This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to...This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.展开更多
Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and...Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.展开更多
Artificial intelligence(AI)is revolutionizing medical imaging,particularly in chronic liver diseases assessment.AI technologies,including machine learning and deep learning,are increasingly integrated with multiparame...Artificial intelligence(AI)is revolutionizing medical imaging,particularly in chronic liver diseases assessment.AI technologies,including machine learning and deep learning,are increasingly integrated with multiparametric ultrasound(US)techniques to provide more accurate,objective,and non-invasive evaluations of liver fibrosis and steatosis.Analyzing large datasets from US images,AI enhances diagnostic precision,enabling better quantification of liver stiffness and fat content,which are essential for diagnosing and staging liver fibrosis and steatosis.Combining advanced US modalities,such as elastography and doppler imaging with AI,has demonstrated improved sensitivity in identifying different stages of liver disease and distinguishing various degrees of steatotic liver.These advancements also contribute to greater reproducibility and reduced operator dependency,addressing some of the limitations of traditional methods.The clinical implications of AI in liver disease are vast,ranging from early detection to predicting disease progression and evaluating treatment response.Despite these promising developments,challenges such as the need for large-scale datasets,algorithm transparency,and clinical validation remain.The aim of this review is to explore the current applications and future potential of AI in liver fibrosis and steatosis assessment using multiparametric US,highlighting the technological advances and clinical relevance of this emerging field.展开更多
BACKGROUND Gastrointestinal stromal tumors(GISTs)are rare mesenchymal neoplasms primarily originating in the stomach or small intestine.Duodenal GISTs are particularly uncommon,accounting for only a small fraction of ...BACKGROUND Gastrointestinal stromal tumors(GISTs)are rare mesenchymal neoplasms primarily originating in the stomach or small intestine.Duodenal GISTs are particularly uncommon,accounting for only a small fraction of GIST cases.These tumors often present with nonspecific symptoms,making early detection challenging.This case discusses a duodenal GIST misdiagnosed as pancreatic cancer due to obstructive jaundice.CASE SUMMARY A 40-year-old male with jaundice and abdominal symptoms underwent imaging,which suggested a malignant periampullary tumor.Preoperative misdiagnosis of pancreatic cancer was made,and surgery was performed.Postoperative histopathology confirmed a duodenal GIST.The role of artificial intelligence in the diagnostic pathway is explored,emphasizing its potential to differentiate between duodenal GISTs and other similar conditions using advanced imaging analysis.CONCLUSION Artificial intelligence in radiomic imaging holds significant promise in enhancing the diagnostic process for rare cancers like duodenal GISTs,ensuring timely and accurate treatment.展开更多
Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.展开更多
Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosupp...Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.展开更多
Nursing education is undergoing a paradigm shift from skill training to clinical thinking cultivation.The integration of artificial intelligence technology offers technical possibilities for this transformation,but it...Nursing education is undergoing a paradigm shift from skill training to clinical thinking cultivation.The integration of artificial intelligence technology offers technical possibilities for this transformation,but it also brings about a deep tension between the cultivation of humanistic qualities and a standardized training.Based on the analysis of the practical forms of nursing smart education,this paper examines the cognitive gap between the deterministic feedback of virtual simulation systems and the complexity of real clinical scenarios,reveals the potential narrowing effect of data-driven ability profiling on the all-round development of nursing students,and then proposes the design logic of intelligent teaching resources centered on real clinical problems,a hierarchical teaching model with clear human-machine division of labor,and a dynamic assessment mechanism for technology application led by professional nursing teachers,in an attempt to find a balance between technological empowerment and humanistic commitment in smart nursing education.展开更多
With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has prog...With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has progressed from the fabrication of simple models(1.0)to the fabrication of permanent implants(2.0),tissue engineering scaffolds(3.0),and complex biostructures utilizing living cells(4.0).Nevertheless,significant challenges remain,particularly in accurately replicating the structure and function of host tissues,selecting appropriate materials,and optimizing printing parameters.The integration of artificial intelligence(AI),especially machine learning,provides promising novel opportunities in bioprinting(5.0).This review systematically summarizes the current applications of AI in bioprinting,discussing both construction strategies and application scenarios.It also explores the potential of AI to improve bioprinting in the preparation of complex functional tissues and in situ tissue repair.Overall,the synergy between AI and bioprinting is poised to drive the development of personalized medicine,facilitate high-throughput preparation of in vitro models,and provide robust tools for regenerative medicine and precision healthcare.展开更多
