Introducing NMR Spectroscopy Using Guided Inquiry and Partial

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Introducing NMR Spectroscopy Using Guided Inquiry and Partial Structure Templating Erin M. Kolonko*,† and Kristopher J. Kolonko‡ †

Department of Chemistry and Biochemistry, Siena College, Loudonville, New York 12211, United States Stewart’s Advanced Instrumentation and Technology Center, Siena College, Loudonville, New York 12211, United States

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S Supporting Information *

ABSTRACT: Strategies for teaching NMR spectral interpretation in the undergraduate organic chemistry curriculum are often faculty-centered and can lead to student reliance on rote memorization and “guess and check” methods rather than critical-thinking skills for structure determination. This article describes a student-focused methodology for the introduction of NMR spectral interpretation. Guided-inquiry tutorials using NMR prediction tools were developed to enable students to investigate the trends and concepts in 13C and 1H NMR spectral interpretation, with an emphasis on making connections between data and foundational chemical knowledge. A systematic approach to solving unknown structure problems is presented, providing a framework for students to organize spectral data and to build molecules from partial structures. The success of this NMR spectroscopy teaching strategy, which can be adapted for either laboratory or lecture environments, was demonstrated both in positive student survey responses as well as in quantitative data showing a significant improvement in exam question scores. KEYWORDS: Second-Year Undergraduate, Organic Chemistry, Laboratory Instruction, Computer-Based Learning, Inquiry-Based/Discovery Learning, NMR Spectroscopy

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results through the investigative process.2,3 Guided-inquirybased NMR experiments have been presented to illustrate simple spectral trends for the general chemistry curriculum4−6 and to predict aromatic shift tables in the organic chemistry curriculum;7 however, a comprehensive introduction to NMR spectroscopy via a guided-inquiry method has not been reported. Regardless of the teaching strategy, NMR theory and 1H NMR spectroscopy are often introduced at a midpoint in the semester, followed by 13C NMR spectroscopy, a format taken by most commonly used organic textbooks.8−16 A notable exception to this trend is the text by Clayden, Greeves, and Warren, which introduces 13C NMR spectroscopy in Chapter 3 and 1H NMR spectroscopy at the more traditional midpoint of the text.17 Similarly, the strategy of introducing 13C NMR spectroscopy prior to 1H NMR spectroscopy has been gaining traction in the literature because the carbon spectra are often much simpler for students to interpret and are less intimidating.6,18−20 After developing a background understanding of NMR spectroscopy and spectral analysis, students are expected to apply these concepts to elucidate an unknown structure given

INTRODUCTION Interpretation of NMR spectroscopy data is a key skill taught in undergraduate organic chemistry courses. In addition to being one of the most important tools in organic chemistry for structure determination, it is a unique platform for the development of critical-thinking skills for undergraduate students. NMR spectral interpretation is a complex process in which students must grapple with different aspects of the data, including the number of signals, chemical shift, integration values, peak shape, and so on. They then must organize and digest this information to ultimately link the spectral data to chemical structure. Gaining experience with this overall process is important for chemistry majors but also provides students whose future is outside of the chemical field with experience evaluating a moderately complex data set to reach an evidence-based conclusion. NMR spectroscopy is frequently taught by a direct instruction method, or faculty-centered approach, in which information is conveyed directly to students via readings in the text or lectures followed by worked examples. In contrast, guided-inquiry-based experiments in the chemistry laboratory encourage students to apply their foundational knowledge and then to use critical-thinking skills, which increases student engagement while still being readily implemented.1 In guidedinquiry-based methods, students are given a question and procedure for investigation but must determine the known © XXXX American Chemical Society and Division of Chemical Education, Inc.

Received: August 13, 2018 Revised: March 27, 2019

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DOI: 10.1021/acs.jchemed.8b00660 J. Chem. Educ. XXXX, XXX, XXX−XXX

