Continuous monitoring of biosignals is essential for advancing early disease detection,personalized treatment,and health management.Flexible electronics,capable of accurately monitoring biosignals in daily life,have g...Continuous monitoring of biosignals is essential for advancing early disease detection,personalized treatment,and health management.Flexible electronics,capable of accurately monitoring biosignals in daily life,have garnered considerable attention due to their softness,conformability,and biocompatibility.However,several challenges remain,including imperfect skin-device interfaces,limited breathability,and insufficient mechanoelectrical stability.On-skin epidermal electronics,distinguished by their excellent conformability,breathability,and mechanoelectrical robustness,offer a promising solution for high-fidelity,long-term health monitoring.These devices can seamlessly integrate with the human body,leading to transformative advancements in future personalized healthcare.This review provides a systematic examination of recent advancements in on-skin epidermal electronics,with particular emphasis on critical aspects including material science,structural design,desired properties,and practical applications.We explore various materials,considering their properties and the corresponding structural designs developed to construct high-performance epidermal electronics.We then discuss different approaches for achieving the desired device properties necessary for long-term health monitoring,including adhesiveness,breathability,and mechanoelectrical stability.Additionally,we summarize the diverse applications of these devices in monitoring biophysical and physiological signals.Finally,we address the challenges facing these devices and outline future prospects,offering insights into the ongoing development of on-skin epidermal electronics for long-term health monitoring.展开更多
Type 2 diabetes mellitus has central complications:Diabetes,a metabolic disorder primarily characterized by hyperglycemia due to insufficient insulin secretion,or impaired insulin signaling,has significant central com...Type 2 diabetes mellitus has central complications:Diabetes,a metabolic disorder primarily characterized by hyperglycemia due to insufficient insulin secretion,or impaired insulin signaling,has significant central complications.Type 2 diabetes mellitus(T2DM),the most prevalent type of diabetes,affects more than 38 million individuals in the United States(approximately 1 in 10)and is defined by chronic hyperglycemia and insulin resistance,which refers to a reduced cellular response to insulin.展开更多
Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is p...Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.展开更多
Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives ...Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.展开更多
Phosphorus(P)is an essential nutrient for crop growth,making it important for maintaining food security as the global population continues to increase.Plants acquire P primarily via the uptake of inorganic phosphate(P...Phosphorus(P)is an essential nutrient for crop growth,making it important for maintaining food security as the global population continues to increase.Plants acquire P primarily via the uptake of inorganic phosphate(Pi)in soil through their roots.Pi,which is usually sequestered in soils,is not easily absorbed by plants and represses plant growth.Plants have developed a series of mechanisms to cope with P deficiency.Moreover,P fertilizer applications are critical for maximizing crop yield.Maize is a major cereal crop cultivated worldwide.Increasing its P-use efficiency is important for optimizing maize production.Over the past two decades,considerable progresses have been achieved in studies aimed at adapting maize varieties to changes in environmental P supply.Here,we present an overview of the morphological,physiological,and molecular mechanisms involved in P acquisition,translocation,and redistribution in maize and combine the advances in Arabidopsis and rice,to better elucidate the progress of P nutrition.Additionally,we summarize the correlation between P and abiotic stress responses.Clarifying the mechanisms relevant to improving P absorption and use in maize can guide future research on sustainable agriculture.展开更多
Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather an...Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.展开更多
Negative logarithm of the acid dissociation constant(pK_(a))significantly influences the absorption,dis-tribution,metabolism,excretion,and toxicity(ADMET)properties of molecules and is a crucial indicator in drug rese...Negative logarithm of the acid dissociation constant(pK_(a))significantly influences the absorption,dis-tribution,metabolism,excretion,and toxicity(ADMET)properties of molecules and is a crucial indicator in drug research.Given the rapid and accurate characteristics of computational methods,their role in predicting drug properties is increasingly important.Although many pK_(a) prediction models currently exist,they often focus on enhancing model precision while neglecting interpretability.In this study,we present GraFpKa,a pK_(a) prediction model using graph neural networks(GNNs)and molecular finger-prints.The results show that our acidic and basic models achieved mean absolute errors(MAEs)of 0.621 and 0.402,respectively,on the test set,demonstrating good predictive performance.Notably,to improve interpretability,GraFpKa also incorporates Integrated Gradients(IGs),providing a clearer visual description of the atoms significantly affecting the pK_(a) values.The high reliability and interpretability of GraFpKa ensure accurate pKa predictions while also facilitating a deeper understanding of the relation-ship between molecular structure and pK_(a) values,making it a valuable tool in the field of pK_(a) prediction.展开更多
Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI pre...Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.展开更多
Maize(Zea mays),which is a vital source of food,feed,and energy feedstock globally,has significant potential for higher yields.However,environmental stress conditions,including drought and salt stress,severely restric...Maize(Zea mays),which is a vital source of food,feed,and energy feedstock globally,has significant potential for higher yields.However,environmental stress conditions,including drought and salt stress,severely restrict maize plant growth and development,leading to great yield losses.Leucine-rich repeat receptor-like kinases(LRR-RLKs)function in biotic and abiotic stress responses in the model plant Arabidopsis(Arabidopsis thaliana),but their roles in abiotic stress responses in maize are not entirely understood.In this study,we determine that the LRR-RLK ZmMIK2,a homolog of the Arabidopsis LRR-RK MALE DISCOVERER 1(MDIS1)-INTERACTING RECEPTOR LIKE KINASE 2(MIK2),functions in resistance to both drought and salt stress in maize.Zmmik2 plants exhibit enhanced resistance to both stresses,whereas overexpressing ZmMIK2 confers the opposite phenotypes.Furthermore,we identify C2-DOMAIN-CONTAINING PROTEIN 1(ZmC2DP1),which interacts with the intracellular region of ZmMIK2.Notably,that region of ZmMIK2 mediates the phosphorylation of ZmC2DP1,likely by increasing its stability.Both ZmMIK2 and ZmC2DP1 are mainly expressed in roots.As with ZmMIK2,knockout of ZmC2DP1 enhances resistance to both drought and salt stress.We conclude that ZmMIK2-ZmC2DP1 acts as a negative regulatory module in maize drought-and salt-stress responses.展开更多