The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unatt...The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unattainable.However,the black-box nature of these systems presents a significant barrier,hindering their operational adoption,regulatory approval,and full scientific validation.This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence(XAI)as applied to geohazard science(GeoXAI),a domain that aims to resolve the long-standing trade-off between model performance and interpretability.A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field.The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment.Methodologically,tree-based ensembles and deep learning models dominate the literature,with SHapley Additive exPlanations(SHAP)frequently adopted as the principal post-hoc explanation technique.More importantly,the review further documents how the role of XAI has shifted:rather than being used solely as a tool for interpreting models after training,it is increasingly integrated into the modeling cycle itself.Recent applications include its use in feature selection,adaptive sampling strategies,and model evaluation.The evidence also shows that GeoXAI extends beyond producing feature rankings.It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms.Nevertheless,several key challenges remain unresolved within the field.These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability,the demanding scholarly task of reliably distinguishing correlation from causation,and the development of appropriate methods for the treatment of complex spatio-temporal dynamics.展开更多
Background:Biliary stent placement during endoscopic retrograde cholangiopancreatography(ERCP)is important for drainage in common bile duct(CBD)strictures,while the stent length is associated with many stent-related c...Background:Biliary stent placement during endoscopic retrograde cholangiopancreatography(ERCP)is important for drainage in common bile duct(CBD)strictures,while the stent length is associated with many stent-related complications.We aimed to develop an artificial intelligence(AI)model for stent length selection during ERCP.Methods:Images of the patients who underwent ERCP and were diagnosed with CBD strictures were collected.Training involved identifying and delineating the duodenoscope,CBD and guidewire,calculating the pixel distance of the target guidewire and determining the required biliary stent length based on the diameter of the duodenoscope.The performance of the model,accuracy for length calculation and the assistance for endoscopists were validated using the testing set.Results:A total of 794 images from 431 patients were included and data augmentation was conducted.The mean intersection over union(mIoU)for duodenoscope,CBD and guidewire were 90.46%,84.79%and 84.64%,respectively.The accuracy in identifying the strictures was 97.58%(121/124).The accuracy for stent length calculation achieved 85.95%(104/121)with an error margin of±1 cm.The mean absolute error(MAE)and mean relative error(MRE)of the AI model was 0.81 cm and 0.13,respectively.The AI model could reduce approximately 202 mGycm^(2)of the radiation exposure for each patient.It significantly improved both MAE and MRE for less experienced endoscopists(P=0.01 and P=0.02,respectively).Conclusions:The AI model could accurately identify duodenoscope,CBD and guidewire,enabling accurate strictures identification and stent length selection.展开更多
Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including ...Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.展开更多
Metabolic dysfunction-associated steatotic liver disease(MASLD)is an increasingly prevalent condition associated with hepatic complications and cardiovascular and renal events.Given its significant clinical impact,the...Metabolic dysfunction-associated steatotic liver disease(MASLD)is an increasingly prevalent condition associated with hepatic complications and cardiovascular and renal events.Given its significant clinical impact,the development of new strategies for early diagnosis and treatment is essential to improve patient outcomes.Over the past decade,the integration of artificial intelligence(AI)into gastroenterology has led to transformative advancements in medical practice.AI represents a major step towards personalized medicine,offering the potential to enhance diagnostic accuracy,refine prognostic assessments,and optimize treatment strategies.Its applications are rapidly expanding.This article explores the emerging role of AI in the management of MASLD,emphasizing its ability to improve clinical prediction,enhance the diagnostic performance of imaging modalities,and support histopathological confirmation.Additionally,it examines the development of AI-guided personalized treatments,where lifestyle modifications and close monitoring play a pivotal role in achieving therapeutic success.展开更多
The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n...The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.展开更多