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spectral data. Experienced chemists generally use a systematic approach for the interpretation of NMR spectra; however, a systematic process is often not explicitly taught in introductory courses. In a study of spectroscopy problem-solving ability, Cartrette and Bodner21 found that key approaches of participants that were successful versus unsuccessful in determining unknown structures from spectral data included (1) a tendency to approach the problems systematically and (2) drawing fragments of the structures at intermediate stages of the process. This approach contrasts with the initial tendency of students to guess at a whole structure with one or two pieces of information and then try to match the rest of the data, or the “guess and check” approach, which often leads to a waste of time, missed pieces of information, or failure to reach a logical structure.21 With this background in mind, a new methodology was developed to restructure how NMR spectroscopy was introduced and taught in a first-semester organic chemistry course. Guided-inquiry-based activities were developed to illustrate the main principles in 1D 13C and 1H NMR spectral interpretation: number of signals, chemical shift, signal intensity, and multiplicity. Additionally, a systematic process was designed for guiding students in spectral analysis using a defined template for signal analysis, followed by proposing “partial structures” for each signal. Through this two-fold approach for introducing NMR spectroscopy, students were able to build a foundation to be more successful in structure determination and to expand their critical-thinking skills.

laboratory component of the first semester in a two-semester organic chemistry sequence. Introducing NMR spectroscopy early in the organic chemistry sequence reinforces general chemistry concepts, nomenclature, and organic structure and provides a building point for the inclusion of spectroscopic concepts throughout the lecture.23−26 Computer-based NMR prediction tools (Mnova, Mestrelab Research) were employed to enable students to visualize spectra without the cost of instrument time, reagents, or solvents. Spectra are clear of additional peaks such as TMS, residual NMR solvents, and so on, which is beneficial to students when they are learning the basics of NMR spectral interpretation. A computer-based format allows this methodology to be used in either a laboratory or a lecture setting. Students can then use the same software to process and analyze the NMR spectra throughout the two-semester sequence and any subsequent advanced courses. Once students had investigated the trends in 13C and 1H NMR spectroscopy, they were taught a systematic method for structure derivation from 1H NMR spectra based on the generation of a partial structure table. This templated and systematic approach to solving NMR spectroscopy problems helped students to organize the complex information into smaller, manageable pieces. Using this information and other data, such as molecular formula, IR spectra, mass spectra, or 13 C NMR spectra, students were able to effectively build the structure from the data.

STRATEGY AND LEARNING OBJECTIVES Prior to the adoption of this methodology, spectroscopic techniques were taught via lectures during the laboratory period focusing on instrumentation and spectral interpretation, followed by assigning students graded worksheets with practice problems. This direct instruction method had several drawbacks, including lack of student engagement, variation in instruction from section to section, presentation of an overwhelming amount of information for students without time to process concepts, a lack of emphasis on critical thinking, and indirect correlation with known chemical concepts, such as structure and bonding. An analysis expressed by various instructors based on graded work and student conversations was that academically stronger students were able to work through the information and devise their own strategies for NMR spectral interpretation; however, moderate to weaker students relied on the use of memorized spectral facts and often employed a “guess and check” method for determining structures, that is, drawing many possible structures for a given molecular formula and checking them against the spectrum. This brute force approach has been shown to be highly inefficient and often unsuccessful.21 Upperlevel chemistry instructors found that students lacked the ability to interpret basic NMR spectra in subsequent years and disliked NMR spectroscopy in general. To increase overall student understanding of NMR spectroscopy, guided-inquiry-based tutorials were used to introduce the concepts and trends of 13C and 1H NMR spectroscopic interpretation. The scaffolded nature of the tutorials enables students to discover the concepts and correlations with known information while making the learning process accessible and manageable.22 Students were also encouraged to collaboratively work through the tutorials with their peers in the laboratory section. The tutorials were presented in the

METHODOLOGY The NMR spectroscopy teaching methods described here were used in the Organic Chemistry I laboratory curriculum for 2 subsequent years, involving a total of 169 students majoring in biology (68%), biochemistry (14%), chemistry (9%), or other sciences (9%). Structural characterization techniques are taught exclusively in the laboratory portion of the curriculum, with infrared spectroscopy, mass spectrometry, and 13C and 1H NMR spectroscopy introduced during the first semester of the two-semester sequence. Aromatic and 2D NMR spectroscopic interpretations are introduced during the second-semester laboratory curriculum. Few of the students had any experience with NMR spectral interpretation prior to the course.

