The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for e...The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for evaluating the efficacy of WGPC.Unlike conventional testing methods,machine learning techniques offer precise and reliable predictions of concrete’s compressive strength,especially in its long-term mechanical properties.In this work,four models,namely Multiple Linear Regression(MLR),Back Propagation Neural Network(BPNN),Support Vector Regression(SVR),and Random Forest Regression(RFR)were employed.Furthermore,particle swarm optimization(PSO)algorithm and cross-validation techniques were applied to fine-tune the model parameters,striving for peak prediction performance.The results indicated that optimized models generally exhibit enhanced predictive accuracy compared to their basic counterparts.Notably,the PSO-RFR model excels among all evaluated models,showcasing superior performance on the testing dataset.It achieves a coefficient of determination(R^(2))of 0.9231,a mean absolute error(MAE)of 2.1073,and a root mean square error(RMSE)of 3.6903.When compared to experimental results,the PSO-RFR and PSO-BPNN models demonstrate exceptional predictive accuracy.Notably,the PSO-BPNN model exhibits the closest R^(2)values between its training and test sets.This close alignment of R^(2)values between the training and testing sets reflects the PSO-BPNN model’s superior generalization ability for unseen data.The findings present an efficient method for predicting concrete’s compressive strength,contributing to the sustainable development of concrete materials,and providing theoretical support for their research and application.展开更多
Alkali-free SiO_(2)-Al_(2)O_(3)-CaO-MgO with different SiO_(2)/Al_(2)O_(3)mass ratios was prepared by conventional melt quenching method.The glass network structure,thermodynamic properties and elastic modulus changes...Alkali-free SiO_(2)-Al_(2)O_(3)-CaO-MgO with different SiO_(2)/Al_(2)O_(3)mass ratios was prepared by conventional melt quenching method.The glass network structure,thermodynamic properties and elastic modulus changes with SiO_(2)and Al_(2)O_(3)ratios were investigated using various techniques.It is found that when SiO_(2)is replaced by Al_(2)O_(3),the Q^(4) to Q^(3) transition of silicon-oxygen network decreases while the aluminum-oxygen network increases,which result in the transformation of Si-O-Si bonds to Si-O-Al bonds and an increase in glass network connectivity even though the intermolecular bond strength decreases.The glass transition temperature(T_(g))increases continuously,while the thermal expansion coefficient increases and high-temperature viscosity first decreases and then increases.Meanwhile,the elastic modulus values increase from 93 to 102 GPa.This indicates that the elastic modulus is mainly affected by packing factor and dissociation energy,and elements with higher packing factors and dissociation energies supplant those with lower values,resulting in increased rigidity within the glass.展开更多
Polyamorphous transition refers to the transformation between two distinct amorphous states with identical composition.This phenomenon is intriguing in the field of physics and offers avenues for glass material design...Polyamorphous transition refers to the transformation between two distinct amorphous states with identical composition.This phenomenon is intriguing in the field of physics and offers avenues for glass material design.Recently,polyamorphous transitions have been frequently observed in glassy materials.However,the transition pathway has yet to be established,which is essential for understanding its structural origins.Here,we present evidence from 12 different types of metallic glasses spanning 7 orders of magnitude in timescales,demonstrating that polyamorphous transitions consistently occur after the devitrification process,between two supercooled liquid phases(Ⅰ and Ⅱ).Notably,we observe a decrease in liquid fragility and heat capacity following the transition,suggesting that the polyamorphous transition is associated with the fragile-to-strong transition(FST)in liquids.These findings elucidate the detailed structural pathway of the polyamorphous transition,via glass I→devitrification→liquid I→fragile-strong transition→liquid II,and incorporate the FST into a cohesive framework for its understanding.展开更多
The glass transition temperature(T_(g))of styrene-butadiene rubber(SBR)is a key parameter determining its low-temperature flexibility and processing performance.Accurate prediction of T_(g)is crucial formaterial desig...The glass transition temperature(T_(g))of styrene-butadiene rubber(SBR)is a key parameter determining its low-temperature flexibility and processing performance.Accurate prediction of T_(g)is crucial formaterial design and application optimisation.Addressing the limitations of traditional experimental measurements and theoretical models in terms of efficiency,cost,and accuracy,this study proposes a machine learning prediction framework that integrates multi-model ensemble and Bayesian optimization by constructing a multi-component feature dataset and algorithm optimization strategy.Based on the constructed high-quality dataset containing 96 SBR samples,ninemachine learning models were employed to predict the T_(g)of SBR and compare their prediction performance.Ultimately,aGPR-XGBoost mixed model was constructed through model ensemble,achieving high-precision prediction with R^(2)values greater than 0.9 on both the training and test sets.Further feature attribution and local effect analysis were conducted using feature analysis methods such as SHAP and ALE,revealing the nonlinear influence patterns of various components on T_(g),providing a theoretical basis for SBR formulation design and T_(g)regulation.The machine learning prediction framework established in this study combines high-precision prediction with interpretability,significantly enhancing the prediction performance of the T_(g)of SBR.It offers an efficient tool for SBR molecular design and holds great potential for promotion and application.展开更多
Enhancing the kinetic stability of glasses typically requires deepening their thermodynamic stability,which increases structural rigidity and degrades ductility;decoupling these properties remains a major challenge.He...Enhancing the kinetic stability of glasses typically requires deepening their thermodynamic stability,which increases structural rigidity and degrades ductility;decoupling these properties remains a major challenge.Here,we demonstrate that spatial patterning in metallic glasses produces exceptional kinetic ultrastability that coexists with a thermodynamically metastable,high-energy state and excellent plasticity.Guided by atomistic simulations using replica exchange molecular dynamics and machine learning interatomic potentials,we reveal that oxygen,through reaction-diffusion-coupled pattern dynamics,self-organizes into oxygen-centered pinned structures(OPSs)that serve as localized kinetic constraints.These motifs drastically slow structural relaxation,delivering kinetic stability comparable to ultrastable glasses even as the system retains the high inherent energy of rapidly quenched states.The OPSs’topology yields a spatially uniform activation of plastic events,promoting strain delocalization under mechanical load.By geometrically tailoring oxygen patterns,we increase the glass transition onset temperature(Tonset)by about 200 K with negligible loss of deformability.Our findings establish a practicable paradigm for decoupling kinetic and thermodynamic stability and point to a scalable,additive route for designing amorphous materials that combine hyperstability with plasticity.展开更多