Functional gastrointestinal disorders(FGIDs),including irritable bowel syndrome(IBS),functional dyspepsia(FD),and gastroesophageal reflux disease(GERD),present persistent diagnostic and therapeutic challenges due to s...Functional gastrointestinal disorders(FGIDs),including irritable bowel syndrome(IBS),functional dyspepsia(FD),and gastroesophageal reflux disease(GERD),present persistent diagnostic and therapeutic challenges due to symptom heterogeneity and the absence of reliable biomarkers.Artificial intelligence(AI)enables the integration of multimodal data to enhance FGID management through precision diagnostics and preventive healthcare.This minireview summarizes recent advancements in AI applications for FGIDs,highlighting progress in diagnostic accuracy,subtype classification,personalized interventions,and preventive strategies inspired by the traditional Chinese medicine concept of“treating the undiseased”.Machine learning and deep learning algorithms have demonstrated value in improving IBS diagnosis,refining FD neuro-gastrointestinal subtyping,and screening for GERD-related complications.Moreover,AI supports dietary,psychological,and integrative medicine-based interventions to improve patient adherence and quality of life.Nonetheless,key challenges remain,including data heterogeneity,limited model interpretability,and the need for robust clinical validation.Future directions emphasize interdisciplinary collaboration,the development of multimodal and explainable AI models,and the creation of patientcentered platforms to facilitate a shift from reactive treatment to proactive prevention.This review provides a systematic framework to guide the clinical application and theoretical innovation of AI in FGIDs.展开更多
Objectives This study aimed to explore the research trends,thematic structures,and core competency domains in the field of nursing-related digital and artificial intelligence(AI)technologies.Methods A bibliometric ana...Objectives This study aimed to explore the research trends,thematic structures,and core competency domains in the field of nursing-related digital and artificial intelligence(AI)technologies.Methods A bibliometric analysis was conducted in accordance with the PRISMA 2020 statement.Peer-reviewed articles published in English from 2015 to 2025 were retrieved from Scopus,Web of Science,and PubMed.Thematic clustering was conducted using the Louvain algorithm and cosine similarity.A subset of 66 frequently cited articles was then qualitatively synthesized to capture core competencies across clusters.Results A total of 83,807 articles were included for bibliometric analysis.Of these,66 articles were chosen for thematic analysis.Five major thematic clusters were identified:remote care in primary settings,oncology and palliative care,nurse education and training,safety and quality in nursing practice,and geriatric and dementia care.Additionally,four competency domains were identified:telehealth and remote communication,health systems and informatics,digital tools in practice,and AI-powered decision support.A clear shift in research focus was observed,with the emphasis transitioning from foundational digital skills before the COVID-19 pandemic to more advanced competencies during the post-pandemic digital transformation,encompassing ethical reasoning,immersive technology use,and AI integration.Conclusions Integrating digital and AI technologies is reshaping nursing practice across various thematic areas and competency domains,highlighting a transition from foundational digital tasks to AI-supported decision-making and ethically informed technology use.This study provides a structured overview of evolving competencies in digital nursing and synthesizes evidence to support future research,curriculum design,and policy planning.展开更多
Aqueous zinc metal batteries(AZMBs)are promising candidates for next-generation energy storage,but their commercialization is hindered by zinc anode challenges,notably parasitic reactions and dendrite growth.Herein,we...Aqueous zinc metal batteries(AZMBs)are promising candidates for next-generation energy storage,but their commercialization is hindered by zinc anode challenges,notably parasitic reactions and dendrite growth.Herein,we present a biodegradable biomass-derived protective layer,primarily composed of curcumin,as a zincophilic interface for AZMBs.The curcumin-based layer,fabricated via a homogeneous solution process,exhibits strong adhesion,uniform coverage,and robust mechanical integrity.Rich polar functional groups in curcumin facilitate homogeneous Zn~(2+)flux and suppress side reactions.The curcumin-based layer shows a favorable affinity for zinc trifluoromethanesulfonate(Zn(OTf)_(2))electrolyte,which is the representative of organic zinc salts,enabling optimal thickness for both protection and ion transport.The protected Zn anodes demonstrate an extended lifespan of 2500 h in symmetrical cells and a high Coulombic efficiency of 99.15%.Furthermore,Zn(OTf)_(2)-based system typically exhibits poor stability at high current densities.Fortunately,the lifespan of symmetrical cells was extended by 40-fold at the high current density.When paired with an Na V_(3)O_(8)·1.5H_(2)O(NVO)cathode,the system achieves 86.5%capacity retention after 3000 cycles at a large specific current density of 10 A g^(-1).These results underscore the efficacy of the curcumin-based protective layer in enhancing the reversibility and stability of metal electrodes,specifically relieving the instability of Zn(OTf)_(2)-based systems at high current densities,advancing its commercial viability.展开更多
This paper presents a novel artificial intelligence(AI)-assisted two-stage method for optimising rock slope stability by integrating advanced 3D modelling with rock support design,aiming at minimising risks,material u...This paper presents a novel artificial intelligence(AI)-assisted two-stage method for optimising rock slope stability by integrating advanced 3D modelling with rock support design,aiming at minimising risks,material usage,and costs.In the first stage,an extended key block analysis identifies key blocks and key block groups,accounting for progressive failure and force interactions.The second stage uses AI algorithms to optimise rockbolting design,balancing stability,cost,and material use.The most efficient algorithms include the multi-objective tree-structured Parzen estimator(MOTPE)and non-dominated sorting genetic algorithms(NSGA-II and NSGA-III).Applied to the Larvik rock slope,the optimised solution uses 18 pre-tensioned cablebolts,providing 13.2 MN of active force and achieving a factor of safety of 1.31 while reducing the average anchorage length by approximately 16%compared to traditional design.The AI-assisted approach also reduces computation time by over 90%compared to Quasi-Monte Carlo(QMC)methods,demonstrating its efficiency for small-scale civil engineering projects and large-scale mining operations.The developed tool is practical,compatible with Building Information Modelling(BIM),and ready for engineering implementation,supporting sustainable and cost-effective rock slope stabilisation.While the method is largely automated,professional judgement remains crucial for verifying ground conditions and selecting the final solution.Future work will focus on integrating data uncertainties,addressing complex block deformation mechanisms,refining optimisation objectives,and improving the performance of multi-objective optimisation for slope rockboling applications to further enhance the method's versatility.展开更多