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Mnova and NMRPredict

The Mestrelab Research Mnova software (formerly Mestre-C) was used as a software platform for the NMR tutorials.27 A perpetual academic site license includes 1D and 2D NMR processing, NMRPredict, and mass spectrum plugins for a onetime cost with the ability to purchase support and update coverage. The program can be used on Mac, PC, and Linux systems and is vendor-independent, supporting major file formats. All students enrolled in the laboratory course are strongly encouraged to install the software on their laptop computers, and departmental computers with the software are also available for student use. Provided with the basic Mnova suite is an NMRPredict plugin, which is the tool used to design the guided-inquiry tutorials for NMR spectral interpretation that allowed each student to draw structures with the software drawing tools and predict 1D NMR spectra. Tutorials for both 13C and 1H NMR spectroscopy were developed using the default settings: CDCl3, 500 MHz/125 MHz, and Mnova Best predictor. If desired, additional settings can be fine-tuned for spectrum B

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Figure 1. Predicted 13C NMR spectra for (A) pentane, (B) 2-methylbutane, and (C) 2,2-dimethylpropane. The spectra were used to illustrate trends in the number of signals and signal intensity. The red prediction labels correspond to the red-labeled carbon atoms in the structures.

• the effect of the number of symmetric carbons and/or the number of attached hydrogen atoms on the intensity of the signal, • the chemical shift trend for the hybridization of carbon atoms, and • the chemical shift trend for the attachment of polar groups to carbon atoms.

prediction including deuterated solvent, resonance frequency, and prediction algorithm. Alternatively, the tutorials could be adapted to use several web-based prediction tools that are currently available at no cost: nmrdb.org,28 SPINUS-WEB,29 or ChemDoodle Web Components.30 These resources can be used in a similar manner as NMRPredict for 13C and 1H NMR spectrum predictions; however, they do not allow for raw data processing or for saving or overlaying spectra.

For each concept, students were given IUPAC names in the tutorial for a series of compounds and drew line-bond structures on separate pages in the software. The process of drawing structures into the software from given IUPAC names reinforced both naming and line-bond drawing skills. Students then predicted the 13C NMR spectra for each series of compounds to illuminate specific trends and answered leading questions to help them to understand why the trends were observed. For example, the effect of symmetry in the molecule to the number of NMR signals was determined by predicting the spectra for the constitutional isomers pentane, 2methylbutane, and 2,2-dimethylpropane (Figure 1). The predicted spectrum indicates the carbon numbers associated with each peak, which enabled students to derive that (1) the number of nonequivalent carbon atoms is equal to the number of signals in the spectrum and (2) the signal intensity

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C NMR Tutorial

The introduction of 13C NMR spectroscopy and subsequently 1 H NMR spectroscopy was incorporated into a 4-week lab sequence involving natural product isolation. During the first week, students were given a 25 min introductory lecture on the principles of NMR spectroscopy, including nuclear spin and spin states, chemical environment and shielding, chemical shift units, and NMR-active isotopes. With instructor guidance, students worked individually on the 13C NMR tutorial handout during the downtime of a reflux isolation experiment for ∼1 h and then finished the tutorial outside of the laboratory period. The key goals of the 13C NMR tutorial were for students to derive from trends in predicted 13C NMR spectra: • the effect of symmetry on the number of signals, C

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Figure 2. Superimposed representation of predicted 1H NMR spectra. (A) Predicted spectra of ethane, trimethylamine, dimethyl ether, 1,2dichloroethane, and ethanedial. (B) Predicted spectra of di-tert-butyl ether, acetone, dimethyl ether, and ethanedial. Individual spectra were predicted in Mnova, and the spectra were superimposed in the software to illustrate the trends. Structures in matching colors were added using ChemDraw to emphasize the trend and do not appear in the Mnova superimposed spectra.

NMR spectra from the guided-inquiry activity; no introductory lecture was provided. With instructor guidance, students worked individually on the handout during a 2 h Soxhlet extraction laboratory experiment and then finished the tutorial outside of the laboratory period. Analogous to the 13C NMR tutorial, the key goals of the 1H NMR tutorial were for students to derive from trends in predicted spectra:

correlates to both the number of equivalent carbon atoms and the number of hydrogen atoms attached to the carbon atom. These spectra can be viewed individually or can be overlaid in the software, allowing students to visualize the differences in the spectra. In total, students predicted spectra for four series of compounds, covering the common chemical shift range for 13 C NMR spectra. They then used this information to answer questions relating chemical concepts to the trends in number of signals, signal intensity, and chemical shift and to develop a chart of general 13C NMR shift ranges. During the second week of the sequence after students had completed the tutorial, instructors presented a summary lecture of the trends that were investigated to reinforce the concepts and answer any student questions. Distortionless enhancement by polarization transfer (DEPT) 13C NMR spectral interpretation was presented in this lecture, and students were assigned a worksheet on 13C NMR spectral analysis to practice the concepts.

