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Stable lattice oxygen redox (l-OR) is the key enabler for achieving attainable high energy density in Li-rich layered oxide cathode materials for Li-ion batteries. However, the unique local structure response to oxygen redox in these materials, resulting in energy inefficiency and hysteresis, still remains elusive, preventing their potential applications. By combining the state-of-the-art neutron pair distribution function with crystal orbital overlap analysis, we directly observe the distinct local structure adaption originated from the potential O–O chemical bonds. The structure adaptability is optimized based on the nature of multi transition metals in our model compound Li1.2Ni0.13Mn0.54Co0.13O2, which accommodates the oxygen redox and at the same time preserves the global layered structure. These findings not only advance the understanding of l-OR, but also provide new perspectives in the rational design of high-energy-density cathode materials with reversible and stable l-OR.

Li- and Mn-rich (LMR) layered cathode materials have demonstrated impressive capacity and specific energy density thanks to their intertwined redox centers including transition metal cations and oxygen anions. Although tremendous efforts have been devoted to the investigation of the electrochemically driven redox evolution in LMR cathode at ambient temperature, their behavior under a mildly elevated temperature (up to ∼100 °C), with or without electrochemical driving force, remains largely unexplored. Here we show a systematic study of the thermally driven surface-to-bulk redox coupling effect in charged Li1.2Ni0.15Co0.1Mn0.55O2. We for the first time observed a charge transfer between the bulk oxygen anions and the surface transition metal cations under ∼100 °C, which is attributed to the thermally driven redistribution of Li ions. This finding highlights the nonequilibrium state and dynamic nature of the LMR material at deeply delithiated state upon a mild temperature perturbation.

Lung adenocarcinoma (LUAD), the most common type of lung cancer, is a leading cause of cancer-related mortality. NT5E, an ecto-5'-nucleotidase enzyme, has been implicated in cancer progression, particularly in efferocytosis. Despite its potential involvement, the prognostic significance of NT5E and relationship with immune cell infiltration in LUAD have not been extensively explored. In this study, we performed a comprehensive analysis to elucidate the expression patterns of NT5E and its prognostic implications in LUAD using data from diverse public databases. Multiple computational algorithms, including CIBERSORT, ESTIMATE, and xCell, were employed to assess the correlation between NT5E expression and immune cell infiltration. We found that NT5E was significantly overexpressed at both the mRNA and protein levels in LUAD tissues. Elevated NT5E expression was significantly linked to multiple clinicopathological factors, including metastasis and pathological stage, and served as a strong predictor of poor prognosis in LUAD patients. Gene Set Enrichment Analysis (GSEA) indicated that NT5E plays a crucial role in regulating immune responses, as evidenced by differential gene expression associated with NT5E levels. A strong positive correlation was observed between NT5E expression and the presence of immune cells, including dendritic cells, macrophages, and CD4+ T cells, as well as the expression of various immune cell markers, suggesting that NT5E may influence the prognosis of LUAD patients by regulating immune cell infiltration. Additionally, drug sensitivity analysis highlights the potential of selumetinib and PD318088, both MEK1/2 inhibitors, to target NT5E in LUAD treatment, suggesting their use as single agents or in combination with other therapies. Collectively, these findings establish NT5E as a promising prognostic biomarker and therapeutic target in LUAD, particularly in the context of immune cell infiltration. [ABSTRACT FROM AUTHOR]

Accurate modeling of lithium-ion battery (LIB) electrode microstructures provides essential references for understanding degradation mechanisms and optimizing materials. Traditional segmentation methods often struggle to accurately capture the complex microstructures of porous LIB electrodes in focused ion beam scanning electron microscopy (FIB-SEM) data. In this work, we develop a deep learning model based on the Swin Transformer to segment FIB-SEM data of a lithium cobalt oxide electrode, utilizing fused secondary and backscattered electron images. The proposed approach outperforms other deep learning methods, enabling the acquirement of 3D microstructure with reduced particle elongated artifacts. Analyses of the segmented microstructures reveal improved electrode tortuosity and pore connectivity crucial for ion and electron transport, emphasizing the necessity of accurate 3D modeling for reliable battery performance predictions. These results suggest a path toward voxel-level degradation analysis through more sensible battery simulation on high-fidelity microstructure models directly twinned from real porous electrodes. [ABSTRACT FROM AUTHOR]

Lithium cobalt oxide (LiCoO2) is an irreplaceable cathode material for lithium‐ion batteries with high volumetric energy density. The prevailing O3 phase LiCoO2 adopts the ABCABC (A, B, and C stand for lattice sites in the close‐packed plane) stacking modes of close‐packed oxygen atoms. Currently, the focus of LiCoO2 development is application at high voltage (>4.55 V versus Li+/Li) to achieve a high specific capacity (>190 mAh g−1). However, cycled with a high cutoff voltage, O3–LiCoO2 suffers from rapid capacity decay. The causes of failure are mostly attributed to the irreversible transitions to H1‐3/O1 phase after deep delithiation and severe interfacial reactions with electrolytes. In addition to O3, LiCoO2 is also known to crystalize in an O2 phase with ABAC stacking. Since its discovery, little is known about the high‐voltage behavior of O2–LiCoO2. Herein, through systematic comparison between electrochemical performances of O3 and O2 LiCoO2 at high voltage, the significantly better stability of O2–LiCoO2 (>4.5 V) than that of O3–LiCoO2 in the same micro‐sized particle morphology is revealed. Combining various characterization techniques and multiscale simulation, the outstanding high‐voltage stability of O2–LiCoO2 is attributed to the high Li diffusivity and ideal mechanical properties. Uniform Li+ distribution and balanced internal stress loading may hold the key to improving the high‐voltage performance of LiCoO2. [ABSTRACT FROM AUTHOR]

The rapid growth of deepfake technology, generating realistic manipulated media, poses a significant threat due to potential misuse. Therefore, effective detection methods are urgently needed to prevent malicious use, as current approaches often focus on single modalities or the simple fusion of audio–visual signals, limiting their accuracy. To solve this problem, we propose a deepfake detection scheme based on bimodal temporal feature prediction, which innovatively introduces the idea of temporal feature prediction into the audio–video bimodal deepfake detection task, aiming at fully exploiting the temporal laws of audio–visual modalities. First, pairs of adjacent audio–video sequence clips are used to construct input quadruples, and a dual-stream network is employed to extract temporal feature representations from video and audio, respectively. A video prediction module and an audio prediction module are designed to capture the temporal inconsistencies within each single modality by predicting future temporal features and comparing them with reference features. Then, a projection layer network is designed to align the audio–visual features, using contrastive loss functions to perform contrastive learning and maximize the differences between real and fake video modalities. Experiments on the FakeAVCeleb dataset demonstrate superior performance with an accuracy of 84.33% and an AUC of 89.91%, outperforming existing methods and confirming the effectiveness of our approach in deepfake detection. [ABSTRACT FROM AUTHOR]