Fluorine-doped Carbon Support Enables Superfast Oxygen Reduction Kinetics by Breaking the Scaling Relationship.

It is well-established that Pt-based catalysts suffer from the unfavorable linear scaling relationship (LSR) between *OOH and *OH (ΔG(*OOH) = ΔG(*OH) + 3.2 ± 0.2 eV) for the oxygen reduction reaction (ORR), resulting in a great challenge to significantly reduced ORR overpotentials. Herein, we propose a universal and feasible strategy of fluorine-doped carbon supports, which optimize interfacial microenvironment of Pt-based catalysts and thus significantly enhance their reactive kinetics. The introduction of C-F bonds not only weakens the *OH binding energy, but also stabilizes the *OOH intermediate, resulting in a break of LSR. Furthermore, fluorine-doped carbon constructs a local super-hydrophobic interface that facilitates the diffusion of H2O and the mass transfer of O2. Electrochemical tests show that the F-doped carbon-supported Pt catalysts exhibit over 2-fold higher mass activities than those without F modification. More importantly, those catalysts also demonstrate excellent stability in both rotating disk electrode (RDE) and membrane electrode assembly (MEA) tests. This study not only validates the feasibility of tuning the electrocatalytic microenvironment to improve mass transport and to break the scaling relationship, but also provides a universal catalyst design paradigm for other gas-involving electrocatalytic reactions.

Sieving-type Electric Double Layer with Hydrogen Bond Interlocking to Stable Zinc Metal Anode.

The stability of aqueous zinc metal batteries is significantly affected by side reactions and dendrite growth on the anode interface, which primarily originate from water and anions. Herein, we introduce a multi H-bond site additive, 2, 2'-Sulfonyldiethanol (SDE), into an aqueous electrolyte to construct a sieving-type electric double layer (EDL) by hydrogen bond interlock in order to address these issues. On the one hand, SDE replaces H2O and SO42- anions that are adsorbed on the zinc anode surface, expelling H2O/SO42- from the EDL and thereby reducing the content of H2O/SO42- at the interface. On the other hand, when Zn2+ are de-solvated at the interface during the plating, the strong hydrogen bond interaction between SDE and H2O/SO42- can trap H2O/SO42- from the EDL, further decreasing their content at the interface. This effectively sieves them out of the zinc anode interface and inhibits the side reactions. Moreover, the unique characteristics of trapped SO42- anions can restrict their diffusion, thereby enhancing the transference number of Zn2+ and promoting dendrite-free deposition and growth of Zn. Consequently, utilizing an SDE/ZnSO4 electrolyte enables excellent cycling stability in Zn//Zn symmetrical cells and Zn//MnO2 full cells with lifespans exceeding 3500 h and 2500 cycles respectively.

MiR-184-3p in the paraventricular nucleus participates in the neurobiology of depression via regulation of the hypothalamus-pituitary-adrenal axis.

Hyperactivity of the hypothalamic-pituitary-adrenal (HPA) axis during chronic stress is essential for the pathogenesis of depression, and increased activity of cAMP response element binding protein (CREB)-regulated transcription co-activator 1 (CRTC1) in the paraventricular nucleus (PVN) plays a critical role. As a well-investigated microRNA (miRNA), miR-184 has two forms, miR-184-3p and miR-184-5p. Recently, miRNAs target genes predictive analysis and dual-luciferase reporter assays identified an inhibitory role of miR-184-3p on CRTC1 expression. Therefore, we speculated that miR-184-3p regulation was responsible for the effects of chronic stress on CRTC1 in the PVN. Various methods, including the chronic social defeat stress (CSDS) model of depression, behavioral tests, Western blotting, co-immunoprecipitation (Co-IP), quantitative real-time reverse transcription PCR (qRT-PCR), immunofluorescence, and adeno-associated virus (AAV)-mediated gene transfer, were used. CSDS evidently downregulated the level of miR-184-3p, but not miR-184-5p, in the PVN. Genetic knockdown and pharmacological inhibition of miR-184-3p in the PVN induced various depressive-like symptoms (e.g., abnormal behaviors, HPA hyperactivity, enhanced CRTC1 function in PVN neurons, downregulation of hippocampal neurogenesis, and decreased brain-derived neurotrophic factor (BDNF) signaling) in naïve male C57BL/6J mice. In contrast, genetic overexpression and pharmacological activation of miR-184-3p in the PVN produced significant beneficial effects against CSDS. MiR-184-3p in the PVN was necessary for the antidepressant actions of two well-known SSRIs, fluoxetine and paroxetine. Collectively. miR-184-3p was also implicated in the neurobiology of depression and may be a viable target for novel antidepressants.