Recent years have witnessed the significant breakthrough in the field of new materials discovery brought about by the artificial intelligence(AI).AI has successfully been applied for predicting the formability,reveali...Recent years have witnessed the significant breakthrough in the field of new materials discovery brought about by the artificial intelligence(AI).AI has successfully been applied for predicting the formability,revealing the properties,and guiding the experimental synthesis of materials.Rapid progress has been made in the integration of increasing database and improved computing power.Though some reviews present the development from their unique aspects,reviews from the view of how AI empowered both discovery of new materials and cognition of existing materials that covers the completed contents with two synergistical aspects are few.Here,the newest development is systematically reviewed in the field of AI empowered materials,reflecting advanced design of the intelligent systems for discovery,synthesis,prediction and validation of materials.First,background and mechanisms are briefed,after which the design for the AI systems with data,machine learning and automated laboratory included is illustrated.Next,strategies are summarized to obtain the AI systems for materials with improved performance which comprehensively cover the aspects from the in-depth cognizance of existing material and the rapid discovery of new materials,and then,the design thought for future AI systems in material science is pointed out.Finally,some perspectives are put forward.展开更多
Background:Medical artificial intelligence(MAI)is a synthesis of medical science and artificial intelligence development,serving as a crucial field in the current advancement and application of AI.In the process of de...Background:Medical artificial intelligence(MAI)is a synthesis of medical science and artificial intelligence development,serving as a crucial field in the current advancement and application of AI.In the process of developing medical AI,there may arise not only legal risks such as infringement of privacy rights and health rights but also ethical risks stemming from violations of the principles of beneficence and non-maleficence.Methods:To effectively address the damages caused by MAI in the future,it is necessary to establish a hierarchical governance system with MAI.This paper examines the systematic collection of local practices in China and the induction and integration of legal remedies for the damage of MAI.Results:To effectively address the ethical and legal challenges of medical artificial intelligence,a hierarchical regulatory system should be established,which based on the impact of intervention measures on natural rights and differences in intervention timing.This paper finally obtains a legal hierarchical governance system corresponding to the ethical risks and legal risks of MAI in China.Conclusion:The Chinese government has formed a multi-agent governance system based on the impact of risks on rights and the timing of legal intervention,which provides a reference for other countries to follow up on the research on MAI risk management.展开更多
To enhance the electrochemical performance of lithium-ion battery anodes with higher silicon content,it is essential to engineer their microstructure for better lithium-ion transport and mitigated volume change as wel...To enhance the electrochemical performance of lithium-ion battery anodes with higher silicon content,it is essential to engineer their microstructure for better lithium-ion transport and mitigated volume change as well.Herein,we suggest an effective approach to control the micropore structure of silicon oxide(SiO_(x))/artificial graphite(AG)composite electrodes using a perforated current collector.The electrode features a unique pore structure,where alternating high-porosity domains and low-porosity domains markedly reduce overall electrode resistance,leading to a 20%improvement in rate capability at a 5C-rate discharge condition.Using microstructure-resolved modeling and simulations,we demonstrate that the patterned micropore structure enhances lithium-ion transport,mitigating the electrolyte concentration gradient of lithium-ion.Additionally,perforating current collector with a chemical etching process increases the number of hydrogen bonding sites and enlarges the interface with the SiO_(x)/AG composite electrode,significantly improving adhesion strength.This,in turn,suppresses mechanical degradation and leads to a 50%higher capacity retention.Thus,regularly arranged micropore structure enabled by the perforated current collector successfully improves both rate capability and cycle life in SiO_(x)/AG composite electrodes,providing valuable insights into electrode engineering.展开更多
基金Supported by Japan Society for the Promotion of Science,No.24K11935.
文摘This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.
文摘Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.
