Chapter 16
Discovery-Based Computational Activities in the Undergraduate Chemistry Curriculum
Using Computational Methods To Teach Chemical Principles Downloaded from pubs.acs.org by UNIV OF ROCHESTER on 05/15/19. For personal use only.
Yana Kholod1 and Dmytro Kosenkov*,1 1Department of Chemistry and Physics, Monmouth University, 400 Cedar Avenue,
West Long Branch, New Jersey 07764, United States *E-mail:
[email protected].
The presented chapter provides a brief overview of recently implemented projects that integrate discovery-based activities into undergraduate chemistry courses. The body of recent literature shows that inquiry- and research-based projects are scalable and can be implemented at smaller undergraduate schools, as well as at larger research universities. The described projects range from purely computational (e.g., “Modeling molecular orbitals with the Hückel method”) to integrative activities with extensive experimental components (e.g., “Cis-trans conformational transitions in N,N-dimethyl-4,4′-azodianiline”). Some of the projects are primarily focused on building students’ skills in computational chemistry (e.g., “Dissociation of formaldehyde”), while others have been designed to produce new scientific knowledge (e.g., “Ligand binding to DNA”). The presented activities can be adapted for general, physical or organic chemistry, or instrumental analysis courses and scaled to fit various institutional settings.
Introduction Computational chemistry provides means for modeling and study of a broad range of chemical and physical phenomena. Thus, computational tools serve well for illustrating scientific concepts to students throughout the entire physical sciences curriculum. Furthermore, molecular modeling is an effective, visually rich instrument for students to explore chemical intricacies on their own as a part of laboratory exercises as well as to participate and contribute to cutting-edge scientific projects. In recent years, there has been a steady growth of interest in enhancing chemistry courses with computational projects. Such interest is particularly noticeable at Primarily Undergraduate Institutions (PUIs), as those computational projects require resources and software packages typically already available at those institutions, with no or very little extra costs involved. There is even a temptation to replace some more expensive chemistry laboratories entirely with computer modeling, simulations or virtual demonstrations. However, we would advocate for integrative projects, where the fusion of computational and experimental components helps students to recreate © 2019 American Chemical Society
a real world situation where theory aims to predict new experimental outcomes and computational results are validated though experiments. Additionally, students experience a collaborative nature of research activities, where diverse approaches are used synergistically to achieve a common goal. Improvement of existing courses can be done through encouraging critical thinking via discovery-based activities. Students either participate in inquiry-based projects where they discover processes known to the instructor, but initially unknown to the students, or work on student-faculty collaborative research projects, resulting in new scientific knowledge. Both approaches have their advantages and challenges. For example, typical inquiry-based activities are more suitable for introductory courses with larger enrollments. In contrast, research projects in many instances can only be implemented in upper-level courses in the area of expertise of the instructor. However, there are exceptions. In either case, discovery-based projects are among high impact activities that provide in-depth coverage of physical and chemical material. Typical challenges of integration of those activities into undergraduate courses are a high investment of faculty and student time and resources (facilities). Thus, administrative support is critically important for success of those curricular innovations. The coverage of material in discovery-based courses is likely to be narrower, while students will obtain more in-depth knowledge in the topic of interest. The discovery-based approach will require more contact hours to cover the same amount of material as compared to traditional lecture and laboratory courses (1, 2). In the following sections of the chapter an overview of recent efforts of integrating discoverybased activities into chemistry courses at Monmouth University and other institutions is presented. We emphasize integrative and collaborative nature of those projects where several chemistry disciplines are integrated to achieve a higher educational impact, and provide our reflections on effectiveness of the described projects. One of the challenges in a typical chemistry laboratory course is a “cookbook” approach where students follow laboratory manuals with limited understanding of performed procedures and obtained results. However, addressing this drawback is difficult, and one of the reasons is fear that a research-based project will require all elements of Course-based Undergraduate Research Experiences (CUREs), (1, 2). Those elements include: i) practicing science - gathering background information, formulating a scientific hypothesis or building an experimental setup; ii) taking an iterative approach that assumes refining procedures and methodologies based on assessment of previously obtained results; iii) discovery and inquiry components implicating that students build their knowledge based on their own experiences; iv) collaboration and teamwork to encourage students to communicate efficiently to achieve their goals; v) science relevance and novelty necessary for genuine research projects. While CURE activities do not necessarily lead to results justifying publications, communicating obtained results though presentations and papers is a significant motivational factor for students and faculty (1). We believe that incorporating all five elements of CURE in each project is not a necessity. The instructor can evaluate each project separately and adapt it to their needs, keeping 2-3 CURE elements as a general guidance. Such an approach, according to our observations, leads to improvement of the course while preserving certain flexibility, in particular at the initial stages of curricular modifications. Significant changes in the chemistry curriculum require a collective effort of the department in multiple if not all disciplines. When multiple disciplines are affected, it is useful to define learning goals at a larger scale for the entire undergraduate program. It is also important that defined goals are aligned with guidelines from the ACS Committee on Professional Training (CPT) due to program certification reasons. For example, Anchoring Concepts Content Maps (ACCM), (3), proposed by 228
the ACS Examinations Institute, have been recently developed for several chemistry sub-disciplines (4–7), including physical chemistry (4). Those maps provide a handy template to define content coverage within chemistry curriculum in 4-year institutions. The following ten “big ideas” represent the top level of ACCM for chemistry courses (Scheme 1):
Scheme 1. Top-level anchoring concepts traditionally covered in physical chemistry courses (4). Reprinted in part with permission from Ref. (4). Copyright 2018 American Chemical Society. Those top levels are covered in areas of kinetics, quantum mechanics, and thermodynamics that are traditional for physical chemistry. ACCMs consist of four hierarchically organized levels (See Table 1). Table 1. Example of four hierarchical ACCM levels (4). Reprinted in part with permission from Ref. (4). Copyright 2018 American Chemical Society. ACCM Levels
Sample Items
1
“II. Bonding: Atoms interact via electrostatic forces to form chemical bonds.”
