Chapter 5
How Can You Measure a Reaction Enthalpy without Going into the Lab?
Using Computational Methods To Teach Chemical Principles Downloaded from pubs.acs.org by UNIV OF ROCHESTER on 05/14/19. For personal use only.
Using Computational Chemistry Data to Draw a Conclusion Melissa S. Reeves,*,1 H. Laine Berghout,2 Mark J. Perri,3 Steven M. Singleton,4 and Robert M. Whitnell5 1Department of Chemistry, Tuskegee University, Tuskegee, Alabama 36088, United States 2Department of Chemistry, Weber State University, Ogden, Utah 84408, United States 3Department of Chemistry, Sonoma State University,
Rohnert Park, California 94928, United States 4Chemistry Department, Coe College, Cedar Rapids, Iowa 52402, United States 5Department of Chemistry, Guilford College,
Greensboro, North Carolina 27410, United States *E-mail:
[email protected].
In the experiment “How can you measure a reaction enthalpy without going into the lab?” we have students use computational thermochemistry to explore the properties and reaction thermodynamics of hydrofluoropropanes. This guided inquiry lab was developed under the Process Oriented Guided Inquiry Lab Physical Chemistry Laboratory (POGIL-PCL) project. Students are asked to find the “best” replacement for the hydrofluoropropane CFC-227ea, which has been used in military fire suppression systems. The compound has been known to decompose at high temperatures to produce poisonous HF, resulting in some casualties. Students are asked to choose an alternative compound based upon properties predicted with computational chemistry. The number of possibilities is large enough that a class will have to pool data to make a selection. As part of their study, students are also asked to evaluate calculational methods for speed and accuracy and to cooperatively choose the “best” method for the class’s analysis. The evaluation of methods requires them to compare computational results with experimental values. Finally, students must use their calculational data to rationalize a choice about the “best” fire suppressant molecule.
© 2019 American Chemical Society
Introduction At a workshop for the Process Oriented Guided Inquiry Learning Physical Chemistry Laboratory (POGIL-PCL) (1) project in Richmond, VA, in 2015, the authors sat around a table discussing what would make an interesting experiment using computational thermochemistry. The POGIL-PCL model for a physical chemistry experiment includes a title which is a question, opportunities for students to make experimental design choices, an outcome which is not a priori known to the students, and the necessity for students to collaboratively pool data to answer the question. For a computational experiment, we sought to develop an application where students could make choices about how to proceed even with little or no experience. It also needed to be an experiment where the results were not readily available, either in experimental or computational data. At the conclusion of the discussions, the experiment chosen was “How do you measure a reaction enthalpy without going into the lab?” Students were introduced to the problem through primary literature: the current fire suppression system in U.S. armored vehicles could produce deadly HF gas and injure or kill personnel (2). A short set of initial calculations introduced students to a subset of computational chemistry’s myriad methods and basis set choices. The National Institute of Standards and Technology (NIST) Computational Chemistry Benchmark Database (CCCBDB) (3) was utilized to validate the results from the calculations and guide students’ choices about which method and basis set to use. Students chose alternative chemicals for fire suppression to compare with the current one. Finally, students had to formulate a recommendation based upon the calculational results. After several rounds of development and “alpha testing” with students, we have an experiment which provokes student engagement, guides the students to make well-reasoned design choices, and results in student reasoning from evidence. Using the Chem Compute site (4), even faculty with no software and novice computer expertise can use the experiment. In this chapter, a brief overview of the POGIL-PCL method will be given, the experiment will be described, and the authors’ experiences with classroom use of the experiment will be discussed. Background The POGIL-PCL project was launched in 2011 through an NSF grant obtained by Sally Hunnicutt, Alex Grushow, and Rob Whitnell (1). The goals of the grant were to develop guided inquiry physical chemistry lab experiments as well as to foster a community of physical chemistry professors who would be the developers and users of the experiments. The development of this experiment showcases the epitome of their grant’s goals: five physical chemistry professors, spread across the country, worked together to develop and test this experiment. POGIL-PCL is part of a larger project, the POGIL project (5). POGIL is a student-centered instructional strategy emphasizing small, self-managed student teams working through guided inquiry activities developed to aid them in constructing content knowledge while prompting them to build process skills (often called soft skills). The method has been described previously (6), compared to other inquiry methods (7), and evaluated in different chemistry settings (8, 9). The instructor’s role in POGIL is to facilitate the student teams as they work through activities. An activity typically has three stages: an exploration stage where students examine a model and relate it to previous knowledge, a concept invention stage where students answer questions relating to the model to discover the concept targeted by the activity, and an application stage where students utilize the concept to solve a problem or analyze a new model (10). The POGIL community and library of available activities is particularly strong in General Chemistry and Physical Chemistry. 52
