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Proton transport in nanofluidic channels is not only fundamentally important but also essential for energy applications. Although various strategies have been developed to improve the concentration of active protons in the nanochannels, it remains challenging to achieve a proton conductivity higher than that of Nafion, the benchmark for proton conductors. Here, taking H 3 Sb 3 P 2 O 14 and HSbP 2 O 8 as examples, we show that the interactions between protons and the layer frameworks in layered solid acid H n M n Z 2 O 3n+5 are substantially reduced at the monolayer limit, which significantly increases the number of active protons and consequently improves the proton conductivities by ∼8 ‒ 66 times depending on the humidity. The membranes assembled by monolayer H 3 Sb 3 P 2 O 14 and HSbP 2 O 8 nanosheets exhibit in-plane proton conductivities of ~ 1.02 and 1.18 S cm −1 at 100% relative humidity and 90 °C, respectively, which are over 5 times higher than the conductivity of Nafion. This work provides a general strategy for facilitating proton transport, which will have broad implications in advancing both nanofluidic research and device applications from energy storage and conversion to neuromorphic computing. High performance proton conductor is highly desired for energy application. Here authors report an increase in the number of active protons in solid acid at the monolayer limit, enabling membranes with proton conductivity 5 times higher than that of Nafion.

Aqueous zinc‐manganese (Zn–Mn) batteries have promising potential in large‐scale energy storage applications since they are highly safe, environment‐friendly, and low‐cost. However, the practicality of Mn‐based materials is plagued by their structural collapse and uncertain energy storage mechanism upon cycling. Herein, this work designs an amorphous manganese borate (a‐MnBOx) material via disordered coordination to alleviate the above issues and improve the electrochemical performance of Zn–Mn batteries. The unique physicochemical characteristic of a‐MnBOx enables the inner a‐MnBOx to serve as a robust framework in the initial energy storage process. Additionally, the amorphous manganese dioxide, amorphous ZnxMnO(OH)2, and Zn4SO4(OH)6·4H2O active components form on the surface of a‐MnBOx during the charge/discharge process. The detailed in situ/ex situ characterization demonstrates that the heterostructure of the inner a‐MnBOx and surface multicomponent phases endows two energy storage modes (Zn2+/H+ intercalation/deintercalation process and reversible conversion mechanism between the ZnxMnO(OH)2 and Zn4SO4(OH)6·4H2O) phases). Therefore, the obtained Zn//a‐MnBOx battery exhibits a high specific capacity of 360.4 mAh g−1, a high energy density of 484.2 Wh kg−1, and impressive cycling stability (97.0% capacity retention after 10 000 cycles). This finding on a‐MnBOx with a dual‐energy storage mechanism provides new opportunities for developing high‐performance aqueous Zn–Mn batteries. A conceptual amorphous manganese borate material for AZIBs is designed via a disordered coordination strategy. The unique physicochemical characteristic of a‐MnBOx can form the a‐MnO2, ZnxMnO(OH)2, and Zn4SO4(OH)6·4H2O phases, realizing multiple energy storage modes for enhancing the charge storage ability.

Solid‐state lithium‐metal batteries based on poly(vinylidene fluoride‐co‐hexafluoropropylene) (PVH) are frequently proposed to address the detrimental safety issue of conventional lithium‐ion batteries by eliminating the use of flammable solvents, but still face a key challenge: low capacity and sluggish charge/discharge rate due to the intrinsic large‐gradient Li+ distribution across the ionically‐inert PVH matrix. Herein, Te vacancies in form of Bi2Te3−x are proposed to polarize the PVH unit to realize efficient decoupling of lithium salts at the atomic level in PVH‐based solid polymeric electrolyte. Te vacancies in the PVH electrolyte doped with Bi2Te3−x (PVBT) induce a high‐throughput and homogenous Li+ flow within the PVH matrices and near the Li metal. Theoretical calculations show that Te vacancies own high adsorption energy with bis(trifluoromethanesulfonyl)imide anions (TFSI−), repulsive effect on Li+, and localized electron distribution, giving rise to a lithium‐ion concentration gradient of 30 mol m−3, the smallest among the PVH‐based inorganic/organic composite electrolytes. Consequently, the polarized electrolyte owns an unprecedented high‐rate battery capacity of 114 mAh g−1 at ∼700 mA g−1 and also superior capacity performances with a cathode loading of 12 mg cm−2, outperforming the state‐of‐art PVH‐based inorganic/organic composite electrolytes in Li||LiFePO4 battery. The work demonstrates an efficient strategy for achieving fast Li+ diffusion dynamics across polymeric matrices of classic solid‐state electrolytes. Te vacancies in Bi2Te3−x regulate the electrostatic microenvironment within PVH‐based solid electrolytes, homogenizing Li+ transport through TFSI− adsorption and Li+ repulsive effects. This strategy minimizes the Li+ gradient to 30 mol m−3 and enables a high‐rate capacity of 114 mAh g−1 at 4 C for lithium‐metal batteries.