Improving the green mechanical strength and thermal shock resistance of silica sol-bonded corundum castables is of great significance for promoting their large-scale application.Silica sol-bonded corundum castables we...Improving the green mechanical strength and thermal shock resistance of silica sol-bonded corundum castables is of great significance for promoting their large-scale application.Silica sol-bonded corundum castables were prepared using brown corundum,dense corundum powder,α-Al_(2)O_(3)micropowder and SiO_(2)micropowder as the main raw materials,and silica sol as the binder.The effects of different additions of chopped glass fibers(0,0.2%,0.4%,0.6%,0.8%and 1%,by mass)on the properties of the castables were studied.The results show that with the increase of the fiber addition,the cold modulus of rupture,cold compressive strength and hot modulus of rupture of the samples first increase and then decrease.After drying at 110℃,the sample containing 0.4%fibers has the cold modulus of rupture of 9.1 MPa and cold compressive strength of 27.4 MPa,increasing by 80.4%and 41.2%,respectively,compared with the one without fiber addition.This is because the fibers bonded with the silica sol-gel interface to form a stressed skeleton,strengthening the bonding between the matrix and the aggregates.When subjected to external stress,the fibers can effectively share the load and prevent crack propagation,thus increasing the strength.In addition,the sample with 0.4%fibers has the highest cold modulus of rupture before and after thermal shock,and its strength retention ratio increases by 16.1%compared to the sample without fibers.Overall,the sample with 0.4%fibers exhibits the best comprehensive performance.展开更多
The yielding transition of amorphous solids remains a fundamental yet poorly understood issue in materials physics.In this work,we employ oscillatory shear to probe the yielding transition in metallic glasses(MGs)with...The yielding transition of amorphous solids remains a fundamental yet poorly understood issue in materials physics.In this work,we employ oscillatory shear to probe the yielding transition in metallic glasses(MGs)with various thermal histories.We identify three distinct deformation regimes depending on the applied strain amplitudes.Below the yield strainγ_(y),the response is elastic and accompanied by aging,through reversible atomic rearrangements that preserve the material's initial memory of thermal history.Slightly aboveγ_(y),the system undergoes a sharp transition during oscillatory cycles,indicated by a sudden rise in potential energy and non-affine displacement,along with the emergence of a shear band.Well aboveγ_(y),plastic deformation dominates,driving samples of various initial stability toward a common steady state,while thermal histories are erased by irreversible rearrangements and shear band formation.These findings advance the understanding of failure mechanisms in MGs and shed light on tuning their mechanical performance in industrial applications involving cyclic loading.展开更多
Received:06 December 2025;Accepted:25 February 2026;Published:30 March 2026 ABSTRACT:In the last decade,the importance of sustainable construction and artificial intelligence(AI)in civil engineering has been underline...Received:06 December 2025;Accepted:25 February 2026;Published:30 March 2026 ABSTRACT:In the last decade,the importance of sustainable construction and artificial intelligence(AI)in civil engineering has been underlined in many studies.Numerous studies highlighted the superiority of AI techniques over simple and mathematical regression analyses,which suffer from relatively poor generalization and an inability to capture highly non-linear relationships among inputs and output(s)parameters.In this study,to evaluate the compressive strength of concrete with glass powder(GP)and recycled aggregates,600 concrete samples were tested in the laboratory,and their results were evaluated.For intelligent assessment of concrete compressive strength(CCS),the study utilized an improved artificial neural network(ANN)with particle swarm optimization(PSO)algorithm and imperialist competitive algorithm(ICA).For training the models,the experimentally obtained data were used.The concrete ingredients formed the inputs of the AI-based predictive models of CCS.The experimental findings reveal that the implementation of recycled coarse aggregates in concrete from a sustainable construction point of view is advantageous and can enhance the CCS by 11.43%.Apart from that,findings indicate that utilization of 10%GP can lead to a nearly 20%increase in CCS(from 44.6 to 54.1 MPa).Additionally,the experimental observations show almost 40%improvement of CCS when 5%micro silica was used in the concrete mixture.Based on the findings,the study suggests the utilization of waste glass powder to partially replace cement in concrete,which can reduce the amount of cement production.This reduction from economic,energy-saving,and environmental(reduction in greenhouse gas emissions)points of view is of interest.On the other hand,the AI results show that the PSO-based ANN model outperforms the ICA-based ANN for the utilized dataset.According to the findings,the PSO-based ANN predictive model(with a coefficient of determination value of 0.939 and root mean square value of 0.113 for testing data)is a capable tool in predicting the CCS.Hence,this study recommends the implementation of AI-based models in CCS assessment.展开更多
Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal...Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal lung tissue,honeycombing lungs,and Ground Glass Opacity(GGO)in CT images is often challenging for radiologists and may lead to misinterpretations.Although earlier studies have proposed models to detect and classify HCL,many faced limitations such as high computational demands,lower accuracy,and difficulty distinguishing between HCL and GGO.CT images are highly effective for lung classification due to their high resolution,3D visualization,and sensitivity to tissue density variations.This study introduces Honeycombing Lungs Network(HCL Net),a novel classification algorithm inspired by ResNet50V2 and enhanced to overcome the shortcomings of previous approaches.HCL Net incorporates additional residual blocks,refined preprocessing techniques,and selective parameter tuning to improve classification performance.The dataset,sourced from the University Malaya Medical Centre(UMMC)and verified by expert radiologists,consists of CT images of normal,honeycombing,and GGO lungs.Experimental evaluations across five assessments demonstrated that HCL Net achieved an outstanding classification accuracy of approximately 99.97%.It also recorded strong performance in other metrics,achieving 93%precision,100%sensitivity,89%specificity,and an AUC-ROC score of 97%.Comparative analysis with baseline feature engineering methods confirmed the superior efficacy of HCL Net.The model significantly reduces misclassification,particularly between honeycombing and GGO lungs,enhancing diagnostic precision and reliability in lung image analysis.展开更多