2
“E. A theoretical construct that describes chemical bonding utilizes the construction of molecular orbitals for the bond based on overlap of atomic orbitals on the constituent atoms.”
3
“1. Different computation models can be used to describe how electrons interact in multiatom, multielectron systems.”
4
“b. The Hartree-Fock, Self Consistent Field (HF-SCF) approximation begins with an initial guess for a trial wavefunction, and is iterated until electron density and energy converge.”
Two top levels represent material covered across chemistry sub-disciplines. In Table 1, levels 3 and 4 are specific to physical and computational chemistry. The concept map helps to organize the content coverage and facilitate the integration of discovery-based projects that bridge several chemistry sub-disciplines. The ACCM also helps to define learning goals for each laboratory unit. After the planning phase, at the beginning of the course, it is critically important to communicate the learning goals and approaches to students as it helps both students and instructor to determine if performed activities help in attaining CUREs goals. For instance, an introduction to the concepts of macroscopic, molecular-level, or mathematical models with specific examples (e.g., ideal and real gases) has been recently suggested for physical chemistry courses (8). A brief review of recent projects that involve several elements of discovery-based activities is provided below. These project range from small (800 students), 229
containing only theoretical/computational component or integrating theory and experiment, covering a narrow topic or bridging several sub-disciplines of chemistry. Providing original research experiences for undergraduates can be accomplished successfully in small PUIs as well as at large research universities (9). A recent successful example of a large (>800 students) project is the Freshman Research Initiative (FRI) at the University of Texas at Austin (9). In this program undergraduates gain their core chemistry competencies participating in one of several “streams” that focused on genuine research projects in one of four areas of chemistry: organic, analytical, inorganic, or biochemistry. Examples of streams are: Supramolecular Sensors, Nanomaterials for Chemical Catalysis, Functional Materials Based on Metal Complexes, Synthesis and Biological Recognition, and Virtual Drug Screening (9). In each stream students gain skills in synthesizing molecules, measuring and analyzing UV-vis spectra, and characterizing chemical molecules, complexes, etc. In the context of computational chemistry, notably, computer modeling has been an integral part of a larger FRI stream, such as the Virtual Drug Screening. In that stream, students perform protein synthesis and purification, and then characterize target proteins with UVVis spectroscopy. As a part of FRI activities students explore 3D structures of proteins, run docking simulations, and analyze obtained results (9). As emphasized by the authors of the FRI model, faculty buy-in and participation of postdoctoral-level research educators play a critical role in the success of larger (30-35 students) FRI streams (9). Another example is a smaller-scale (33 students) one-semester CURE in Physical Chemistry laboratory at Emory University as reported by Williams et al. (10). It has been shown that the project helped to standardize undergraduate research training, and increased student sense of ownership of their projects. Chemistry majors worked on investigating of intermolecular interactions of ligands (uremic toxins) with human serum albumin (10). The ultimate goal of that research project was to improve dialysis methods. During their CURE activities, students used stopped-flow kinetics, isothermal titration calorimetry, and molecular dynamics simulations to study ligand binding (10). Molecular modeling can be integrated into organic chemistry laboratory. For example, in the project on investigation of the dimerization of isobutylene students performed synthesis, isolation, and characterization of obtained trimethylpentene isomers by Schuster et al. (11). The characterization employed NMR, IR, CG-MS, and molecular modeling techniques. Students performed geometry optimization of possible isomers with the B3LYP/6-31G(d) method, studied the Lowest Unoccupied Molecular Orbital (LUMO) of the intermediate structure in order to understand the mechanism of the reaction. In this example, addition of molecular modeling helped to gain a better understanding of the mechanism of the isobutylene dimerization reaction. A research-related laboratory course focused on modeling organic photovoltaics has been also proposed by Schellhammer et al. (12). In that course, students modeled dipyrromethene molecules that can be potentially used as donor materials in organic photovoltaics. Students used densityfunctional theory-based tight-binding (DFTB) method implemented in DFTB+ package to run the simulations (13). Classical molecular dynamics (MD) simulation package (e.g. Amber) (14), can be used to cover concepts of diffusion and radial distribution functions (RDFs) in physical chemistry laboratory as reported by Kinnaman et al. (15). These MD simulations help to interpret the physical meaning of RDFs and diffusion coefficients and compare various water models (e.g. TIP3P, OPC, etc.), (15). There are also a few recent projects beyond traditional computational chemistry where molecules are constituted and optimized with standard molecular modeling and/or electronic structure packages. A special molecular dynamics software has been created in the Foley lab (16) to 230
interactively model real gases using the Lennard-Jones potential approximation. The package enables quantitative measurement of deviations in properties (e.g. macroscopic compressibility) of real gases from ideal behavior. Recently developed software (17, 18), which integrates YAeHMOP and Avogadro Molecular Editor and Visualizer packages enables calculation of electronic band structure and other properties of bulk materials traditionally covered in physical chemistry or materials science. The calculations are based on the extended Hückel theory and accounts for the periodicity of materials in 1, 2, or 3 dimensions. The software enables calculation of band structure (crystal orbitals), density of states (DOS), crystal orbital overlaps, etc. It has been shown by Avery et al. (19) that the software is capable to model electronic band structure and DOS of such important materials as silicon and graphite. One of the main advantages of the software is that students can start modeling electronic band structure of materials with the software even before they completely mastered underlying quantum mechanics. Chemical kinetics of oscillating chemical systems (e.g. Belousov-Zhabotinsky reaction) can be simulated with the Brusselator model (20). This model has been implemented by Lozano-Parada et al. (20) with Comsol Multiphysics, Matlab, and Microsoft Excel packages. This approach provides undergraduate students to quantitatively model complex kinetics of autocatalytic chemical reactions. There is an interesting idea of a classroom lecture demonstration/student laboratory exercise proposed by Brom (21), which integrates theoretical and experimental components to cover the wave–particle duality as one of fundamental concepts of quantum physics. First, the photon wavelength is derived through the calculation of the interference of quantum probability amplitudes. Then, this theoretical result is confirmed by the light scattering experiment to determine the wavelength of the photon. This activity provides an opportunity for students to directly experience and explore the wave–particle duality in the experiment and describe it in the precise mathematical form.