A central part of the POGIL-PCL paradigm is the learning cycle (11). In each experiment, students build deeper and deeper conceptual understanding through successive predict-experimentanalyze cycles. This experiment includes three learning cycles. A typical POGIL-PCL experiment has an initial “quick and dirty” experimental cycle to provide students with familiarity with the experimental setup and a small dataset from which to make more refined predictions. Students also use pooled class data and hence can solve a problem more complex than one or two students working with their own data. There are already two computationally-based POGIL-PCL experiments developed by two of the authors (12). The first is “What makes an electron a valence electron?”, an ab initio experiment designed to explore the orbital structures of first and second row atoms and diatomics. The second is “What factors govern the escapability of a molecule from a liquid?” This experiment is an introduction to molecular dynamics to examine the intermolecular forces governing liquid cohesion. Additional experience with implementing these two experiments is described in a separate chapter in this volume. The experiment described in this chapter is the first POGIL-PCL computational experiment applied to a reaction system. Real Life Setting for a Problem The title of a POGIL-PCL experiment is formulated as a question a student would be interested in answering – this is the first step to engaging the student. For this experiment, the title refers to the general technique of computational thermochemistry rather than a specific application. The strongest appeal to engagement in this experiment is the application: the fire suppressant HFC-227ea (1,1,1,2,3,3,3-heptafluoropropane) was found to have caused deaths of U.S. military personnel in an armored vehicle after a hit from a rocket-propelled grenade (2). Students are guided to explore the properties and reaction enthalpies of various fluoropropanes (there are 27 possibilities) to determine whether they can suggest an alternative fire suppressant. The opportunity to use chemistry to potentially save lives increases engagement, at least anecdotally, for most of the students. A convenient feature of this experiment is that, at this time, the experimental and computational results for these compounds are unavailable in the literature. This experiment cannot be termed an exercise of “confirm the known results.” The fluoropropane HFC-227ea, also known as FM-200, is the replacement for halon 1301 whose production was banned in 1994. The mechanism of fire suppression by HFC-227ea is a combination of a high heat capacity and some ability to react with the free radicals in combustion (13). The scavenging of free radicals can lead to toxic HF production; this mechanism can be reduced by adding particulate NaHCO3 in the formulation. At high temperatures, HFC-227ea can decompose by elimination to produce a fluoropropene and HF (see Figure 1). This reaction has an experimentally determined activation barrier of 291 kJ mol-1 at 1200 K (14) and an enthalpy of reaction of 126 kJ mol-1 at 298 K (15). Theoretical calculations put the transition state energy at 333 kJ mol-1 and the reaction enthalpy at 146 kJ mol-1 (16). Analogs of the reaction in Figure 1 with various fluoropropanes substituted for the HFC-227ea are the subject of the computational studies performed by the students. With 27 choices, from the three mono-substituted fluoropropanes to the other heptasubstituted fluoropropane, it is unlikely that a class will explore all the choices. There are also enough choices that each team can explore multiple fluoropropanes.
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Figure 1. Elimination of HF from HFC-227ea. Computational Chemistry as a Tool, Not a Topic While this experiment utilizes ab initio computational chemistry methods, it is intended as a thermodynamics experiment. The prerequisite quantum knowledge is at the general chemistry level; that is, students are expected to know what atomic and molecular orbitals are. The comparison of methods and basis sets does not require additional background for the students. The goal was to use computational chemistry to answer a question, in a manner we imagine many chemists (who are not computational chemists) would approach the use of these tools. There are many instances of computational thermochemistry lab experiments in the literature. Barbiric, Tribe, and Soriano used computational chemistry to estimate calories in food, although at the general chemistry level (17). Martini and Hartzell report including computational chemistry into a full course in classical thermodynamics (18). Lever, Howe, and Whisnant applied computational chemistry to the spontaneity of stratospheric ozone reactions with Cl2O4 (19). Bumpus, et al., used computational chemistry at various levels to assess thermochemistry of various explosives (20). Finally, the popular (but apparently out of print) Physical Chemistry lab textbook by Halpern and McBane has an experiment studying the 2 NO2 ⇌ N2O4 reaction at high levels of theory (21). Using Data To Make a Decision Another feature of POGIL-PCL is promotion of higher-order learning (analysis, evaluation and synthesis). In this experiment, students choose and justify choices of computational method and molecules to target. Then, after pooling class data, students make a recommendation from the results about which molecule would serve as a preferred fire suppressant compound. Data analysis must lead, in this experiment, to an appropriately justified, recommended compound.