Purpose Immunohistochemical staining of programmed death-ligand 1 (PD-L1) in tumor biopsies acquired through invasive procedures is routinely employed in clinical practice to identify patients who are most likely to benefit from anti-programmed cell death protein 1 (PD-1) therapy. Nevertheless, PD-L1 expression is observed in various cellular subsets within tumors and their microenvironments, including tumor cells, dendritic cells, and macrophages. The impact of PD-L1 expression across these different cell types on the responsiveness to anti-PD-1 treatment is yet to be fully understood. Methods We synthesized polymer-based lysosome-targeting chimeras (LYTACs) that incorporate both PD-L1-targeting motifs and liver cell-specific asialoglycoprotein receptor (ASGPR) recognition elements. Small-animal positron emission tomography (PET) imaging of PD-L1 expression was also conducted using a PD-L1-specific radiotracer 89 Zr-αPD-L1/Fab. Results The PD-L1 LYTAC platform was capable of specifically degrading PD-L1 expressed on liver cancer cells through the lysosomal degradation pathway via ASGPR without impacting the PD-L1 expression on host cells. When coupled with whole-body PD-L1 PET imaging, our studies revealed that host cell PD-L1, rather than tumor cell PD-L1, is pivotal in the antitumor response to anti-PD-1 therapy in a mouse model of liver cancer. Conclusion The LYTAC strategy, enhanced by PET imaging, has the potential to surmount the limitations of knockout mouse models and to provide a versatile approach for the selective degradation of target proteins in vivo. This could significantly aid in the investigation of the roles and mechanisms of protein functions associated with specific cell subsets in living subjects.

Trajectory representation learning transforms raw trajectory data (sequences of spatiotemporal points) into low-dimensional representation vectors to improve downstream tasks such as trajectory similarity computation, prediction, and classification. Existing models primarily adopt self-supervised learning frameworks, often employing models like Recurrent Neural Networks (RNNs) as encoders to capture local dependency in trajectory sequences. However, individual mobility within urban areas exhibits regular and periodic patterns, suggesting the need for a more comprehensive representation from both local and global perspectives. To address this, we propose TrajRL-TFF, a trajectory representation learning method based on time-domain and frequency-domain feature fusion. First, considering the heterogeneous distribution of trajectory data in space, a quadtree is employed for spatial partitioning and coding. Then, each trajectory is converted into a quadtree-code based time series (i.e., time-domain signal), with its corresponding frequency-domain signal derived via Discrete Fourier Transform (DFT). Finally, a trajectory encoder, combining an RNN-based time-domain encoder and a Transformer-based frequency domain encoder, is constructed to capture the trajectory’s local and global features, respectively, and trained by a self-supervised sequence encoding-decoding framework with trajectory perturbation-reconstruction task. Experiments demonstrate that TrajRL-TFF outperforms baselines in downstream tasks including trajectory querying and prediction, confirming that integrating time- and frequency-domain signals enables a more comprehensive representation of human mobility regularities and patterns, which provides valuable guidance for trajectory representation learning and trajectory modeling in future studies.

18F-fluorodeoxyglucose (18F-FDG) is the most widely used radiotracer for positron emission tomography (PET) imaging in clinical oncology, owing to the elevated glycolytic activity of tumor cells. However, transient post-radiotherapy (RT) “metabolic flares” of 18F-FDG uptake are frequently observed in patients and are traditionally attributed to localized inflammatory responses. Whether these flares are linked to immune cell dynamics, particularly tumor-infiltrating T cells, and the mechanisms involved remain poorly understood. Here, we demonstrate that RT markedly upregulates intracellular adhesion molecule-1 (ICAM-1) expression and promotes T cell infiltration in tumors, as observed in both patients and mouse models. Genetic ablation of ICAM-1 significantly attenuates RT-induced metabolic flares in irradiated tumors, primarily due to reduced 18F-FDG uptake by tumor-infiltrating T cells rather than myeloid cells. Mechanistically, ICAM-1 engages with lymphocyte function-associated antigen 1 (LFA-1) to facilitate T cell clustering, thereby promoting their intratumoral accumulation and activating glycolysis and the tricarboxylic acid (TCA) cycle via the PI3K-AKT-mTOR signaling pathway. These findings identify ICAM-1 as a critical regulator of T cell metabolic reprogramming and tumor infiltration following RT, offering a mechanistic explanation for 18F-FDG PET flares. Clinical monitoring of post-RT tumor ICAM-1 expression may enhance PET interpretation and aid in distinguishing pseudoprogression from true tumor progression.

Wind erosion is largely determined by wind erosion climatic erosivity. In this study, we examined changes in wind erosion climatic erosivity during 4 seasons across northern China from 1981–2016 using 2 models: the wind erosion climatic erosivity of the Wind Erosion Equation (WEQ) model and the weather factor from the Revised Wind Erosion Equation (RWEQ) model. Results showed that wind erosion climatic erosivity derived from the 2 models was highest in spring and lowest in winter with high values over the Kumtag Desert, the Qaidam Basin, the boundary between Mongolia and China, and the Hulunbuir Sandy Land. In spring and summer, wind erosion climatic erosivity showed decreasing trends in whole of northern China from 1981–2016, whereas there was an increasing trend in wind erosion climatic erosivity over the Gobi Desert from 1992–2011. For the weather factor of the RWEQ model, the difference between northern Northwest China and the Gobi Desert and eastern-northern China was much larger than that of the wind erosion climatic erosivity of the WEQ model. In addition, in contrast to a decreasing trend in the weather factor of the RWEQ model over southern Northwest China during spring and summer from 1981–2016, the wind erosion climatic erosivity of the WEQ model showed a decreasing trend for 1981–1992 and an increasing trend for 1992–2011 over southern Northwest China. According to a comparison between dust emission and wind erosion climatic erosivity, the 2 models have the ability to project changes in future wind erosion in northern China.