Activity cliffs(ACs)are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target.ACs offer crucial...Activity cliffs(ACs)are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target.ACs offer crucial insights that aid medicinal chemists in optimizing molecular structures.Nonetheless,they also form a major source of prediction error in structure-activity relationship(SAR)models.To date,several studies have demonstrated that deep neural networks based on molecular images or graphs might need to be improved further in predicting the potency of ACs.In this paper,we integrated the triplet loss in face recognition with pre-training strategy to develop a prediction model ACtriplet,tailored for ACs.Through extensive comparison with multiple baseline models on 30 benchmark datasets,the results showed that ACtriplet was significantly better than those deep learning(DL)models without pretraining.In addition,we explored the effect of pre-training on data representation.Finally,the case study demonstrated that our model's interpretability module could explain the prediction results reasonably.In the dilemma that the amount of data could not be increased rapidly,this innovative framework would better make use of the existing data,which would propel the potential of DL in the early stage of drug discovery and optimization.展开更多
As batteries become increasingly essential for energy storage technologies,battery prognosis,and diagnosis remain central to ensure reliable operation and effective management,as well as to aid the in-depth investigat...As batteries become increasingly essential for energy storage technologies,battery prognosis,and diagnosis remain central to ensure reliable operation and effective management,as well as to aid the in-depth investigation of degradation mechanisms.However,dynamic operating conditions,cell-to-cell inconsistencies,and limited availability of labeled data have posed significant challenges to accurate and robust prognosis and diagnosis.Herein,we introduce a time-series-decomposition-based ensembled lightweight learning model(TELL-Me),which employs a synergistic dual-module framework to facilitate accurate and reliable forecasting.The feature module formulates features with physical implications and sheds light on battery aging mechanisms,while the gradient module monitors capacity degradation rates and captures aging trend.TELL-Me achieves high accuracy in end-of-life prediction using minimal historical data from a single battery without requiring offline training dataset,and demonstrates impressive generality and robustness across various operating conditions and battery types.Additionally,by correlating feature contributions with degradation mechanisms across different datasets,TELL-Me is endowed with the diagnostic ability that not only enhances prediction reliability but also provides critical insights into the design and optimization of next-generation batteries.展开更多
The rising prevalence of drug-resistant Gram-positive pathogens,particularly methicillin-resistant Staphy-lococcus aureus(MRSA)and vancomycin-resistant Enterococci(VRE),poses a substantial clinical challenge.Biofilm-a...The rising prevalence of drug-resistant Gram-positive pathogens,particularly methicillin-resistant Staphy-lococcus aureus(MRSA)and vancomycin-resistant Enterococci(VRE),poses a substantial clinical challenge.Biofilm-associated infections exacerbate this problem due to their inherent antibiotic resistance and complex structure.Current antibiotic treatments struggle to penetrate biofilms and eradicate persister cells,leading to prolonged antibiotic use and increased resistance.Host defense peptides(HDPs)have shown promise,but their clinical application is limited by factors such as enzymatic degradation and difficulty in largescale preparation.Synthetic HDP mimics,such as poly(2-oxazoline),have emerged as effective alter-natives.Herein,we found that the poly(2-oxazoline),Gly-POX_(20),demonstrated rapid and potent activity against clinically isolated multidrug-resistant Gram-positive strains.Gly-POX_(20) showed greater stability under physiological conditions compared to natural peptides,including resistance to protease degradation.Importantly,Gly-POX_(20) inhibited biofilm formation and eradicated mature biofilm and demonstrated superior in vivo therapeutic efficacy to vancomycin in a MRSA biofilm-associated mouse keratitis model,suggesting its potential as a novel antimicrobial agent against drug-resistant Gram-positive bacteria,especially biofilm-associated infections.展开更多
CO_(2)hydrogenation to value-added light olefins(C_(2-4)=)is crucial for the utilization and cycling of global carbon resource.Moderate CO_(2)activation and carbon chain growth ability are key factors for iron-based c...CO_(2)hydrogenation to value-added light olefins(C_(2-4)=)is crucial for the utilization and cycling of global carbon resource.Moderate CO_(2)activation and carbon chain growth ability are key factors for iron-based catalysts for efficient CO_(2)conversion to target C_(2-4)=products.The electronic interaction and confinement effect of electron-deficient graphene inner surface on the active phase are effective to improve surface chemical properties and enhance the catalytic performance.Here,we report a core-shell FeCo alloy catalyst with graphene layers confinement prepared by a simple sol-gel method.The electron transfer from Fe species to curved graphene inner surface modifies the surface electronic structure of the active phaseχ-(Fe_(x)Co_(1-x))_(5)C_(2)and improves CO_(2)adsorption capacity,enhancing the efficient conversion of CO_(2)and moderate C-C coupling.Therefore,the catalyst FeCoK@C exhibits C_(2-4)=selectivity of 33.0%while maintaining high CO_(2)conversion of 52.0%.The high stability without obvious deactivation for over 100 h and unprecedented C_(2-4)=space time yield(STY)up to 52.9 mmolCO_(2)·g^(-1)·h^(-1)demonstrate its potential for practical application.This work provides an efficient strategy for the development of high-performance CO_(2)hydrogenation catalysts.展开更多
