce the run-time to less than 7 minutes while maintaining resolution. As the pH approaches a minimum in the resolution map, the students can observe the corresponding co-elution of sample components as, for example, phenytoin undergoes a change from a neutral to protonated form and is therefore eluted more rapidly. This allows the run-time to be reduced but, unless the pH is carefully controlled, can easily result in a loss of resolution. Students are therefore asked to identify the pH regions under which the separation will be most robust, that is, least affected by small variations in mobile-phase pH owing to operator or instrumental errors. The goal of a run-time of less than 7 minutes is attainable, but only at a pH of almost 13. Students are asked to determine whether this is physically realistic by consulting the manufacturer’s specification for the column used in the original chromatogram. Since pH 13 is well beyond the working range of silica columns, students are then asked to propose alternative methods for reducing run-time, which helps reinforce concepts from earlier courses such as the influence of column length, flow-rate, and the availability of alternate stationary-phase support materials. The final part of this exercise looks at the reverse-phase gradient separation of a series of large, complex dye molecules. Students are asked to pick one particular peak from the sample chromatogram, examine the structure of the molecule, and predict whether changing the initial acetonitrile content of the mobile phase from 80% to 50% would increase or decrease its retention time. In this case, the aqueous portion of the mobile phase is pure water so the effect of pH is negligible. As a result, this question tests the students’ recall of solvent polarity effects in reverse-phase chromatography from third-year lectures and laboratory experiments. This is subwww.JCE.DivCHED.org

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sequently made explicit by having the students perform calculations on what would happen under equivalent isocratic conditions using Snyder’s polarity index, P´ (22). This is helpful because the use of such calculations is covered in some detail in most undergraduate texts and provides a useful starting point for discussing the use of solvent gradients. After making a prediction for the isocratic case, students are asked to determine the effect of changing the gradient and to comment on the results. As with the pH example, students can use the resolution map to show a graph of the minimum resolution within the chromatogram as a function of the solvent gradient. One interesting feature of the specimen chromatogram is that, as the rate of change of mobile-phase composition is reduced, there is a change in elution order in addition to the expected increase in retention times. This primarily reflects differences in the net polarity of the individual sample components and provides a useful warning to students to verify peak identities when optimizing solvent gradients. Another important pedagogical aspect is that, although gradient elution can offer superior separation of complex mixtures, it requires reequilibration of the column between runs. This can negate the advantage of reduced run-time in high-throughout analysis, where a short turnaround between samples is essential. This is emphasized in the final part of the exercise where students are asked to adjust the solvent gradient so that it is effectively isocratic and see if an acetonitrile–water mixture can be found that will provide full resolution with a runtime of less than 11 minutes. With this particular system, a run-time of 7 minutes or less is in fact possible using 90% acetonitrile. Student Performance and Survey Results Students were given a two-week period to complete each virtual laboratory experiment. Each exercise typically required 2 to 3 hours to complete, depending on the student’s prior experience and comfort using computers. In addition to their report and optimized chromatograms, students were asked to complete a short survey at the end of the second module. The survey results for the past two academic sessions are summarized in Table 4. The survey included room for comments, a selection of which are included in Table 5. Results for the first year are not included since the survey questions where not the same, while only half the class opted to take the virtual laboratory that year. Overall, students responded favorably to the use of the software and exercises. Their feedback enabled progressive improvements to be made, with particular emphasis on the GC module following year two. This probably accounts for the notable improvement in ratings between the 2003兾4 and 2004兾5 sessions. Looking at the survey results in more detail revealed some interesting results. One surprise was that even students who gave an overall rating to the exercises of “somewhat helpful” (a score of 4 or 5 out of 7) still reported that the modules were “helpful” (6 or 7 out of 7) in gaining a better understanding of elution in chromatography. This was true even of students who had run 20 or more chromatograms. In fact, one mature student who had run several thousand chromatograms in industry prior to the course rated both modules as


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“extremely helpful” in understanding elution mechanisms! This perhaps speaks to the effectiveness of the questions included with both modules in stimulating thought and reflection on the part of the students. Of course, students might feel that they gained a greater understanding of chromatography without actually having done so. Time did not allow for a formal quantitative outcome-based study but only a qualitative evaluation based on students’ written work in assignments and exams. In particular, comparing the level of description and understanding displayed in answering a problem set on separation and chromatography theory prior to the first virtual laboratory exercise with similar questions on the final exam showed a clear improvement in the scope and depth of the students’ understanding of the separation process. One of the pedagogical goals identified in developing these exercises was that the software itself should not be an obstacle or distraction to the task of manipulating the chromatographic conditions and monitoring the outcome of those changes. This was, in fact, the area of biggest concern during the initial development and implementation of these exercises. It was therefore somewhat surprising to obtain only a couple of negative comments, which had more to do with the quality of the computers than the software or instructions. Ironically, the student who made the most negative comment (Table 5) was also the only one to achieve a perfect mark on both labs. I agree with this student that it would be better to have more real labs, but timetable and cost pressures make this impractical. The primary goal in developing these virtual chromatography laboratories was to take advantage of advances in computer technology and chromatographic theory to enhance the teaching of chromatography at the undergraduate level. Specifically, it was believed that the ability to continuously vary chromatographic parameters while directly observing the effects in real time, would add a powerful dynamic element to the learning experience and enhance student understanding. The fact that this could be done without having to wait while each chromatogram develops on-screen is considered an advantage, since repetitive viewing of such animations provides little additional benefit. This is particularly true for advanced courses where students have had prior exposure to chromatography, since the time required for each animation becomes a delay in completing the assignment.

