Precision Chemistry最新文献

Single-molecule bioelectronic sensing, a groundbreaking domain in biological research, has revolutionized our understanding of molecules by revealing deep insights into fundamental biological processes. The advent of emergent technologies, such as nanogapped electrodes and nanopores, has greatly enhanced this field, providing exceptional sensitivity, resolution, and integration capabilities. However, challenges persist, such as complex data sets with high noise levels and stochastic molecular dynamics. Artificial intelligence (AI) has stepped in to address these issues with its powerful data processing capabilities. AI algorithms effectively extract meaningful features, detect subtle changes, improve signal-to-noise ratios, and uncover hidden patterns in massive data. This review explores the synergy between AI and single-molecule bioelectronic sensing, focusing on how AI enhances signal processing and data analysis to boost accuracy and reliability. We also discuss current limitations and future directions for integrating AI, highlighting its potential to advance biological research and technological innovation.

Recently, the chain-walking ethylene polymerization strategy has garnered widespread attention as an efficient and straightforward method for preparing polyolefin elastomers. In this study, a series of 2,4,8-triarylnaphthyl iminopyridyl nickel catalysts were synthesized and used in ethylene polymerization. These catalysts demonstrated moderate catalytic activity (10⁵ g mol^-1 h^-1), producing high-molecular-weight (up to 145.5 kg/mol) polyethylene materials with high branching degrees (75-95/1000C) and correspondingly low melting points. Detailed analysis using ¹³C NMR spectroscopy revealed that the polyethylenes primarily featured methyl and long-chain branches. Mechanical testing of the polyethylene samples obtained from catalysts Ni1-Ni3 exhibited moderate stress at break (4.64-6.97 MPa) coupled with a very high strain at break (1650-3752%), indicating their very good ductility. Furthermore, these polyethylenes showcased great elastic recovery abilities, with strain recovery values ranging from 72% to 85%. In contrast, the polyethylene produced by Ni4 displayed notably inferior tensile strength (0.16 MPa) and tensile recovery (43%). To the best of our knowledge, this study represents the inaugural utilization of a nickel iminopyridyl catalyst in the preparation of a polyethylene thermoplastic elastomer.

Recently, the chain-walking ethylene polymerization strategy has garnered widespread attention as an efficient and straightforward method for preparing polyolefin elastomers. In this study, a series of 2,4,8-triarylnaphthyl iminopyridyl nickel catalysts were synthesized and used in ethylene polymerization. These catalysts demonstrated moderate catalytic activity (10⁵ g mol^–1 h^–1), producing high-molecular-weight (up to 145.5 kg/mol) polyethylene materials with high branching degrees (75–95/1000C) and correspondingly low melting points. Detailed analysis using ¹³C NMR spectroscopy revealed that the polyethylenes primarily featured methyl and long-chain branches. Mechanical testing of the polyethylene samples obtained from catalysts Ni1–Ni3 exhibited moderate stress at break (4.64–6.97 MPa) coupled with a very high strain at break (1650–3752%), indicating their very good ductility. Furthermore, these polyethylenes showcased great elastic recovery abilities, with strain recovery values ranging from 72% to 85%. In contrast, the polyethylene produced by Ni4 displayed notably inferior tensile strength (0.16 MPa) and tensile recovery (43%). To the best of our knowledge, this study represents the inaugural utilization of a nickel iminopyridyl catalyst in the preparation of a polyethylene thermoplastic elastomer.

Single-molecule bioelectronic sensing, a groundbreaking domain in biological research, has revolutionized our understanding of molecules by revealing deep insights into fundamental biological processes. The advent of emergent technologies, such as nanogapped electrodes and nanopores, has greatly enhanced this field, providing exceptional sensitivity, resolution, and integration capabilities. However, challenges persist, such as complex data sets with high noise levels and stochastic molecular dynamics. Artificial intelligence (AI) has stepped in to address these issues with its powerful data processing capabilities. AI algorithms effectively extract meaningful features, detect subtle changes, improve signal-to-noise ratios, and uncover hidden patterns in massive data. This review explores the synergy between AI and single-molecule bioelectronic sensing, focusing on how AI enhances signal processing and data analysis to boost accuracy and reliability. We also discuss current limitations and future directions for integrating AI, highlighting its potential to advance biological research and technological innovation.