文摘Artificial intelligence(AI)is revolutionizing medical imaging,particularly in chronic liver diseases assessment.AI technologies,including machine learning and deep learning,are increasingly integrated with multiparametric ultrasound(US)techniques to provide more accurate,objective,and non-invasive evaluations of liver fibrosis and steatosis.Analyzing large datasets from US images,AI enhances diagnostic precision,enabling better quantification of liver stiffness and fat content,which are essential for diagnosing and staging liver fibrosis and steatosis.Combining advanced US modalities,such as elastography and doppler imaging with AI,has demonstrated improved sensitivity in identifying different stages of liver disease and distinguishing various degrees of steatotic liver.These advancements also contribute to greater reproducibility and reduced operator dependency,addressing some of the limitations of traditional methods.The clinical implications of AI in liver disease are vast,ranging from early detection to predicting disease progression and evaluating treatment response.Despite these promising developments,challenges such as the need for large-scale datasets,algorithm transparency,and clinical validation remain.The aim of this review is to explore the current applications and future potential of AI in liver fibrosis and steatosis assessment using multiparametric US,highlighting the technological advances and clinical relevance of this emerging field.
文摘BACKGROUND Gastrointestinal stromal tumors(GISTs)are rare mesenchymal neoplasms primarily originating in the stomach or small intestine.Duodenal GISTs are particularly uncommon,accounting for only a small fraction of GIST cases.These tumors often present with nonspecific symptoms,making early detection challenging.This case discusses a duodenal GIST misdiagnosed as pancreatic cancer due to obstructive jaundice.CASE SUMMARY A 40-year-old male with jaundice and abdominal symptoms underwent imaging,which suggested a malignant periampullary tumor.Preoperative misdiagnosis of pancreatic cancer was made,and surgery was performed.Postoperative histopathology confirmed a duodenal GIST.The role of artificial intelligence in the diagnostic pathway is explored,emphasizing its potential to differentiate between duodenal GISTs and other similar conditions using advanced imaging analysis.CONCLUSION Artificial intelligence in radiomic imaging holds significant promise in enhancing the diagnostic process for rare cancers like duodenal GISTs,ensuring timely and accurate treatment.
文摘Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
文摘Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.
基金Funding Project for Ideological and Political Model Courses of“Epidemic Fighting”Courses in Henan Province(Project No.:531,2020)University-level Curriculum Ideological and Political Demonstration Course Support Project of Zhengzhou Sias University(Project No.:34,2024)+2 种基金University-level Key Discipline Support Project of Zhengzhou Sias University(Project No.:1,2022)2025 Key Scientific Research Projects of Henan Universities(Project No.:25B360003)Henan Province Private Brand Professional Support Project(Project No.:527,2019)。
文摘Nursing education is undergoing a paradigm shift from skill training to clinical thinking cultivation.The integration of artificial intelligence technology offers technical possibilities for this transformation,but it also brings about a deep tension between the cultivation of humanistic qualities and a standardized training.Based on the analysis of the practical forms of nursing smart education,this paper examines the cognitive gap between the deterministic feedback of virtual simulation systems and the complexity of real clinical scenarios,reveals the potential narrowing effect of data-driven ability profiling on the all-round development of nursing students,and then proposes the design logic of intelligent teaching resources centered on real clinical problems,a hierarchical teaching model with clear human-machine division of labor,and a dynamic assessment mechanism for technology application led by professional nursing teachers,in an attempt to find a balance between technological empowerment and humanistic commitment in smart nursing education.
基金financially supported by the National Natural Science Foundation of China(Nos.32471396,82230071,82172098,82201716,and 61973206)the National Key R&D Program of China(No.2023YFC2411303)+4 种基金the Integrated Project of Major Research Plan of the National Natural Science Foundation of China(No.92249303)the Shanghai Committee of Science and Technology(No.23141900600,Laboratory Animal Research Project)the Shanghai Clinical Research Plan of SHDC2023CRT01the Young Elite Scientist Sponsorship Program by the China Association for Science and Technology(No.YESS20230049)the Baoshan District Health Commission Talents(Excellent Academic Leaders)Program(No.BSWSYX-2024-05)。
文摘With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has progressed from the fabrication of simple models(1.0)to the fabrication of permanent implants(2.0),tissue engineering scaffolds(3.0),and complex biostructures utilizing living cells(4.0).Nevertheless,significant challenges remain,particularly in accurately replicating the structure and function of host tissues,selecting appropriate materials,and optimizing printing parameters.The integration of artificial intelligence(AI),especially machine learning,provides promising novel opportunities in bioprinting(5.0).This review systematically summarizes the current applications of AI in bioprinting,discussing both construction strategies and application scenarios.It also explores the potential of AI to improve bioprinting in the preparation of complex functional tissues and in situ tissue repair.Overall,the synergy between AI and bioprinting is poised to drive the development of personalized medicine,facilitate high-throughput preparation of in vitro models,and provide robust tools for regenerative medicine and precision healthcare.
