In the Classroom
Computational Investigations for Undergraduate Organic Chemistry: Modeling a TLC Exercise to Investigate Molecular Structure and Intermolecular Forces
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Rita K. Hessley† Department of Chemistry, Rose-Hulman Institute of Technology, Terre Haute, IN 47803;
[email protected] Background In an effort to link molecular modeling and laboratory activity, this paper describes an exercise in which molecular modeling is used in conjunction with thin-layer chromatography (TLC). It fits at the beginning of the sophomore-level organic course when it is difficult to find laboratory exercises that complement fundamental concepts of bonding and structure in organic molecules and do not require sophisticated laboratory skills. In a variety of sophomore-level organic laboratory texts (1), thin-layer chromatography is described in detail with regard to its theory, polarity of molecules, and interaction with the stationary phase. Exercises are wide ranging. They include the separation of inks and food colorings (1a, 1b), extraction and isolation of natural products from food (1c) or other commercial products (1d, 1e), identification of unknowns (1f ), monitoring the course of a reaction (1g), and determining the purity of a synthetic product (1h). Another example (2) describes the use of TLC to explore intermolecular forces of attraction and to support discovery of relationships between the structure of a molecule and its properties. The utility of the exercise described here is that it links TLC with molecular modeling and with hypothesis-based experimental design. After carrying out the exercises students are able to evaluate the value of the computational data, and to discuss the implications of their adherence to or deviation from the predicted experimental outcome. The exercise achieves four learning goals. It reinforces structure–property relationships for a range of related organic molecules; it illustrates the role of hypothesis-driven experimentation; it engages students in critical thinking about lab work; and it involves students in good laboratory techniques and practices from the outset of the course. This paper describes how the assignment can be carried out and provides some reflections on how it has operated in the classroom setting. All students at Rose-Hulman Institute of Technology have personal notebook computers equipped with identical software. Students who enroll in chemistry courses after the first year (chemistry or chemical engineering) are required to purchase PC Spartan-Plus molecular modeling software (3), and it is used routinely in class and for assignments. This exercise is suitable for any similar modeling software. The Exercise For this exercise, students are directed to build the 6 models in the set chosen, perform an energy minimization (SYBYL), and then carry out a geometry optimization (or † Current address: College of Applied Science, University of Cincinnati, Cincinnati, OH 45202.
single-point computation) at the semiempirical AM1 level to obtain the molecular dipole moment value. It is also instructive to have students acquire and view the electron potential mapped onto the density surface in order to visualize charge distribution. One set of molecules we use includes decane, 1-decene, 2-decanol, decanoic acid, 2-decanone, and butyl butyrate (2). In addition to representing a distribution of polarities for unbranched molecules in a narrow molecularweight range, they represent different functional groups, a topic introduced near the beginning of the course. These compounds are readily available, are not volatile or hazardous, and are easily handled and stored. Their similarity also permits consideration of other physical properties, such as solubility, presence of hydrogen bonding, and melting point. Another set that works equally well includes decyl amine, decanal, 1-chlorodecane, butyl butyrate, decane, and 2-decanol (2). The modeling assignment and a reading assignment describing the purpose of the computational exercise are given at least 3 days in advance of the lab meeting. The reading includes the theory and technique of TLC. Students are told they will use their computational data to plan a TLC experiment to rank the set of compounds according to their intermolecular forces of attraction. It is stated explicitly that they must have the computational data in hand in order to participate in the discussion and laboratory. The discussion of effects of structure on properties, including intermolecular forces of attraction, is part of the text and class material, so no unusual additional time is needed to develop principles underlying the lab. Prior to carrying out the lab, one class period (50 minutes) is used to discuss and plan the procedure. Ideally this will also occur in advance of the actual lab period, but it can take place at the beginning of the lab session and still permit completion of the exercise. The guided discussion includes consideration of solute interaction with the adsorbent and solute–solvent interaction. From this information and their computed dipole moment values, students assess the relative interaction the molecules are expected to exhibit with silica gel on the TLC slide, and predict mobility when each solute is eluted with a given solvent. Planning the strategy for carrying out the exercise is a novel experience if students are accustomed to having an experimental procedure provided to them. The approach I use is to link the meaning of R f values and the truism “like dissolves like” to elicit from the class the hypothesis that the compounds are expected to elute in inverse relationship to their polarity (more polar, lower R f) on silica. Using their computed dipole moment values I write the list on the board in increasing or decreasing order. This gives the group as a whole a visual “working” data set and also reveals variations and ambiguities in computations. I then re-pose the problem: on the basis of the computational information about relative
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In the Classroom
Figure 1. Student data for a bracket assessment of elution (Rf) on silica gel for the series of compounds. Computed dipole moment values are in [brackets], Rf values in (parentheses) and rank in {braces}.