The functional properties of glasses are governed by their formation history and the complex relaxation processes they undergo.However,under extreme conditions,glass behaviors are still elusive.In this study,we employ...The functional properties of glasses are governed by their formation history and the complex relaxation processes they undergo.However,under extreme conditions,glass behaviors are still elusive.In this study,we employ simulations with varied protocols to evaluate the effectiveness of different descriptors in predicting mechanical properties across both low-and high-pressure regimes.Our findings demonstrate that conventional structural and configurational descriptors fail to correlate with the mechanical response following pressure release,whereas the activation energy descriptor exhibits robust linearity with shear modulus after correcting for pressure effects.Notably,the soft mode parameter emerges as an ideal and computationally efficient alternative for capturing this mechanical behavior.These findings provide critical insights into the influence of pressure on glassy properties,integrating the distinct features of compressed glasses into a unified theoretical framework.展开更多
Optimizing the microchannel design of the next generation of chips requires an understanding of the in situ property evolution of the chip-based materials under fast cooling.This work overcomes the conventional relian...Optimizing the microchannel design of the next generation of chips requires an understanding of the in situ property evolution of the chip-based materials under fast cooling.This work overcomes the conventional reliance on reheating data of melt-quenched glasses by demonstrating direct observations of glass transition on cooling curves utilizing the most advanced fast differential scanning calorimetry.By leveraging an MEMS chip sensor that allows for rapid heat extraction from microgram-sized samples to a purged gas coolant,the device is able to reach ultra-fast cooling rates of up to 40,000 K·s^(−1).Four thermal regions are identified by examining the cooling behaviors of two metallic glasses.This is because the actual rate of the specimen can differ from the programmed rate,especially at high set rate when the actual rate decreases before the glass transition is completed.We define the operational window for reliable cooling curve analysis,build models with empirical and theoretical analyses to determine the maximum feasible cooling rate,and demonstrate how optimizing sample mass and environment temperature broaden this window.The method avoids deceptive structural relaxation effects verified by fictivetemperature analysis and permits the capture of full glass transition during cooling.展开更多
For the development of high-performance metallic glasses,enhancing their stability against viscous flow and crystallization is a primary objective.Vapor deposition or prolonged annealing is an effective method to impr...For the development of high-performance metallic glasses,enhancing their stability against viscous flow and crystallization is a primary objective.Vapor deposition or prolonged annealing is an effective method to improve glass stability,shown by increased glass transition temperature(Tg)and crystallization temperature(Tx).This contributes to the development of ultra-stable metallic glasses.Herein,we demonstrate that modulating the quenching temperature can also produce ultra-stable metallic glasses,as evidenced by an increase in Tx of 17-30 K in Cu-based metallic glasses.By modulating the quenching temperature,separated primary phases,secondary phases,and even nano-oxides can be obtained in the metallic glasses.Notably,metastable phases such as Cu-rich precipitates arising from secondary phase separation play a crucial role in enhancing glass stability.However,the enhancement of the stability of the glass has only a negligible effect on its mechanical properties.This study implies that different melt thermodynamic states generated by liquid-liquid separation and transition collectively determine the frozen-in glass structure.The results of this study will be helpful for the development of ultra-stable bulk glasses.展开更多
The low-melting glass of Bi2O_(3)-B2O_(3)-SiO_(2)(BiBSi)system was used for the first time for laser sealing of vacuum glazing.Under the condition of constant boron content,how the structure and properties vary with B...The low-melting glass of Bi2O_(3)-B2O_(3)-SiO_(2)(BiBSi)system was used for the first time for laser sealing of vacuum glazing.Under the condition of constant boron content,how the structure and properties vary with Bi/Si ratio in low-melting glass was investigated.In addition,the relationships between laser power,low-melting glass solder with different Bi/Si ratios and laser sealing shear strength were revealed.The results show that a decrease in the Bi/Si ratio can cause a contraction of the glass network of the low-melting glass,leading to an increase of its characteristic temperature and a decrease of its coefficient of thermal expansion.During laser sealing,the copper ions in the low-melting glass play an endothermic role.A change in the Bi/Si ratio will affect the valence state transition of the copper ions in the low-melting glass.The absorbance of the low-melting glass does not follow the expected correlation with the Bi/Si ratio,but shows a linear correlation with the content of divalent copper ions.The greater the concentration of divalent copper ions,the greater the absorbance of the low-melting glass,and the lower the laser power required for laser sealing.The shear strength of the low melting glass solder after laser sealing was tested,and it was found that the maximum shear strength of Z1 glass sample was the highest up to 2.67 MPa.展开更多
