This month, I am going to talk about a different spectroscopy that rivals Raman in its usefulness. A-TEEM stands for Absorption, Transmission, Excitation/Emission Matrix Fluorescence. While it does not give direct information on the structure of a molecule, its value lies in its ability to detect subtle changes in structure due to changes in conformation of large organic molecules or changes in interactions with other molecules. While this is not a technology with which I have been directly involved, I have been impressed with its potential and thought this column would be a good place to share my thoughts.

The charge transfer complex of drugs is an important means to study the binding mechanism of drugs and receptors. In this study, the ground state structure and CT transition of the complex formed by the reaction of hydrogen peroxide (H2O2) with the five antibiotics (TMP, NOR, OFL, CIP, and SMR) were theoretically investigated by density functional theory (DFT). Excitations at 247.3 and 212.5 nm (f = 0.139 and f = 0.097) for H2O2-TMP, 280.2 and 239.2 nm (f = 0.142 and f = 0.358) for H2O2-NOR, 254.5 and 239.5 nm (f = 0.887 and f = 0.365) for H2O2-CIP, 285.2, 268.2, and 254.5 nm (f = 0.177, 0.832, and 0.226) for H2O2-OFL, and 241.3, 205.5, and 193.6 nm (f = 0.289, 0.318, and 0.169) for H2O2-SMR complex played a key role in the formation of UV-Vis spectra. HOMO and LUMO energies indicated calculation of molecular and atomic properties and shown CT occurrence in molecules. The synthesized CT complexes were characterized using various spectral techniques, including UV-Vis spectroscopy, IR spectroscopy and 1H NMR spectroscopy, and the bonding sites between the H2O2 and antibiotics were identified. These theoretical calculation results provide a scientific basis for experimental validation.

This article features Earle K. Plyler, a pioneering figure in molecular spectroscopy. Plyler’s contributions to infrared spectroscopy, molecular vibrations, and optical materials characterization established new standards for precision in spectroscopic measurement and analysis. Widely recognized for his instrumental innovations and meticulous experimental work, Plyler also mentored future leaders in the field, including Walter Gordy. His enduring impact is honored through the Earle K. Plyler Prize for Molecular Spectroscopy & Dynamics, awarded annually by the American Physical Society since 1977.

In this second installment, we continue examining C.V. Raman’s amazing discovery of the Raman Effect in 1928, a milestone that fundamentally altered the landscape of physics and catalyzed advancements in quantum spectroscopy. Raman’s observation that light scattering could induce wavelength shifts provided empirical support for quantum theory, establishing a foundation for modern spectroscopic methodologies. His pioneering research earned him the 1930 Nobel Prize in Physics and continues to influence disciplines ranging from analytical chemistry to biomedical imaging. This discussion traces Raman’s scientific contributions, emphasizing his transformative role in the revolution of quantum spectroscopy and its contemporary applications. Across this two-part series, we have explored Raman’s quest to characterize what he described as “a new kind of radiation” and its profound impact on scientific progress.

This month’s column investigates the elemental composition of electrolytes in lithium-ion batteries (LIBs) using inductively coupled plasma–mass spectrometry (ICP-MS). In addition to offering quantitative results for the target elements, the technique also provided valuable insight into the semiquantitative analysis of up to 78 elements in the samples. To complete the study, method detection limits and long-term stability in LIB electrolyte matrices were also evaluated.

Heavy metal poisoning via food is a leading cause of kidney disease and cancer in many parts of the world. Pollution of staple food products has led to serious health effects across the globe. This research investigates the application of laser-induced breakdown spectroscopy (LIBS) and machine learning (ML) for detecting elemental composition of food, using rice as an example. Six varieties of rice samples were measured using LIBS. The resulting spectral data were analyzed, and elemental assignments were made based on characteristic wavelengths. The study leveraged the inherent capabilities of LIBS to provide rapid and non-destructive elemental analysis of the samples. Principal component analysis (PCA) was performed to extract meaningful information from the complex LIBS spectral dataset to obtain a holistic understanding of the variations and similarities among the rice samples. A back propagation artificial neural network (BP-ANN) was trained using the PCA scores, reaching an overall classification accuracy of 88.68% on test data. The correlation between specific wavelengths and elemental content was established, providing an understanding on the elemental variability in different rice varieties.

Classification of flowers is required to promote their appreciation, value, and application. In this study, laser-induced breakdown spectroscopy (LIBS) was applied in conjunction with principal component analysis (PCA) to identify and classify flower species. Three flowers of similar color or shape were selected as the experimental samples. Laser pulses were directed at the sample surface to form plasma, from which light was collected, and the spectra were analyzed to identify the surface material. The spectral data of 240 groups of experimental samples were obtained using LIBS. Spectral data were pretreated, and the 28 characteristic spectral intensity ratios of six characteristic elements (Na, Mg, Al, K, Si, Ca) of flower species after normalization were chosen to constitute a matrix (240×28) of spectral data. The data were compressed, after which their dimensionality was reduced to reduce the number of output variables of the model for PCA from 28 to 3. Finally, cluster analysis of the flowers was performed, and a standard sample database of flowers was established. Subsequently, a template matching model (TMM) was used as a model for training. The accuracy of the entire TMM model was 97%.

It has occurred to me that, in the 10+ years I have been writing about molecular vibrations, I have never introduced my readers to its basic theory! I will rectify that now. Some of this is new material, and some will be review. Either way, it is important that all this material be covered in one place.

Microplastics (MPs) and nanoplastics (NPs) are emerging contaminants requiring robust analytical techniques for identification and quantification in diverse environmental and biological matrices. This review highlights various spectroscopy methods, such as Raman, FT-IR, NIR, ICP-MS, Fluorescence, X-ray, and NMR detailing their methodologies, sample handling, and applications for characterizing MPs and NPs.