Luis Velarde

Doubly resonant infrared-visible sum frequency generation spectra of diverse origins are analyzed using a spectral overlap function determined from in situ measurements. New and previously published datasets are compared through both homodyne (intensity) and internal heterodyne (phase-resolved) spectra across varied visible and infrared excitation conditions. We demonstrate that the conventional damping of excitation spectra for vibrational modes—a long-standing practice in the literature—is not required to reproduce the experimental observations when the spectral overlap function is obtained experimentally. Moreover, we show that the doubly resonant character cannot be inferred from the absorption spectrum alone, as resonance conditions extend well beyond the energy range implied by the absorption peak. Finally, this all-experimental method for data analysis accurately predicts the vibrational-electronic phase extracted from phase-resolved spectra, a critical parameter for robust spectral interpretation and an independent validation of the approach.

Accurate characterization of heterogeneous postconsumer textiles remains a major challenge for textile-to-textile (T2T) recycling. Here, we demonstrate the utility of near-infrared hyperspectral imaging (NIR-HSI) for visualizing textile heterogeneity and detecting localized minor constituents that may represent recycling disruptors but are frequently overlooked by conventional characterization approaches. Unlike single-point or spatially averaged spectroscopic measurements, NIR-HSI provides spatially resolved chemical information throughout the imaged textile region, enabling the identification of localized fibers, trims, coatings, optically accessible layers, and other secondary constituents. NIR-HSI also enables the localization and differentiation of chemical contaminants that may compromise recycling feedstock quality. The resulting supervised material-distribution maps reveal compositional complexity not readily captured by bulk measurements and demonstrate how spatial averaging can obscure localized material heterogeneity and contaminant distributions. These findings highlight the value of NIR-HSI for improved textile characterization, feedstock assessment, and informed decision-making within emerging circular textile systems.

Deep learning architectures are increasingly used for spectral classification because of their ability to achieve high prediction accuracies and adapt to complex, heterogeneous data. However, their limited interpretability and sensitivity to data set heterogeneity remain as barriers to chemical insight and deployment in real-world technologies. Here, we evaluate a one-dimensional convolutional neural network (1D-CNN) trained on mid-infrared (MIR) spectra of virgin polymers and postconsumer plastic waste, with emphasis on additive-induced spectral variability that is often underrepresented in pristine polymer spectral libraries and simulated databases. Using one-dimensional gradient-weighted class activation mapping (1D Grad-CAM++), we identify the MIR spectral features most influential for classification of poly(ethylene terephthalate), high-density polyethylene, low-density polyethylene, polypropylene, and polystyrene. Fourier-transform infrared (FTIR) spectra were projected into a two-dimensional space using t-distributed stochastic neighbor embedding (t-SNE) to identify class overlap and spectral feature contamination. To improve model performance, polyolefin formulations containing common polymer additives, including erucamide (CH3(CH2)7CH═CH(CH2)11CONH2), a slip agent, and calcium carbonate (CaCO3), a mineral filler, were extruded using a melt-mixer and incorporated as “chemistry-informed” training data. This targeted experimental augmentation improved the prediction accuracy of standard machine learning classifiers, including random forest, k-nearest neighbors, support vector machine, and logistic regression, from 79.4% to 86.6% without altering the training, validation, or testing parameters. Overall, this work establishes an integrated workflow that combines explainable deep learning, low-dimensional embedding, and targeted experimental data acquisition to improve MIR-based classification of additive-containing polymers. More broadly, the approach provides a practical foundation for autonomous material classification technologies designed for small, chemically heterogeneous spectral data sets.

2H SnSe ₂ and 1T VSe ₂ van der Waals materials were synthesized and then subjected to redox exfoliation. Decoration of these 2D sheets with iron oxide nanoparticles produced a synergistic magnetization response that exceeded that of iron oxide alone.

