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.

Nexus between green financing, renewable energy generation, and energy efficiency: empirical insights through DEA technique.

The study aims to test the nexus of green financing with renewable electricity generation and energy efficiency. The study used data envelopment analysis (DEA) technique during the year of 2016 to 2020 in developed and developing countries. The findings show that there is a 24% possibility of worldwide rise in expenditures in renewable energy through energy efficiency projects and probably could fall around 17% much further in 2017 and 2018. This may jeopardize the Sustainable Development Goals (SDGs) and the Paris climate change agreement. Lack of access to private financing slows the development of green initiatives. Now that sustainable energy is not about science and technology, it is all about getting financing in developed and developing countries. As policy measure, the study suggested to value environmental initiatives, like other infrastructure initiatives, for greater electricity generation and energy efficiency in developed and developing countries. Such infrastructural projects need long-term financing and capital intensiveness. It is further suggested to sustain growth, development, and energy poverty reduction, and around $26 trillion would be required, in terms of green financing, in the developed and developing countries alone by the year 2030 to enhance energy efficiency. To achieve energy sustainability goals in developed and developing countries, recent research suggested some policy implication considering the post COVID-19 time. If such policy implications are implemented successfully, there are chances that green financing would make energy generation and energy efficiency effective.

Environmental secondhand smoke exposure and policy assessment at five venues in Zhejiang Province, China.

The objective of this study was to assess environmental secondhand smoke exposure and tobacco control policy at 5 venues. A cross-sectional study was conducted involving 134 settings and 2727 adults in Zhejiang, China. The results show that the proportions of venues that had complete smoking ban were as follows: health administrative organizations (71.9%), hospitals (70.0%), schools (66.7%), public transportation vehicles (24.0%), and government agencies (11.8%). The proportions of venues where smoking was noticed were as follows: public transportation vehicles (88.0%), government agencies (47.1%), hospitals (46.7%), health administrative organizations (40.6%), and schools (30.0%). Venues with completely indoor smoking ban were 5 times more likely to be smoke-free at the time of survey than other venues without smoking ban (odds ratio = 5.39, 95% confidence interval = 1.92-15.14). It indicated that implementation of indoor smoking ban can reduce indoor secondhand smoke exposure.