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Jiang, Bin

Publications and source records attributed to Jiang, Bin.

Dynamics of collision-induced energy transfer

Collisions are of fundamental importance in many relevant physico-chemical phenomena in the gas phase and at the gas-surface interface. Collision-induced energy transfer not only offers a sensitive probe of interatomic interactions, but also impacts an array of processes ranging from transport to reactivity. This perspective highlights some recent advances on collision-induced energy transfer dynamics. In the gas phase, cold collisions revealed remarkable quantum effects and stereodynamics of bimolecular inelastic scattering, which motivated development of new state-to-state quantum scattering methods. For surface processes, experiments of hydrogen atom scattering from various surfaces combined with first-principles molecular dynamics simulations shed valuable light on the relative importance of adiabatic energy transfer to surface phonons and nonadiabatic energy transfer to surface electrons. We outline further challenges in these fields and expect more fruitful interplay between experiment and theory.

Cold collision↗

Identification of Blue Horizontal Branch Stars with Multimodal Fusion

Blue Horizontal Branch stars (BHBs) are ideal tracers to probe the global structure of the milky Way (MW), and the increased size of the BHB star sample could be helpful to accurately calculate the MW's enclosed mass and kinematics. Large survey telescopes have produced an increasing number of astronomical images and spectra. However, traditional methods of identifying BHBs are limited in dealing with the large scale of astronomical data. A fast and efficient way of identifying BHBs can provide a more significant sample for further analysis and research. Therefore, in order to fully use the various data observed and further improve the identification accuracy of BHBs, we have innovatively proposed and implemented a Bi-level attention mechanism-based Transformer multimodal fusion model, called Bi-level Attention in the Transformer with Multimodality (BATMM). The model consists of a spectrum encoder, an image encoder, and a Transformer multimodal fusion module. The Transformer enables the effective fusion of data from two modalities, namely image and spectrum, by using the proposed Bi-level attention mechanism, including cross-attention and self-attention. As a result, the information from the different modalities complements each other, thus improving the accuracy of the identification of BHBs. The experimental results show that the F1 score of the proposed BATMM is 94.78%, which is 21.77% and 2.76% higher than the image and spectral unimodality, respectively. It is therefore demonstrated that higher identification accuracy of BHBs can be achieved by means of using data from multiple modalities and employing an efficient data fusion strategy.

79 ASTRONOMY AND ASTROPHYSICS↗

Design of Multicomponent Peptide Fibrils with Ordered and Programmable Compositional Patterns

Advanced applications of biomacromolecular assemblies require a stringent degree of control over molecular arrangement, which is a challenge to current synthetic methods. Here we used a neighbor-controlled patterning strategy to build multicomponent peptide fibrils with an unprecedented capacity to manipulate local composition and peptide positions. Eight peptides were designed to have regulable nearest neighbors upon co-assembly, which, by simulation, afforded 412 different patterns within fibrils, with varied compositions and/or peptide positions. The fibrils with six prescribed patterns were experimentally constructed with high accuracy. The controlled patterning also applies to functionalities appended to the peptides, as exemplified by arranging carbohydrate ligands at nanoscale precision for protein recognition. Importantly, this study offers a route to molecular editing of inner structures of peptide assemblies, prefiguring the uniqueness and richness of patterning-based material design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