Lunar impact glasses have been identified as crucial indicators of geochemical information regarding their source regions. Impact glasses can be categorized as either local or exotic. Those preserving geochemical sign...Lunar impact glasses have been identified as crucial indicators of geochemical information regarding their source regions. Impact glasses can be categorized as either local or exotic. Those preserving geochemical signatures matching local lithologies (e.g., mare basalts or their single minerals) or regolith bulk soil compositions are classified as “local”. Otherwise, they could be defined as “exotic”. The analysis of exotic glasses provides the opportunity to explore previously unsampled lunar areas. This study focuses on the identification of exotic glasses within the Chang’e-5 (CE-5) soil sample by analyzing the trace elements of 28 impact glasses with distinct major element compositions in comparison with the CE-5 bulk soil. However, the results indicate that 18 of the analyzed glasses exhibit trace element compositions comparable to those of the local CE-5 materials. In particular, some of them could match the local single mineral component in major and trace elements, suggesting a local origin. Therefore, it is recommended that the investigation be expanded from using major elements to including nonvolatile trace elements, with a view to enhancing our understanding on the provenance of lunar impact glasses. To achieve a more accurate identification of exotic glasses within the CE-5 soil sample, a novel classification plot of Mg# versus La is proposed. The remaining 10 glasses, which exhibit diverse trace element variations, were identified as exotic. A comparative analysis of their chemical characteristics with remote sensing data indicates that they may have originated from the Aristarchus, Mairan, Sharp, or Pythagoras craters. This study elucidates the classification and possible provenance of exotic materials within the CE-5 soil sample, thereby providing constraints for the enhanced identification of local and exotic components at the CE-5 landing site.展开更多
To analyze the impact of bubbles on the mechanical behavior of glasses,by controlling the refining time,we prepared three borosilicate glasses with the same composition and different porosity.By the analysis software ...To analyze the impact of bubbles on the mechanical behavior of glasses,by controlling the refining time,we prepared three borosilicate glasses with the same composition and different porosity.By the analysis software integrated within the optical microscope,the diameter and number of the bubbles on the surface of three borosilicate glasses were quantified.From the hardness and crack initiation resistance(CR),we built the relationship between the porosity and the mechanical performance of these borosilicate glasses.展开更多
Silicone rubber(SR)exhibits superior breathability and high-temperature resistance.However,SR is prone to degradation under extreme heat or combustion,limiting its effectiveness in mitigating secondary hazards.In this...Silicone rubber(SR)exhibits superior breathability and high-temperature resistance.However,SR is prone to degradation under extreme heat or combustion,limiting its effectiveness in mitigating secondary hazards.In this study,phosphate glass powder was used to calcinate zinc borate,lanthanum oxide,and cerium oxide.Methylphenyl polysiloxane was then grafted onto the surface of the glass powder,resulting in the modified pow-ders designated as Methylphenyl polysiloxane-grafted zinc borate-modified phosphate glass powder(GF-ZnBM),Methylphenyl polysiloxane-grafted lanthanum oxide-modified phosphate glass powder(GF-LaM),and Methylphenyl polysiloxane-grafted cerium oxide-modified phosphate glass powder(GF-CeM).The modified powders were sub-sequently incorporated into silicone rubber composites to enhance the ceramicization capability of silicone rubber at high temperatures.Specifically,GF-CeM and GF-LaM significantly increased the limiting oxygen index(LOI)to 33%and reduced the tendency for combustion propagation.Additionally,GF-CeM notably contributed to enhancing ceramicization strength.The presence of cerium oxide helps in the melting of the glass powder and enhances its adhesion to the silicone rubber matrix.SR/ZnB-GF exhibited the lowest activation energy among the tested composites,along with the best protective capability.The inclusion of modified glass powder has a minor impact on the rheological properties,indicating that the composite retains its ability to flow and deform under stress.This confirms that the material remains flexible under normal conditions and forms a ceramic structure when heated,thereby exhibiting self-supporting properties.This study provides a practical methodology for the targeted modification of glass powders,thereby further enhancing the fire safety of silicone-based composites.展开更多
High-density germanate glasses doped with Tb^(3+)ions were synthesized via the melt-quenching meth-od.The physical and luminescent properties of these glasses were characterized through various techniques,in-cluding d...High-density germanate glasses doped with Tb^(3+)ions were synthesized via the melt-quenching meth-od.The physical and luminescent properties of these glasses were characterized through various techniques,in-cluding density measurement,differential scanning calorimetry(DSC),photoluminescence(PL)spectroscopy,X-ray excited luminescence(XEL)spectroscopy,and fluorescence decay analysis.The densities of the germanate glasses were greater than 6.1 g/cm^(3).Upon excitations of ultraviolet(UV)light and X-rays,the glasses emitted in-tense green emissions.The fluorescence lifetime of the strongest emission peak at 544 nm,measured under 377 nm excitation,ranged from 1.52 ms to 1.32 ms.In the glass specimens,the maximum XEL integral intensity reached roughly 26%of that of the commercially available Bi_(4)Ge_(3)O_(12)(BGO)crystal.These results indicate that Tb^(3+)-doped high-density germanate scintillating glasses hold potential as scintillation materials for X-ray imaging applications.展开更多
文摘The application and promotion of waste glass powder concrete(WGPC)cansignificantly alleviate the pressure of concrete material scarcity and environmental pollution.Compressive strength(CS)is a critical parameter for evaluating the efficacy of WGPC.Unlike conventional testing methods,machine learning techniques offer precise and reliable predictions of concrete’s compressive strength,especially in its long-term mechanical properties.In this work,four models,namely Multiple Linear Regression(MLR),Back Propagation Neural Network(BPNN),Support Vector Regression(SVR),and Random Forest Regression(RFR)were employed.Furthermore,particle swarm optimization(PSO)algorithm and cross-validation techniques were applied to fine-tune the model parameters,striving for peak prediction performance.The results indicated that optimized models generally exhibit enhanced predictive accuracy compared to their basic counterparts.Notably,the PSO-RFR model excels among all evaluated models,showcasing superior performance on the testing dataset.It achieves a coefficient of determination(R^(2))of 0.9231,a mean absolute error(MAE)of 2.1073,and a root mean square error(RMSE)of 3.6903.When compared to experimental results,the PSO-RFR and PSO-BPNN models demonstrate exceptional predictive accuracy.Notably,the PSO-BPNN model exhibits the closest R^(2)values between its training and test sets.This close alignment of R^(2)values between the training and testing sets reflects the PSO-BPNN model’s superior generalization ability for unseen data.The findings present an efficient method for predicting concrete’s compressive strength,contributing to the sustainable development of concrete materials,and providing theoretical support for their research and application.