The Experiment Objectives and Prerequisites All POGIL-PCL labs have a combination of content and process learning objectives. “How do you measure a reaction enthalpy without going into the lab?” has four listed content objectives, the first of which is to obtain thermochemical quantities from electronic structure calculations. The other content objectives relate to the difference in zeroes of energy between enthalpy of formation and electronic structure, when calculations can be useful in the absence of experimental data, and how to use the specific results of this experiment to choose the best fire suppressant. Process learning objectives are an essential feature in the POGIL method. The “official” process goals include oral and written communication, problem solving, and critical thinking among others (22). Most of the POGIL-PCL lab experiments include those four process skills, but in addition have lab-oriented process learning objectives. The process learning objectives for this experiment are to develop approaches to quantum chemistry calculations which maximize accuracy while utilizing 54
computational resources effectively and to use available databases to make comparisons among the students’ calculations and known computational and experimental data. The experiment would be most appropriate in the thermodynamics semester of Physical Chemistry. It does not require quantum mechanics or thermodynamics preparation beyond what students receive in General Chemistry. Bond enthalpies are used for an early calculation. The POGIL-PCL philosophy is to have students build concepts; hence, they are designed to be used without a tight relationship to the lecture. This is especially helpful at larger schools using round robin methods in the laboratory. As with other POGIL-PCL experiments, the student handout, Instructor’s Handbook with answers and facilitation notes, and supplemental information are available upon request. Pre-Experiment Questions Pre-experiment questions are used in a POGIL-PCL lab to prompt students to remember what they already know about the concepts in the experiment, to have students look up and record needed data, and to encourage students to organize that information to make a prediction relevant to the experiment. In this experiment, students are first asked to read the Zierold and Chauviere paper and determine the fire suppressant molecule’s identity and structure. They predict the enthalpy of the elimination reaction in Figure 1 using standard general chemistry bond enthalpies. Then students are directed to construct Hess’s Law diagrams relating to the standard enthalpy change and for an electronic energy calculation; these two diagrams differ primarily in the zero of energy (see Figure 2). The thermochemical convention for enthalpies is that the zero of energy is for elements in their standard states, and is usually tabulated for 298.15 K. For electronic energies, however, the zero of energy is for infinitely separated nuclei and electrons at 0 K. Our experience is that, even with these examples, students require guidance to create a correct diagram. Finally, students make an initial prediction about which fluoropropane may be a suitable replacement for HFC-227ea. Facilitating the POGIL-PCL experiments is an art, and faculty workshops have included training in facilitation. The prelab questions are generally not a source of discussion in the Physical Chemistry lab, but POGIL-PCL has team and class discussion prior to the experimentation phase. The final prelab question where students make a prediction requires class facilitation: putting the team consensus predictions on the board or some other visible place is important. Revisions of the predictions will occur in the successive experimental cycles, and students gain an appreciation for the new information as it is juxtaposed against previous decisions. Cycle 1 and Properties Calculations In the first experimental cycle, the class is divided into teams which perform HF/6-31G* calculations on either CH3F or CHF3. The teams compare results to ensure everyone has completed the calculations successfully. Then the results of electronic energy, geometry, vibrational frequencies, and heat capacities are compared and contrasted between the two molecules. Neither molecule takes long (less than 20 cpuseconds on a single node of Chem Compute), so the first cycle should be done relatively quickly.
