Magnesium-based materials,including magnesium alloys,have emerged as a promising class of biodegradable materials with potential applications in cancer therapy due to their unique properties,including biocompatibility...Magnesium-based materials,including magnesium alloys,have emerged as a promising class of biodegradable materials with potential applications in cancer therapy due to their unique properties,including biocompatibility,biodegradability,and the ability to modulate the tumor microenvironment.The main degradation products of magnesium alloys are magnesium ions(Mg^(2+)),hydrogen(H_(2)),and magnesium hydroxide(Mg(OH)_(2)).Magnesium ions can regulate tumor growth and metastasis by mediating the inflammatory response and oxidative stress,maintaining genomic stability,and affecting the tumor microenvironment.Similarly,hydrogen can inhibit tumorigenesis through antioxidant and anti-inflammatory properties.Moreover,Mg(OH)_(2) can alter the pH of the microenvironment,impacting tumorigenesis.Biodegradable magnesium alloys serve various functions in clinical applications,including,but not limited to,bonefixation,coronary stents,and drug carriers.Nonetheless,the anti-tumor mechanism associated with magnesium-based materials has not been thoroughly investigated.This review provides a comprehensive overview of the current state of magnesium-based therapies for cancer.It highlights the mechanisms of action,identifies the challenges that must be addressed,and discusses prospects for oncological applications.展开更多
Mg alloys have the defects of low stiffness,low strength,and high coefficient of thermal expansion(CTE).The composites strategy and its architecture design are effective approaches to improve the comprehensive perform...Mg alloys have the defects of low stiffness,low strength,and high coefficient of thermal expansion(CTE).The composites strategy and its architecture design are effective approaches to improve the comprehensive performance of materials,but the processing difficulty,especially in ceramics forming,limits the control and innovation of material architecture.Here,combined with 3D printing and squeeze infiltration technology,two precisely controllable architectures of AZ91/Al_(2)O_(3)interpenetrating phase composites(IPC)with ceramic scaffold were prepared.The interface,properties and impact of different architecture on IPC performance were studied by experiments and finite element simulation.The metallurgical bonding of the interface was realized with the formation of MgAl_(2)O_(4)reaction layer.The IPC with 1 mm circular hole scaffold(1C-IPC)exhibited significantly improved elastic modulus of 164 GPa,high compressive strength of 680 MPa,and good CTE of 12.91×10^(-6)K^(−1),which were 3.64 times,1.98 times and 55%of the Mg matrix,respectively.Their elastic modulus,compressive strength,and CTE were superior to the vast majority of Mg alloys and Mg based composites.The reinforcement and matrix were bicontinuous and interpenetrating each other,which played a critical role in ensuring the potent strengthening effect of the Al_(2)O_(3)reinforcement by efficient load transfer.Under the same volume fraction of reinforcements,compared to IPC with 1 mm hexagonal hole scaffold(1H-IPC),the elastic modulus and compressive strength of 1C-IPC increased by 15%and 28%,respectively,which was due to the reduced stress concentration and more uniform stress distribution of 1C-IPC.It shows great potential of architecture design in improving the performance of composites.This study provides architectural design strategy and feasible preparation method for the development of high performance materials.展开更多
Aiming to provide optimal solutions to the sluggish kinetics of Mg(BH_(4))_(2),this study proposes,for the first time,a novel machine learning model to predict dehydrogenation behaviors of modified Mg(BH_(4))_(2).Nota...Aiming to provide optimal solutions to the sluggish kinetics of Mg(BH_(4))_(2),this study proposes,for the first time,a novel machine learning model to predict dehydrogenation behaviors of modified Mg(BH_(4))_(2).Notably,numerous data points are collected from temperatureprogrammed,isothermal,and cyclic dehydrogenation behaviors,a neural network model is proposed by using multi-head attention mechanisms,which exhibits the highest predictive performance compared to traditional machine learning models.The study also ranks different variables influencing dehydrogenation processes,employing interpretable analysis to identify critical variable thresholds,offering guidance for the experimental parameter design.The model can also be adapted to scenarios involving co-doping of hydrides and catalysts in Mg(BH_(4))_(2) system and proved high accuracy and scalability in predicting dehydrogenation curves under diverse conditions.Employing the model,performance predictions for a series of undeveloped Mg(BH_(4))_(2) co-doping systems can be made,and superior dehydrogenation catalytic effects of fluorinated graphite(FGi)are uncovered.Real-world experimental validation of the optimal Mg(BH_(4))_(2)-LiBH_(4)-FGi system confirms consistency with model predictions,and performance enhancement attributes to experimental parameter optimization.Further characterizations provide mechanistic insights into the synergistic interactions of FGi and LiBH_(4).This work paves the way for advancing utilization of machine learning in the high-capacity hydrogen storage field.展开更多
文摘Magnesium-based materials,including magnesium alloys,have emerged as a promising class of biodegradable materials with potential applications in cancer therapy due to their unique properties,including biocompatibility,biodegradability,and the ability to modulate the tumor microenvironment.The main degradation products of magnesium alloys are magnesium ions(Mg^(2+)),hydrogen(H_(2)),and magnesium hydroxide(Mg(OH)_(2)).Magnesium ions can regulate tumor growth and metastasis by mediating the inflammatory response and oxidative stress,maintaining genomic stability,and affecting the tumor microenvironment.Similarly,hydrogen can inhibit tumorigenesis through antioxidant and anti-inflammatory properties.Moreover,Mg(OH)_(2) can alter the pH of the microenvironment,impacting tumorigenesis.Biodegradable magnesium alloys serve various functions in clinical applications,including,but not limited to,bonefixation,coronary stents,and drug carriers.Nonetheless,the anti-tumor mechanism associated with magnesium-based materials has not been thoroughly investigated.This review provides a comprehensive overview of the current state of magnesium-based therapies for cancer.It highlights the mechanisms of action,identifies the challenges that must be addressed,and discusses prospects for oncological applications.