Layered transition metal oxides have emerged as promising cathode materials for sodium ion batteries.However,irreversible phase transitions cause structural distortion and cation rearrangement,leading to sluggish Na+d...Layered transition metal oxides have emerged as promising cathode materials for sodium ion batteries.However,irreversible phase transitions cause structural distortion and cation rearrangement,leading to sluggish Na+dynamics and rapid capacity decay.In this study,we propose a medium-entropy cathode by simultaneously introducing Fe,Mg,and Li dopants into a typical P2-type Na_(0.75)Ni_(0.25)Mn_(0.75)O_(2)cathode.The modified Na_(0.75)Ni_(0.2125)Mn_(0.6375)Fe_(0.05)Mg_(0.05)Li_(0.05)O_(2)cathode predominantly exhibits a main P2 phase(93.5%)with a minor O3 phase(6.5%).Through spectroscopy techniques and electrochemical investigations,we elucidate the redox mechanisms of Ni^(2+/3+/4+),Mn^(3+/4+),Fe^(3+/4+),and O_(2)-/O_(2)^(n-)during charging/discharging.The medium-entropy doping mitigates the detrimental P2-O_(2)phase transition at high-voltage,replacing it with a moderate and reversible structural evolution(P2-OP4),thereby enhancing structural stability.Consequently,the modified cathode exhibits a remarkable rate capacity of 108.4 mAh·g^(-1)at 10C,with a capacity retention of 99.0%after 200 cycles at 1C,82.5%after 500 cycles at 5C,and 76.7%after 600 cycles at 10C.Furthermore,it also demonstrates superior electrochemical performance at high cutoff voltage of 4.5 V and extreme temperature(55 and 0℃).This work offers solutions to critical challenges in sodium ion batteries cathode materials.展开更多
Traditional Chinese medicine formula(TCMF)represents a fundamental component of Chinese medical practice,incorporating medical knowledge and practices from both Han Chinese and various ethnic minorities,while providin...Traditional Chinese medicine formula(TCMF)represents a fundamental component of Chinese medical practice,incorporating medical knowledge and practices from both Han Chinese and various ethnic minorities,while providing comprehensive insights into health and disease.The foundation of TCMF lies in its holistic approach,manifested through herbal compatibility theory,which has emerged from extensive clinical experience and evolved into a highly refined knowledge system.Within this framework,Chinese herbal medicines exhibit intricated characteristics,including multi-component interactions,diverse target sites,and varied biological pathways.These complexities pose significant challenges for understanding their molecular mechanisms.Contemporary advances in artificial intelligence(AI)are reshaping research in traditional Chinese medicine(TCM),offering immense potential to transform our understanding of the molecular mechanisms underlying TCMFs.This review explores the application of AI in uncovering these mechanisms,highlighting its role in compound absorption,distribution,metabolism,and excretion(ADME)prediction,molecular target identification,compound and target synergy recognition,pharmacological mechanisms exploration,and herbal formula optimization.Furthermore,the review discusses the challenges and opportunities in AI-assisted research on TCMF molecular mechanisms,promoting the modernization and globalization of TCM.展开更多
How the subduction direction of the Paleo-Pacific plate beneath the Eurasian plate changes in the Early Cretaceous remains highly controversial due to the disappearance of the subducted oceanic plate.Intraplate deform...How the subduction direction of the Paleo-Pacific plate beneath the Eurasian plate changes in the Early Cretaceous remains highly controversial due to the disappearance of the subducted oceanic plate.Intraplate deformation structures in the east Asian continent,however,provide excellent opportunities for reconstructing paleostress fields in continental interior in relation to the Paleo-Pacific/Eurasian plate interaction.Anisotropy of magnetic susceptibility(AMS),geological,and geochronological analyses of post-kinematic mafic dykes intruding the detachment fault zone of the Wulian metamorphic core complex(WL MCC)in Jiaodong Peninsula exemplify emplacement of mantle-sourced dykes in a WNW-ESE(301°-121°)oriented tectonic extensional setting at ca.120 Ma.In combination with the results from our previous kinematic analysis of the MCC,a ca.21°clockwise change in the direction of intraplate extension is obtained for early(135-122 Ma)extensional exhumation of the MCC to late(122-108 Ma)emplacement of the dykes.Such a change is suggested to be related to the variation in subduction direction of the Paleo-Pacific plate beneath the Eurasian plate,from westward(pre-122 Ma)to west-northwestward(post-122 Ma).展开更多
Aegilops speltoides,the closest ancestor of the wheat B subgenome,has been well studied genomically.However,the epigenetic landscape of Ae.speltoides and the effects of epigenetics on its growth and development remain...Aegilops speltoides,the closest ancestor of the wheat B subgenome,has been well studied genomically.However,the epigenetic landscape of Ae.speltoides and the effects of epigenetics on its growth and development remain poorly understood.Here,we present a comprehensive multi-omics atlas of leaves and roots in Ae.speltoides,encompassing transcriptome,DNA methylation,histone modifications,and small RNA profiling.Divergent DNA methylation levels were detected between leaves and roots,and were associated with differences in accumulated 24-nt siRNAs.DNA methylation changes in promoters and gene bodies showed strong connections with altered expression between leaves and roots.Transcriptional regulatory networks(TRN)reconstructed between leaves and roots were driven by tissue-specific TF families.DNA methylation and histone modification act together as switches that shape root and leaf morphogenesis by modulating the binding of tissue-specific TFs to their target genes.The TRNs in leaves and roots reshaped during wheat polyploidization were associated with alterations in epigenetic modi-fications.Collectively,these results not only shed light on the critical contribution of epigenetic regulation in the morphogenesis of leaves and roots in Ae.speltoides but also provide new insights for future investigations into the complex interplay of genetic and epigenetic factors in the developmental biology of common wheat.展开更多