Laboratory Units Implemented at Monmouth University In order to improve students’ critical thinking and avoid “cookbook” labs we have developed a series of project-based laboratory units that integrate elements of scientific research into undergraduate courses at Monmouth University. Each project is constructed to balance instructional and research objectives. While literature examples show that CUREs can be implemented in larger classes of 30-35 students (8, 9) the described units implemented at Monmouth University have primarily been used in Physical Chemistry Laboratory (2 semesters) and Computational Chemistry courses that typically enroll 8-10 students per section. The experiments have been performed in our Physical Chemistry Laboratory equipped with typical instrumentation (e.g. FTIR, UV-Vis spectrometers, pH probes, etc.) to carry out traditional physical chemistry experiments. Some additional equipment (e.g. microcontrollers) and supplies needed for the implementation of laboratory units were purchased with departmental lab-fee funds and from external grants. The full list of equipment and supplies needed to carry out the experiments is given in the corresponding references. Development of Laboratory Instrumentation: A Titration Experiment The project is focused on development and integration of chemistry laboratory instrumentation (e.g., automatic titrator) into the industrial internet. The industrial internet incorporates physical hardware and software, which is able to receive data from sensors, analyze it, and carry out necessary laboratory procedures automatically. Students participation in this project build and calibrate an 231
automatic titrator (22). The titrator is based on an open-source microcontroller Arduino platform (23) and novel methodology of data integration developed in our lab (Figure 1), (22). The titrator is programmed by students to perform automatic acid/base titration. Skills learned in this lab by students can be also used to construct the hardware and software necessary to automate, remotely control, and integrate a variety of sensors commonly used in chemistry and environmental science (e.g. salinity, temperature, pressure, etc.) into the Internet. The main goal of the first part of the unit is to give students a basic knowledge and understanding of the microcontroller board and electronic circuit components. Upon completion of the unit, students will also be able to create a simple circuit by connecting the microcontroller board and a pH sensor. Then students build the titrant dispenser circuit and volumetric assembly. The instrument is calibrated with the desired titrant (e.g., NaOH solution).
Figure 1. Automatic titrator: an electronic circuit and Arduino software perform automatic measurements of pH. Control valve is used to dispense titration solution. The software performs recording and analysis of the obtained pH values. Adapted with modifications with permission from Ref. (22). Copyright 2016 American Chemical Society. The unit integrates topics in chemical kinetics, equilibrium, electric circuits and building instrumentation for research purposes. Students involved in this laboratory unit develop a sense of ownership of the project. Critical thinking is encouraged when they work on assembling and calibrating the instrument, debugging the software, and troubleshooting hardware problems. In our experience, a team of two students working on the project is best, as they can exchange ideas, collaborate, and stay motivated. The titration experiment includes the following CURE elements: Practicing Science – students build an experimental setup by themselves (2); Iterative Approach – the experiment has been revised a few time to improve performance (e.g., increasing signal to noise ratio) (4); Collaboration and Teamwork – students work in teams to build the titrator. This laboratory unit covers the following ACCM concepts: chemical measurements: electrochemical methods (IX.D.3.a), appropriate experimental design is required to obtain 232
trustworthy data (IX.F.1-3), proper safety precautions must be taken to minimize risks associated with chemical experiments (IX.G.1.a). Modeling Ligand Binding to DNA Binding of small organic ligands to DNA minor groove (24) as well as stabilization of human telomeric DNA high-order structures (e.g. G-quadruplexes) has become a promising approach to introduce novel anticancer drugs (25). In the proposed unit students investigate binding of naphthalene diimide (26) and diminazene (Figure 2) ligands (27) in order to evaluate the principal factors determining contributions to DNA-ligand binding. A molecular docking technique and ab initio based fragment molecular orbital method are used to estimate binding affinities of ligands. Students select ligands based on primary literature search and based on recommendations by the instructor. Then, students find the optimal ligand based on results of binding energies and docking modes (positions of the ligands with respect to the DNA) using the computational protocol developed in our lab (27). The unit integrates topics in molecular modeling, thermodynamics, kinetics, and quantum theory. The modeling ligand binding to DNA laboratory unit embeds the following CURE elements: Practicing Science – students formulate a hypothesis about potential abilities of organic molecules to bind to DNA; Discovery and Inquiry – students verified their hypotheses performing molecular docking simulations; Science Relevance and Novelty – students are offered structures of newly synthesized ligands to check their affinity to DNA.