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Figure 2. Hess’s Law diagrams similar to templates presented for students to revise. These diagrams show an example of an exothermic reaction although the bond enthalpy calculation for HF elimination gives an endothermic result. In (a), the diagram is for standard enthalpy changes. For (b), the diagram refers to an electronic structure calculation. Cycle 2 and Methods Evaluations The second round of experimental work has students use the model fluoromethanes to examine how basis set and methods choices affect accuracy and computational time. The choices suggested in the experiment are shown in Table 1, where the C-F bond lengths calculated are compared to the experimentally determined value (23). Depending on an instructor’s time and number of students, this set of choices can be truncated or expanded. Table 1. Basis set and methods suggested for students to test in Cycle 2 with sample data for CH3F C-F bond length (Å)a Method Basis set
HF
B3LYP
MP2
6-31G*
1.36
1.38
1.39
6-311G*
1.36
1.39
1.39
cc-pVDZ
1.38
1.42
1.38
a Experimental value is 1.38 Å (23).
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Using the electronic energy, C-F bond length, C-F and C-H stretching frequencies, and heat capacity, students create tables of results similar in layout to Table 1. These are compared to the experimental and computational results at the CCBDB3, and students determine the best method to reproduce accurate results. There are enough different options that teams will need to share data to complete the analysis. After choosing a method based upon accuracy, students are introduced to the concept of N4 scaling of computational methods and prompted to predict calculational times for fluoropropanes. Our testing suggests N4 is an overestimate, but it is a reasonable rule of thumb (see Figure 3). Students then revise their decision about the “best” method for calculation.
Figure 3. Scaling analysis for propane through perfluoropropane geometry optimization (employing symmetry and initial MM optimization). Optimization times are 10x faster for HF 6-31G* than B3LYP 6-31G*. HF optimization scales as N3.5; B3LYP scales as N2.4. All calculations were done on 1 core (Jetstream) using GAMESS version 20 Apr 2017 (R1). Finally, teams are asked to reevaluate their prediction of which fluoropropane would best replace HFC-227ea using the results from Cycles 1 and 2. The work in the experiment to this point is directed at providing students with sufficient experience and information to make a prediction about which fluoropropane would make a suitable replacement and to make a cost-benefit decision about which computational method would be preferred. The experiment will produce an analyzable result even if students make “wrong” choices here, but if students choose a calculational method which will not allow them time to do all the calculations, guidance may be necessary to push them toward a better option. Cycle 3 and Selecting a Final Candidate In order to determine the enthalpy of the HF elimination reaction, students must perform geometry optimizations and frequency calculations on the reactant fluoropropane as well as the product fluoropropene and HF using the same basis set and method. A particular fluoropropene is the product for two different fluoropropanes, so student teams are encouraged to consider two 57
fluoropropane reactions at a time. Students were also required to have at least one team perform calculations on HFC-227ea for comparison. After performing the calculations, students record Eelec (0 K) and Cv. Through guiding questions, students are led to obtain the zero point energy and the thermal correction to the enthalpy to correct their values for ΔEelec (0 K) for the reaction to (298 K). Postlab Questions and Experiment Extensions In POGIL-PCL labs, students work in their teams, with instructor guidance, to complete data analysis cooperatively. Hence, post-lab questions presume the students have already reached their conclusions. For this experiment, one question encourages the students to explore discrepancies between the standard bond enthalpy value and the electronic structure values for reaction enthalpy—as it turns out, fluorine compounds have wide discrepancies in bond enthalpies. Another has students evaluate the effect of the number of fluorines on the vibrational zero point energy and the vibrational thermal correction to the enthalpy. Increasing fluorines should decrease the zero point energy since the much larger mass results in lower frequency vibrations. The vibrational thermal energy correction is larger, however, since the lower frequency vibrations are populated and C-H stretches are too high to be populated at room temperature. A few other questions have students explore whether fluorinated butanes or cyclopropanes would make suitable fire suppressants. These questions can be answered relatively superficially, but some could be expanded to include another round of calculations or a separate experiment or project. Our expectation is that only a few would be assigned. This computational experiment could also lead to more sophisticated statistical mechanics calculations of heat capacities and enthalpies at various temperatures. We considered that as a fourth stage of the experiment, but rejected it as making this experiment too unwieldy. Lab Reports The subject material of this experiment would lend itself to alternative forms of lab reports. Teams could give oral presentations of the data in order to defend a particular replacement option. The lab can be framed as a request from a work superior for the analysis, and the report could be written as a response to such a request.