polarity, they are to carry out as many comparative elutions as needed using pairs of molecules so that when R f values are ranked, they will be conclusive enough to verify or contradict the hypothesis. Because we use pre-coated 2 × 7.5-cm slides, only 2 lanes per slide can be accommodated. One of the first ideas usually generated is to pair the most polar sequentially with each compound of lower polarity; then the 2nd most polar with each, and so on. Being attuned to time, students evaluate this approach as requiring a minimum of 15 trials (for 6 compounds) of 2-compound pairs. I then ask them to consider using the bracket schematic shown in Figure 1 (2), and if no one suggests how, I ask them to critique my idea that the proposed “end result” [hypothesis] can be stepped backward to some starting pair of compounds. They then need to decide which pair to elute first and the subsequent pairs. There are different possible starting pairs from which subsequent pairings of more polar with less polar compounds must be followed logically to test the hypothesis. Most see that this process reduces the number of paired trials to nine, but some decide to use the alternative method. I do not dissuade anyone from carrying out the more lengthy procedure, and 2.5 hours has been sufficient for everyone to complete data acquisition. Besides delight that this second approach will require fewer trials, there has been a sense of satisfaction in the class as a whole for having worked out the experimental design, albeit with help, instead of having had a procedural recipe provided to them. Materials We use precoated silica slides available from Fisher Scientific. All compounds were obtained from Aldrich Chemical Co. Liquids are used neat; pea-sized samples of solids are dissolved in a small vial with a few drops of solvent. Two solvents, n-hexane and a 50:50 (v/v) mixture of n-hexane and dichloromethane are provided. A hand-held UV lamp (Fisher Scientific) is used to visualize the eluted samples. Students work in pairs and use commercially available (Fisher) TLC jars with lids. Capillary tubes are used to apply the sample. Students are reminded that the nonpolar substances are not expected to elute with the polar solvent, and that the
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solvent mixture may be needed to differentiate between compounds of similar polarity. They are forewarned that they may need to repeat a trial with the alternative solvent if their initial data do not permit them to arrive at a conclusion about the relative R f values. In ref 2, steps 1, 2, 3, 5, and 7 use neat n-hexane; the other 4 steps use the solvent mixture. Results and Formal Report Table 1 gives some typical data for the computed dipole moment values, and one scheme for pairing the six compounds. The elution pattern and Rf data shown in the figure are student data. When they have completed the analysis, partners pool their Rf data for the formal report. They provide a tabulated summary (and bracket) of the data and explain whether the data confirm or contradict the hypothesis. They must compare and contrast the utility of computed data (dipole moment)
Table 1. Typical Dipole Moment Values Using PCSpartan-Plus (AM1) Compound
Dipole Paired with Moment/ B r a c k et Compound D
Solvent
Decane
0.03
1
2-Decanone
1-Decene
0.23
4
Decane
n-hexane n-hexane/CH2 Cl2
2-Decanol
1.52
3
Decanone
n-hexane
Butyl butyrate
1.77
2
Decane
n-hexane
Decanoic acid
1.96
6
2-Decanone
n-hexane/CH2 Cl2
2-Decanone
2.78
1
Decane
n-hexane
NOTE: The set of related compounds and one possible sequence for completing the paired elution are provided. Our experience is that the dipole moment and the placement of the dipole vector computed by PC Spartan-Plus is not always unambiguous. While this may not lead to conflicting data, self-consistent data are important for students new to molecular modeling. Advising students of the limitations of modeling is critical, but meaningful data are more readily obtained if students build molecules in a consistent fashion, so that functional groups are extended in space with comparable steric relationships. We have found using the Single Point computation somewhat more satisfying with regard to the assigned dipole vector. AM1 is satisfactory and more rapid than higherlevel computations.
Journal of Chemical Education • Vol. 77 No. 2 February 2000 • JChemEd.chem.wisc.edu
In the Classroom
and experimental data (Rf) for assessing the polar character of compounds. They must also consider the ability of each parameter to predict relationships among other physical properties they are studying, such as melting or boiling point. Finally, they are asked to make specific mention of the types of intermolecular forces of attraction associated with each compound and with the adsorbent and with the solvent, and to discuss the relationships existing between the structure of each and its polarity. We have found this entire exercise to be a valuable extension of some traditional thin layer chromatographic analyses because it gives the students a direct experience of the structure–property relationships. WSupplemental
Material
Supplemental material for this article is available in this issue of JCE Online.
Literature Cited 1. (a) Eaton, D. C. Laboratory Investigations in Organic Chemistry; McGraw-Hill, Inc.: New York, 1989; pp 167–168. (b) Pavia, D. L.; Lampman, G. M.; Kriz, G. S. Introduction to Organic Laboratory Techniques; Saunders: Philadelphia, 1976; pp 276–279. (c) Most, C. F. Jr. Experimental Organic Chemistry; Wiley: New York, 1988; pp 143–144. (d) Wilcox, C. F. Jr. Experimental Organic Chemistry: A Small-Scale Approach; Macmillan: New York, 1988, pp 119–120. (e) Ault, A. Techniques and Experiments for Organic Chemistry, 5th ed.; Allyn and Bacon: Boston, 1987; pp 127–130. (f ) Lehman, J. W. Operational Organic Chemistry a Laboratory Course; Allyn and Bacon: Boston, 1981; pp 179–180. (g) Mayo, D. W.; Pike, R. M.; Trumper, P. K. Microscale Organic Laboratory, 3rd ed.; Wiley: New York, 1994, pp 173–179. (h) Williamson, K. L. Microscale Organic Experiments; Heath: Lexington, MA, 1987; pp 292–293. 2. Holman, R. W.; Hessley, R. K. Contemporary Microscale Organic Chemistry; Kendall-Hunt: Dubuque, IA, 1991; pp 137–150. 3. Wavefunction, Inc., 18401 Von Karman, Suite 370, Irvine, CA 92612.
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