Search

Activating water and methanol is crucial in numerous catalytic, electrocatalytic, and photocatalytic reactions. Despite extensive research, the optimal active sites for water/methanol activation are yet to be unequivocally elucidated. Here, we combine transition-state searches and electronic charge analyses on various structurally different materials to identify two features of favorable O–H bond cleavage in H2O, CH3OH, and hydroxyl: (1) low barriers appear when the charge of H moieties remains approximately constant during the dissociation process, as observed on metal oxides, MXenes, and metal/oxide interfaces. Such favorable kinetics is closely related to adsorbate/substrate hydrogen bonding and is enhanced by nearly linear O–H–O angles and short O–H distances. (2) Fast dissociation is observed when the rotation of O–H bonds is facile, which is favored by weak adsorbate binding and effective orbital overlap. Interestingly, we find that the two features are energetically proportional. Finally, we find conspicuous differences between H2O/CH3OH and OH activation, which hints toward the use of carefully engineered interfaces.

A new Li–Cu–Ag ternary alloy was developed with controlled lithophilic/lithiophobic gradients to induce bottom-up Li deposition.

We successfully synthesized PdPtIr porous nanotubes. The mass activity of PdPtIr PNTs-400 is 1.26 A mgPd+Pt+Ir−1 and the specific activity is 1.3 A cm−2 in acidic media.

Predictive modeling of new biochemical systems with small data is a great challenge. To fill this gap, transfer learning, a subdomain of machine learning that serves to transfer knowledge from a generalized model to a more domain-specific model, provides a promising solution. While transfer learning has been used in natural language processing, image analysis, and chemical engineering fault detection, its application within biochemical engineering has not been systematically explored. In this study, we demonstrated the benefits of transfer learning when applied to predict dynamic behaviors of new biochemical processes. Two different case studies were presented to investigate the accuracy, reliability, and advantage of this innovative modeling approach. We thoroughly discussed the different transfer learning strategies and the effects of topology on transfer learning, comparing the performance of the transfer learning models against benchmark kinetic and data-driven models. Furthermore, strong connections between the underlying process mechanism and the transfer learning model's optimal structure were highlighted, suggesting the interpretability of transfer learning to enable more accurate prediction than a naive data-driven modeling approach. Therefore, this study shows a novel approach to effectively combining data from different resources for bioprocess simulation.

The development of efficient methods for the synthesis of substituted polycyclic arenes with various topologies is in high demand due to their excellent electrical and optical properties. In this work, a series of gem-dimethylcyclopentane-fused arenes with more than ten topologies were synthesized via a 1,5,7-Triazabicyclo[4.4.0]dec-5-ene (TBD)-mediated dehydro-Diels-Alder reaction with moderate to good yields. The introduction of the near-planar gem-dimethylcyclopentane moiety not only impacts the molecular conjugative system but also regulates the intermolecular π-π interactions and crystal packing, which are critical for the photoelectric performance of arenes. The photophysical properties, molecular geometry, molecular packing of these compounds, and electrochemical properties were investigated by UV-vis absorption, fluorescence emission spectra, DFT calculations, single-crystal X-ray structure analysis, and cyclic voltammetry study.

Gold catalysts have been reported as highly effective catalysts in various oxidation reactions. However, for chemoselective hydrogenation reactions, gold-based catalysts normally show much lower catalytic activity than platinum group metals, even though their selectivities are excellent. Here, we report that the chemoselective hydrogenation activity of 3-nitrostyrene to 3-vinylaniline over Au/TiO2 can be enhanced up to 3.3 times through the hydrogen reduction strategy. It is revealed that strong metal-support interaction, between gold nanoparticles (NPs) and TiO2 support, is introduced through hydrogen reduction, resulting in partial dispersion of reduced TiOx on the Au surface. The partially covered Au not only increases the perimeter of the interface between the gold NPs and the support, but also benefits H2 activation. Reaction kinetic analysis and H2-D2 exchange reaction show that H2 activation is the critical step in the hydrogenation of 3-nitrostyrene to 3-vinylaniline. Density functional theory calculations verify that hydrogen dissociation and hydrogen transfer are favored at the interface of gold NPs and TiO2 over the hydrogen-reduced Au/TiO2. This study provides insights for fabricating highly active gold-based catalysts for chemoselective hydrogenation reactions.