基金Supported by the National Key Research Program(No.2024-1129-954-112)National Natural Science Foundation of China(No.52372033)Guangxi Science and Technology Major Program(No.AA24263054)。
文摘Alkali-free SiO_(2)-Al_(2)O_(3)-CaO-MgO with different SiO_(2)/Al_(2)O_(3)mass ratios was prepared by conventional melt quenching method.The glass network structure,thermodynamic properties and elastic modulus changes with SiO_(2)and Al_(2)O_(3)ratios were investigated using various techniques.It is found that when SiO_(2)is replaced by Al_(2)O_(3),the Q^(4) to Q^(3) transition of silicon-oxygen network decreases while the aluminum-oxygen network increases,which result in the transformation of Si-O-Si bonds to Si-O-Al bonds and an increase in glass network connectivity even though the intermolecular bond strength decreases.The glass transition temperature(T_(g))increases continuously,while the thermal expansion coefficient increases and high-temperature viscosity first decreases and then increases.Meanwhile,the elastic modulus values increase from 93 to 102 GPa.This indicates that the elastic modulus is mainly affected by packing factor and dissociation energy,and elements with higher packing factors and dissociation energies supplant those with lower values,resulting in increased rigidity within the glass.
基金supported by the National Science Foundation of China(NSFC 52571185,52201180 and 52371148)the China Postdoctoral Science Foundation(2023T160241 and 2023M731176)+2 种基金the Natural Science Foundation of Chongqing(CSTB2025NSCQ-GPX1026)the Science and Technology Research Program of Chongqing Municipal Education Commission of China(KJQN202500526)the Foundation of Chongqing Normal University(No.24XLB019).
文摘Polyamorphous transition refers to the transformation between two distinct amorphous states with identical composition.This phenomenon is intriguing in the field of physics and offers avenues for glass material design.Recently,polyamorphous transitions have been frequently observed in glassy materials.However,the transition pathway has yet to be established,which is essential for understanding its structural origins.Here,we present evidence from 12 different types of metallic glasses spanning 7 orders of magnitude in timescales,demonstrating that polyamorphous transitions consistently occur after the devitrification process,between two supercooled liquid phases(Ⅰ and Ⅱ).Notably,we observe a decrease in liquid fragility and heat capacity following the transition,suggesting that the polyamorphous transition is associated with the fragile-to-strong transition(FST)in liquids.These findings elucidate the detailed structural pathway of the polyamorphous transition,via glass I→devitrification→liquid I→fragile-strong transition→liquid II,and incorporate the FST into a cohesive framework for its understanding.
基金supported by the National Natural Science Foundation of China(grant numbers 52250357 and 52203003).
文摘The glass transition temperature(T_(g))of styrene-butadiene rubber(SBR)is a key parameter determining its low-temperature flexibility and processing performance.Accurate prediction of T_(g)is crucial formaterial design and application optimisation.Addressing the limitations of traditional experimental measurements and theoretical models in terms of efficiency,cost,and accuracy,this study proposes a machine learning prediction framework that integrates multi-model ensemble and Bayesian optimization by constructing a multi-component feature dataset and algorithm optimization strategy.Based on the constructed high-quality dataset containing 96 SBR samples,ninemachine learning models were employed to predict the T_(g)of SBR and compare their prediction performance.Ultimately,aGPR-XGBoost mixed model was constructed through model ensemble,achieving high-precision prediction with R^(2)values greater than 0.9 on both the training and test sets.Further feature attribution and local effect analysis were conducted using feature analysis methods such as SHAP and ALE,revealing the nonlinear influence patterns of various components on T_(g),providing a theoretical basis for SBR formulation design and T_(g)regulation.The machine learning prediction framework established in this study combines high-precision prediction with interpretability,significantly enhancing the prediction performance of the T_(g)of SBR.It offers an efficient tool for SBR molecular design and holds great potential for promotion and application.
基金supported by the National Natural Science Foundation of China(Grants Nos.T2325004)the Advanced Materials-National Science and Technology Major Project(Grant No.2024ZD0606900)+3 种基金the Talent Hub for‘AI+New Materials’Basic Researchsupported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant Nos.XDB0620103 and XDB0510301)the National Natural Science Foundation of China(Grant No.12472112)R.S.acknowledges the Young Scientists Fund of the National Natural Science Foundation of China(51801046).