Implementation Details by the Authors The experiment was developed to be platform-independent. Platforms previously used by the authors include Gaussian03W (24), WebMO (25) (as a front end for Gaussian), NWChem (26) with ECCE (27) as a front end, and Chem Compute as a front end for GAMESS (28). All the authors are reasonably facile with both computational software and computing environments, however, and being platform-independent does little to help instructors lacking experience, hardware, and software. The work by Perri with Chem Compute (described further in Chapter 7 of this volume) has been done to address difficulties faced by professors with minimal experience. Local Environments Three of the authors have run this experiment with students. Two are at small schools and one at a medium-sized (on-campus enrollment of 18,000) regional university. All have small physical 58
chemistry enrollments. The computing environments at the schools differ, however, as shown in Table 2. While all utilized the bring your own device (BYOD) model, local software resources included NWChem, WebMO as a front end to Gaussian, or Gaussview (29) as a front end to Gaussian. Two of us used the Chem Compute server. Table 2. Local teaching and computing environments for the authors Author
Location
Times expt. used
Average no. of students
Chem Compute used
Environment
Berghout
Utah
3
7
Y(1)a
BYODc
Reeves
Alabama
3
3
Y(2)b
BYODc
Singleton
Iowa
2
1.5
N
BYODc
a Used
once with Chem Compute; other times with NWChem. time with Gaussian 03W. c Bring Your Own Device.
b Used
twice with Chem Compute; other
Computational Barriers and Errors Teaching a lab which utilizes computational chemistry has multiple barriers for the students. First, there is the lab content itself. Novices to the computational chemistry software will struggle with an array of menus and contextual click actions (especially when building complicated molecules). The computational environment itself leads to additional difficulties; for example, filename conventions, default “save” locations, unfamiliar operating systems (unix shells, terminal commands), and supercomputer queueing are not commonly encountered by chemistry majors. In the initial phases of developing this experiment with the Chem Compute server, students were building their molecules with downloads of shareware programs MacMolPlt (30) or Avogadro (31). In the “bring your own device” (BYOD) model, some students were using one graphical interface or the other depending on whether their laptop was Mac or PC. There were more problems with helping students navigate these programs than with completing the calculations and analyzing the results. The latest version of Chem Compute (chemcompute.org) includes a database search functionality. Typing in “1,1,1,2,3,3,3-heptafluoropropane” will load in the appropriate molecule. A molecule drawing javascript applet is also included so that students can draw molecules on the web page as well. A batch submission interface is provided as well, so that instructors can enter all the fluoropropanes into a csv file and Chem Compute will analyze all of them, returning information about the run time required and thermodynamics values / energies calculated. This gives instructors an easy way to evaluate parameters such as basis set choice before assigning to students. Facilitation Notes Guided inquiry methods require instructors to monitor student discussion or at least to have checkpoints to ensure that student teams don’t veer too far afield. In this experiment, there are several sticky points for students. First, having students develop a Hess’s Law diagram (as in Figure 2) specifically for a chosen reaction requires patience. In our initial naiveté, we thought assigning the question was sufficient. Students do not, as we learned, typically know what a Hess’s Law diagram is without prompting. In the end, we provided templates for them. While the templates helped, it did not make the question “easy.” 59
In three places, students have to make a choice and rationalize it. They must • Choose a basis set and method using scaling calculations and cpu data from CH3F and CF3H calculations • Choose a fluorocarbon to replace HFC-227ea and rationalize why it might be a reasonable choice based upon descriptions of the mechanism of HF elimination and the mechanism of fire suppression • Evaluate, using the electronic structure calculations, whether any of the chosen molecules will make suitable replacements for HFC-227ea. At all three of these points, it is vital to encourage appropriate decisions from the data; one method is to require the students to articulate their decision with the Toulmin model (32), making explicit use of claim (answer), evidence (data), and warrant (reasoning). As an example, consider the choice prompted at the end of cycle 2 where students balance accuracy of calculations against computational time from N4 scaling. The student handout reads “Review the information from the previous two questions in the context of the time available to perform further calculations. Does that change the class perspective on which method/basis set combinations