This study investigates the influence of major climatic modes on the interannual variability of the annual minimum extent of Antarctic sea ice.It shows that the Southern Annular Mode(SAM),the Indian Ocean Dipole(IOD),...This study investigates the influence of major climatic modes on the interannual variability of the annual minimum extent of Antarctic sea ice.It shows that the Southern Annular Mode(SAM),the Indian Ocean Dipole(IOD),and the El Niño-Southern Oscillation(ENSO),along with the total sea ice condition during the preceding spring,serve as precursor signals of February sea ice extent(SIE).These climate modes interact,energizing the Pacific-South American pattern(PSA),which deepens and shifts the Amundsen Sea Low(ASL)westward in spring.This pattern generates a dipole sea ice anomaly characterized by an increase in sea ice in the northern Ross Sea but a decrease in ice in the Bellingshausen and northern Weddell Seas.However,as the season transitions into summer,the ASL exerts a pronounced delayed effect,contributing to widespread sea ice loss across West Antarctica.Strong southerly winds on the western flank of the ASL push sea ice away from the inner Ross Sea,exposing coastal waters that absorb solar radiation,thereby accelerating ice melt through positive ice-albedo feedback.Simultaneously,northwesterly winds on the eastern flank transport warm air toward the Bellingshausen and northern Weddell Seas,intensifying ice loss in these regions.Furthermore,the active PSA is accompanied by a tripole sea surface temperature pattern characterized by warming in the Weddell Sea,which promotes continued ice melt.The co-occurrence of an exceptionally positive SAM,a La Niña,and a strong negative IOD during spring 2022,combined with lower-than-normal total spring SIE,ultimately contributed to the record-low Antarctic SIE observed in February 2023.展开更多
Fluoride-based electrolyte exhibits extraordinarily high oxidative stability in high-voltage lithium metal batteries(h-LMBs) due to the inherent low highest occupied molecular orbital(HOMO) of fiuorinated solvents. Ho...Fluoride-based electrolyte exhibits extraordinarily high oxidative stability in high-voltage lithium metal batteries(h-LMBs) due to the inherent low highest occupied molecular orbital(HOMO) of fiuorinated solvents. However, such fascinating properties do not bring long-term cyclability of h-LMBs. One of critical challenges is the interface instability in contacting with the Li metal anode, as fiuorinated solvents are highly susceptible to exceptionally reductive metallic Li attributed to its low lowest unoccupied molecular orbital(LUMO), which leads to significant consumption of the fiuorinated components upon cycling.Herein, attenuating reductive decomposition of fiuorinated electrolytes is proposed to circumvent rapid electrolyte consumption. Specifically, the vinylene carbonate(VC) is selected to tame the reduction decomposition by preferentially forming protective layer on the Li anode. This work, experimentally and computationally, demonstrates the importance of pre-passivation of Li metal anodes at high voltage to attenuate the decomposition of fiuoroethylene carbonate(FEC). It is expected to enrich the understanding of how VC attenuate the reactivity of FEC, thereby extending the cycle life of fiuorinated electrolytes in high-voltage Li-metal batteries.展开更多
This review details the advancement in the development of V–Ti-based hydrogen storage materials for using in metal hydride(MH)tanks to supply hydrogen to fuel cells at relatively ambient temperatures and pressures.V...This review details the advancement in the development of V–Ti-based hydrogen storage materials for using in metal hydride(MH)tanks to supply hydrogen to fuel cells at relatively ambient temperatures and pressures.V–Tibased solid solution alloys are excellent hydrogen storage materials among many metal hydrides due to their high reversible hydrogen storage capacity which is over 2 wt%at ambient temperature.The preparation methods,structure characteristics,improvement methods of hydrogen storage performance,and attenuation mechanism are systematically summarized and discussed.The relationships between hydrogen storage properties and alloy compositions as well as phase structures are discussed emphatically.For large-scale applications on MH tanks,it is necessary to develop low-cost and high-performance V–Ti-based solid solution alloys with high reversible hydrogen storage capacity,good cyclic durability,and excellent activation performance.展开更多
基金supported by National Natural Science Foundation of China(Grant Nos.52025055,52375576,52350349)Key Research and Development Program of Shaanxi(Program No.2022GXLH-01-12)+2 种基金Joint Fund of Ministry of Education for Equipment Pre-research(No.8091B03012304)Aeronautical Science Foundation of China(No.2022004607001)the Fundamental Research Funds for the Central Universities(No.xtr072024031).
文摘Continuous monitoring of biosignals is essential for advancing early disease detection,personalized treatment,and health management.Flexible electronics,capable of accurately monitoring biosignals in daily life,have garnered considerable attention due to their softness,conformability,and biocompatibility.However,several challenges remain,including imperfect skin-device interfaces,limited breathability,and insufficient mechanoelectrical stability.On-skin epidermal electronics,distinguished by their excellent conformability,breathability,and mechanoelectrical robustness,offer a promising solution for high-fidelity,long-term health monitoring.These devices can seamlessly integrate with the human body,leading to transformative advancements in future personalized healthcare.This review provides a systematic examination of recent advancements in on-skin epidermal electronics,with particular emphasis on critical aspects including material science,structural design,desired properties,and practical applications.We explore various materials,considering their properties and the corresponding structural designs developed to construct high-performance epidermal electronics.We then discuss different approaches for achieving the desired device properties necessary for long-term health monitoring,including adhesiveness,breathability,and mechanoelectrical stability.Additionally,we summarize the diverse applications of these devices in monitoring biophysical and physiological signals.Finally,we address the challenges facing these devices and outline future prospects,offering insights into the ongoing development of on-skin epidermal electronics for long-term health monitoring.
基金supported by grants from NIH T32(DK007260,to WC)the Steno North American Fellowship awarded by the Novo Nordisk Foundation(NNF23OC0087108,to WC)+6 种基金STI2030-Major Projects(2021ZD0202700,to HY)the National Natural Science Foundation of China(32241004,to HY)the Natural Science Foundation of Zhejiang Province of China(LR24C090001,to HY)Key R&D Program of Zhejiang Province(2024SSYS0017,to HY)CAMS Innovation Fund for Medical Sciences(2019-12M-5-057,to HY)Fundamental Research Funds for the Central Universities(226-2022-00193,to HY)the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences(2023-PT310-01,to HY)。
文摘Type 2 diabetes mellitus has central complications:Diabetes,a metabolic disorder primarily characterized by hyperglycemia due to insufficient insulin secretion,or impaired insulin signaling,has significant central complications.Type 2 diabetes mellitus(T2DM),the most prevalent type of diabetes,affects more than 38 million individuals in the United States(approximately 1 in 10)and is defined by chronic hyperglycemia and insulin resistance,which refers to a reduced cellular response to insulin.
基金supported by National Natural Science Foundation of China(No.52025055 and 52275571)Basic Research Operation Fund of China(No.xzy012024024).
