Bayesian Optimization-guided Discovery of High-performance Methane Combustion Catalysts based on Multi-component PtPd@CeZrOx Core–Shell Nanospheres

Formula regulation of multi-component catalysts by manual search is undoubtedly a time-consuming task, which has severely impeded the development efficiency of high-performance catalysts. In this work, PtPd@CeZrO_x core–shell nanospheres, as a successful case study, is explicitly demonstrated how Bayesian optimization (BO) accelerates the discovery of methane combustion catalysts with the optimal formula ratio (the Pt/Pd mole ratio ranges from 1/2.33–1/9.09, and Ce/Zr from 1/0.22–1/0.35), which directly results in a lower conversion temperature (T₅₀ approaching to 330 °C) than ones reported hitherto. Consequently, the best sample obtained could be efficiently developed after two rounds of iterations, containing only 18 experiments in all that is far less than the common human workload via the traditional trial-and-error search for optimal compositions. Further, this BO-based machine learning strategy can be straightforward extended to serve the autonomous discovery in multi-component material systems, for other desired properties, showing promising opportunities to practical applications in future.     

Structure- and Data-Driven Protein Engineering of Transaminases for Improving Activity and Stereoselectivity

Amine transaminases (ATAs) are powerful biocatalysts for the stereoselective synthesis of chiral amines. Machine learning provides a promising approach for protein engineering, but activity prediction models for ATAs remain elusive due to the difficulty of obtaining high-quality training data. Thus, we first created variants of the ATA from Ruegeria sp. (3FCR) with improved catalytic activity (up to 2000-fold) as well as reversed stereoselectivity by a structure-dependent rational design and collected a high-quality dataset in this process. Subsequently, we designed a modified one-hot code to describe steric and electronic effects of substrates and residues within ATAs. Finally, we built a gradient boosting regression tree predictor for catalytic activity and stereoselectivity, and applied this for the data-driven design of optimized variants which then showed improved activity (up to 3-fold compared to the best variants previously identified). We also demonstrated that the model can predict the catalytic activity for ATA variants of another origin by retraining with a small set of additional data.     

基于同伴学习法的模电教学改革实践

在工程教育认证的以“学生为中心”的理念指导下，结合信息技术发展的大背景，高等教育的教学模式急需新的变革。构建基于学习通平台和同伴学习法的教学新模式，并以“模拟电子技术”课程为研究对象，进行了教学实践。通过问卷调查和课程目标达成情况分析，证实本教学模式得到了学生的认可，有效地提高了学生的学习效果、效率。     

Computer-Driven Development of Ylide Functionalized Phosphines for Palladium-Catalyzed Hiyama Couplings

Palladium-catalyzed couplings of silicon enolates with aryl electrophiles are of great synthetic utility, but often limited to expensive bromide substrates. A comparative experimental study confirmed that none of the established ligand systems allows to couple inexpensive aryl chlorides with α-trimethylsilyl alkylnitriles. In contrast, ylide functionalized phosphines (YPhos) led to encouraging results. A statistical model was developed that correlates the reaction yields with ligand features. It was employed to predict catalyst structures with superior performance. With this cheminformatics approach, YPhos ligands were tailored specifically to the demands of Hiyama couplings. The newly synthesized ligands displayed record-setting activities, enabling the elusive coupling of aryl chlorides with α-trimethylsilyl alkyl nitriles. The preparative utility of the catalyst system was demonstrated by the synthesis of pharmaceutically meaningful α-aryl alkylnitriles, α-arylcarbonyls and biaryls.     

Recent studies on advance spectroscopic techniques for the identification of microorganisms: A review

The continuous development of resistance to antibiotic drugs by microorganisms causes high mortality and morbidity. Pathogens with distinct features and biochemical abilities make them destructive to human health. Therefore, early identification of the pathogen is of substantial importance for quick ailments and healthcare outcomes. Several phenotype methods are used for the identification and resistance determination but most of the conventional procedures are time-consuming, costly, and give qualitative results. Recently, great focus has been made on the utilization of advanced techniques for microbial identification. This review is focused on the research studies performed in the last five years for the identification of microorganisms particularly, bacteria using advanced spectroscopic techniques including mass spectrometry (MS), infrared (IR) spectroscopy, Raman spectroscopy (RS), and nuclear magnetic resonance (NMR) spectroscopy. Among all the techniques, MS techniques, particularly MALDI-TOF/MS have been widely utilized for microbial identification. A total of 44 bacteria i.e., 6 Staphylococcus spp., 3 Enterococcus spp., 6 Bacillus spp., 4 Streptococcus spp., 6 Salmonella spp., and one from each genus including Escherichia, Acinetobacter, Pseudomonas, Proteus, Clostridioides, Candida, Brucella, Burkholderia, Francisella, Yersinia, Moraxella, Vibrio, Shigella, Serratia, Citrobacter, and Haemophilus (spp.) were discussed in the review for their identification using the above-mentioned techniques. Among all the identified microorganisms, 21% of studies have been conducted for the identification of E. coli, 14% for S. aureus followed by 37% for other microorganisms.