should be used for further calculations?” There is no absolutely correct answer to this question. Students are asked to make a judgment call where accuracy must be balanced against available time. One class may choose to do a “quick and dirty” calculation because they are in a time crunch. Another class may choose to work outside class and do longer calculations with higher accuracy. The experimental objective is to have the students make a judgment call within the set of choices and to rationalize that judgment from the available data. Similarly, the teams may not pick the ideal candidates for testing as substitute fires suppressants. The individual instructor may choose to provide hints about heat capacity or permit other rationalizations as long as they are adequately justified. Related to the issue with the Hess’s Law diagram is guiding students to recognize the corrections which must be made to ΔEelec (0 K) to obtain (298 K). The correction terms are not obvious, and the places to find them in the output also require guidance. The last sticky point to mention is in data organization. Ensuring that all the necessary fluoropropanes and fluoropropenes (and HF) are calculated and the data are saved in recognizable files is not automatic for the students. Student Reception and Response The most gratifying aspect of this experiment is the student engagement with the “real-world” problem. Many of us found the students eager to test whether they could find an acceptable alternative to HFC-227ea. Classroom discussion about the article was lively. The structure of this experiment forces students to draw a conclusion. Without specific interventions, student lab reports have a tendency to present results and calculations without further interpretation. In this experiment, the “specific intervention” is the requirement that an alternative fire suppressant molecule be tested and either recommended or not. The report isn’t complete with a presentation of only the numerical values. We have worked with small numbers of students; hence, we can only offer an anecdotal result that students will make a choice and support it with the data in their notebooks and lab reports.
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Community of Teaching Before the launch of the POGIL-PCL project, none of the five authors knew each other. Before the 2015 workshop where the work on this experiment began, only two of the authors had previously collaborated (Reeves and Whitnell). The evolution of this experiment has involved three workshops, several Skype meetings, piloting at three institutions, and has resulted in two presentations (at the 2016 BCCE and the Fall 2017 ACS National Meeting). The collaboration for this experiment is an example of the success of the POGIL-PCL grant in fostering a community of physical chemistry professors.
Conclusions With this experiment, there are now three computationally-based POGIL-PCL experiments with differing learning objectives. While designed to be platform-independent, they are all capable of being run on the web-based Chem Compute server free of charge. This experiment, exploring the thermochemistry of HF elimination from fluoropropanes, has been found to engage students due to the real-world problem it addresses. With the spread in popularity of web-based servers using graphical interfaces such as WebMO and Chem Compute, computational chemistry is accessible to even the casually aquainted chemistry professor. It is no longer necessary to have either the institutional resources or personal abilities to purchase and maintain the software. With the set of POGIL-PCL computational experiments, we hope to make guided inquiry computational experiments accessible and available to anyone interested in using them. Finally, we recognize that without the POGIL-PCL project, this collaboration would not have occurred. The workshop model which brought together professors and lab coordinators was a driving force to create new experiments using the guided inquiry paradigm.
Acknowledgements The authors would like to thank their respective departments and all their students for support. Many thanks go to the other participating faculty in POGIL-PCL as well as PIs Sally Hunnicutt and Alex Grushow. (Four of us also thank PI Rob Whitnell.) The POGIL-PCL project was funded under NSF-DUE 1044624.
References 1.
2. 3. 4.
5.
Hunnicutt, S. S.; Grushow, A.; Whitnell, R. Guided-Inquiry Experiments for Physical Chemistry: The POGIL-PCL Model. J. Chem. Educ. 2015, 92, 262–268. https://doi.org/10. 1021/ed5003916. Zierold, D.; Chauviere, M. Hydrogen Fluoride Inhalation Injury Because of a Fire Suppression System. Mil. Med. 2012, 177, 108–112. https://doi.org/10.7205/MILMED-D-11-00165. NIST Computational Chemistry Comparison and Benchmark Database; NIST Standard Reference Database; NIST, 2018. Perri, M. J.; Weber, S. H. Web-Based Job Submission Interface for the GAMESS Computational Chemistry Program. J. Chem. Educ. 2014, 91, 2206–2208. https://doi.org/ 10.1021/ed5004228. POGIL | Home https://pogil.org/ (accessed Dec 15, 2018). 61
6. 7.
8.
9. 10. 11. 12.
13.
14. 15.
16.
17.
18.
19.
20.
21.