文摘Tilted metasurface nanostructures,with excellent physical properties and enormous application potential,pose an urgent need for manufacturing methods.Here,electric-field-driven generative-nanoimprinting technique is proposed.The electric field applied between the template and the substrate drives the contact,tilting,filling,and holding processes.By accurately controlling the introduced included angle between the flexible template and the substrate,tilted nanostructures with a controllable angle are imprinted onto the substrate,although they are vertical on the template.By flexibly adjusting the electric field intensity and the included angle,large-area uniform-tilted,gradient-tilted,and high-angle-tilted nanostructures are fabricated.In contrast to traditional replication,the morphology of the nanoimprinting structure is extended to customized control.This work provides a cost-effective,efficient,and versatile technology for the fabrication of various large-area tilted metasurface structures.As an illustration,a tilted nanograting with a high coupling efficiency is fabricated and integrated into augmented reality displays,demonstrating superior imaging quality.
基金supported by the National Natural Science Foundation of China(Grant No.U2342208)support from NSF/Climate Dynamics Award#2025057。
文摘Predicting monsoon climate is one of the major endeavors in climate science and is becoming increasingly challenging due to global warming. The accuracy of monsoon seasonal predictions significantly impacts the lives of billions who depend on or are affected by monsoons, as it is essential for the water cycle, food security, ecology, disaster prevention, and the economy of monsoon regions. Given the extensive literature on Asian monsoon climate prediction, we limit our focus to reviewing the seasonal prediction and predictability of the Asian Summer Monsoon (ASM). However, much of this review is also relevant to monsoon predictions in other seasons and regions. Over the past two decades, considerable progress has been made in the seasonal forecasting of the ASM, driven by an enhanced understanding of the sources of predictability and the dynamics of seasonal variability, along with advanced development in sophisticated models and technologies. This review centers on advances in understanding the physical foundation for monsoon climate prediction (section 2), significant findings and insights into the primary and regional sources of predictability arising from feedback processes among various climate components (sections 3 and 4), the effects of global warming and external forcings on predictability (section 5), developments in seasonal prediction models and techniques (section 6), the challenges and limitations of monsoon climate prediction (section 7), and emerging research trends with suggestions for future directions (section 8). We hope this review will stimulate creative activities to enhance monsoon climate prediction.
基金supported by grants from the National Key Research and Development Program of China(2021YFF1000500)the National Natural Science Foundation of China(32370272,31970273,and 31921001).
文摘Phosphorus(P)is an essential nutrient for crop growth,making it important for maintaining food security as the global population continues to increase.Plants acquire P primarily via the uptake of inorganic phosphate(Pi)in soil through their roots.Pi,which is usually sequestered in soils,is not easily absorbed by plants and represses plant growth.Plants have developed a series of mechanisms to cope with P deficiency.Moreover,P fertilizer applications are critical for maximizing crop yield.Maize is a major cereal crop cultivated worldwide.Increasing its P-use efficiency is important for optimizing maize production.Over the past two decades,considerable progresses have been achieved in studies aimed at adapting maize varieties to changes in environmental P supply.Here,we present an overview of the morphological,physiological,and molecular mechanisms involved in P acquisition,translocation,and redistribution in maize and combine the advances in Arabidopsis and rice,to better elucidate the progress of P nutrition.Additionally,we summarize the correlation between P and abiotic stress responses.Clarifying the mechanisms relevant to improving P absorption and use in maize can guide future research on sustainable agriculture.
基金in part supported by the National Natural Science Foundation of China(Grant Nos.42288101,42405147 and 42475054)in part by the China National Postdoctoral Program for Innovative Talents(Grant No.BX20230071)。
文摘Conducting predictability studies is essential for tracing the source of forecast errors,which not only leads to the improvement of observation and forecasting systems,but also enhances the understanding of weather and climate phenomena.In the past few decades,dynamical numerical models have been the primary tools for predictability studies,achieving significant progress.Nowadays,with the advances in artificial intelligence(AI)techniques and accumulations of vast meteorological data,modeling weather and climate events using modern data-driven approaches is becoming trendy,where FourCastNet,Pangu-Weather,and GraphCast are successful pioneers.In this perspective article,we suggest AI models should not be limited to forecasting but be expanded to predictability studies,leveraging AI's advantages of high efficiency and self-contained optimization modules.To this end,we first remark that AI models should possess high simulation capability with fine spatiotemporal resolution for two kinds of predictability studies.AI models with high simulation capabilities comparable to numerical models can be considered to provide solutions to partial differential equations in a data-driven way.Then,we highlight several specific predictability issues with well-determined nonlinear optimization formulizations,which can be well-studied using AI models,holding significant scientific value.In addition,we advocate for the incorporation of AI models into the synergistic cycle of the cognition–observation–model paradigm.Comprehensive predictability studies have the potential to transform“big data”to“big and better data”and shift the focus from“AI for forecasts”to“AI for science”,ultimately advancing the development of the atmospheric and oceanic sciences.
基金upported by the National Key Research and Development Program of China(Grant No.:2023YFF1204904)the National Natural Science Foundation of China(Grant Nos.:U23A20530 and 82173746)Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism(Shanghai Municipal Education Commission,China).
文摘Negative logarithm of the acid dissociation constant(pK_(a))significantly influences the absorption,dis-tribution,metabolism,excretion,and toxicity(ADMET)properties of molecules and is a crucial indicator in drug research.Given the rapid and accurate characteristics of computational methods,their role in predicting drug properties is increasingly important.Although many pK_(a) prediction models currently exist,they often focus on enhancing model precision while neglecting interpretability.In this study,we present GraFpKa,a pK_(a) prediction model using graph neural networks(GNNs)and molecular finger-prints.The results show that our acidic and basic models achieved mean absolute errors(MAEs)of 0.621 and 0.402,respectively,on the test set,demonstrating good predictive performance.Notably,to improve interpretability,GraFpKa also incorporates Integrated Gradients(IGs),providing a clearer visual description of the atoms significantly affecting the pK_(a) values.The high reliability and interpretability of GraFpKa ensure accurate pKa predictions while also facilitating a deeper understanding of the relation-ship between molecular structure and pK_(a) values,making it a valuable tool in the field of pK_(a) prediction.
