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Droplet-Based EPR Spectroscopy for Real-Time Monitoring of Liquid-Phase Catalytic Reactions.
Small Methods
January 2025
Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 1, Zürich, 8093, Switzerland.
In situ monitoring is essential for catalytic process design, offering real-time insights into active structures and reactive intermediates. Electron paramagnetic resonance (EPR) spectroscopy excels at probing geometric and electronic properties of paramagnetic species during reactions. Yet, state-of-the-art liquid-phase EPR methods, like flat cells, require custom resonators, consume large amounts of reagents, and are unsuited for tracking initial kinetics or use with solid catalysts.
View Article and Find Full Text PDFStudy of spectral overlap and heterogeneity in agriculture based on soft classification techniques.
MethodsX
June 2025
Department of Biological and Pharmaceutical Environmental Sciences and Technologies, University of Campania "L. Vanvitelli", Via Antonio Vivaldi, 43, Caserta 81100, CE, Italy.
This study explores the application of fuzzy soft classification techniques combined with vegetation indices to address spectral overlap and heterogeneity in agricultural image processing. The methodology focuses on the integration of three key vegetation indices: Soil-Adjusted Vegetation Index (SAVI), Modified Soil-Adjusted Vegetation Index (MSAVI), and Modified Chlorophyll Absorption in Reflectance Index (MCARI), with Modified Possibilistic C-Means (MPCM) clustering. The analysis involves preprocessing the image data, calculating the vegetation indices, and applying the MPCM algorithm to perform soft classification, allowing pixels to belong to multiple classes with varying degrees of membership.
View Article and Find Full Text PDFBuilding a near-infrared (NIR) soil spectral dataset and predictive machine learning models using a handheld NIR spectrophotometer.
Data Brief
February 2025
Woodwell Climate Research Center, 149 Woods Hole Rd., Falmouth, MA, 02540, United States.
This near-infrared spectral dataset consists of 2,106 diverse mineral soil samples scanned, on average, on six different units of the same low-cost commercially available handheld spectrophotometer. Most soil samples were selected from the USDA NRCS National Soil Survey Center-Kellogg Soil Survey Laboratory (NSSC-KSSL) soil archives to represent the diversity of mineral soils (0-30 cm) found in the United States, while 90 samples were selected from Ghana, Kenya, and Nigeria to represent available African soils in the same archive. All scanning was performed on dried and sieved (<2 mm) soil samples.
View Article and Find Full Text PDFQuantification of modal mineralogy in molybdenite-bearing drill-core samples by laser-induced breakdown spectroscopy.
Heliyon
January 2025
Laboratorio de Trazas elementales y Especiación, Departamento de Química Analítica e Inorgánica, Facultad de Ciencias Químicas, Universidad de Concepción, Concepción, Chile.
Quantification of modal mineralogy in drill-core samples is crucial for understanding the geology and metal deportment in a mining operation. This study assesses conventional procedures to quantify modal mineralogy, that includes an initial drill-core logging, followed by petrographic descriptions and SEM-based automated mineralogy analyses performed in selected regions of interest, against a novel approach using laser-induced breakdown spectroscopy (LIBS). Our proposed methodology aims to quantify the modal mineralogy directly in a drill-core sample, avoiding previous stages of selection and preparation of samples.
View Article and Find Full Text PDFSparse kernel -means clustering.
J Appl Stat
June 2024
Graduate School, Department of Urban Big Data Convergence, University of Seoul, Seoul, South Korea.
Clustering is an essential technique that groups similar data points to uncover the underlying structure and features of the data. Although traditional clustering methods such as -means are widely utilized, they have limitations in identifying nonlinear clusters. Thus, alternative techniques, such as kernel -means and spectral clustering, have been developed to address this issue.
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