• the effect of neighboring electronegative atoms on chemical shift, • the effect of carbon hybridization on the chemical shift of hydrogen atoms, • how integration values correspond to the number of symmetric hydrogen atoms, and • simple first-order coupling patterns (doublet, triplet, quartet, etc.) based on the number of neighboring hydrogen atoms. In the tutorial, students predicted spectra from a series of compounds from IUPAC names, filled in a data table, and then answered leading questions to help them to determine the desired trend or concept. For example, students predicted the 1 H NMR spectra for ethane, trimethylamine, dimethyl ether, 1,2-dichloroethane, and ethanedial, which all have a single 1H NMR signal, to determine the effect on the 1H NMR chemical shift due to the presence of electronegative atoms attached directly to the same carbon (Figure 2A). Subsequently, the chemical shifts for the hydrogen atoms in ethanedial and

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H NMR Tutorial

After students had gained a foundation of the concepts of chemical shift and peak intensity from the 13C NMR tutorial, the concepts of integration and multiplicity were introduced using 1H NMR predicted spectra in the second tutorial during the final 2 weeks of the sequence. During the first laboratory session, students received an 1H NMR tutorial worksheet. Students were expected to derive trends and concepts in 1H D

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Figure 3. Predicted 1H NMR spectra for 1,1,2-tribromoethane (A), 1,1-dibromoethane (B), and bromoethane (C). Figures were modified from the Mnova output to illustrate the key concept of multiplicity. Atom colors have been adjusted from the default settings, the scales have been removed, and the signals have been aligned to highlight the differences in multiplicity rather than the differences in chemical shift.

spectra. With this strategy, students first organize 1H NMR spectral data in a template and then apply the extracted, tabulated data to generate partial structures that directly relate to the data (Figure 4). Finally, any additional information (e.g., molecular formula and IR, mass, or 13C NMR data) is used with these pieces to build a complete structure. This process largely alleviated the tendency of students to try to “guess and check” structures and reinforced the link between the spectral information and chemical structure. The 1H NMR spectrum for C8H10 (ethylbenzene) is used to illustrate this process (Figure 4). The signal at 1.25 ppm has an integration value of three, indicating three equivalent hydrogen atoms, and appears as a simple triplet (1:2:1) pattern. To determine a partial structure, the integration value is interpreted first, which indicates a CH3, or methyl unit.31 This segment of the partial structure is circled to show that it represents the hydrogen atoms from which the signal arises. Using the n+1 rule and observed multiplicity, the structure of the adjacent carbon atom(s) is proposed. On the basis of the observed triplet pattern, the adjacent carbon atom(s) must contain bonds to two hydrogen atoms. For a methyl unit, the only possibility is for a CH2 unit as a neighbor. This portion of the structure is underlined to indicate that the information is derived from the multiplicity of the signal. Finally, the chemical shift is used to infer if any neighboring groups are influencing the hydrogen atoms (e.g., O, N, halogens, carbonyl, etc.). Because the chemical shift (1.25 ppm) falls in the range of primary alkyl groups, no additional partial structure information can be derived from this signal. The signal at 2.65 ppm integrates for two hydrogen atoms and appears as a quartet (1:3:3:1) pattern. From integration

dimethyl ether were compared with those of acetone and ditert-butyl ether, respectively (Figure 2B), to illustrate that the number of bonds between the hydrogen atom and the electronegative atom affects the magnitude of the downfield shift. To investigate the concept of multiplicity, students were instructed to predict 1H NMR spectra of 1,1,2-tribromoethane, 1,1-dibromoethane, and bromoethane (Figure 3) and to note the number of signals, chemical shifts, number of hydrogen atoms on the neighboring carbon, and signal multiplicity. From this analysis, students were able to derive that the multiplicity of the signal corresponded to the number of neighboring hydrogen atoms plus one. To reinforce previously illustrated chemical shift trends, the change in chemical shift of the hydrogen atoms due to the addition of bromine can be highlighted by the instructor or added as a student question to the tutorial. During the second week of the sequence, instructors presented a lecture (∼45 min) overview to (1) summarize the key concepts from the tutorial, (2) explain how multiplicity is derived, and (3) discuss more complicated multiplet patterns (e.g., doublet of doublets). The instructors and students then worked several problems together as a class using a systematic partial structure approach, discussed below. After building a foundation of knowledge from the tutorial, instructors found that students were able to ask questions and were much more engaged in the summary lecture. Partial Structure Analysis