Postoperative facial prediction for mandibular defect based on surface mesh deformation.

OBJECTIVES: This study aims to introduce a novel predictive model for the post-operative facial contours of patients with mandibular defect, addressing limitations in current methodologies that fail to preserve geometric features and lack interpretability. METHODS: Utilizing surface mesh theory and deep learning, our model diverges from traditional point cloud approaches by employing surface triangular mesh grids. We extract latent variables using a Mesh Convolutional Restricted Boltzmann Machines (MCRBM) model to generate a three-dimensional deformation field, aiming to enhance geometric information preservation and interpretability. RESULTS: Experimental evaluations of our model demonstrate a prediction accuracy of 91.2 %, which represents a significant improvement over traditional machine learning-based methods. CONCLUSIONS: The proposed model offers a promising new tool for pre-operative planning in oral and maxillofacial surgery. It significantly enhances the accuracy of post-operative facial contour predictions for mandibular defect reconstructions, providing substantial advancements over previous approaches.

A review of deep learning methods for ligand based drug virtual screening.

Drug discovery is costly and time consuming, and modern drug discovery endeavors are progressively reliant on computational methodologies, aiming to mitigate temporal and financial expenditures associated with the process. In particular, the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic. Recently, the performance of deep learning methods in drug virtual screening has been particularly prominent. It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening, select different models for different drug screening problems, exploit the advantages of deep learning models, and further improve the capability of deep learning in drug virtual screening. This review first introduces the basic concepts of drug virtual screening, common datasets, and data representation methods. Then, large numbers of common deep learning methods for drug virtual screening are compared and analyzed. In addition, a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening. Finally, the existing challenges and future directions in the field of virtual screening are presented.

Extracellular vesicles loaded with ApoB-100 protein affect the occurrence of coronary heart disease in patients after injury of spinal cord.

Spinal cord injury (SCI) patients have an increased susceptibility to coronary heart disease (CHD) due to dysregulated lipid deposition. We conducted a comprehensive investigation to gain insights into the specific roles of Apolipoprotein B-100 (APOB-100) in the development of CHD in patients suffering from SCI. First, we established an SCI rat model through semitransection. APOB-100 expression in plasma exosomes obtained from patients were determined. Subsequently, we found APOB-100 affected macrophage polarization when treating co-cultured neurons/macrophages lacking Sortilin with extracellular vesicles derived from SCI rats, where APOB-100 co-immunoprecipitated with Sortilin. Moreover, APOB-100 upregulation reduced neuronal cell viability and triggered apoptosis by upregulating Sortilin, leading to a decline in the Basso, Beattie, and Bresnahan (BBB) scale, exacerbation of neuron injury, increased macrophage infiltration, and elevated blood lipid-related indicators in SCI rats, which could be reversed by silencing Sortilin. In conclusion, APOB-100 from post-SCI patients' extracellular vesicles upregulates Sortilin, thereby endangering those patients to CHD.

The role of cytokine receptor-like factor 1 (CRLF1) in facet joint osteoarthritis pathogenesis.

BACKGROUND: Facet joint osteoarthritis (FJOA) is a prevalent condition contributing to low back pain, particularly in the elderly population. This study aimed to investigate the potential role of Cytokine Receptor-like Factor 1 (CRLF1) in FJOA pathogenesis and its therapeutic implications. METHODS: Bioinformatics analysis was utilized to identify CRLF1 as the target gene, followed by quantification of CRLF1 expression levels and joint degeneration degree using immunohistochemistry (IHC). In primary chondrocytes, the inhibition of CRLF1 expression by siRNA was performed, and Western blot analysis was conducted to evaluate the involvement of the extracellular matrix and MAPK/ERK signaling pathway. Flow cytometry was employed to assess the apoptosis rate of chondrocytes, while immunofluorescence (IF) was utilized to evaluate the localization of CRLF1, cleaved-caspase3, MMP13, COL2A1, and ERK. RESULTS: The expression of CRLF1 was found to be significantly elevated in FJOA tissues compared to normal tissues. Through the use of loss-of-function assays, it was determined that CRLF1 not only enhanced the rate of apoptosis in chondrocytes, but also facilitated the degradation of the extracellular matrix in vitro. Furthermore, CRLF1 was found to activate the ERK1/2 pathways. The pro-arthritic effects elicited by CRLF1 were mitigated by treatment with the MEK inhibitor U0126 in chondrocytes. CONCLUSION: These results suggest that CRLF1 enhances chondrocytes apoptosis and extracellular matrix degration in FJOA and thus may therefore be a potential therapeutic target for FJOA.