Farrell, J. J.; Moog, R. S.; Spencer, J. N. A Guided-Inquiry General Chemistry Course. J. Chem. Educ. 1999, 76, 570. https://doi.org/10.1021/ed076p570. Eberlein, T.; Kampmeier, J.; Minderhout, V.; Moog, R. S.; Platt, T.; Varma-Nelson, P.; White, H. B. Pedagogies of Engagement in Science: A Comparison of PBL, POGIL, and PLTL. Biochem. Mol. Biol. Educ. 2008, 36, 262–273. https://doi.org/10.1002/bmb.20204. Straumanis, A.; Simons, E. A. A Multi-Institutional Assessment of the Use of POGIL in Organic Chemistry. In Process Oriented Guided Inquiry Learning (POGIL); Moog, R. S., Spencer, J. N., Eds.; ACS Symposium Series 994; American Chemical Society: Washington, DC, 2008; pp 226–239. https://doi.org/10.1021/bk-2008-0994.ch019. Lewis, S. E.; Lewis, J. E. Departing from Lectures: An Evaluation of a Peer-Led Guided Inquiry Alternative. J. Chem. Educ. 2005, 82, 135. https://doi.org/10.1021/ed082p135. Characteristics and Types of POGIL Activities; The POGIL Project. See, for example, Figure 1 in reference 1. Reeves, M. S.; Whitnell, R. M. New Computational Physical Chemistry Experiments: Using POGIL Techniques with ab Initio and Molecular Dynamics Calculations. In Addressing the Millennial Student in Undergraduate Chemistry; Potts, G. E., Dockery, C. R., Eds.; ACS Symposium Series 1180; American Chemical Society: Washington, DC, 2014; pp 71–90. Skaggs, R. R. Assessment of the Fire Suppression Mechanics for HFC-227ea Combined with NaHCO3. In Proceedings of the 12th Halon Options Technical Working Conference; NIST SP 984; NIST: Albuquerque, NM, 2002; Vol. 12. Hynes, R. G.; Mackie, J. C.; Masri, A. R. Shock Tube Study of the Oxidation of C3F6 by N2O. J. Phys. Chem. A 1999, 103, 5967–5977. https://doi.org/10.1021/jp991065v. Copeland, G.; Lee, E. P. F.; Dyke, J. M.; Chow, W. K.; Mok, D. K. W.; Chau, F. T. Study of 2-H-Heptafluoropropane and Its Thermal Decomposition Using UV Photoelectron Spectroscopy and Ab Initio Molecular Orbital Calculations. J. Phys. Chem. A 2010, 114, 3540–3550. https://doi.org/10.1021/jp1000607. Peterson, S. D.; Francisco, J. S. Theoretical Study of the Thermal Decomposition Pathways of 2-H Heptafluoropropane. J. Phys. Chem. A 2002, 106, 3106–3113. https://doi.org/10.1021/ jp012763u. Barbiric, D.; Tribe, L.; Soriano, R. Computational Chemistry Laboratory: Calculating the Energy Content of Food Applied to a Real-Life Problem. J. Chem. Educ. 2015, 92, 881–885. https://doi.org/10.1021/ed2008894. Martini, S. R.; Hartzell, C. J. Integrating Computational Chemistry into a Course in Classical Thermodynamics. J. Chem. Educ. 2015, 92, 1201–1203. https://doi.org/10.1021/ ed500924u. Lever, L. S.; Howe, J. J.; Whisnant, D. M. Cl2O4 in the Stratosphere: A Collaborative Computational Physical Chemistry Project. J. Chem. Educ. 2000, 77, 1648. https://doi.org/ 10.1021/ed077p1648. Cramer, C. J.; Bumpus, J. A.; Lewis, A.; Stotts, C. Characterization of High Explosives and Other Energetic Compounds by Computational Chemistry and Molecular Modeling. J. Chem. Educ. 2007, 84, 329. https://doi.org/10.1021/ed084p329. Halpern, A.; McBane, G. Experimental Physical Chemistry: A Laboratory Textbook, Third ed.; W. H. Freeman: New York, 2006. 62