Atomic simulations aim to understand and predict complex physical phenomena, the success of which relies largely on the accuracy of the potential energy surface description and the efficiency to capture important rare events. LASP software (large-scale atomic simulation with a Neural Network Potential), released in 2018, incorporates the key ingredients to fulfill the ultimate goal of atomic simulations by combining advanced neural network potentials with efficient global optimization methods. This review introduces the recent development of the software along two main streams, namely, higher intelligence and more automation, to solve complex material and reaction problems. The latest version of LASP (LASP 3.7) features the global many-body function corrected neural network (G-MBNN) to improve the PES accuracy with low cost, which achieves a linear scaling efficiency for large-scale atomic simulations. The key functionalities of LASP are updated to incorporate (i) the ASOP and ML-interface methods for finding complex surface and interface structures under grand canonic conditions; (ii) the ML-TS and MMLPS methods to identify the lowest energy reaction pathway. With these powerful functionalities, LASP now serves as an intelligent data generator to create computational databases for end users. We exemplify the recent LASP database construction in zeolite and the metal-ligand properties for a new catalyst design.

Atomic simulations aim to understand and predict complex physical phenomena, the success of which relies largely on the accuracy of the potential energy surface description and the efficiency to capture important rare events. LASP software (large-scale atomic simulation with a Neural Network Potential), released in 2018, incorporates the key ingredients to fulfill the ultimate goal of atomic simulations by combining advanced neural network potentials with efficient global optimization methods. This review introduces the recent development of the software along two main streams, namely, higher intelligence and more automation, to solve complex material and reaction problems. The latest version of LASP (LASP 3.7) features the global many-body function corrected neural network (G-MBNN) to improve the PES accuracy with low cost, which achieves a linear scaling efficiency for large-scale atomic simulations. The key functionalities of LASP are updated to incorporate (i) the ASOP and ML-interface methods for finding complex surface and interface structures under grand canonic conditions; (ii) the ML-TS and MMLPS methods to identify the lowest energy reaction pathway. With these powerful functionalities, LASP now serves as an intelligent data generator to create computational databases for end users. We exemplify the recent LASP database construction in zeolite and the metal–ligand properties for a new catalyst design.

This Perspective explores the integration of machine learning potentials (MLPs) in the research of heterogeneous catalysis, focusing on their role in identifying in situ active sites and enhancing the understanding of catalytic processes. MLPs utilize extensive databases from high-throughput density functional theory (DFT) calculations to train models that predict atomic configurations, energies, and forces with near-DFT accuracy. These capabilities allow MLPs to handle significantly larger systems and extend simulation times beyond the limitations of traditional ab initio methods. Coupled with global optimization algorithms, MLPs enable systematic investigations across vast structural spaces, making substantial contributions to the modeling of catalyst surface structures under reactive conditions. The review aims to provide a broad introduction to recent advancements and practical guidance on employing MLPs and also showcases several exemplary cases of MLP-driven discoveries related to surface structure changes under reactive conditions and the nature of active sites in heterogeneous catalysis. The prevailing challenges faced by this approach are also discussed.

This Perspective explores the integration of machine learning potentials (MLPs) in the research of heterogeneous catalysis, focusing on their role in identifying in situ active sites and enhancing the understanding of catalytic processes. MLPs utilize extensive databases from high-throughput density functional theory (DFT) calculations to train models that predict atomic configurations, energies, and forces with near-DFT accuracy. These capabilities allow MLPs to handle significantly larger systems and extend simulation times beyond the limitations of traditional ab initio methods. Coupled with global optimization algorithms, MLPs enable systematic investigations across vast structural spaces, making substantial contributions to the modeling of catalyst surface structures under reactive conditions. The review aims to provide a broad introduction to recent advancements and practical guidance on employing MLPs and also showcases several exemplary cases of MLP-driven discoveries related to surface structure changes under reactive conditions and the nature of active sites in heterogeneous catalysis. The prevailing challenges faced by this approach are also discussed.