文摘The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unattainable.However,the black-box nature of these systems presents a significant barrier,hindering their operational adoption,regulatory approval,and full scientific validation.This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence(XAI)as applied to geohazard science(GeoXAI),a domain that aims to resolve the long-standing trade-off between model performance and interpretability.A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field.The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment.Methodologically,tree-based ensembles and deep learning models dominate the literature,with SHapley Additive exPlanations(SHAP)frequently adopted as the principal post-hoc explanation technique.More importantly,the review further documents how the role of XAI has shifted:rather than being used solely as a tool for interpreting models after training,it is increasingly integrated into the modeling cycle itself.Recent applications include its use in feature selection,adaptive sampling strategies,and model evaluation.The evidence also shows that GeoXAI extends beyond producing feature rankings.It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms.Nevertheless,several key challenges remain unresolved within the field.These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability,the demanding scholarly task of reliably distinguishing correlation from causation,and the development of appropriate methods for the treatment of complex spatio-temporal dynamics.
基金supported by grants from the Taishan Scholars Program of Shandong Province(tsqn202312333)the National Natural Science Foundation of China(82470695).
文摘Background:Biliary stent placement during endoscopic retrograde cholangiopancreatography(ERCP)is important for drainage in common bile duct(CBD)strictures,while the stent length is associated with many stent-related complications.We aimed to develop an artificial intelligence(AI)model for stent length selection during ERCP.Methods:Images of the patients who underwent ERCP and were diagnosed with CBD strictures were collected.Training involved identifying and delineating the duodenoscope,CBD and guidewire,calculating the pixel distance of the target guidewire and determining the required biliary stent length based on the diameter of the duodenoscope.The performance of the model,accuracy for length calculation and the assistance for endoscopists were validated using the testing set.Results:A total of 794 images from 431 patients were included and data augmentation was conducted.The mean intersection over union(mIoU)for duodenoscope,CBD and guidewire were 90.46%,84.79%and 84.64%,respectively.The accuracy in identifying the strictures was 97.58%(121/124).The accuracy for stent length calculation achieved 85.95%(104/121)with an error margin of±1 cm.The mean absolute error(MAE)and mean relative error(MRE)of the AI model was 0.81 cm and 0.13,respectively.The AI model could reduce approximately 202 mGycm^(2)of the radiation exposure for each patient.It significantly improved both MAE and MRE for less experienced endoscopists(P=0.01 and P=0.02,respectively).Conclusions:The AI model could accurately identify duodenoscope,CBD and guidewire,enabling accurate strictures identification and stent length selection.
基金Supported by Xuhui District Health Commission,No.SHXH202214.
文摘Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.
文摘Metabolic dysfunction-associated steatotic liver disease(MASLD)is an increasingly prevalent condition associated with hepatic complications and cardiovascular and renal events.Given its significant clinical impact,the development of new strategies for early diagnosis and treatment is essential to improve patient outcomes.Over the past decade,the integration of artificial intelligence(AI)into gastroenterology has led to transformative advancements in medical practice.AI represents a major step towards personalized medicine,offering the potential to enhance diagnostic accuracy,refine prognostic assessments,and optimize treatment strategies.Its applications are rapidly expanding.This article explores the emerging role of AI in the management of MASLD,emphasizing its ability to improve clinical prediction,enhance the diagnostic performance of imaging modalities,and support histopathological confirmation.Additionally,it examines the development of AI-guided personalized treatments,where lifestyle modifications and close monitoring play a pivotal role in achieving therapeutic success.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950).
文摘The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.
基金Supported by The Natural Science Foundation of China,No.82374292the Plans for Major Provincial Science and Technology Projects of Anhui Province,No.202303a07020003the Innovation Team and Talents Cultivation Program of the National Administration of Traditional Chinese Medicine,No.ZYYCXTD-C-202401.