Rationally Designed L12-Pt2RhFe Intermetallic Catalyst with High CO-Tolerance for Alkaline Methanol Electrooxidation.

It is a grand challenge to deep understanding of and precise control over functional sites for the rational design of highly efficient catalysts for methanol electrooxidation. Here, an L12-Pt2RhFe intermetallic catalyst with integrated functional components is demonstrated, which exhibits exceptional CO tolerance. The Pt2RhFe/C achieves a superior mass activity of 6.43 A mgPt -1, which is 2.23-fold and 3.53-fold higher than those of PtRu/C and Pt/C. Impressively, the Pt2RhFe/C exhibits a significant enhancement in durability owing to its high CO-tolerance and stability. Density functional theory calculations reveal that high performance of Pt2RhFe intermetallic catalyst arises from the synergistic effect: the strong OH binding energy (OHBE) at Fe sites induce stably adsorbed OH species and thus facilitate the dehydrogenation step of methanol via rapid hydrogen transfer, while moderate OHBE at Rh sites promote the formation of the transition state (Pt-CO···OH-Rh) with a low activation barrier for CO removal. This work provides new insights into the role of OH binding strength in the removal of CO species, which is beneficial for the rational design of highly efficient catalysts.

A Microscopically Heterogeneous Colloid Electrolyte for Extremely Fast-Charging and Long-Calendar-Life Silicon-Based Lithium-Ion Batteries.

Fast-charging capability and calendar life are critical metrics in rechargeable batteries, especially in silicon-based batteries that are susceptible to sluggish Li+ desolvation kinetics and HF-induced corrosion. No existing electrolyte simultaneously tackles both these pivotal challenges. Here we report a microscopically heterogeneous covalent organic nanosheet (CON) colloid electrolyte for extremely fast-charging and long-calendar-life Si-based lithium-ion batteries. Theoretical calculations and operando Raman spectroscopy reveal the fundamental mechanism of the multiscale noncovalent interaction, which involves the mesoscopic CON attenuating the microscopic Li+-solvent coordination, thereby expediting the Li+ desolvation kinetics. This electrolyte design enables extremely fast-charging capabilities of the full cell, both at 8C (83.1% state of charge) and 10C (81.3% state of charge). Remarkably, the colloid electrolyte demonstrates record-breaking cycling performance at 10C (capacity retention of 92.39% after 400 cycles). Moreover, benefiting from the robust adsorption capability of mesoporous CON towards HF and water, a notable improvement is observed in the calendar life of the full cell. This study highlights the role of microscopically heterogeneous colloid electrolytes in enhancing the fast-charging capability and calendar life of Si-based Li-ion batteries. Our work offers fresh perspectives on electrolyte design with multiscale interactions, providing insightful guidance for the development of alkali-ion/metal batteries operating under harsh environments.

Analysis of gene expression in microglial apoptotic cell clearance following spinal cord injury based on machine learning algorithms.

Spinal cord injury (SCI) is a severe neurological complication following spinal fracture, which has long posed a challenge for clinicians. Microglia play a dual role in the pathophysiological process after SCI, both beneficial and detrimental. The underlying mechanisms of microglial actions following SCI require further exploration. The present study combined three different machine learning algorithms, namely weighted gene co-expression network analysis, random forest analysis and least absolute shrinkage and selection operator analysis, to screen for differentially expressed genes in the GSE96055 microglia dataset after SCI. It then used protein-protein interaction networks and gene set enrichment analysis with single genes to investigate the key genes and signaling pathways involved in microglial function following SCI. The results indicated that microglia not only participate in neuroinflammation but also serve a significant role in the clearance mechanism of apoptotic cells following SCI. Notably, bioinformatics analysis and lipopolysaccharide + UNC569 (a MerTK-specific inhibitor) stimulation of BV2 cell experiments showed that the expression levels of Anxa2, Myo1e and Spp1 in microglia were significantly upregulated following SCI, thus potentially involved in regulating the clearance mechanism of apoptotic cells. The present study suggested that Anxa2, Myo1e and Spp1 may serve as potential targets for the future treatment of SCI and provided a theoretical basis for the development of new methods and drugs for treating SCI.

