Enhancing Chemical Reaction Monitoring with a Deep Learning Model for NMR Spectra Image Matching to Target Compounds.

In the synthetic laboratory, researchers typically rely on nuclear magnetic resonance (NMR) spectra to elucidate structures of synthesized products and confirm whether they match the desired target compounds. As chemical synthesis technology evolves toward intelligence and continuity, efficient computer-assisted structure elucidation (CASE) techniques are required to replace time-consuming manual analysis and provide the necessary speed. However, current CASE methods typically aim to derive precise chemical structures from spectroscopic data, yet they suffer from drawbacks such as low accuracy, high computational cost, and reliance on chemical libraries. In meticulously designed chemical synthesis reactions, researchers prioritize confirming the attainment of the target product based on NMR spectra, rather than focusing on identifying the specific product obtained. For this purpose, we innovatively developed a binary classification model, termed as MatCS, to directly predict the relationship between NMR spectra image (including 1H NMR and 13C NMR) and the molecular structure of the target compound. After evaluating various feature extraction methods, MatCS employs a combination of the Graph Attention Networks and Graph Convolutional Networks to learn the structural features of molecular graphs and the pretrained ResNet101 network with a Convolutional Block Attention Module to extract features from NMR spectra images. The results show that on a challenging Testsim data set, which poses difficulty in distinguishing spectra of similar molecular structures, MatCS achieves comprehensive evaluation metrics with an F1-score of 0.81 and an AUC value of 0.87. Simultaneously, it exhibited commendable performance on an external SDBS data set containing experimental NMR spectra, showcasing substantial potential for structural verification tasks in real automated chemical synthesis.

The structural, antioxidant and emulsifying properties of cellulose nanofiber-dihydromyricetin mixtures: Effects of composite ratio.

In this work, effects of cellulose nanofiber/dihydromyricetin (CNF/DMY) ratio on the structural, antioxidant and emulsifying properties of the CNF/DMY mixtures were investigated. CNF integrated with DMY via hydrogen bonding and the antioxidant capacity of mixtures increased with decreasing CNF/DMY ratio (k). The oxidative stability of emulsions enhanced as the DMY content increased. Emulsions formed at Φ = 0.5 displayed larger size (about 25 µm), better viscoelasticity and centrifugal stability than those at Φ = 0.3 (about 23 µm). The emulsions at k = 17:3 and Φ = 0.5 exhibited the most excellent viscoelasticity. In conclusion, the DMY content in mixtures and the oil phase fraction exhibited distinct synergistic effects on the formation and characteristics of emulsions, and the emulsions could demonstrate superior oxidative and storage stability. These findings could provide a novel strategy to extend the shelf life of cellulose-based emulsions and related products.

Characterization and application of a novel bioemulsifier in crude oil degradation by Acinetobacter beijerinckii ZRS.

Bioemulsifiers can be applicated in a variety of areas such as bioremediation and microbial-enhanced oil recovery. The present study was aimed at bioemulsifier production, optimization, stability studies, and applications of the bioemulsifier produced by one of these strains, Acinetobacter beijerinckii ZRS. When Acinetobacter beijerinckii ZRS is cultured with hexadecane as a carbon source, it produces a novel extracellular emulsifying agent that does not cause remarkable reductions in surface tension. In order to enhance bioemulsifier production, response surface methodology was applied to optimize the culture medium. The bioemulsifier was subjected to thin-layer chromatography, Fourier transform infrared spectroscopy (FTIR), gel filtration chromatography, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF), and nuclear magnetic resonance (NMR), which allowed for the identification of a novel polymeric bioemulsifier. The bioemulsifier retained its properties at a wide range of pH values, high temperatures and high salinities (up to 5% [w/v] Na(+) and 24% Ca(2+)). To deduce the role of this bioemulsifier in a coastal zone oil spill, the propagation of oil-degrading bacteria on oil-coated grains of gravel immersed in seawater was investigated in beach-simulating tanks. The bioemulsifier played a positive role in the degradation of these hydrocarbons and increasing the light crude oil degradation rate of the bacterial strain from 37.5 to 58.3% within 56 days. Therefore, this bioemulsifier shows strong potential to be used for bioremediation of oil pollution in marine environments.