Hollow-SiO@Cu Zn Mg Al-LDHs as catalyst precursors for CO hydrogenation to methanol.

We report a new synthetic strategy for preparing well-organised, spherical and mesoporous, mixed-metal, hollow-core@layered double hydroxides. Hollow-SiO@Cu Zn Mg Al-LDHs ( + + = 2.32 ± 0.

Subtle Modifications in Interface Configurations of Iron/Cobalt Phthalocyanine-Based Electrocatalysts Determine Molecular CO Reduction Activities.

Strain engineering has emerged as a powerful approach in steering material properties. However, the mechanism and potential limitations remain poorly understood. Here we report that subtle changes in molecular configurations can profoundly affect, conducively or adversely, the catalytic selectivity and product turnover frequencies (TOFs) of CO reduction reaction.

HFTCRNet: Hierarchical Fusion Transformer for Interbank Credit Rating and Risk Assessment.

As a prominent application of deep neural networks in financial literature, bank credit ratings play a pivotal role in safeguarding global economic stability and preventing crises. In the contemporary financial system, interconnectivity among banks has reached unprecedented levels. However, many existing credit risk models continue to assess each bank independently, resulting in inevitable suboptimal performance.

Imaging Anisotropic Proton Intercalation in Photochromic MoO.

Protonation represents a fundamental chemical process with promising applications in the fields of energy, environment, and memory devices. Probing the protonation mechanism, however, presents a formidable challenge owing to the elusiveness of intercalated protons. In this work, we utilize the atomic and electronic structure changes associated with protonation to directly image the proton intercalation pathways in α-MoO induced by UV illumination.

Synthesis of core@shell catalysts guided by Tammann temperature.

Designing high-performance thermal catalysts with stable catalytic sites is an important challenge. Conventional wisdom holds that strong metal-support interactions can benefit the catalyst performance, but there is a knowledge gap in generalizing this effect across different metals. Here, we have successfully developed a generalizable strong metal-support interaction strategy guided by Tammann temperatures of materials, enabling functional oxide encapsulation of transition metal nanocatalysts.

Memorize, Associate and Match: Embedding Enhancement via Fine-Grained Alignment for Image-Text Retrieval.

Image-text retrieval aims to capture the semantic correlation between images and texts. Existing image-text retrieval methods can be roughly categorized into embedding learning paradigm and pair-wise learning paradigm. The former paradigm fails to capture the fine-grained correspondence between images and texts.