基金supported by the National Key Research and Development Program of China(No.2022YFB3708400)the National Natural Science Foundation of China(No.52305158)+1 种基金the Young Elite Scientists Sponsorship Program by CAST(No.2022QNRC001)the Science Innovation Foundation of Shanghai Academy of Spaceflight Technology(No.USCAST2021-18).
文摘Mg alloys have the defects of low stiffness,low strength,and high coefficient of thermal expansion(CTE).The composites strategy and its architecture design are effective approaches to improve the comprehensive performance of materials,but the processing difficulty,especially in ceramics forming,limits the control and innovation of material architecture.Here,combined with 3D printing and squeeze infiltration technology,two precisely controllable architectures of AZ91/Al_(2)O_(3)interpenetrating phase composites(IPC)with ceramic scaffold were prepared.The interface,properties and impact of different architecture on IPC performance were studied by experiments and finite element simulation.The metallurgical bonding of the interface was realized with the formation of MgAl_(2)O_(4)reaction layer.The IPC with 1 mm circular hole scaffold(1C-IPC)exhibited significantly improved elastic modulus of 164 GPa,high compressive strength of 680 MPa,and good CTE of 12.91×10^(-6)K^(−1),which were 3.64 times,1.98 times and 55%of the Mg matrix,respectively.Their elastic modulus,compressive strength,and CTE were superior to the vast majority of Mg alloys and Mg based composites.The reinforcement and matrix were bicontinuous and interpenetrating each other,which played a critical role in ensuring the potent strengthening effect of the Al_(2)O_(3)reinforcement by efficient load transfer.Under the same volume fraction of reinforcements,compared to IPC with 1 mm hexagonal hole scaffold(1H-IPC),the elastic modulus and compressive strength of 1C-IPC increased by 15%and 28%,respectively,which was due to the reduced stress concentration and more uniform stress distribution of 1C-IPC.It shows great potential of architecture design in improving the performance of composites.This study provides architectural design strategy and feasible preparation method for the development of high performance materials.
基金the National Natural Science Foundation of China(No.52171223 and U20A20237).
文摘Aiming to provide optimal solutions to the sluggish kinetics of Mg(BH_(4))_(2),this study proposes,for the first time,a novel machine learning model to predict dehydrogenation behaviors of modified Mg(BH_(4))_(2).Notably,numerous data points are collected from temperatureprogrammed,isothermal,and cyclic dehydrogenation behaviors,a neural network model is proposed by using multi-head attention mechanisms,which exhibits the highest predictive performance compared to traditional machine learning models.The study also ranks different variables influencing dehydrogenation processes,employing interpretable analysis to identify critical variable thresholds,offering guidance for the experimental parameter design.The model can also be adapted to scenarios involving co-doping of hydrides and catalysts in Mg(BH_(4))_(2) system and proved high accuracy and scalability in predicting dehydrogenation curves under diverse conditions.Employing the model,performance predictions for a series of undeveloped Mg(BH_(4))_(2) co-doping systems can be made,and superior dehydrogenation catalytic effects of fluorinated graphite(FGi)are uncovered.Real-world experimental validation of the optimal Mg(BH_(4))_(2)-LiBH_(4)-FGi system confirms consistency with model predictions,and performance enhancement attributes to experimental parameter optimization.Further characterizations provide mechanistic insights into the synergistic interactions of FGi and LiBH_(4).This work paves the way for advancing utilization of machine learning in the high-capacity hydrogen storage field.
