Imaging Anisotropic Proton Intercalation in Photochromic MoO3.

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-MoO3 induced by UV illumination. We reveal the anisotropic intercalation behavior which is initiated by photocatalyzed water dissociation preferentially at the (001) edges and then propagates along the c axis, transforming α-MoO3 into HxMoO3 to realize photochromism. This photochromic process can be reversed via heating in air, leading to anisotropic proton deintercalation, also preferentially along the c axis. The observed anisotropic behavior can be attributed to the intrinsically low energy barriers for both proton migration along the c axis and water dissociation/formation at (001) edges.

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. As an illustrative example, Co@BaAl2O4 core@shell is synthesized and tracked in real-time through in-situ microscopy and spectroscopy, revealing an unconventional strong metal-support interaction encapsulation mechanism. Notably, Co@BaAl2O4 exhibits exceptional activity relative to previously reported core@shell catalysts, displaying excellent long-term stability during high-temperature chemical reactions and overcoming the durability and reusability limitations of conventional supported catalysts. This pioneering design and widely applicable approach has been validated to guide the encapsulation of various transition metal nanoparticles for environmental tolerance functionalities, offering great potential to advance energy, catalysis, and environmental fields.

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. The latter paradigm achieves fine-grained alignment between regions and words, but the high cost of pair-wise computation leads to slow retrieval speed. In this paper, we propose a novel method named MEMBER by using Memory-based EMBedding Enhancement for image-text Retrieval (MEMBER), which introduces global memory banks to enable fine-grained alignment and fusion in embedding learning paradigm. Specifically, we enrich image (resp., text) features with relevant text (resp., image) features stored in the text (resp., image) memory bank. In this way, our model not only accomplishes mutual embedding enhancement across two modalities, but also maintains the retrieval efficiency. Extensive experiments demonstrate that our MEMBER remarkably outperforms state-of-the-art approaches on two large-scale benchmark datasets.