Table 4. Student Survey Results Academic Session

2003/4

No. of students taking the virtual lab

2004/5

18

21

Undergraduates

14

18

Graduates

04

No. of sur vey responses (response rate) 11 (61%)

03 16 (76%)

Did you find the exercises helpful? Helpful (score of 6 or 7)

05 (45%)

11 (69%)

Somewhat helpful (score of 4 or 5)

06 (55%)

05 (31%)

Not helpful (score of 1–3)

00 0(0%)

00 0(0%)

Have you done much practical chromatography? No

04 (36%)

00 (0 %)

Some (typically between 2 and 5)

05 (45%)

04 (25%)

Yes (anywhere from 10 to 100)

02 (19%)

12 (75%)

Did this help you understand elution in GC? Helpful (score of 6 or 7)

07 (64%)

13 (81%)


04 (36%)

03 (19%)


00 0(0%)

00 0(0%)

Did this help you understand elution in HPLC? Helpful (score of 6 or 7)

08 (73%)

12 (75%)


03 (27%)

03 (19%)


00 0(0%)

00 0(0%)

No response

00 0(0%)

01 0(6%)

The observed improvements in students’ understanding, together with the positive survey results, confirm that simulation software can indeed play a key role in the effective teaching of chromatography. The highly visual nature of the software combined with carefully selected specimen chromatograms and well-structured exercises allows students to see firsthand how chromatographic parameters influence separation and retention and make connections between molecular properties, physical processes, and chromatographic development. A significant additional benefit is that the software provides an opportunity to cover the subject in greater depth than typical undergraduate texts. Topics highlighted in these exercises include the concept of robustness in method development, the role of pH in HPLC optimization, and the

Table 5. Selected Student Comments • With a more involved virtual lab this part of the course could easily become the most useful. • The overall experience aided to put the theory to practice. • I found this exercise did help me to see the effects of changing the parameters on the resulting chromatograph. This is especially good since I have never run any chromatograms, with the exception of the marker colour on paper in grade school, and third year laboratory. • [I liked] the ability to change the pH and see the chromatogram change right [before] your eyes without actually doing the experiment. • The overall experience aided to put the theory to practice. • The software exercise let us visualize how the parameters change the chromatogram. It was fun. [I] hope that one day it will be available for all chemistry students. • I disliked the computer problems. I guess I always will. • Actually have a lab component and not this horrible virtual lab.

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importance and application of fundamental physical properties through qualitative structure–activity relationships. The samples chosen also help to illustrate the breadth of chromatographic applications and the importance of this subject to society at large. Practical Implementation A very real issue associated with using any software package for educational purposes is cost. In this case, the software used was obtained free-of-charge through a limited time beta-test agreement. This resulted in a different version being used each year. While this eliminated some software problems encountered in the first year, it also meant that instructions and screen-captures had to be updated each year. At the time of writing, the educational cost for the simulation package used here is $1195 for a single installation licence (9); the full method development suite is not required to run these exercises. The educational price for the competing product is $750 (8). Both products allow chromatographic data to be imported or entered directly by the user, so creation of custom files for exercises such as those described here is straightforward. Although this cost is not insignificant, it need only occur once since the software is quite mature, and upgrades are unlikely to confer any additional benefit as a far as these exercises are concerned. Clearly, it would be unrealistic to acquire sufficient licences to issue every student with a copy; some thought therefore needs to be given to installation and student access. One possibility is a walk-up installation, consisting of a single computer running the software within a laboratory, with either a sign-up sheet or designated times as part of the normal laboratory experiment rotation. This would provide an opportunity for students to test their predictions directly in the laboratory. This configuration has the advantage of limiting costs to a single license, but limits the number of students who can use the software. This in turn would largely prevent the virtual lab from being as closely integrated with lecture material. Though not a problem for small classes, this would certainly be unfeasible for classes with more than a dozen students. An alternative (used here) is to install the software on a limited number of stations in a computer lab, and provide multiple times over a one- or two-week period for students to complete the exercises outside of scheduled classes. This incurs greater initial cost, but effectively provides for larger classes. This option would also work well for courses divided into alternating lab sections. For either scenario, the limiting factors are the number of time slots available and the course enrolment. As mentioned earlier, this particular set of exercises was developed to supplement an existing lecture-only course. The exercises therefore replace earlier written assignments. Since these are performed independently outside the classroom and since there is considerable variation in individual timetables, a limited number of installations can accommodate a larger number of students provided that the computer facility is readily available. Total enrolment in the most recent academic session was 35 students and was accommodated using four installations available three mornings a week, for two weeks per exercise.

