Discrimination of coal geographical origins through HS-GC-IMS assisted with machine learning algorithms in larceny case.

The process of globalization and industrialization has resulted in a rise in the theft of coal and other related products, thereby becoming a focal point for forensic science. This situation has engendered an escalated demand for effective detection and monitoring technologies. The precise identification of coal trace evidence presents a challenge with current methods, owing to its minute quantity, fine texture, and intricate composition. In this study, we integrated machine learning with the identification of volatiles to accurately differentiate coal geographical origins through the application of headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS). The topographic distribution of volatiles in coals was visually depicted to elucidate the subtle distinctions through spectra and fingerprint analysis. Additionally, four supervised machine learning algorithms were developed to quantitatively predict the geographical origins of natural coals utilizing the HS-GC-IMS dataset, and these were subsequently compared with unsupervised models. Remarkable volatile compounds were identified through the quantitative analysis and optimal Random Forest model, which offered a rapid readout and achieved an average accuracy of 100 % in coal identification. Our findings indicate that the integration of HS-GC-IMS and machine learning is anticipated to enhance the efficiency and accuracy of coal geographical traceability, thereby providing a foundation for litigation and trials.

Carbon aerogel from forestry biomass as a peroxymonosulfate activator for organic contaminants degradation.

The carbon catalyst has been widely used as a peroxymonosulfate (PMS) activator to degrade organic contaminants. The biomass carbon aerogel (CA) derived from poplar powder was synthesized in this study. CA with three-dimensional structure exhibited an excellent degradation performance of PMS activation for different types of organic contaminants including bisphenol A (BPA), rhodamine 6 G, phenol, and p-chlorophenol with the removal efficiencies up to 91%, 100%, 100%, and 60% within 60 min, respectively. It was found that singlet oxygen (1O2) dominated the non-radical pathway worked for BPA removal in CA/PMS system. The possible mechanism for PMS activation was discussed. A portion of 1O2 was produced through the transformation of superoxide radical (O2•-) in CA/PMS system. Electronic impedance spectroscopy (EIS) proved that the hierarchical structure of CA contributed to the electron transfer process for PMS activation. The ketonic/carbonyl groups (CË­O) on the surface of CA could serve as a possible active site to facilitate the generation of 1O2. In addition, CA showed superior degradation performance in actual water bodies and reusability with high-temperature regeneration treatment. This study developed an efficient and environmentally benign catalyst for water remediation of organic pollutants.