Emerging Areas in Undergraduate Analytical Chemistry Education: Microfluidics, Microcontrollers, and Chemometrics.

Analytical chemistry is a fast-paced field with frequent introduction of new techniques via research labs; however, incorporation of new techniques into academic curricula lags their adoption in research and industry. This review describes the recent educational literature on microfluidics, microcontrollers, and chemometrics in the undergraduate analytical chemistry curriculum. Each section highlights opportunities for nonexpert faculty to get started with these techniques and more advanced implementations suitable for experienced practitioners. While the addition of new topics to any curriculum brings some opportunity costs, student engagement with cutting edge techniques brings many benefits, including enhanced preparation for graduate school and professional careers and development of transferable skills, such as coding. Formal assessment of student outcomes is encouraged to promote broader adoption of these techniques.

Comparison of GC stationary phases for the separation of fatty acid methyl esters in biodiesel fuels.

The fatty acid methyl ester (FAME) content of biodiesel fuels has traditionally been determined using gas chromatography with a polar stationary phase. In this study, a direct comparison of the separation of FAMEs present in various biodiesel samples on three polar stationary phases and one moderately polar stationary phase (with comparable column dimensions) was performed. Retention on each column was based on solubility in and polarity of the phase. Quantitative metrics describing the resolution of important FAME pairs indicate high resolution on all polar columns, yet the best resolution, particularly of geometric isomers, is achieved on the cyanopropyl column. In addition, the separation of four C18 monounsaturated isomers was optimized and the elution order determined on each column. FAME composition of various biodiesel fuel types was determined on each column to illustrate (1) chemical differences in biodiesels produced from different feedstocks and (2) chemical similarities in biodiesels of the same feedstock type produced in different locations and harvest seasons.

Analysis of biodiesel-diesel blends: Does ultrafast gas chromatography provide for similar separation in a fraction of the time?

Ultrafast gas chromatography (UFGC) using a moderately polar column was compared to traditional gas chromatography (GC) for evaluation of biodiesel-diesel blended fuels. Several biodiesel feedstocks (soybean, tallow, canola, palm, camelina) and concentrations (1-20%) were evaluated, with specific attention to the separation of fatty acid methyl esters (FAMEs) from the biodiesel component. UFGC is compared to traditional GC using a similar column chemistry. Principal component analysis (PCA) is performed to identify clustering based on feedstock and concentration. UFGC proves an effective and fast technique, comparable to traditional GC, for the analysis of biodiesel-diesel blended fuels.

Thermodynamic and kinetic characterization of a bridged-ethylene hybrid C18 stationary phase.

In this study, a series of polycyclic aromatic hydrocarbons (PAHs) and nitrogen-containing polycyclic aromatic hydrocarbons (NPAHs) is separated on a hybrid stationary phase using methanol and acetonitrile mobile phases. Temperature is varied from 283 to 313 K in order to determine thermodynamic and kinetic parameters of the separation. Thermodynamic behavior is characterized by the retention factor and associated changes in molar enthalpy, whereas kinetic behavior is characterized by the rate constants and associated activation energies. In this study, the retention factors for the NPAHs are smaller than those for the parent PAHs in methanol, while they are more similar to the parent PAHs in acetonitrile. The changes in molar enthalpy are very similar for all solutes, yet are more negative in acetonitrile than in methanol. The rate constants for the NPAHs are smaller than those for their parent PAHs in both mobile phases. Moreover, the rate constants in acetonitrile are one to four orders of magnitude smaller than those in methanol. Based on these thermodynamic and kinetic results, the hybrid stationary phase is compared to traditional silica stationary phases. In addition, the relative contributions from the partition and adsorption mechanisms are discussed.

Chemometric analysis of diesel fuel for forensic and environmental applications.

Diesel fuel samples were analyzed using gas chromatography-mass spectrometry (GC-MS) and chemometric procedures to associate and discriminate samples for potential use in forensic and environmental applications. Twenty-five diesel samples, representing 13 different brands, were collected from service stations in the Lansing, Michigan area. From the GC-MS data, mass-to-charge ratios were identified to represent aliphatic (m/z 57) and aromatic (m/z 91 and 141) compounds. The total ion chromatogram (TIC) and extracted ion chromatograms (EICs) of the chosen ions were evaluated using Pearson product moment correlation (PPMC) and principal component analysis (PCA). Diesel samples from the same brand showed higher PPMC coefficients, while those from different brands showed lower values. EICs generally provided a wider range of correlation coefficients than the TIC, with correspondingly increased discrimination among samples for EIC m/z 91. PCA grouped the diesel samples into four distinct clusters for the TIC. The first cluster consisted of four samples from the same brand, two clusters contained one diesel sample each of different brands, and the fourth cluster contained the remaining diesel samples. The same trend was observed using each EIC, with an increase in the number of clusters formed for EIC m/z 57 and 91. Both statistical procedures suggest aromatic components (specifically, those with m/z 91) provide the greatest discrimination among diesel samples. This conclusion was supported by identifying the chemical components that contribute the most to the variance. The relative amount of aliphatic versus aromatic components was found to cause the greatest discrimination among samples in the data set.