文摘Enhancing the kinetic stability of glasses typically requires deepening their thermodynamic stability,which increases structural rigidity and degrades ductility;decoupling these properties remains a major challenge.Here,we demonstrate that spatial patterning in metallic glasses produces exceptional kinetic ultrastability that coexists with a thermodynamically metastable,high-energy state and excellent plasticity.Guided by atomistic simulations using replica exchange molecular dynamics and machine learning interatomic potentials,we reveal that oxygen,through reaction-diffusion-coupled pattern dynamics,self-organizes into oxygen-centered pinned structures(OPSs)that serve as localized kinetic constraints.These motifs drastically slow structural relaxation,delivering kinetic stability comparable to ultrastable glasses even as the system retains the high inherent energy of rapidly quenched states.The OPSs’topology yields a spatially uniform activation of plastic events,promoting strain delocalization under mechanical load.By geometrically tailoring oxygen patterns,we increase the glass transition onset temperature(Tonset)by about 200 K with negligible loss of deformability.Our findings establish a practicable paradigm for decoupling kinetic and thermodynamic stability and point to a scalable,additive route for designing amorphous materials that combine hyperstability with plasticity.
文摘Improving the green mechanical strength and thermal shock resistance of silica sol-bonded corundum castables is of great significance for promoting their large-scale application.Silica sol-bonded corundum castables were prepared using brown corundum,dense corundum powder,α-Al_(2)O_(3)micropowder and SiO_(2)micropowder as the main raw materials,and silica sol as the binder.The effects of different additions of chopped glass fibers(0,0.2%,0.4%,0.6%,0.8%and 1%,by mass)on the properties of the castables were studied.The results show that with the increase of the fiber addition,the cold modulus of rupture,cold compressive strength and hot modulus of rupture of the samples first increase and then decrease.After drying at 110℃,the sample containing 0.4%fibers has the cold modulus of rupture of 9.1 MPa and cold compressive strength of 27.4 MPa,increasing by 80.4%and 41.2%,respectively,compared with the one without fiber addition.This is because the fibers bonded with the silica sol-gel interface to form a stressed skeleton,strengthening the bonding between the matrix and the aggregates.When subjected to external stress,the fibers can effectively share the load and prevent crack propagation,thus increasing the strength.In addition,the sample with 0.4%fibers has the highest cold modulus of rupture before and after thermal shock,and its strength retention ratio increases by 16.1%compared to the sample without fibers.Overall,the sample with 0.4%fibers exhibits the best comprehensive performance.
基金supported by the National Natural Science Foundation of China(Grant Nos.52201169 and 52575352)the Key Research&Development Plan of Anhui Province(Grant No.2022a05020016)。
文摘The yielding transition of amorphous solids remains a fundamental yet poorly understood issue in materials physics.In this work,we employ oscillatory shear to probe the yielding transition in metallic glasses(MGs)with various thermal histories.We identify three distinct deformation regimes depending on the applied strain amplitudes.Below the yield strainγ_(y),the response is elastic and accompanied by aging,through reversible atomic rearrangements that preserve the material's initial memory of thermal history.Slightly aboveγ_(y),the system undergoes a sharp transition during oscillatory cycles,indicated by a sudden rise in potential energy and non-affine displacement,along with the emergence of a shear band.Well aboveγ_(y),plastic deformation dominates,driving samples of various initial stability toward a common steady state,while thermal histories are erased by irreversible rearrangements and shear band formation.These findings advance the understanding of failure mechanisms in MGs and shed light on tuning their mechanical performance in industrial applications involving cyclic loading.
文摘Received:06 December 2025;Accepted:25 February 2026;Published:30 March 2026 ABSTRACT:In the last decade,the importance of sustainable construction and artificial intelligence(AI)in civil engineering has been underlined in many studies.Numerous studies highlighted the superiority of AI techniques over simple and mathematical regression analyses,which suffer from relatively poor generalization and an inability to capture highly non-linear relationships among inputs and output(s)parameters.In this study,to evaluate the compressive strength of concrete with glass powder(GP)and recycled aggregates,600 concrete samples were tested in the laboratory,and their results were evaluated.For intelligent assessment of concrete compressive strength(CCS),the study utilized an improved artificial neural network(ANN)with particle swarm optimization(PSO)algorithm and imperialist competitive algorithm(ICA).For training the models,the experimentally obtained data were used.The concrete ingredients formed the inputs of the AI-based predictive models of CCS.The experimental findings reveal that the implementation of recycled coarse aggregates in concrete from a sustainable construction point of view is advantageous and can enhance the CCS by 11.43%.Apart from that,findings indicate that utilization of 10%GP can lead to a nearly 20%increase in CCS(from 44.6 to 54.1 MPa).Additionally,the experimental observations show almost 40%improvement of CCS when 5%micro silica was used in the concrete mixture.Based on the findings,the study suggests the utilization of waste glass powder to partially replace cement in concrete,which can reduce the amount of cement production.This reduction from economic,energy-saving,and environmental(reduction in greenhouse gas emissions)points of view is of interest.On the other hand,the AI results show that the PSO-based ANN model outperforms the ICA-based ANN for the utilized dataset.According to the findings,the PSO-based ANN predictive model(with a coefficient of determination value of 0.939 and root mean square value of 0.113 for testing data)is a capable tool in predicting the CCS.Hence,this study recommends the implementation of AI-based models in CCS assessment.