Pt3Sn0.5Mn0.5 Intermetallic Electrocatalyst with Superior Stability for CO-Resilient Methanol Oxidation.

The sluggish kinetics of methanol oxidation reaction (MOR) and poor long-term durability of catalysts are the main restrictions of the large-scale applications of direct methanol fuel cells (DMFCs). Herein, we demonstrated an inspirational ternary Pt3Sn0.5Mn0.5/DMC intermetallic catalyst that reached 4.78 mA cm-2 and 2.39 A mg-1Pt for methanol oxidation, which were 2.50/2.44 and 5.62/5.31 times that of commercial PtRu/C and Pt/C. After the durability test, Pt3Sn0.5Mn0.5/DMC presented a very low current density attenuation (38.5%), which was significantly lower than those for commercial PtRu/C catalyst (84.2%) and Pt/C (93.1%). Density functional theory (DFT) calculations revealed that the coregulation of Sn and Mn altered the surface electronic structure and endowed Pt3Sn0.5Mn0.5 with selective adsorption of Pt for CO and Sn for OH, which optimized the adsorption strength for intermediates and improved the reaction kinetics of MOR. Beyond offering an advanced electrocatalyst, this study provided a new point of view for the rational design of superior methanol oxidation catalysts for DMFC.

A Novel Hierarchical Cross-Stream Aggregation Neural Network for Semantic Segmentation of 3-D Dental Surface Models.

Accurate teeth delineation on 3-D dental models is essential for individualized orthodontic treatment planning. Pioneering works like PointNet suggest a promising direction to conduct efficient and accurate 3-D dental model analyses in end-to-end learnable fashions. Recent studies further imply that multistream architectures to concurrently learn geometric representations from different inputs/views (e.g., coordinates and normals) are beneficial for segmenting teeth with varying conditions. However, such multistream networks typically adopt simple late-fusion strategies to combine features captured from raw inputs that encode complementary but fundamentally different geometric information, potentially hampering their accuracy in end-to-end semantic segmentation. This article presents a hierarchical cross-stream aggregation (HiCA) network to learn more discriminative point/cell-wise representations from multiview inputs for fine-grained 3-D semantic segmentation. Specifically, based upon our multistream backbone with input-tailored feature extractors, we first design a contextual cross-steam aggregation (CA) module conditioned on interstream consistency to boost each view's contextual representation learning jointly. Then, before the late fusion of different streams' outputs for segmentation, we further deploy a discriminative cross-stream aggregation (DA) module to concurrently update all views' discriminative representation learning by leveraging a specific graph attention strategy induced by multiview prototype learning. On both public and in-house datasets of real-patient dental models, our method significantly outperformed state-of-the-art (SOTA) deep learning methods for teeth semantic segmentation. In addition, extended experimental results suggest the applicability of HiCA to other general 3-D shape segmentation tasks. The code is available at https://github.com/ladderlab-xjtu/HiCA.

Multi-scale revealing how real catalyst layer interfaces dominate the local oxygen transport resistance in ultra-low platinum PEMFC.

In view of a catalyst layer (CL) with low-Pt causing higher local transport resistance of O2 (Rlocal), we propose a multi-study methodology that combines CO poisoning, the limiting current density method, and electrochemical impedance spectroscopy to reveal how real CL interfaces dominate Rlocal. Experimental results indicate that the ionomer is not evenly distributed on the catalyst surface, and the uniformity of ionomer distribution does not show a positive correlation with the ionomer content. When the ionomer coverage on the supported catalyst surface is below 20 %, the ECSA is only 10 m2·g-1, and the ionomer coverage on the supported catalyst surface reaches 60 %, the ECSA is close to 40 m2·g-1. The ECSA has a positive correlation with ionomer coverage. Because the ECSA is measured by CO poisoning, it can be inferred that the platinum contacted with ionomer can generate effective active sites. Furthermore, a more uniform distribution of ionomer can create additional proton transport channels and reduce the distance for oxygen transport from the catalyst layer bulk to the active sites. A higher ECSA and a shorter distance for oxygen transport will reduce the Rlocal, leading to better performance.

Pt1.8Pd0.2CuGa Intermetallic Nanocatalysts with Enhanced Methanol Oxidation Performance for Efficient Hybrid Seawater Electrolysis.