Figure 2. Organic ligand diminazene (DMZ) docked to the minor grove of DNA. Adapted with modifications with permission from Ref. (27). Copyright 2018 American Chemical Society. Main ACCM concepts covered in this unit include: finding minimal energy structures with quantum chemistry methods (III.C.1.a,c), intermolecular forces (IV.A.1.a-d, IV.A.2.a-b) in general and hydrogen bonding specifically (IV.C.7.a-c) determines conformations of large biological molecules (IV.B.1.a-c). Solvatochromism of Organic Dyes Organic solvatochromic molecules, such as Reichardt’s betaine Et30 (2,6-diphenyl-4-(2,4,6triphenyl-1-pyridinio) phenolate) and Brooker’s merocyanine (4-[(1-methyl-4(1H)233
pyridinylidene) ethylidene]-2,5-cyclohexadien-1-one) dyes change their color depending on polarity of the surrounding solvent (28). Thus, they are used to investigate the intermolecular interactions between solute and solvent molecules and find their applications as sensors for identification of organic solvents. In this unit students study solvatochromic effects in organic molecules. This solvatochromic behavior is largely due to intermolecular charge transfer under electronic excitation. Students participating in this project have investigated the solvatochromic behavior of Reichardt’s and Brooker’s dyes by UV-Vis spectroscopy in solvents of varying polarity (Figure 3).
Figure 3. UV-Vis absorption spectra of the Reichardt’s betaine Et30 dye (shown in inset) in solvent of various polarities demonstrate a strong solvatoshimic shift of absorption maxima (marked with arrows). The unit can be enhanced by adding a computational component – quantum chemical modeling of solvatochromic shifts using our fragmentation-based approach (29). In the course of the experiment, students choose a solvatochromic dye and determine appropriate solvents to dissolve the molecule. Comparing measured and simulated UV-Vis spectra, students determine which electronic excited states contribute to the solvatochromic behavior and which solvents can be discriminated using their molecule chosen as a sensor. The unit integrates topics in electronic spectroscopy, computer simulations, and quantum theory. This experiment includes three CURE elements: Practicing Science – students read primary literature on solvatochromic properties of organic compounds; Taking an Iterative Approach – students refine experimental procedure finding the best conditions (e.g. solvents, concentrations of solutions) to observe solvatochromic properties of organic dyes; Discovery and Inquiry – running spectroscopic experiments students observe solvatochromic shifts of given samples and can compare obtained results with their expectations based on literature data. The ACCM concepts covered here include molecular spectroscopy (IX.D.1.a,e), various intermolecular interactions (IV.A.1.a-d, IV.A.2.a-b, IV.C.7.a-c), effects of solvents (IV.E.1.b) and their polarity (IX.C.2.a-b) on molecular properties. Transient UV-Vis Spectroscopy of cis-trans Conformational Transitions in Diazo Dyes. Molecular diazo dyes (Figure 4) undergo a light-induced trans-cis isomerization and transform back to the trans conformation in the dark. These compounds recently received widespread attention because of their applications in neuroscience (30). In their extended trans conformations they are able to block engineered ion channels in neurons, which opens a possibility of controlling neurons 234
with pulses of laser light. The time scale of the cis-trans transition in diazo dyes is long enough (10-4-10-2s) to make it possible to determine rates of photoinduced reactions using a KRONOS flash photolysis instrument (31), available in our laboratory. The unique feature of this experimental setup is a custom-made temperature control unit assembled by students (Figure 4), (32). Using this setup students study the kinetics of cis-trans transition at various temperatures, which allows them to determine the activation energy of the cis-trans isomerization in diazo dyes experimentally using the Arrhenius plot.
Figure 4. “Transient UV-Vis spectroscopy of cis-trans conformational transitions in N,N-dimethyl-4,4′azodianiline” Kinetics of isomerization is studied at various temperatures in order to estimate the activation energy of cis-trans transition. Adapted with modifications with permission from Ref. (32). Copyright 2016 American Chemical Society. Students can also compare experimental results with computational (DFT) study, and analyze molecular structures and relative Gibbs free energies between the model isomers in the gas-phase and in solution in order to characterize potential photoswitches. The transient UV-Vis spectroscopy of cis-trans conformational transitions in diazo dyes experiment involves three CURE elements, similarly to the titration experiment: Practicing Science – an experimental setup is built by students; Iterative Approach – the experiment has been revised and the procedure has been fine-tuned by the participating students; Collaboration and Teamwork – students work in teams to build the temperature-controlled sample holder. The experiment above covers the following ACCM concepts: experimental molecular UV/ Vis spectroscopy (IX.D.1.a,e), temperature control and measurement of temperature effects (IX.D.2a,b), spectroscopic observation of reaction intermediates (IX.F.2.a) and Arrhenius equation (V.C.1.b) . Computational Laboratory Units The two laboratory units described below purely computational and require only a computer laboratory and R software. They are offered as a part of our physical chemistry laboratory course, but can be implemented in a computational chemistry course as well. 235
In the two computational laboratory units described below (“Michaelis–Menten Kinetics”, “Hückel Molecular Orbital Method”), the most important CURE component is Discovery and Inquiry – students check proposed hypotheses about changes in results of simulations (concentrations of products of enzymatic reactions or energies and shapes of molecular orbitals) as a result of changes in initial parameters of simulations (e.g., an initial concertation of the substrate or energies of atomic orbitals). ACCM concepts covered by those laboratory units include chemical kinetics topics (VII.B.1.a-f) including the reaction mechanism (VII.C.1.a-b), the Michaelis–Menten model of enzymatic kinetics (VII.E.3.a), Visualization and estimation of energies of π-orbitals with the Hückel method (X.A.4.b). Michaelis–Menten Kinetics This computational laboratory unit is dedicated to modeling of chemical reactions catalyzed by enzymes in accordance with the Michaelis–Menten kinetics model (Eq. 1), (33):
where E is the enzyme, S is the substrate, ES is the enzyme-substrate complex, and P is the product. It is emphasized that the reaction scheme is rather general. However, the approach can be used to model aminoacylase catalyzed hydrolysis reaction (34). A brief and excellent introduction to basics of Michaelis-Menten and Briggs-Haldane Kinetics is available at the Prof. W.M. Atkins website (35). Students model concentrations of reactants and produce of functions of rate constants and initial concentrations of substrate and enzyme. Students also investigate the steady-state, free ligand, rapid equilibrium approximations (35). In our implementation of the laboratory unit, students use Rlanguage (36) to explore the kinetic model (Scheme 2):
Scheme 2. Excerpt of the R-script used by students to model enzymatic kinetics. Hückel Molecular Orbital Method One of the few basic approximations in computational quantum chemistry is the molecular orbital theory which commonly employs a linear combination of atomic orbitals molecular orbitals (MOs) (37). In the Hückel approximation the diagonal matrix of MO energies (ε) and matrix of coefficients (c) of molecular orbitals are obtained by solving the eigenvalue problem (Eq. 2) diagonalizing the Hamiltonian matrix (H): 236
In this laboratory unit, the matrix formulation of the Hückel method is employed to compute and interpret energies and coefficients that show contributions of separate atomic p-orbitals to molecular orbitals of simple conjugated systems. Students define and diagonalize the Hamiltonian matrix using R-language in order to obtain energies and coefficients of molecular orbitals (Scheme 3).