文摘Honeycombing Lung(HCL)is a chronic lung condition marked by advanced fibrosis,resulting in enlarged air spaces with thick fibrotic walls,which are visible on Computed Tomography(CT)scans.Differentiating between normal lung tissue,honeycombing lungs,and Ground Glass Opacity(GGO)in CT images is often challenging for radiologists and may lead to misinterpretations.Although earlier studies have proposed models to detect and classify HCL,many faced limitations such as high computational demands,lower accuracy,and difficulty distinguishing between HCL and GGO.CT images are highly effective for lung classification due to their high resolution,3D visualization,and sensitivity to tissue density variations.This study introduces Honeycombing Lungs Network(HCL Net),a novel classification algorithm inspired by ResNet50V2 and enhanced to overcome the shortcomings of previous approaches.HCL Net incorporates additional residual blocks,refined preprocessing techniques,and selective parameter tuning to improve classification performance.The dataset,sourced from the University Malaya Medical Centre(UMMC)and verified by expert radiologists,consists of CT images of normal,honeycombing,and GGO lungs.Experimental evaluations across five assessments demonstrated that HCL Net achieved an outstanding classification accuracy of approximately 99.97%.It also recorded strong performance in other metrics,achieving 93%precision,100%sensitivity,89%specificity,and an AUC-ROC score of 97%.Comparative analysis with baseline feature engineering methods confirmed the superior efficacy of HCL Net.The model significantly reduces misclassification,particularly between honeycombing and GGO lungs,enhancing diagnostic precision and reliability in lung image analysis.
基金supported by the National Natural Science Foundation of China (Grant Nos.T2325004 and 52161160330)the National Natural Science Foundation of China (Grants No.12504233)+2 种基金Advanced MaterialsNational Science and Technology Major Project (Grant No.2024ZD0606900)the Talent Hub for “AI+New Materials” Basic Researchthe Key Research and Development Program of Ningbo (Grant No.2025Z088)。
文摘The functional properties of glasses are governed by their formation history and the complex relaxation processes they undergo.However,under extreme conditions,glass behaviors are still elusive.In this study,we employ simulations with varied protocols to evaluate the effectiveness of different descriptors in predicting mechanical properties across both low-and high-pressure regimes.Our findings demonstrate that conventional structural and configurational descriptors fail to correlate with the mechanical response following pressure release,whereas the activation energy descriptor exhibits robust linearity with shear modulus after correcting for pressure effects.Notably,the soft mode parameter emerges as an ideal and computationally efficient alternative for capturing this mechanical behavior.These findings provide critical insights into the influence of pressure on glassy properties,integrating the distinct features of compressed glasses into a unified theoretical framework.
基金supported by the National Natural Science Foundation of China (Grant Nos.92580120 and 52471188)。
文摘Optimizing the microchannel design of the next generation of chips requires an understanding of the in situ property evolution of the chip-based materials under fast cooling.This work overcomes the conventional reliance on reheating data of melt-quenched glasses by demonstrating direct observations of glass transition on cooling curves utilizing the most advanced fast differential scanning calorimetry.By leveraging an MEMS chip sensor that allows for rapid heat extraction from microgram-sized samples to a purged gas coolant,the device is able to reach ultra-fast cooling rates of up to 40,000 K·s^(−1).Four thermal regions are identified by examining the cooling behaviors of two metallic glasses.This is because the actual rate of the specimen can differ from the programmed rate,especially at high set rate when the actual rate decreases before the glass transition is completed.We define the operational window for reliable cooling curve analysis,build models with empirical and theoretical analyses to determine the maximum feasible cooling rate,and demonstrate how optimizing sample mass and environment temperature broaden this window.The method avoids deceptive structural relaxation effects verified by fictivetemperature analysis and permits the capture of full glass transition during cooling.
基金supported by the National Natural Science Foundation of China(Nos.51827801,52371152,and 51971120).
文摘For the development of high-performance metallic glasses,enhancing their stability against viscous flow and crystallization is a primary objective.Vapor deposition or prolonged annealing is an effective method to improve glass stability,shown by increased glass transition temperature(Tg)and crystallization temperature(Tx).This contributes to the development of ultra-stable metallic glasses.Herein,we demonstrate that modulating the quenching temperature can also produce ultra-stable metallic glasses,as evidenced by an increase in Tx of 17-30 K in Cu-based metallic glasses.By modulating the quenching temperature,separated primary phases,secondary phases,and even nano-oxides can be obtained in the metallic glasses.Notably,metastable phases such as Cu-rich precipitates arising from secondary phase separation play a crucial role in enhancing glass stability.However,the enhancement of the stability of the glass has only a negligible effect on its mechanical properties.This study implies that different melt thermodynamic states generated by liquid-liquid separation and transition collectively determine the frozen-in glass structure.The results of this study will be helpful for the development of ultra-stable bulk glasses.
基金Funded by the National Natural Science Foundation of China(No.52472012)Opening Project of State Silica-Based Materials Laboratory of Anhui Province(No.2022KF11)the Research and Development of Glass Powder for Laser Sealing and Its Sealing Technology(No.K24556)。
文摘The low-melting glass of Bi2O_(3)-B2O_(3)-SiO_(2)(BiBSi)system was used for the first time for laser sealing of vacuum glazing.Under the condition of constant boron content,how the structure and properties vary with Bi/Si ratio in low-melting glass was investigated.In addition,the relationships between laser power,low-melting glass solder with different Bi/Si ratios and laser sealing shear strength were revealed.The results show that a decrease in the Bi/Si ratio can cause a contraction of the glass network of the low-melting glass,leading to an increase of its characteristic temperature and a decrease of its coefficient of thermal expansion.During laser sealing,the copper ions in the low-melting glass play an endothermic role.A change in the Bi/Si ratio will affect the valence state transition of the copper ions in the low-melting glass.The absorbance of the low-melting glass does not follow the expected correlation with the Bi/Si ratio,but shows a linear correlation with the content of divalent copper ions.The greater the concentration of divalent copper ions,the greater the absorbance of the low-melting glass,and the lower the laser power required for laser sealing.The shear strength of the low melting glass solder after laser sealing was tested,and it was found that the maximum shear strength of Z1 glass sample was the highest up to 2.67 MPa.
