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361.
Sakshi Yadav Schmid Kacper Lachowski Huat Thart Chiang Lilo Pozzo Jim De Yoreo Shuai Zhang 《Angewandte Chemie (International ed. in English)》2023,62(48):e202309725
Biomolecular self-assembly of hierarchical materials is a precise and adaptable bottom-up approach to synthesizing across scales with considerable energy, health, environment, sustainability, and information technology applications. To achieve desired functions in biomaterials, it is essential to directly observe assembly dynamics and structural evolutions that reflect the underlying energy landscape and the assembly mechanism. This review will summarize the current understanding of biomolecular assembly mechanisms based on in situ characterization and discuss the broader significance and achievements of newly gained insights. In addition, we will also introduce how emerging deep learning/machine learning-based approaches, multiparametric characterization, and high-throughput methods can boost the development of biomolecular self-assembly. The objective of this review is to accelerate the development of in situ characterization approaches for biomolecular self-assembly and to inspire the next generation of biomimetic materials. 相似文献
362.
Given the vast number of data representations that people encounter daily, it is imperative that people become critical consumers of the representations they will face. This requires the development of particular habits of mind. The purpose of this paper is to share the critical statistical literacy habits of mind framework. We articulate how we drew on the literature related to statistical literacy, critical mathematics, and critical statistical literacy to identify the habits of mind needed to enact critical statistical literacy in the context of consuming data representations. We describe the refinement process using qualitative interview data. To illustrate what each of the critical statistical literacy habits of mind looks like when enacted, we share examples from statistics teachers making sense of a data representation. 相似文献
363.
Xilan Feng Xiangrui Gong Prof. Dapeng Liu Prof. Yang Li Prof. Ying Jiang Prof. Yu Zhang 《Angewandte Chemie (International ed. in English)》2023,62(47):e202313068
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@CeZrOx 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. 相似文献
364.
Dr. Yu-Fei Ao Shuxin Pei Dr. Chao Xiang Marian J. Menke Prof. Lin Shen Dr. Chenghai Sun Dr. Mark Dörr Dr. Stefan Born Prof. Matthias Höhne Prof. Uwe T. Bornscheuer 《Angewandte Chemie (International ed. in English)》2023,62(23):e202301660
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. 相似文献
365.
Jonas F. Goebel Julian Löffler Zhongyi Zeng Jens Handelmann Albert Hermann Ilja Rodstein Dr. Tobias Gensch Prof. Dr. Viktoria H. Gessner Prof. Dr. Lukas J. Gooßen 《Angewandte Chemie (International ed. in English)》2023,62(9):e202216160
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. 相似文献
366.
367.
《Arabian Journal of Chemistry》2023,16(3):104521
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. 相似文献