The final goal of this methodology was for students to utilize a systematic approach to determine unknown structures from E

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are more fully analyzed using multiplicity and coupling constants after aromaticity is covered in the lecture course. The final step in deciphering the structure is to combine the partial structures such that the matching parts overlap. To overlap partial structures, atoms that are circled in one partial structure should be underlined in the other and vice versa (Figure 4). Students must use the formula, additional data, and critical-thinking skills to connect the partial structures that do not overlap explicitly. This systematic approach is further illustrated by the worked examples in the Supporting Information. If students analyze all of the components of the data (integration, multiplicity, and chemical shift) to derive partial structures, then it is rare that they are unable to determine the correct structure when given structures of a difficulty level in line with introductory organic chemistry courses. The most common pitfall for students in structure elucidation is neglecting to incorporate chemical shift information into the partial structures, which can lead to incorrect linkages in the final structure (e.g., linking an ester with the carbonyl and oxygen in reverse positions). On occasion, there are spectra that could rationally be interpreted as two possible organic structures based on student understanding (e.g., 2-phenylethyl acetate and 4-phenoxybutan-2one). Compounds such as these can be used to illustrate that the identification of a unique structure often requires the evaluation of characterization data from multiple techniques. Instructors can choose either to accept any reasonable structures, given the students’ knowledge base, or to provide additional data (e.g., mp, mass spectrum, or IR and 13C NMR spectra), depending on what has been covered in the course.

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ASSESSMENT The effectiveness of this strategy for teaching NMR spectroscopy via guided-inquiry tutorials and partial structure determination was assessed by (1) an end-of-semester student survey regarding the laboratory curriculum and (2) a comparison of student scores on structure elucidation questions of exams. At the end of the 2016 and 2017 fall semesters, students responded to an online, anonymous survey regarding the laboratory curriculum with response rates of 94 and 92%, respectively, to give 157 total responses. On the basis of the survey, over four-fifths of students responded that the 1H and 13 C NMR tutorials and the use of the Mnova software to work up their own spectra provided “great” help in their learning in the course (Figure 5). Eighty-five percent of students also responded that they had made great (52%) or good (33%) gain in interpreting spectroscopic data to elucidate organic structures. The effectiveness of the methodology was also assessed by comparing exam question scores that focused on NMR spectroscopy in years prior to and after the introduction of the MNova tutorials and partial structure strategy. Questions dealing with structure elucidation by interpretation of 1H and 13 C NMR data for 3 years before (2013−2015) and for 2 years after (2016−2017) the introduction of the new method were compared (n = 316 and 168 students, respectively). The data demonstrate that the median scores (67 ± 1% before and 79 ± 1% after) increased significantly after the introduction of this approach to teaching spectroscopy. An analysis of the distribution of scores (Figure 6) indicates that the strategies discussed herein enabled most students to be largely successful

Figure 4. Partial structure analysis method can be summarized in three steps: (1) initial interpretation of each signal (integration, multiplicity, and chemical shift), (2) tabulation of the data and proposal of a partial structure for each signal, and (3) connection of the partial structures to form a full organic structure.

information, assignment of a CH2 unit as the source of the signals (circled) is warranted. The multiplicity (quartet) indicates that the CH2 has three hydrogen neighbors on adjacent carbon atoms. Alone, this information can be interpreted as either (1) a CH3 on one side and quaternary carbon (or other atom without attached hydrogen atoms) on the other side or (2) two adjacent carbon atoms containing one and two hydrogen atoms, respectively. Finally, the downfield chemical shift indicates that a deshielding group (X), likely a carbonyl or aromatic ring due to the chemical shift, is attached. On the basis of this information and the presence of only one other signal in the alkyl group region, the first partial structure option above can be rationally chosen. The final signal at 7.26 ppm is a multiplet in the aromatic region that has an integration value of five. During the first semester course, students are taught to interpret aromatic signals as one multiplet in which the integration denotes the number of hydrogen atoms attached to the ring system. Thus, by using the formula, degrees of unsaturation, and integration, students are able to interpret the signal as a monosubstituted benzene ring. In the second semester course, aromatic signals F

DOI: 10.1021/acs.jchemed.8b00660 J. Chem. Educ. XXXX, XXX, XXX−XXX

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surveys as well as a comparison of exam question scores indicated that the strategy is an effective means for the introduction of NMR spectroscopy concepts and structure determination to the student population at large.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available on the ACS Publications website at DOI: 10.1021/acs.jchemed.8b00660. 13

C NMR tutorial (PDF, DOCX) C NMR tutorial key (PDF, DOCX) 1 H NMR tutorial (PDF, DOCX) 1 H NMR tutorial key (PDF, DOCX) Structure determination example problems (PDF, DOCX) Worked example problems (PDF) Step-by-step guide to creating a partial structure table (PDF) Representative exam problems (PDF) 13

Figure 5. Survey response data for NMR-related questions.