基金funded by the National Natural Science Foundation of China (Grant Nos. 42241103 and 62227901)the Key Research Program of the Institute of Geology and Geophysics, Chinese Academy of Sciences (Grant Nos. IGGCAS-202101 and IGGCAS-202401)
文摘Lunar impact glasses have been identified as crucial indicators of geochemical information regarding their source regions. Impact glasses can be categorized as either local or exotic. Those preserving geochemical signatures matching local lithologies (e.g., mare basalts or their single minerals) or regolith bulk soil compositions are classified as “local”. Otherwise, they could be defined as “exotic”. The analysis of exotic glasses provides the opportunity to explore previously unsampled lunar areas. This study focuses on the identification of exotic glasses within the Chang’e-5 (CE-5) soil sample by analyzing the trace elements of 28 impact glasses with distinct major element compositions in comparison with the CE-5 bulk soil. However, the results indicate that 18 of the analyzed glasses exhibit trace element compositions comparable to those of the local CE-5 materials. In particular, some of them could match the local single mineral component in major and trace elements, suggesting a local origin. Therefore, it is recommended that the investigation be expanded from using major elements to including nonvolatile trace elements, with a view to enhancing our understanding on the provenance of lunar impact glasses. To achieve a more accurate identification of exotic glasses within the CE-5 soil sample, a novel classification plot of Mg# versus La is proposed. The remaining 10 glasses, which exhibit diverse trace element variations, were identified as exotic. A comparative analysis of their chemical characteristics with remote sensing data indicates that they may have originated from the Aristarchus, Mairan, Sharp, or Pythagoras craters. This study elucidates the classification and possible provenance of exotic materials within the CE-5 soil sample, thereby providing constraints for the enhanced identification of local and exotic components at the CE-5 landing site.
基金Funded by the National Natural Science Foundation of China(No.52172007)。
文摘To analyze the impact of bubbles on the mechanical behavior of glasses,by controlling the refining time,we prepared three borosilicate glasses with the same composition and different porosity.By the analysis software integrated within the optical microscope,the diameter and number of the bubbles on the surface of three borosilicate glasses were quantified.From the hardness and crack initiation resistance(CR),we built the relationship between the porosity and the mechanical performance of these borosilicate glasses.
基金supported by National Natural Science Foundation of China(51991352 and 51874266).
文摘Silicone rubber(SR)exhibits superior breathability and high-temperature resistance.However,SR is prone to degradation under extreme heat or combustion,limiting its effectiveness in mitigating secondary hazards.In this study,phosphate glass powder was used to calcinate zinc borate,lanthanum oxide,and cerium oxide.Methylphenyl polysiloxane was then grafted onto the surface of the glass powder,resulting in the modified pow-ders designated as Methylphenyl polysiloxane-grafted zinc borate-modified phosphate glass powder(GF-ZnBM),Methylphenyl polysiloxane-grafted lanthanum oxide-modified phosphate glass powder(GF-LaM),and Methylphenyl polysiloxane-grafted cerium oxide-modified phosphate glass powder(GF-CeM).The modified powders were sub-sequently incorporated into silicone rubber composites to enhance the ceramicization capability of silicone rubber at high temperatures.Specifically,GF-CeM and GF-LaM significantly increased the limiting oxygen index(LOI)to 33%and reduced the tendency for combustion propagation.Additionally,GF-CeM notably contributed to enhancing ceramicization strength.The presence of cerium oxide helps in the melting of the glass powder and enhances its adhesion to the silicone rubber matrix.SR/ZnB-GF exhibited the lowest activation energy among the tested composites,along with the best protective capability.The inclusion of modified glass powder has a minor impact on the rheological properties,indicating that the composite retains its ability to flow and deform under stress.This confirms that the material remains flexible under normal conditions and forms a ceramic structure when heated,thereby exhibiting self-supporting properties.This study provides a practical methodology for the targeted modification of glass powders,thereby further enhancing the fire safety of silicone-based composites.
文摘High-density germanate glasses doped with Tb^(3+)ions were synthesized via the melt-quenching meth-od.The physical and luminescent properties of these glasses were characterized through various techniques,in-cluding density measurement,differential scanning calorimetry(DSC),photoluminescence(PL)spectroscopy,X-ray excited luminescence(XEL)spectroscopy,and fluorescence decay analysis.The densities of the germanate glasses were greater than 6.1 g/cm^(3).Upon excitations of ultraviolet(UV)light and X-rays,the glasses emitted in-tense green emissions.The fluorescence lifetime of the strongest emission peak at 544 nm,measured under 377 nm excitation,ranged from 1.52 ms to 1.32 ms.In the glass specimens,the maximum XEL integral intensity reached roughly 26%of that of the commercially available Bi_(4)Ge_(3)O_(12)(BGO)crystal.These results indicate that Tb^(3+)-doped high-density germanate scintillating glasses hold potential as scintillation materials for X-ray imaging applications.