Scheme 3. Excerpt of the R-script used by students to obtain energies and coefficients of molecular orbitals of cyclobutadiene with the Hückel Molecular Orbital Method. Skill-Building Laboratory Units There are several skill-building laboratory units where students learn specific experimental techniques (e.g. FTIR, UV-Vis spectroscopy, etc.), apply theoretical models learned in the lecture courses (harmonic oscillator, particle in a box model, etc.), and gain skills in standard computational quantum chemistry tools and methods (e.g., geometry optimization with Gaussian (38) or Spartan (39) packages, etc.) A brief overview of those laboratory units is given below. Skill-building laboratory units are, indeed, more traditional labs. They may not contain substantial research or inquiry components at this moment. However, those labs help students to master basic skills that are critical for further labs (e.g. research-based lab on UV-Vis spectroscopy of cis-trans conformational transitions in diazo dyes). Skill-building labs cover ACCM topics on molecular IR spectroscopy (IX.D.1.a-f), isotopic effects on IR spectra (1.A.3.a) simple quantum mechanical models (e.g. particle in a box) – (X.A.1.e), transition state theory (VII.D.4.d), modeling of transition states (X.D.2.b, III.C.3.c) with quantum chemistry methods. Vibrational Spectroscopy: Isotopic Shift Another laboratory unit integrating experimental and computational components is dedicated to principles of vibrational spectroscopy and the harmonic oscillator model. Students record FTIR spectra of water (H2O) and deuterium oxide (D2O) (40), and then interpret observed isotopic shift applying the harmonic oscillator model. Harmonic frequency of the O–H stretching vibrational modes (Eq. 3):
237
where k is force constant, and μ is reduced mass μx (x = H2O or D2O). The reduced mass μx = momx/(mo + mx) for water or deuterium oxide are computed from masses of oxygen atom (mo) and either hydrogen (mH) or deuterium (mD) respectively. Then students estimate the effect of the reduced mass on the vibrational frequency, which can be presented as the ratio (Eq. 4):
Then, students assign experimental vibrational modes and learn selection rules of FTIR spectroscopy performing obtained results with normal mode analysis with traditional DFT methods (e.g., B3LYP/6-31G*), (41–44). UV-Vis Spectroscopy of Conjugated Dyes Students record UV-Vis spectra of a series of conjugated dyes (e.g. 3,3′-diethylthiacarbocyanine iodide, 3,3′-diethylthiadicarbocyanine iodide, and 3,3′-diethylthiatricarbocyanine iodide) with increasing length of the conjugated chain and determine maxima of their absorption due to electronic π- π* transitions. Then, the particle in a box model is used to predict the relationship between the lengths of the molecules and maxima of their UV-Vis spectra (longer molecules have lower wavelength of absorption) as proposed earlier (45). Additionally, students learn basics of molecular modeling building 3D models of the dyes and computing molecular orbitals with DFT methods (41). Dissociation of Formaldehyde The laboratory unit is focused on investigation of the dissociation of formaldehyde with formation of carbon monoxide and hydrogen (46). Students optimize formaldehyde, carbon monoxide, and hydrogen molecules. Then they optimize the transition state in the dissociation reaction with B3LYP/6-31G* method. Students learn basics of the Transition State Theory (TST) exploring the mechanism and estimating the activation energy of the reaction. Then, students calculate the rate constants of the forward and reverse reactions with the simplified version of the Eyring equation (Eq. 5), (47):
where Δ‡E°, is the activation energy of the reaction, calculated as energy difference between the transition state and reactant, kB is the Boltzmann constant and h is the Planck constant, and T is the temperature. All quantum chemical calculations in this lab are performed with Gaussian package (38). Another skill-building lab where students use Spartan package (39) is focused on a concept of intermolecular interactions and specifically hydrogen bonding. Students build various dimers of formic acid to determine which dimers are stabilized by hydrogen bonds, analyze the effect of hydrogen bonding on vibrational frequencies of O-H stretching vibrations and estimate energies of hydrogen bonds. 238
Assessment Approach The goals of our revisions to the courses is to give students hands-on experience in modern chemistry using research-based projects. The proposed units aim to introduce students to electronic and vibrational spectroscopy, thermodynamics, chemical kinetics, and computer simulations with state-of-the-art computational quantum chemistry software. We broadly define objectives of our courses as follows: students should be able to devise and perform experiments, to analyze the data obtained, to assess the significance of the results and to write about their work in a professional manner. They should be able to: i) explain the fundamental concepts underlying the major computational and experimental techniques used in the lab and, as a consequence, be able to critically evaluate evidence in the chemical literature; ii) apply modern laboratory techniques and use the interpretations of chemical concepts therein as a part of the practice of science in their future careers; iii) use the connection between the hands-on practice and the mathematical formalism of quantum mechanics to create appropriate interpretations of observed phenomena; iv) demonstrate collegial interaction as an integral part of the learning process. Evaluation of Laboratory Units The key learning goals and objectives given above are communicated to students at the