文摘Functional gastrointestinal disorders(FGIDs),including irritable bowel syndrome(IBS),functional dyspepsia(FD),and gastroesophageal reflux disease(GERD),present persistent diagnostic and therapeutic challenges due to symptom heterogeneity and the absence of reliable biomarkers.Artificial intelligence(AI)enables the integration of multimodal data to enhance FGID management through precision diagnostics and preventive healthcare.This minireview summarizes recent advancements in AI applications for FGIDs,highlighting progress in diagnostic accuracy,subtype classification,personalized interventions,and preventive strategies inspired by the traditional Chinese medicine concept of“treating the undiseased”.Machine learning and deep learning algorithms have demonstrated value in improving IBS diagnosis,refining FD neuro-gastrointestinal subtyping,and screening for GERD-related complications.Moreover,AI supports dietary,psychological,and integrative medicine-based interventions to improve patient adherence and quality of life.Nonetheless,key challenges remain,including data heterogeneity,limited model interpretability,and the need for robust clinical validation.Future directions emphasize interdisciplinary collaboration,the development of multimodal and explainable AI models,and the creation of patientcentered platforms to facilitate a shift from reactive treatment to proactive prevention.This review provides a systematic framework to guide the clinical application and theoretical innovation of AI in FGIDs.
基金supported by grants for development of new faculty staff,Ratchadaphiseksomphot Fund,Chulalongkorn University,Thailand.
文摘Objectives This study aimed to explore the research trends,thematic structures,and core competency domains in the field of nursing-related digital and artificial intelligence(AI)technologies.Methods A bibliometric analysis was conducted in accordance with the PRISMA 2020 statement.Peer-reviewed articles published in English from 2015 to 2025 were retrieved from Scopus,Web of Science,and PubMed.Thematic clustering was conducted using the Louvain algorithm and cosine similarity.A subset of 66 frequently cited articles was then qualitatively synthesized to capture core competencies across clusters.Results A total of 83,807 articles were included for bibliometric analysis.Of these,66 articles were chosen for thematic analysis.Five major thematic clusters were identified:remote care in primary settings,oncology and palliative care,nurse education and training,safety and quality in nursing practice,and geriatric and dementia care.Additionally,four competency domains were identified:telehealth and remote communication,health systems and informatics,digital tools in practice,and AI-powered decision support.A clear shift in research focus was observed,with the emphasis transitioning from foundational digital skills before the COVID-19 pandemic to more advanced competencies during the post-pandemic digital transformation,encompassing ethical reasoning,immersive technology use,and AI integration.Conclusions Integrating digital and AI technologies is reshaping nursing practice across various thematic areas and competency domains,highlighting a transition from foundational digital tasks to AI-supported decision-making and ethically informed technology use.This study provides a structured overview of evolving competencies in digital nursing and synthesizes evidence to support future research,curriculum design,and policy planning.
基金the financial support from Research Institute for Smart Energy at the Hong Kong Polytechnic University(Grant No.CDB2)the support of the Hong Kong PhD Fellowship Scheme(Grant No.PF21-65328)。
文摘Aqueous zinc metal batteries(AZMBs)are promising candidates for next-generation energy storage,but their commercialization is hindered by zinc anode challenges,notably parasitic reactions and dendrite growth.Herein,we present a biodegradable biomass-derived protective layer,primarily composed of curcumin,as a zincophilic interface for AZMBs.The curcumin-based layer,fabricated via a homogeneous solution process,exhibits strong adhesion,uniform coverage,and robust mechanical integrity.Rich polar functional groups in curcumin facilitate homogeneous Zn~(2+)flux and suppress side reactions.The curcumin-based layer shows a favorable affinity for zinc trifluoromethanesulfonate(Zn(OTf)_(2))electrolyte,which is the representative of organic zinc salts,enabling optimal thickness for both protection and ion transport.The protected Zn anodes demonstrate an extended lifespan of 2500 h in symmetrical cells and a high Coulombic efficiency of 99.15%.Furthermore,Zn(OTf)_(2)-based system typically exhibits poor stability at high current densities.Fortunately,the lifespan of symmetrical cells was extended by 40-fold at the high current density.When paired with an Na V_(3)O_(8)·1.5H_(2)O(NVO)cathode,the system achieves 86.5%capacity retention after 3000 cycles at a large specific current density of 10 A g^(-1).These results underscore the efficacy of the curcumin-based protective layer in enhancing the reversibility and stability of metal electrodes,specifically relieving the instability of Zn(OTf)_(2)-based systems at high current densities,advancing its commercial viability.
基金support from Research Council of Norway via STIPINST PhD grant(Grant No.323307),Bever Control AS,and Bane NOR.