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(57% had a score of 75% or greater) at interpreting NMR data and predicting an unknown structure.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected].

CONCLUSIONS A novel methodology for introducing undergraduate students to NMR spectroscopy and interpretation was developed and implemented. This strategy involved the use of guided-inquiry tutorials utilizing computer-based prediction tools in Mnova NMR processing software to enable students to predict series of spectra designed to illuminate NMR spectroscopy trends and concepts. Students investigated both 13C and 1H 1D NMR spectra, with the less complex 13C NMR spectra introduced first. After the introduction of 13C and 1H NMR spectroscopy, students were taught a systematic approach for the determination of an unknown organic structure from spectral data. This approach provides a template for students to organize the 1H NMR data and determine partial structures for each signal. The methodology guides students to more efficiently and effectively determine an unknown structure using a systematic process. Assessment through student

ORCID

Erin M. Kolonko: 0000-0002-9054-6642 Kristopher J. Kolonko: 0000-0003-4001-7133 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank Thomas Hughes, Hilary Hofstein, Hans Reich, and Daniel Wherritt for helpful conversations, Pablo Monje for technical assistance with the prediction tools, and the Organic Chemistry laboratory faculty and instructors at Siena College who have taught with the curriculum and provided valuable feedback. We also thank Information Technology Services at Siena College for funding to purchase and maintain the Mnova NMR processing software.

Figure 6. Exam data before (orange) and after (blue) implementation of the methodology. The distribution of student NMR spectroscopy-based question scores is shown along with the median values (vertical lines). The median scores on NMR-based structure identification problems increased significantly after the introduction of the tutorials and partial structure strategies (nbefore = 316, nafter = 168; standard error (SE = SD/√n) is reported). G

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(23) Reeves, P. C.; Chaney, C. P. A Strategy for Incorporating 13C NMR into the Organic Chemistry Lecture and Laboratory Courses. J. Chem. Educ. 1998, 75 (8), 1006. (24) Amburgey-Peters, J. C.; Bonvallet, P. A. Spectroscopy First” for Interweaving and Scaffolded Learning in Organic Chemistry. ACS Symp. Ser. 2016, 1221, 41−59. (25) Bonvallet, P. A.; Amburgey-Peters, J. C. Data versus Dogma: Introducing NMR Early in Organic Chemistry to Reinforce Key Concepts. ACS Symp. Ser. 2013, 1128, 45−55. (26) Chapman, O. L.; Russell, A. A. Structure, Chirality, and FTNMR in Sophomore Organic Chemistry. A Modern Approach to Teaching. J. Chem. Educ. 1992, 69 (10), 779. (27) Cobas, J. C.; Sardina, F. J. Nuclear Magnetic Resonance Data Processing. MestRe-C: A Software Package for Desktop Computers. Concepts Magn. Reson. 2003, 19A, 80−96. (28) Steinbeck, C.; Krause, S.; Kuhn, S. NMRShiftDB Constructing a Free Chemical Information System with Open-Source Components. J. Chem. Inf. Comput. Sci. 2003, 43 (6), 1733−1739. (29) Gasteiger, J.; Aires de Sousa, J.; Hemmer, M. C.; Binev, Y.; Corvo, M. SPINUS - Structure-Based Predictions in Nuclear Magnetic Resonance Spectroscopy. http://www2.chemie.unierlangen.de/services/spinus/ (accessed March 2019). (30) IChemLabs. Simulate NMR and MS | ChemDoodle Web Components. https://web.chemdoodle.com/demos/simulate-nmrand-ms/ (accessed March 2019). (31) Another possible interpretation for this signal could be three magnetically equivalent CH groups. We ask students to consider all possibilities; however, they are encouraged to start with the simplest explanation first, unless it becomes inconsistent with other data (e.g., the partial structure is not consistent with others or suggests a different number of carbon atoms than the known molecular formula).

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