beginning of the course. In order to assess the learning outcomes the following tools are used: direct pre/posttests, lab reports, comprehension checks, and reflective assessments to track students’ performance for each unit. The pre/post-test questions are designed in the way to test each learning outcome at three levels of knowledge: understanding (ability to explain or identify basic concepts), analysis (ability to critically examine relationships between provided data or observations), and generalization (ability to gain new knowledge or broaden the context from provided or obtained information). Based on their projects, students write reports following standards of chemical research papers that are used to assess analysis and generalization skills. We have received direct feedback from students during and after the laboratory units. Additionally, a comprehensive evidence-based portfolio approach is used to put assessment into a broader context (48). Specific results of evaluation of laboratory units has been recently presented in our publications (22, 27, 32). Students actively participating in the described projects co-authored 5 papers (22, 26, 27, 32, 49), and gave 26 presentations at undergraduate and professional conferences, including ACS Regional and National Meetings. Over four consecutive years (2013-2016), they received Dean’s awards for their research presentations at MU. Five students were recognized with external grants, scholarships, and awards by ACS, NSF, and The Independent College Fund of New Jersey (ICFNJ). Students also received awards for their research presentations at the Undergraduate Research Symposium at William Paterson University (2016), the New Jersey Water Environment Association (NJWEA) Meeting (2016), ACS-North Jersey Meeting at Fairleigh Dickinson University (2015), Independent College Fund of New Jersey (ICFNJ) Undergraduate Research Symposium (2015), the Conference on Current Trends in Computational Chemistry (2013), the Southern School on Computational Chemistry and Material Science (2013) at Jackson State University.
Conclusions Based on our experience it is evident that regular involvement of students in research activities and gradual increasing of complexity of assignments is a key for student success in undergraduate research. As it follows from the developed laboratory units it is possible to implement CUREs with a 239
subset of its elements. It is evident that involvement of undergraduate students in research activities and gradual increasing of complexity of assignments is a key for students’ success. Student participation in conferences and meetings to present results obtained from the labs, external fellowships, participation in summer research, co-authorship in publications, and ability to enter to desired graduate and professional programs indicates the success of the proposed approach. The feedback obtained from students is continuously used to adjust the course to achieve the stated learning outcomes. This helps us to gradually integrate research-based projects into the courses. A full-size research based laboratory course may seem a distant goal for someone who just starts. Creating a fully CURE-based course with all CURE elements included is a great challenge. However, that should not prevent one, out of discouragement, from gradually implementing research and inquiry-based activities in their courses. Our experience suggests that a combination of more traditional, skill-building laboratory units, followed by one or several research and inquiry-based units provide a balance necessary for an instructor to comfortably handle a laboratory course, at least during the transition period. Skill building labs help to achieve learning objectives with traditional methods and provide practice necessary for building students confidence in their ability to perform basic lab procedures.
Acknowledgements Financial support was partially provided by the Research Corporation for Science Advancement through a Cottrell Scholar Award, the Independent College Fund of New Jersey, National Science Foundation MRI #1662030, Donors of the American Chemical Society Petroleum Research Fund though grant #58019-UR6, and Monmouth University though the School of Science SRP program and Creativity Grants.
References 1.
2.
3.
4.
5.
Ross, J. L.; Atkins Elliott, L.; Bur, S. K.; Gilbertson, J. D.; Kosenkov, D.; Sigmann, S. B. Continuum of Labs. In Expanding the CURE Model: Course-Based Undergraduate Research Experience; Waterman, R., Heemstra, J., Eds.; Research Corporation for Science Advancement: Tucson, AZ, 2018; pp 29−59. Heemstra, J. M.; Waterman, R.; Antos, J. M.; Beuning, P. J.; Bur, S. K.; Columbus, L.; Feig, A. L.; Fuller, A. A.; Gillmore, J. G.; Leconte, A. M.; Londergan, C. H.; Pomerantz, W. C. K.; Prescher, J. A.; Stanley, L. M. Throwing Away the Cookbook: Implementing CourseBased Undergraduate Research Experiences (CUREs). In Chemistry in Educational and Outreach Projects from the Cottrell Scholars Collaborative Undergraduate and Graduate Education Volume 1; Waterman, R., Reig, A., Eds.; ACS Symposium Series 1248, American Chemical Society: Washington, DC, 2017; pp 33–63. Murphy, K.; Holme, T.; Zenisky, A.; Caruthers, H.; Knaus, K. Building the ACS Exams Anchoring Concept Content Map for Undergraduate Chemistry. J. Chem. Educ. 2012, 89, 715–720. Holme, T. A.; Reed, J. J.; Raker, J. R.; Murphy, K. L. The ACS Exams Institute Undergraduate Chemistry Anchoring Concepts Content Map IV: Physical Chemistry. J. Chem. Educ. 2018, 95, 238–241. Holme, T.; Luxford, C.; Murphy, K. Updating the General Chemistry Anchoring Concepts Content Map. J. Chem. Educ. 2015, 92, 1115–1116. 240
6.
7.
8.
9.
10.
11.
12. 13. 14.
15. 16.
17. 18. 19. 20.