基金supported by the National Natural Science Foundation of China(Nos.82173746 and U23A20530)Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism(Shanghai Municipal Education Commission)。
文摘Accurate prediction of drug-target interactions(DTIs)plays a pivotal role in drug discovery,facilitating optimization of lead compounds,drug repurposing and elucidation of drug side effects.However,traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features.In this study,we proposed KG-CNNDTI,a novel knowledge graph-enhanced framework for DTI prediction,which integrates heterogeneous biological information to improve model generalizability and predictive performance.The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm,which were further enriched with contextualized sequence representations obtained from ProteinBERT.For compound representation,multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated.The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor.Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods,particularly in terms of Precision,Recall,F1-Score and area under the precision-recall curve(AUPR).Ablation analysis highlighted the substantial contribution of knowledge graph-derived features.Moreover,KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease,resulting in 40 candidate compounds.5 were supported by literature evidence,among which 3 were further validated in vitro assays.
基金supported by the National Key Research and Development Program of China(2021YFD1200703 and 2022YFF1001602)the National Science Foundation of China(32272024 and 32171940)+2 种基金the Pinduoduo-China Agricultural University Research Fund(PC2023B01001)the Chinese Universities Scientific Fund(2022TC142)the 2115 Talent Development Program of China Agricultural University。
文摘Maize(Zea mays),which is a vital source of food,feed,and energy feedstock globally,has significant potential for higher yields.However,environmental stress conditions,including drought and salt stress,severely restrict maize plant growth and development,leading to great yield losses.Leucine-rich repeat receptor-like kinases(LRR-RLKs)function in biotic and abiotic stress responses in the model plant Arabidopsis(Arabidopsis thaliana),but their roles in abiotic stress responses in maize are not entirely understood.In this study,we determine that the LRR-RLK ZmMIK2,a homolog of the Arabidopsis LRR-RK MALE DISCOVERER 1(MDIS1)-INTERACTING RECEPTOR LIKE KINASE 2(MIK2),functions in resistance to both drought and salt stress in maize.Zmmik2 plants exhibit enhanced resistance to both stresses,whereas overexpressing ZmMIK2 confers the opposite phenotypes.Furthermore,we identify C2-DOMAIN-CONTAINING PROTEIN 1(ZmC2DP1),which interacts with the intracellular region of ZmMIK2.Notably,that region of ZmMIK2 mediates the phosphorylation of ZmC2DP1,likely by increasing its stability.Both ZmMIK2 and ZmC2DP1 are mainly expressed in roots.As with ZmMIK2,knockout of ZmC2DP1 enhances resistance to both drought and salt stress.We conclude that ZmMIK2-ZmC2DP1 acts as a negative regulatory module in maize drought-and salt-stress responses.
基金supported by the National Natural Science Foundation of China(Grant Nos.:U23A20530,82273858,and 82173746)the National Key Research and Development Programof China(Grant No.:2023YFF1204904)Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism(Shanghai Municipal Education Commission,China).
文摘Activity cliffs(ACs)are generally defined as pairs of similar compounds that only differ by a minor structural modification but exhibit a large difference in their binding affinity for a given target.ACs offer crucial insights that aid medicinal chemists in optimizing molecular structures.Nonetheless,they also form a major source of prediction error in structure-activity relationship(SAR)models.To date,several studies have demonstrated that deep neural networks based on molecular images or graphs might need to be improved further in predicting the potency of ACs.In this paper,we integrated the triplet loss in face recognition with pre-training strategy to develop a prediction model ACtriplet,tailored for ACs.Through extensive comparison with multiple baseline models on 30 benchmark datasets,the results showed that ACtriplet was significantly better than those deep learning(DL)models without pretraining.In addition,we explored the effect of pre-training on data representation.Finally,the case study demonstrated that our model's interpretability module could explain the prediction results reasonably.In the dilemma that the amount of data could not be increased rapidly,this innovative framework would better make use of the existing data,which would propel the potential of DL in the early stage of drug discovery and optimization.
基金supported by the National Natural Science Foundation of China(22379021 and 22479021)。
文摘As batteries become increasingly essential for energy storage technologies,battery prognosis,and diagnosis remain central to ensure reliable operation and effective management,as well as to aid the in-depth investigation of degradation mechanisms.However,dynamic operating conditions,cell-to-cell inconsistencies,and limited availability of labeled data have posed significant challenges to accurate and robust prognosis and diagnosis.Herein,we introduce a time-series-decomposition-based ensembled lightweight learning model(TELL-Me),which employs a synergistic dual-module framework to facilitate accurate and reliable forecasting.The feature module formulates features with physical implications and sheds light on battery aging mechanisms,while the gradient module monitors capacity degradation rates and captures aging trend.TELL-Me achieves high accuracy in end-of-life prediction using minimal historical data from a single battery without requiring offline training dataset,and demonstrates impressive generality and robustness across various operating conditions and battery types.Additionally,by correlating feature contributions with degradation mechanisms across different datasets,TELL-Me is endowed with the diagnostic ability that not only enhances prediction reliability but also provides critical insights into the design and optimization of next-generation batteries.
基金financially supported by the National Key Research and Development Program of China(no.2022YFC2303100)National Natural Science Foundation of China(nos.T2325010,22305082,52203162,and 22075078)+1 种基金Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism(Shanghai Municipal Education Commission),the Fundamental Research Funds for the Central Universities(nos.JKVD1241029 and JKD01241701)Open Research Fund of State Key Laboratory of Polymer Physics and Chemistry(Changchun Institute of Applied Chemistry,Chinese Academy of Sciences),the Open Project of Engineering Research Center of Dairy Quality and Safety Control Technology(Ministry of Education,no.R202201).