Elucidating the crystalline‐amorphous interface during decrystallization processes in semi‐crystalline polyethylene (PE) is crucial for the advancement of polymer theory and plastic‐to‐plastic recycling technologies. In this study, we carried out an in‐depth investigation of PE thin films undergoing melting or dissolution using a temperature‐controlled liquid flow‐cell experimental setup which provided in situ mid‐infrared (MIR, 4000–700 cm⁻¹) and near‐infrared (NIR, 6000–4000 cm⁻¹) spectra in real time. The spectroscopic results yielded molecular‐level information regarding PE decrystallization and chain disentanglement via fundamental vibrations, combination bands, and overtones which were correlated using hetero‐spectral two‐dimensional correlation spectroscopy (2D‐COS). A quantitative procedure for the calculation of PE degree of crystallinity was developed to track transformations of crystalline domains during melting and dissolution. This semi‐empirical model achieved a strong linear correlation of at least +0.93 in four spectral regions: 750–700 cm⁻¹, 1500–1400 cm⁻¹, 3000–2800 cm⁻¹, and 4400–4200 cm⁻¹. This analysis revealed important spectral trends about the interfacial solvation environment during these processes. Lastly, the time evolution of the unraveling, terminal methyl (CH₃) groups of PE cilia was examined in relation to the decrystallization mechanism of PE. The insights obtained from this study advance the fundamental understanding necessary for developing new depolymerization and dissolution‐precipitation recycling strategies.

Experimental methods capable of selectively probing water at the DNA minor groove, major groove, and phosphate backbone are crucial for understanding how hydration influences DNA structure and function. Chiral-selective sum frequency generation spectroscopy (chiral SFG) is unique among vibrational spectroscopies because it can selectively probe water molecules that form chiral hydration structures around biomolecules. However, interpreting chiral SFG spectra is challenging since both water and the biomolecule can produce chiral SFG signals. Here, we combine experiment and computation to establish a theoretical framework for the rigorous interpretation of chiral SFG spectra of DNA. We demonstrate that chiral SFG detects the N–H stretch of DNA base pairs and the O–H stretch of water, exclusively probing water molecules in the DNA first hydration shell. Our analysis reveals that DNA transfers chirality to water molecules only within the first hydration shell, so they can be probed by chiral SFG spectroscopy. Beyond the first hydration shell, the electric field-induced water structure is symmetric and, therefore, precludes chiral SFG response. Furthermore, we find that chiral SFG can differentiate chiral subpopulations of first hydration shell water molecules at the minor groove, major groove, and phosphate backbone. Our findings challenge the scientific perspective dominant for more than 40 years that the minor groove “spine of hydration” is the only chiral water structure surrounding the DNA double helix. By identifying the molecular origins of the DNA chiral SFG spectrum, we lay a robust experimental and theoretical foundation for applying chiral SFG to explore the chemical and biological physics of DNA hydration.

Experimental methods capable of selectively probing water at the DNA minor groove, major groove, and phosphate backbone are crucial for understanding how hydration influences DNA structure and function. Chiral-selective sum frequency generation spectroscopy (chiral SFG) is unique among vibrational spectroscopies because it can selectively probe water molecules that form chiral hydration structures around biomolecules. However, interpreting chiral SFG spectra is challenging since both water and the biomolecule can produce chiral SFG signals. Here, we combine experiment and computation to establish a theoretical framework for rigorous interpretation of chiral SFG spectra of DNA. We demonstrate that chiral SFG detects the N-H stretch of DNA base pairs and the O-H stretch of water, exclusively probing water molecules in the DNA first hydration shell. Our analysis reveals that DNA transfers chirality to water molecules only within the first hydration shell, so they can be probed by chiral SFG spectroscopy. Beyond the first hydration shell, electric field-induced water structure is symmetric and therefore precludes chiral SFG response. Furthermore, we find that chiral SFG can differentiate chiral subpopulations of first hydration shell water molecules at the minor groove, major groove, and phosphate backbone. Our findings challenge the scientific perspective dominant for more than 40 years that the minor groove “spine of hydration” is the only chiral water structure surrounding the DNA double helix. By identifying the molecular origins of the DNA chiral SFG spectrum, we lay a robust experimental and theoretical foundation for applying chiral SFG to explore the chemical and biological physics of DNA hydration.