Seawater electrolysis is a potentially cost-effective approach to green hydrogen production, but it currently faces substantial challenges for its high energy consumption and the interference of chlorine evolution reaction (ClER). Replacing the energy-demanding oxygen evolution reaction with methanol oxidation reaction (MOR) represents a promising alternative, as MOR occurs at a significantly low anodic potential, which cannot only reduce the voltage needed for electrolysis but also completely circumvents ClER. To this end, developing high-performance MOR catalysts is a key. Herein, a novel quaternary Pt1.8Pd0.2CuGa/C intermetallic nanoparticle (i-NP) catalyst is reported, which shows a high mass activity (11.13 A mgPGM -1), a large specific activity (18.13 mA cmPGM -2), and outstanding stability toward alkaline MOR. Advanced characterization and density functional theory calculations reveal that the introduction of atomically distributed Pd in Pt2CuGa intermetallic markedly promotes the oxidation of key reaction intermediates by enriching electron concentration around Pt sites, resulting in weak adsorption of carbon-containing intermediates and favorable adsorption of synergistic OH- groups near Pd sites. MOR-assisted seawater electrolysis is demonstrated, which continuously operates under 1.23 V for 240 h in simulated seawater and 120 h in natural seawater without notable degradation.

Facet-Dependent Surface Restructuring on Nickel (Oxy)hydroxides: A Self-Activation Process for Enhanced Oxygen Evolution Reaction.

Unraveling the catalyst surface structure and behavior during reactions is essential for both mechanistic understanding and performance optimization. Here we report a phenomenon of facet-dependent surface restructuring intrinsic to ß-Ni(OH)2 catalysts during oxygen evolution reaction (OER), discovered by the correlative ex situ and operando characterization. The ex situ study after OER reveals ß-Ni(OH)2 restructuring at the edge facets to form nanoporous Ni1-xO, which is Ni deficient containing Ni3+ species. Operando liquid transmission electron microscopy (TEM) and Raman spectroscopy further identify the active role of the intermediate ß-NiOOH phase in both the OER catalysis and Ni1-xO formation, pinpointing the complete surface restructuring pathway. Such surface restructuring is shown to effectively increase the exposed active sites, accelerate Ni oxidation kinetics, and optimize *OH intermediate bonding energy toward fast OER kinetics, which leads to an extraordinary activity enhancement of ∼16-fold. Facilitated by such a self-activation process, the specially prepared ß-Ni(OH)2 with larger edge facets exhibits a 470-fold current enhancement than that of the benchmark IrO2, demonstrating a promising way to optimize metal-(oxy)hydroxide-based catalysts.

ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs.

The integration of Computer-Aided Diagnosis (CAD) with Large Language Models (LLMs) presents a promising frontier in clinical applications, notably in automating diagnostic processes akin to those performed by radiologists and providing consultations similar to a virtual family doctor. Despite the promising potential of this integration, current works face at least two limitations: (1) From the perspective of a radiologist, existing studies typically have a restricted scope of applicable imaging domains, failing to meet the diagnostic needs of different patients. Also, the insufficient diagnostic capability of LLMs further undermine the quality and reliability of the generated medical reports. (2) Current LLMs lack the requisite depth in medical expertise, rendering them less effective as virtual family doctors due to the potential unreliability of the advice provided during patient consultations. To address these limitations, we introduce ChatCAD+, to be universal and reliable. Specifically, it is featured by two main modules: (1) Reliable Report Generation and (2) Reliable Interaction. The Reliable Report Generation module is capable of interpreting medical images from diverse domains and generate high-quality medical reports via our proposed hierarchical in-context learning. Concurrently, the interaction module leverages up-to-date information from reputable medical websites to provide reliable medical advice. Together, these designed modules synergize to closely align with the expertise of human medical professionals, offering enhanced consistency and reliability for interpretation and advice. The source code is available at GitHub.

Hspb1 and Lgals3 in spinal neurons are closely associated with autophagy following excitotoxicity based on machine learning algorithms.

Excitotoxicity represents the primary cause of neuronal death following spinal cord injury (SCI). While autophagy plays a critical and intricate role in SCI, the specific mechanism underlying the relationship between excitotoxicity and autophagy in SCI has been largely overlooked. In this study, we isolated primary spinal cord neurons from neonatal rats and induced excitotoxic neuronal injury by high concentrations of glutamic acid, mimicking an excitotoxic injury model. Subsequently, we performed transcriptome sequencing. Leveraging machine learning algorithms, including weighted correlation network analysis (WGCNA), random forest analysis (RF), and least absolute shrinkage and selection operator analysis (LASSO), we conducted a comprehensive investigation into key genes associated with spinal cord neuron injury. We also utilized protein-protein interaction network (PPI) analysis to identify pivotal proteins regulating key gene expression and analyzed key genes from public datasets (GSE2599, GSE20907, GSE45006, and GSE174549). Our findings revealed that six genes-Anxa2, S100a10, Ccng1, Timp1, Hspb1, and Lgals3-were significantly upregulated not only in vitro in neurons subjected to excitotoxic injury but also in rats with subacute SCI. Furthermore, Hspb1 and Lgals3 were closely linked to neuronal autophagy induced by excitotoxicity. Our findings contribute to a better understanding of excitotoxicity and autophagy, offering potential targets and a theoretical foundation for SCI diagnosis and treatment.