文摘The rising prevalence of drug-resistant Gram-positive pathogens,particularly methicillin-resistant Staphy-lococcus aureus(MRSA)and vancomycin-resistant Enterococci(VRE),poses a substantial clinical challenge.Biofilm-associated infections exacerbate this problem due to their inherent antibiotic resistance and complex structure.Current antibiotic treatments struggle to penetrate biofilms and eradicate persister cells,leading to prolonged antibiotic use and increased resistance.Host defense peptides(HDPs)have shown promise,but their clinical application is limited by factors such as enzymatic degradation and difficulty in largescale preparation.Synthetic HDP mimics,such as poly(2-oxazoline),have emerged as effective alter-natives.Herein,we found that the poly(2-oxazoline),Gly-POX_(20),demonstrated rapid and potent activity against clinically isolated multidrug-resistant Gram-positive strains.Gly-POX_(20) showed greater stability under physiological conditions compared to natural peptides,including resistance to protease degradation.Importantly,Gly-POX_(20) inhibited biofilm formation and eradicated mature biofilm and demonstrated superior in vivo therapeutic efficacy to vancomycin in a MRSA biofilm-associated mouse keratitis model,suggesting its potential as a novel antimicrobial agent against drug-resistant Gram-positive bacteria,especially biofilm-associated infections.
文摘CO_(2)hydrogenation to value-added light olefins(C_(2-4)=)is crucial for the utilization and cycling of global carbon resource.Moderate CO_(2)activation and carbon chain growth ability are key factors for iron-based catalysts for efficient CO_(2)conversion to target C_(2-4)=products.The electronic interaction and confinement effect of electron-deficient graphene inner surface on the active phase are effective to improve surface chemical properties and enhance the catalytic performance.Here,we report a core-shell FeCo alloy catalyst with graphene layers confinement prepared by a simple sol-gel method.The electron transfer from Fe species to curved graphene inner surface modifies the surface electronic structure of the active phaseχ-(Fe_(x)Co_(1-x))_(5)C_(2)and improves CO_(2)adsorption capacity,enhancing the efficient conversion of CO_(2)and moderate C-C coupling.Therefore,the catalyst FeCoK@C exhibits C_(2-4)=selectivity of 33.0%while maintaining high CO_(2)conversion of 52.0%.The high stability without obvious deactivation for over 100 h and unprecedented C_(2-4)=space time yield(STY)up to 52.9 mmolCO_(2)·g^(-1)·h^(-1)demonstrate its potential for practical application.This work provides an efficient strategy for the development of high-performance CO_(2)hydrogenation catalysts.
基金supported by the National Natural Science Foundation of China(No.21805018)by Sichuan Science and Technology Program(Nos.2022ZHCG0018,2023NSFSC0117 and 2023ZHCG0060)Yibin Science and Technology Program(No.2022JB005)and China Postdoctoral Science Foundation(No.2022M722704).
文摘Layered transition metal oxides have emerged as promising cathode materials for sodium ion batteries.However,irreversible phase transitions cause structural distortion and cation rearrangement,leading to sluggish Na+dynamics and rapid capacity decay.In this study,we propose a medium-entropy cathode by simultaneously introducing Fe,Mg,and Li dopants into a typical P2-type Na_(0.75)Ni_(0.25)Mn_(0.75)O_(2)cathode.The modified Na_(0.75)Ni_(0.2125)Mn_(0.6375)Fe_(0.05)Mg_(0.05)Li_(0.05)O_(2)cathode predominantly exhibits a main P2 phase(93.5%)with a minor O3 phase(6.5%).Through spectroscopy techniques and electrochemical investigations,we elucidate the redox mechanisms of Ni^(2+/3+/4+),Mn^(3+/4+),Fe^(3+/4+),and O_(2)-/O_(2)^(n-)during charging/discharging.The medium-entropy doping mitigates the detrimental P2-O_(2)phase transition at high-voltage,replacing it with a moderate and reversible structural evolution(P2-OP4),thereby enhancing structural stability.Consequently,the modified cathode exhibits a remarkable rate capacity of 108.4 mAh·g^(-1)at 10C,with a capacity retention of 99.0%after 200 cycles at 1C,82.5%after 500 cycles at 5C,and 76.7%after 600 cycles at 10C.Furthermore,it also demonstrates superior electrochemical performance at high cutoff voltage of 4.5 V and extreme temperature(55 and 0℃).This work offers solutions to critical challenges in sodium ion batteries cathode materials.
基金supported by the National Key R&D Program of China(No.2022YFC3502005)the three-year Action Plan for Shanghai TCM Development and Inheritance Program[No.ZY(2021-2023)-0401]the National Natural Science Foundation of China(No.82104521)。
文摘Traditional Chinese medicine formula(TCMF)represents a fundamental component of Chinese medical practice,incorporating medical knowledge and practices from both Han Chinese and various ethnic minorities,while providing comprehensive insights into health and disease.The foundation of TCMF lies in its holistic approach,manifested through herbal compatibility theory,which has emerged from extensive clinical experience and evolved into a highly refined knowledge system.Within this framework,Chinese herbal medicines exhibit intricated characteristics,including multi-component interactions,diverse target sites,and varied biological pathways.These complexities pose significant challenges for understanding their molecular mechanisms.Contemporary advances in artificial intelligence(AI)are reshaping research in traditional Chinese medicine(TCM),offering immense potential to transform our understanding of the molecular mechanisms underlying TCMFs.This review explores the application of AI in uncovering these mechanisms,highlighting its role in compound absorption,distribution,metabolism,and excretion(ADME)prediction,molecular target identification,compound and target synergy recognition,pharmacological mechanisms exploration,and herbal formula optimization.Furthermore,the review discusses the challenges and opportunities in AI-assisted research on TCMF molecular mechanisms,promoting the modernization and globalization of TCM.
基金supported by the National Natural Science Foundation of China(Grant Nos:42130801,41430211,90814006,and 42072226)the“Deep-time Digital Earth”Science and Technology Leading Talents Team Funds for the Central Universities for the Frontiers Science Center for Deep-time Digital Earth,CUGB(Fundamental Research Funds for the Central UniversitiesGrant No:2652023001).