Raker, J.; Holme, T.; Murphy, K. The ACS Exams Institute Undergraduate Chemistry Anchoring Concepts Content Map II: Organic Chemistry. J. Chem. Educ. 2013, 90, 1443–1445. Marek, K. A.; Raker, J. R.; Holme, T. A.; Murphy, K. L. The ACS Exams Institute Undergraduate Chemistry Anchoring Concepts Content Map III: Inorganic Chemistry. J. Chem. Educ. 2018, 95, 233–237. Bruce, C. D. Beyond the Syllabus: Using the First Day of Class in Physical Chemistry as an Introduction to the Development of Macroscopic, Molecular-Level, and Mathematical Models. J. Chem. Educ. 2013, 90, 1180–1185. Ghanem, E.; Long, S. R.; Rodenbusch, S. E.; Shear, R. I.; Beckham, J. T.; Procko, K.; DePue, L.; Stevenson, K. J.; Robertus, J. D.; Martin, S.; Holliday, B.; Jones, R. A.; Anslyn, E. V.; Simmons, S. L. Teaching through Research: Alignment of Core Chemistry Competencies and Skills within a Multidisciplinary Research Framework. J. Chem. Educ. 2018, 95, 248–258. Williams, L. C.; Reddish, M. J. Integrating Primary Research into the Teaching Lab: Benefits and Impacts of a One-Semester CURE for Physical Chemistry. J. Chem. Educ. 2018, 95, 928–938. Schuster, M. L.; Peterson, K. P.; Stoffregen, S. A. Isobutylene Dimerization: A DiscoveryBased Exploration of Mechanism and Regioselectivity by NMR Spectroscopy and Molecular Modeling. J. Chem. Educ. 2018, 95, 1040–1044. Schellhammer, K. S.; Cuniberti, G. Competence-Based, Research-Related Lab Courses for Materials Modeling: The Case of Organic Photovoltaics. J. Chem. Educ. 2017, 94, 90–194. Aradi, B.; Hourahine, B.; Frauenheim, T. DFTB+, a Sparse Matrix-Based Implementation of the DFTB Method. J. Phys. Chem. A 2007, 111, 5678–5684. Case, D. A.; Darden, T. A.; Cheatham, T. E., III; Simmerling, C. L.; Wang, J.; Duke, R. E.; Luo, R.; Walker, W.; Zhang, K. M.; Merz, B.; Roberts, S.; Hayik, A.; Roitberg, G.; Seabra, J.; Swails, A. W.; Götz, I.; Kolossváry, R. C.; Wong, K. F.; Paesani, F.; Vanicek, J.; Wolf, R. M.; Liu, J.; Wu, X.; Brozell, S. R.; Steinbrecher, T.; Gohlke, H.; Cai, X.; Ye, J.; Wang, M.-J.; Hsieh, G.; Cui, D. R.; Roe, D. H.; Mathews, M. G.; Seetin, R.; Salomon-Ferrer, C.; Sagui, Q.; Babin, V.; Luchko, T.; Gusarov, S.; Kovalenko, A.; Kollman, P. A. AMBER 12; University of California: San Francisco, 2012. Kinnaman, L. J.; Roller, R. M.; Miller, C. S. Comparing Classical Water Models Using Molecular Dynamics To Find Bulk Properties. J. Chem. Educ. 2018, 95, 888–894. Swee, C.; Akinfenwa, O.; Foley, J. J. Facilitating Students’ Interaction with Real Gas Properties Using a Discovery-Based Approach and Molecular Dynamics Simulations. J. Chem. Educ. 2018, 95, 384–392. YAeHMOP. http://yaehmop.sourceforge.net (accessed September 2018). Avogadro. https://avogadro.cc/ (accessed September 2018). Avery, P.; Ludowieg, H.; Autschbach, J.; Zurek, E. Extended Hückel Calculations on Solids Using the Avogadro Molecular Editor and Visualizer. J. Chem. Educ. 2018, 95, 331–337. Lozano-Parada, J. H.; Burnham, H.; Machuca Martinez, F. Pedagogical Approach to the Modeling and Simulation of Oscillating Chemical Systems with Modern Software: The Brusselator Model. J. Chem. Educ. 2018, 95, 758–766.
241
21. Brom, J. M. Quantum Interference: How To Measure the Wavelength of a Particle. J. Chem. Educ. 2017, 94, 577–581. 22. Famularo, N.; Kholod, Y.; Kosenkov, D. Integrating Chemistry Laboratory Instrumentation into the Industrial Internet: Building, Programming, and Experimenting with an Automatic Titrator. J. Chem. Educ. 2016, 93, 175–181. 23. The Arduino name is a trademark of Arduino LLC. https://www.arduino.cc/ (accessed September 2018). 24. Wang, C.; Carter-Cooper, B.; Du, Y.; Zhou, J.; Saeed, M. A.; Liu, J.; Guo, M.; Roembke, B.; Mikek, C.; Lewis, E. A.; Lapidus, R. G.; Sintim, H. O. Alkyne-Substituted Diminazene as GQuadruplex Binders with Anticancer Activities. Eur. J. Med. Chem. 2016, 118, 266–275. 25. Neidle, S. Human Telomeric G-Quadruplex: The Current Status of Telomeric G-Quadruplexes as Therapeutic Targets in Human Cancer. FEBS J. 2010, 277, 1118–1125. 26. Prato, G.; Silvent, S.; Saka, S.; Lamberto, M.; Kosenkov, D. Thermodynamics of Binding of Di- and Tetrasubstituted Naphthalene Diimide Ligands to DNA G-Quadruplex. J. Phys. Chem. B 2015, 119, 3335–3347. 