Heterogeneous MoS2 Nanosheets on Porous TiO2 Nanofibers toward Fast and Reversible Sodium-Ion Storage.

2D layered molybdenum disulfide (MoS2) has garnered considerable attention as an attractive electrode material in sodium-ion batteries (SIBs), but sluggish mass transfer kinetic and capacity fading make it suffer from inferior cycle capability. Herein, hierarchical MoS2 nanosheets decorated porous TiO2 nanofibers (MoS2 NSs@TiO2 NFs) with rich oxygen vacancies are engineered by microemulsion electrospinning method and subsequent hydrothermal/heat treatment. The MoS2 NSs@TiO2 NFs improves ion/electron transport kinetic and long-term cycling performance through distinctive porous structure and heterogeneous component. Consequently, the electrode exhibits excellent long-term Na storage capacity (298.4 mAh g-1 at 5 A g-1 over 1100 cycles and 235.6 mAh g-1 at 10 A g-1 over 7200 cycles). Employing Na3V2(PO4)3 as cathode, the full cell maintains a desirable capacity of 269.6 mAh g-1 over 700 cycles at 1.0 A g-1. The stepwise intercalation-conversion and insertion/extraction endows outstanding Na+ storage performance, which yields valuable insight into the advancement of fast-charging and long-cycle life SIBs anode materials.

VOx Matrix Confinement Approach to Generate Sub-3 nm L10-Pt-Based Intermetallic Catalysts for Fuel Cell Cathode.

Pt-based intermetallic compounds (IMCs) are considered as a class of promising fuel cell electrocatalysts, owing to their outstanding intrinsic activity and durability. However, the synthesis of uniformly dispersed IMCs with small sizes presents a formidable challenge during the essential high-temperature annealing process. Herein, a facile and generally applicable VOx matrix confinement strategy is demonstrated for the controllable synthesis of ordered L10-PtM (M = Fe, Co, and Mn) nanoparticles, which not only enhances the dispersion of intermetallic nanocrystals, even at high loading (40 wt%), but also simplifies the oxide removal and acid-washing procedures. Taking intermetallic PtCo as an example, the as-prepared catalyst displays a high-performance oxygen reduction activity (mass activity of 1.52 A mgPt -1) and excellent stability in the membrane electrode assemblies (MEAs) (the ECSA has just 7% decay after durability test). This strategy provides an economical and scalable route for the controlled synthesis of Pt-based intermetallic catalysts, which can pave a way for the commercialization of fuel cell technologies.

A Dielectric MXene-Induced Self-Built Electric Field in Polymer Electrolyte Triggering Fast Lithium-Ion Transport and High-Voltage Cycling Stability.

Quasi-solid polymer electrolyte (QPE) lithium (Li)-metal battery holds significant promise in the application of high-energy-density batteries, yet it suffers from low ionic conductivity and poor oxidation stability. Herein, a novel self-built electric field (SBEF) strategy is proposed to enhance Li+ transportation and accelerate the degradation dynamics of carbon-fluorine bond cleavage in LiTFSI by optimizing the termination of MXene. Among them, the SBEF induced by dielectric Nb4C3F2 MXene effectively constructs highly conductive LiF-enriched SEI and CEI stable interfaces, moreover, enhances the electrochemical performance of the QPE. The related Li-ion transfer mechanism and dual-reinforced stable interface are thoroughly investigated using ab initio molecular dynamics, COMSOL, XPS depth profiling, and ToF-SIMS. This comprehensive approach results in a high conductivity of 1.34 mS cm-1, leading to a small polarization of approximately 25 mV for Li//Li symmetric cell after 6000 h. Furthermore, it enables a prolonged cycle life at a high voltage of up to 4.6 V. Overall, this work not only broadens the application of MXene for QPE but also inspires the great potential of the self-built electric field in QPE-based high-voltage batteries.