文摘How the subduction direction of the Paleo-Pacific plate beneath the Eurasian plate changes in the Early Cretaceous remains highly controversial due to the disappearance of the subducted oceanic plate.Intraplate deformation structures in the east Asian continent,however,provide excellent opportunities for reconstructing paleostress fields in continental interior in relation to the Paleo-Pacific/Eurasian plate interaction.Anisotropy of magnetic susceptibility(AMS),geological,and geochronological analyses of post-kinematic mafic dykes intruding the detachment fault zone of the Wulian metamorphic core complex(WL MCC)in Jiaodong Peninsula exemplify emplacement of mantle-sourced dykes in a WNW-ESE(301°-121°)oriented tectonic extensional setting at ca.120 Ma.In combination with the results from our previous kinematic analysis of the MCC,a ca.21°clockwise change in the direction of intraplate extension is obtained for early(135-122 Ma)extensional exhumation of the MCC to late(122-108 Ma)emplacement of the dykes.Such a change is suggested to be related to the variation in subduction direction of the Paleo-Pacific plate beneath the Eurasian plate,from westward(pre-122 Ma)to west-northwestward(post-122 Ma).
基金supported by the National Key Research and Development Program of China(2023YFD1200403).
文摘Aegilops speltoides,the closest ancestor of the wheat B subgenome,has been well studied genomically.However,the epigenetic landscape of Ae.speltoides and the effects of epigenetics on its growth and development remain poorly understood.Here,we present a comprehensive multi-omics atlas of leaves and roots in Ae.speltoides,encompassing transcriptome,DNA methylation,histone modifications,and small RNA profiling.Divergent DNA methylation levels were detected between leaves and roots,and were associated with differences in accumulated 24-nt siRNAs.DNA methylation changes in promoters and gene bodies showed strong connections with altered expression between leaves and roots.Transcriptional regulatory networks(TRN)reconstructed between leaves and roots were driven by tissue-specific TF families.DNA methylation and histone modification act together as switches that shape root and leaf morphogenesis by modulating the binding of tissue-specific TFs to their target genes.The TRNs in leaves and roots reshaped during wheat polyploidization were associated with alterations in epigenetic modi-fications.Collectively,these results not only shed light on the critical contribution of epigenetic regulation in the morphogenesis of leaves and roots in Ae.speltoides but also provide new insights for future investigations into the complex interplay of genetic and epigenetic factors in the developmental biology of common wheat.
基金supported by the National Natural Science Foundation of China (Grant Nos.42288101 and 42375045)
文摘This study investigates the influence of major climatic modes on the interannual variability of the annual minimum extent of Antarctic sea ice.It shows that the Southern Annular Mode(SAM),the Indian Ocean Dipole(IOD),and the El Niño-Southern Oscillation(ENSO),along with the total sea ice condition during the preceding spring,serve as precursor signals of February sea ice extent(SIE).These climate modes interact,energizing the Pacific-South American pattern(PSA),which deepens and shifts the Amundsen Sea Low(ASL)westward in spring.This pattern generates a dipole sea ice anomaly characterized by an increase in sea ice in the northern Ross Sea but a decrease in ice in the Bellingshausen and northern Weddell Seas.However,as the season transitions into summer,the ASL exerts a pronounced delayed effect,contributing to widespread sea ice loss across West Antarctica.Strong southerly winds on the western flank of the ASL push sea ice away from the inner Ross Sea,exposing coastal waters that absorb solar radiation,thereby accelerating ice melt through positive ice-albedo feedback.Simultaneously,northwesterly winds on the eastern flank transport warm air toward the Bellingshausen and northern Weddell Seas,intensifying ice loss in these regions.Furthermore,the active PSA is accompanied by a tripole sea surface temperature pattern characterized by warming in the Weddell Sea,which promotes continued ice melt.The co-occurrence of an exceptionally positive SAM,a La Niña,and a strong negative IOD during spring 2022,combined with lower-than-normal total spring SIE,ultimately contributed to the record-low Antarctic SIE observed in February 2023.
基金supported by the National Natural Science Foundation of China (Nos. 22379121, 62005216)Basic Public Welfare Research Program of Zhejiang (No. LQ22F050013)+1 种基金Zhejiang Province Key Laboratory of Flexible Electronics Open Fund (2023FE005)Shenzhen Foundation Research Program (No. JCYJ20220530112812028)。
文摘Fluoride-based electrolyte exhibits extraordinarily high oxidative stability in high-voltage lithium metal batteries(h-LMBs) due to the inherent low highest occupied molecular orbital(HOMO) of fiuorinated solvents. However, such fascinating properties do not bring long-term cyclability of h-LMBs. One of critical challenges is the interface instability in contacting with the Li metal anode, as fiuorinated solvents are highly susceptible to exceptionally reductive metallic Li attributed to its low lowest unoccupied molecular orbital(LUMO), which leads to significant consumption of the fiuorinated components upon cycling.Herein, attenuating reductive decomposition of fiuorinated electrolytes is proposed to circumvent rapid electrolyte consumption. Specifically, the vinylene carbonate(VC) is selected to tame the reduction decomposition by preferentially forming protective layer on the Li anode. This work, experimentally and computationally, demonstrates the importance of pre-passivation of Li metal anodes at high voltage to attenuate the decomposition of fiuoroethylene carbonate(FEC). It is expected to enrich the understanding of how VC attenuate the reactivity of FEC, thereby extending the cycle life of fiuorinated electrolytes in high-voltage Li-metal batteries.
基金supported by the Key-Area Research and Development Program of Guangdong Province(No.2023B0909060001)the National Natural Science Foundation of China(No.52271213)。
文摘This review details the advancement in the development of V–Ti-based hydrogen storage materials for using in metal hydride(MH)tanks to supply hydrogen to fuel cells at relatively ambient temperatures and pressures.V–Tibased solid solution alloys are excellent hydrogen storage materials among many metal hydrides due to their high reversible hydrogen storage capacity which is over 2 wt%at ambient temperature.The preparation methods,structure characteristics,improvement methods of hydrogen storage performance,and attenuation mechanism are systematically summarized and discussed.The relationships between hydrogen storage properties and alloy compositions as well as phase structures are discussed emphatically.For large-scale applications on MH tanks,it is necessary to develop low-cost and high-performance V–Ti-based solid solution alloys with high reversible hydrogen storage capacity,good cyclic durability,and excellent activation performance.