22. Operationalized POGIL Process Skill Definitions. The POGIL Project November 17, 2015. 23. Demaison, J.; Breidung, J.; Thiel, W.; Papousek, D. The Equilibrium Structure of Methyl Fluoride. Struct. Chem. 1999, 10, 129–133. https://doi.org/10.1023/A:1022085314343. 24. Frisch, M.; Trucks, G.; Schlegel, H.; Scuseria, G.; Robb, M.; Cheeseman, J.; Montgomery, J.; Vreven, T.; Kudin, K.; Burant, J.; Millam, J.; Iyengar, S.; Tomasi, J.; Barone, V.; Mennucci, B.; Cossi, M.; Scalmani, G.; Rega, N.; Petersson, G.; Nakatsuji, H.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; Kitao, O.; Nakai, H.; Klene, M.; Li, X.; Knox, J.; Hratchian, H.; Cross, J.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R.; Yazyev, O.; Austin, A.; Cammi, R.; Pomelli, C.; Ochterski, J.; Ayala, P.; Morokuma, K.; Voth, G.; Salvador, P.; Dannenberg, J.; Zakrzewski, V.; Dapprich, S.; Daniels, A.; Strain, M.; Farkas, O.; Malick, D.; Rabuck, A.; Raghavachari, K.; Foresman, J.; Ortiz, J.; Cui, Q.; Baboul, A.; Clifford, S.; Cioslowski, J.; Stefanov, B.; Liu, G.; Liashenko, A.; Piskorz, P.; Komaromi, I.; Martin, R.; Fox, D.; Keith, T.; Laham, A.; Peng, C.; Nanayakkara, A.; Challacombe, M.; Gill, P.; Johnson, B.; Chen, W.; Wong, M.; Gonzalez, C.; Pople, J. Gaussian 03, Revision C.02; Gaussian, Inc.: Wallingford CT, 2003. 25. Schmidt, J. R.; Polik, W. F. WebMO; WebMO LLC: Holland, MI, 2018. 26. Valiev, M.; Bylaska, E. J.; Govind, N.; Kowalski, K.; Straatsma, T. P.; Van Dam, H. J. J.; Wang, D.; Nieplocha, J.; Apra, E.; Windus, T. L.; de Jong, W. A. NWChem: A Comprehensive and Scalable Open-Source Solution for Large Scale Molecular Simulations. Comput. Phys. Commun. 2010, 181, 1477–1489. https://doi.org/10.1016/j.cpc.2010.04.018. 27. Black, G.; Schuchardt, K.; Gracio, D.; Palmer, B. The Extensible Computational Chemistry Environment: A Problem Solving Environment for High Performance Theoretical Chemistry. In Computational Science — ICCS 2003; Sloot, P. M. A., Abramson, D., Bogdanov, A. V., Gorbachev, Y. E., Dongarra, J. J., Zomaya, A. Y., Eds.; Lecture Notes in Computer Science; Springer: Berlin, Heidelberg, 2003; pp 122–131. 28. Gordon, M. S.; Schmidt, M. W. Advances in Electronic Structure Theory: GAMESS a Decade Later. In Theory and Applications of Computational Chemistry; Dykstra, C. E., Frenking, G., Kim, K. S., Scuseria, G. E., Eds.; Elsevier: Amsterdam, 2005; Chapter 41, pp 1167–1189. https://doi.org/10.1016/B978-044451719-7/50084-6. 29. Dennington, R.; Keith, T.; Millam, J. GaussView, Version 5; Semichem, Inc.: Shawnee Mission. KS, 2009. 30. Bode, B. M.; Gordon, M. S. Macmolplt: A Graphical User Interface for GAMESS. J. Mol. Graph. Model. 1998, 16, 133–138. https://doi.org/10.1016/S1093-3263(99)00002-9. 31. Hanwell, M. D.; Curtis, D. E.; Lonie, D. C.; Vandermeersch, T.; Zurek, E.; Hutchison, G. R. Avogadro: An Advanced Semantic Chemical Editor, Visualization, and Analysis Platform. J. Cheminformatics 2012, 4, 17. https://doi.org/10.1186/1758-2946-4-17. 32. Becker, N.; Rasmussen, C.; Sweeney, G.; Wawro, M.; Towns, M.; Cole, R. Reasoning Using Particulate Nature of Matter: An Example of a Sociochemical Norm in a University-Level Physical Chemistry Class. Chem. Educ. Res. Pract. 2013, 14, 81–94. https://doi.org/10.1039/ C2RP20085F.
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