Precision Chemistry最新文献

The integration of multiple chromophore units into a single molecule is expected to improve the performance of photon upconversion based on triplet-triplet annihilation (TTA-UC) that can convert low energy photons to higher energy photons at low excitation intensity. In this study, a macrocyclic parallel dimer of 9,10-diphenylanthracene (DPA) with a precisely parallel orientation, named MPD-2, is synthesized, and its TTA-UC properties are investigated. MPD-2 shows a green-to-blue TTA-UC emission in the presence of a triplet sensitizer, platinum octaethylporphyrin (PtOEP). Compared to monomeric DPA, MPD-2 results in an enhancement of the spin statistical factor of TTA and a decrease in the excitation light intensity due to the intramolecular TTA process. The obtained structure-property relationship provides important information for the further improvement of TTA-UC properties.

The integration of multiple chromophore units into a single molecule is expected to improve the performance of photon upconversion based on triplet–triplet annihilation (TTA-UC) that can convert low energy photons to higher energy photons at low excitation intensity. In this study, a macrocyclic parallel dimer of 9,10-diphenylanthracene (DPA) with a precisely parallel orientation, named MPD-2, is synthesized, and its TTA-UC properties are investigated. MPD-2 shows a green-to-blue TTA-UC emission in the presence of a triplet sensitizer, platinum octaethylporphyrin (PtOEP). Compared to monomeric DPA, MPD-2 results in an enhancement of the spin statistical factor of TTA and a decrease in the excitation light intensity due to the intramolecular TTA process. The obtained structure–property relationship provides important information for the further improvement of TTA-UC properties.

The interfacial proton transfer (PT) reaction on the metal oxide surface is an important step in many chemical processes including photoelectrocatalytic water splitting, dehydrogenation, and hydrogen storage. The investigation of the PT process, in terms of thermodynamics and kinetics, has received considerable attention, but the individual free energy barriers and solvent effects for different PT pathways on rutile oxide are still lacking. Here, by applying a combination of ab initio and deep potential molecular dynamics methods, we have studied interfacial PT mechanisms by selecting the rutile SnO₂(110)/H₂O interface as an example of an oxide with the characteristic of frequently interfacial PT processes. Three types of PT pathways among the interfacial groups are found, i.e., proton transfer from terminal adsorbed water to bridge oxygen directly (surface-PT) or via a solvent water (mediated-PT), and proton hopping between two terminal groups (adlayer PT). Our simulations reveal that the terminal water in mediated-PT prefers to point toward the solution and forms a shorter H-bond with the assisted solvent water, leading to the lowest energy barrier and the fastest relative PT rate. In particular, it is found that the full solvation environment plays a crucial role in water-mediated proton conduction, while having little effect on direct PT reactions. The PT mechanisms on aqueous rutile oxide interfaces are also discussed by comparing an oxide series composed of SnO₂, TiO₂, and IrO₂. Consequently, this work provides valuable insights into the ability of a deep neural network to reproduce the ab initio potential energy surface, as well as the PT mechanisms at such oxide/liquid interfaces, which can help understand the important chemical processes in electrochemistry, photoelectrocatalysis, colloid science, and geochemistry.

The interfacial proton transfer (PT) reaction on the metal oxide surface is an important step in many chemical processes including photoelectrocatalytic water splitting, dehydrogenation, and hydrogen storage. The investigation of the PT process, in terms of thermodynamics and kinetics, has received considerable attention, but the individual free energy barriers and solvent effects for different PT pathways on rutile oxide are still lacking. Here, by applying a combination of ab initio and deep potential molecular dynamics methods, we have studied interfacial PT mechanisms by selecting the rutile SnO₂(110)/H₂O interface as an example of an oxide with the characteristic of frequently interfacial PT processes. Three types of PT pathways among the interfacial groups are found, i.e., proton transfer from terminal adsorbed water to bridge oxygen directly (surface-PT) or via a solvent water (mediated-PT), and proton hopping between two terminal groups (adlayer PT). Our simulations reveal that the terminal water in mediated-PT prefers to point toward the solution and forms a shorter H-bond with the assisted solvent water, leading to the lowest energy barrier and the fastest relative PT rate. In particular, it is found that the full solvation environment plays a crucial role in water-mediated proton conduction, while having little effect on direct PT reactions. The PT mechanisms on aqueous rutile oxide interfaces are also discussed by comparing an oxide series composed of SnO₂, TiO₂, and IrO₂. Consequently, this work provides valuable insights into the ability of a deep neural network to reproduce the ab initio potential energy surface, as well as the PT mechanisms at such oxide/liquid interfaces, which can help understand the important chemical processes in electrochemistry, photoelectrocatalysis, colloid science, and geochemistry.

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.