文摘This paper presents a novel artificial intelligence(AI)-assisted two-stage method for optimising rock slope stability by integrating advanced 3D modelling with rock support design,aiming at minimising risks,material usage,and costs.In the first stage,an extended key block analysis identifies key blocks and key block groups,accounting for progressive failure and force interactions.The second stage uses AI algorithms to optimise rockbolting design,balancing stability,cost,and material use.The most efficient algorithms include the multi-objective tree-structured Parzen estimator(MOTPE)and non-dominated sorting genetic algorithms(NSGA-II and NSGA-III).Applied to the Larvik rock slope,the optimised solution uses 18 pre-tensioned cablebolts,providing 13.2 MN of active force and achieving a factor of safety of 1.31 while reducing the average anchorage length by approximately 16%compared to traditional design.The AI-assisted approach also reduces computation time by over 90%compared to Quasi-Monte Carlo(QMC)methods,demonstrating its efficiency for small-scale civil engineering projects and large-scale mining operations.The developed tool is practical,compatible with Building Information Modelling(BIM),and ready for engineering implementation,supporting sustainable and cost-effective rock slope stabilisation.While the method is largely automated,professional judgement remains crucial for verifying ground conditions and selecting the final solution.Future work will focus on integrating data uncertainties,addressing complex block deformation mechanisms,refining optimisation objectives,and improving the performance of multi-objective optimisation for slope rockboling applications to further enhance the method's versatility.
基金supported by the Hong Kong Polytechnic University(Project No.4-ZZW1,4-YWER,97D9,4-W443)。
文摘Recent years have witnessed the significant breakthrough in the field of new materials discovery brought about by the artificial intelligence(AI).AI has successfully been applied for predicting the formability,revealing the properties,and guiding the experimental synthesis of materials.Rapid progress has been made in the integration of increasing database and improved computing power.Though some reviews present the development from their unique aspects,reviews from the view of how AI empowered both discovery of new materials and cognition of existing materials that covers the completed contents with two synergistical aspects are few.Here,the newest development is systematically reviewed in the field of AI empowered materials,reflecting advanced design of the intelligent systems for discovery,synthesis,prediction and validation of materials.First,background and mechanisms are briefed,after which the design for the AI systems with data,machine learning and automated laboratory included is illustrated.Next,strategies are summarized to obtain the AI systems for materials with improved performance which comprehensively cover the aspects from the in-depth cognizance of existing material and the rapid discovery of new materials,and then,the design thought for future AI systems in material science is pointed out.Finally,some perspectives are put forward.
基金funded by China Law Society 2025 Annual Legal Research,Project grant number:CLS(2025)Y04.
文摘Background:Medical artificial intelligence(MAI)is a synthesis of medical science and artificial intelligence development,serving as a crucial field in the current advancement and application of AI.In the process of developing medical AI,there may arise not only legal risks such as infringement of privacy rights and health rights but also ethical risks stemming from violations of the principles of beneficence and non-maleficence.Methods:To effectively address the damages caused by MAI in the future,it is necessary to establish a hierarchical governance system with MAI.This paper examines the systematic collection of local practices in China and the induction and integration of legal remedies for the damage of MAI.Results:To effectively address the ethical and legal challenges of medical artificial intelligence,a hierarchical regulatory system should be established,which based on the impact of intervention measures on natural rights and differences in intervention timing.This paper finally obtains a legal hierarchical governance system corresponding to the ethical risks and legal risks of MAI in China.Conclusion:The Chinese government has formed a multi-agent governance system based on the impact of risks on rights and the timing of legal intervention,which provides a reference for other countries to follow up on the research on MAI risk management.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.NRF-2021M3H4A1A02048529)the Ministry of Trade,Industry and Energy(MOTIE)of the Korean government under grant No.RS-2022-00155854support from the DGIST Supercomputing and Big Data Center.
文摘To enhance the electrochemical performance of lithium-ion battery anodes with higher silicon content,it is essential to engineer their microstructure for better lithium-ion transport and mitigated volume change as well.Herein,we suggest an effective approach to control the micropore structure of silicon oxide(SiO_(x))/artificial graphite(AG)composite electrodes using a perforated current collector.The electrode features a unique pore structure,where alternating high-porosity domains and low-porosity domains markedly reduce overall electrode resistance,leading to a 20%improvement in rate capability at a 5C-rate discharge condition.Using microstructure-resolved modeling and simulations,we demonstrate that the patterned micropore structure enhances lithium-ion transport,mitigating the electrolyte concentration gradient of lithium-ion.Additionally,perforating current collector with a chemical etching process increases the number of hydrogen bonding sites and enlarges the interface with the SiO_(x)/AG composite electrode,significantly improving adhesion strength.This,in turn,suppresses mechanical degradation and leads to a 50%higher capacity retention.Thus,regularly arranged micropore structure enabled by the perforated current collector successfully improves both rate capability and cycle life in SiO_(x)/AG composite electrodes,providing valuable insights into electrode engineering.