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Discussion and Conclusions As noted earlier, these exercises provide plenty of opportunities for emphasizing fundamental chemical principles and their application, making connections between what can otherwise be quite disparate subject areas. Indeed, it has been suggested that chromatography and electrophoresis should be used to illustrate chemical principles in general chemistry courses (23). One example used here is the importance of molecular structure and geometry, its connection with intermolecular forces such as hydrogen bonding and van der Waals interactions, and the influence of such forces on solubility, boiling point, and vapor pressure. The use of the octanol– water partition constant (as either log P or the pH-dependent log D) further reinforces this theme and provides the necessary connection to solvent extraction and acid–base behavior. These topics are revisited in the course when dealing with ion and ion-exclusion chromatography, solid-phase and liquid–solid extraction, and headspace and thermal desorption gas chromatography. In fact, students are provided at the beginning of the course with a list of first-year topics that will be used extensively in discussing the course subject matter. In conclusion, chromatographic simulation software can play a significant role in delivering an effective and informative course in separation science. Students benefit both from an alternative means of delivering course content and from increased firsthand involvement with the subject material. Virtual laboratory exercises can provide a meaningful extension of undergraduate laboratory experiments by allowing students to examine a much broader range of conditions in a short space of time. Finally, such exercises offer an opportunity to enrich and enhance the scope of topics covered at the undergraduate level in separation science. Acknowledgments The author would like to thank Advanced Chemistry Development, Inc. for providing the software used in developing the virtual laboratory exercises; Michael McBrien and Scott MacDonald (ACD) for assistance with the software; and finally Dan Mathers and Mima Staikova (Chemistry, University of Toronto) for hosting the virtual lab and providing computer support to the project. The author has no commercial connection with Advanced Chemistry Development, Inc. and received no financial compensation for this work. WSupplemental

Material

Instructions for the students are available in this issue of JCE Online. Literature Cited 1. Saltzer, R. Anal. Bioanal. Chem. 2004, 378, 28–32. 2. Christian, G. D. Anal. Chem. 1995, 67, 532A–538A. 3. Perone, S. P.; Pesek, J.; Stone, C.; Englert, P. J. Chem. Educ. 1998, 75, 1444–1452. 4. Project Chemlab, JCE Laboratory Search. http://www.jce. divched.org/JCEWWW/Features/Chemlab/index.html (accessed Jun 2007).


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In the Laboratory 5. ACD/Web Librarian. Advanced Chemistry Development, Inc. http://www.chromdb.com/ (accessed Jun 2007). 6. McNair, H. M. Am. Lab. 1972, 4 (5), 21–24. 7. Yuan, M. J. Chem. Educ. 1977, 54, 364. 8. DryLab. http://www.molnar-institut.com/cd/e/dlw/drylab1.html (accessed Jun 2007). 9. ACD/Method Development Suite 9.0, Advanced Chemistry Development, Inc. http://www.acdlabs.com/ (accessed Jun 2007). 10. Rittenhouse, R. C. J. Chem. Educ. 1988, 65, 1050–1051. 11. Rittenhouse, R. C. J. Chem. Educ. 1995, 72, 1086. 12. Armitage, B. D. J. Chem. Educ. 1999, 76, 287. 13. GC and HPLC virtual instruments. JCE Software Advanced Chemistry Collection CD-ROM. http://www.jce.divched.org/ JCESoft/Programs/Collections/ACC/index.html (accessed Jun 2007). 14. Gu, T. Chromulator Version 1.1 and Version 2.0. http:// www.ent.ohiou.edu/~guting/CHROM/ (accessed Jun 2007).

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15. Wilensky, U. NetLogo Gas Chromatography model. http:// www.ccl.sesp.northwestern.edu/netlogo/models/GasChromatography (accessed Jun 2007). 16. Spaziani, M. A.; Fermann, J. T.; Vining, W. J. Chemical Educator 1999, 4, 226–230. 17. Grob, R. L.; Barry, E. F.; Leepipatpiboon, S.; Ombaba, J. M.; Colon, L. A. J. Chromatogr. Sci. 1992, 30, 177–183. 18. Haddad, P. R.; Shaw, M. J.; Madden, J. E.; Dicinoski, G. W. J. Chem. Educ. 2004, 81, 1293–1298. 19. Hansch, C.; Leo, A. J. Substituent Constants for Correlation Analysis in Chemistry and Biology; Wiley: New York, 1979. 20. Xing, L.; Glen, R. C. J. Chem. Inf. Comput. Sci. 2002, 42, 796–805. 21. Tetko, I. G.; Bruneau, P. J. Pharm. Sci. 2004, 93, 3103–3110. 22. Snyder, L. R. J. Chromatogr. Sci. 1978, 16, 223. 23. Thomas, J. D. R. Fresenius J. Anal. Chem. 1996, 354, 136– 139.


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