27. Kholod, Y.; Hoag, E.; Muratore, K.; Kosenkov, D. Computer-Aided Drug Discovery: Molecular Docking of Diminazene Ligands to DNA Minor Groove. J. Chem. Educ. 2018, 95, 882–887. 28. Reichardt, C. Solvatochromic Dyes as Solvent Polarity Indicators. Chem. Rev. 1994, 94, 2319–2358. 29. Kosenkov, D.; Slipchenko, L. V. Solvent Effects on the Electronic Transitions in p-Nitroaniline: QM/EFP Study. J. Phys. Chem. A 2010, 115 (4), 392–401. 30. Mourot, A.; Fehrentz, T.; Le Feuvre, Y.; Smith, C. M.; Herold, C.; Dalkara, D.; Nagy, F.; Trauner, D.; Kramer, R. H. Rapid Optical Control of Nociception with an Ion-Channel Photoswitch. Nat. Methods. 2012, 9, 396–402. 31. Kronos. Educational Flash Photolysis Spectrometer. User Manual Version 3.3 Ultrafast Systems LLC: Sarasota, FL, 2006−2012. 32. Kosenkov, D.; Shaw, J.; Zuczek, J.; Kholod, Y. Transient Absorption Spectroscopy of cis-trans Isomerization of N,N-Dimethyl-4,4′-Azodianiline with 3D-Printed Temperature Controlled Sample Holder. J. Chem. Educ. 2016, 93, 1299–1304. 33. Michaelis, L.; Menten, M. Die kinetik der invertinwirkung. Biochemistry Zeitung 1913, 49, 333–369. 34. Olsen, R. J.; Olsen, J A.; Giles, G. A. An Enzyme Kinetics Experiment for the Undergraduate Organic Chemistry Laboratory. J. Chem. Educ. 2010, 87, 956–957. 35. Michaelis-Menten Kinetics and Briggs-Haldane Kinetics. https://depts.washington.edu/ wmatkins/kinetics/michaelis-menten.html (accessed September 2018). 36. The R Project for Statistical Computing. https://www.r-project.org/ (accessed September 2018). 37. Atkins, P.; De Paula, J. Physical Chemistry, 9th ed.; W. H. Freeman and Co.: New York, 2010; pp 395−401. 38. Frisch, M. J.; Trucks, G. W.; Schlegel, H. B.; Scuseria, G. E.; Robb, M. A.; Cheeseman, J. R.; Scalmani, G.; Barone, V.; Mennucci, B.; Petersson, G. A.; Nakatsuji, H.; Caricato, M.; Li, X.; Hratchian, H. P.; Izmaylov, A. F.; Bloino, J.; Zheng, G.; Sonnenberg, J. L.; Hada, M.; Ehara, M.; Toyota, K.; Fukuda, R.; Hasegawa, J.; Ishida, M.; Nakajima, T.; Honda, Y.; 242
Kitao, O.; Nakai, H.; Vreven, T.; Montgomery, J. A., Jr.; Peralta, J. E.; Ogliaro, F.; Bearpark, M.; Heyd, J. J.; Brothers, E.; Kudin, K. N.; Staroverov, V. N.; Kobayashi, R.; Normand, J.; Raghavachari, K.; Rendell, A.; Burant, J. C.; Iyengar, S. S.; Tomasi, J.; Cossi, M.; Rega, N.; Millam, J. M.; Klene, M.; Knox, J. E.; Cross, J. B.; Bakken, V.; Adamo, C.; Jaramillo, J.; Gomperts, R.; Stratmann, R. E.; Yazyev, O.; Austin, A. J.; Cammi, R.; Pomelli, C.; Ochterski, J. W.; Martin, R. L.; Morokuma, K.; Zakrzewski, V. G.; Voth, G. A.; Salvador, P.; Dannenberg, J. J.; Dapprich, S.; Daniels, A. D.; Farkas, Ö.; Foresman, J. B.; Ortiz, J. V.; Cioslowski, J.; Fox, D. J. Gaussian, Inc. Gaussian 09, Revision D.01; Wallingford, CT, 2009. 39. Hehre, W.; Ohlinger, S. Spartan Tutorial and User’s Guide; Wavefunction, Inc.: Irvine, CA, 2010. 40. Max, J.-J.; Chapados, C. Isotope effects in liquid water by infrared spectroscopy. III. H2O and 41. 42.
43. 44. 45. 46. 47. 48. 49.
D2O spectra from 6000 to 0 cm-1. J. Chem. Phys. 2009, 131, 184505. Hertwig, R. H.; Koch, W. On the Parameterization of the Local Correlation Functional: What is Becke-3-LYP? Chem. Phys. Lett. 1997, 268, 345–351. Hehre, W. J.; Ditchfield, R.; Pople, J. A. Self-Consistent Molecular Orbital Methods. XII. Further Extensions of Gaussian-Type Basis Sets for Use in Molecular Orbital Studies of Organic Molecules. Chem. Phys. 1972, 56, 2257–2261. Rassolov, V. A.; Ratner, M. A.; Pople, J. A.; Redfern, P. C.; Curtiss, L. A. 6-31G* Basis Set for Third-Row Atoms. J. Comput. Chem. 2001, 22, 976–984. Hariharan, P. C.; Pople, J. A. The Influence of Polarization Functions on Molecular Orbital Hydrogenation Energies. Theoret. Chim. Acta 1973, 28, 213–222. Garland, C. W.; Nibler, J. B.; Shoemaker, D. P. Experiments in Physical Chemistry, 8th ed.; McGraw-Hill: New York, 2009. Foresman, J. B.; Frisch, Æ. Exploring chemistry with electronic structure methods, 2nd ed.; Gaussian: Pittsburgh, PA, 1996. Eyring, H.; Walter, J.; Kimball, G. Quantum Chemistry; J. Wiley and Sonds Inc.: New York, 1944. Best and Innovative Practices in Higher Education Assessment; Hanover Research: Arlington, VA, 2013. Kholod, Y.; DeFilippo, M.; Reed, B.; Valdez, D.; Gillan, G.; Kosenkov, D. Excitation Energy Transfer Pathways in Light-Harvesting Proteins: Modeling with PyFREC. J. Comp. Chem. 2018, 39, 438–449.
243
