Deformation characteristics and range of optimized hot working parameters of a 6.5 tons GH3535 superalloy ingot with an average columnar grain size of over 1 mm in diameter were investigated. Axial compression experim...Deformation characteristics and range of optimized hot working parameters of a 6.5 tons GH3535 superalloy ingot with an average columnar grain size of over 1 mm in diameter were investigated. Axial compression experiments were performed in temperature range of 900-1240 ℃ and strain rate range of 0.001-30 s;at a total strain of 0.8. The hot deformation activation energy of the experimental GH3535 alloy is calculated to be 483.22 kJ/mol. Furthermore, the deformation constitutive equation is established by the peak stresses obtained from the stress-strain curves under various conditions. The hot working window of the alloy ingot at a strain of 0.8 can be preliminarily discussed based on the deformed microstructures and processing maps. The optimized hot working window was thus determined at the strain of 0.95 for 6.5 tons GH3535 alloy ingot by the supplementary compression tests. A large-size GH3535 superalloy ring with a dimension of 03010 mm x 410 mm was ultimately manufactured.展开更多
The effect of long-term thermal exposure on the grain boundary carbides and the tensile behavior of two kinds of Ni–Mo–Cr superalloys with different silicon contents（0 and 0.46 wt%） was investigated. Experimental ...The effect of long-term thermal exposure on the grain boundary carbides and the tensile behavior of two kinds of Ni–Mo–Cr superalloys with different silicon contents（0 and 0.46 wt%） was investigated. Experimental results showed granular M2C carbides formed at the grain boundaries after exposure for 100 h for the non-silicon alloy. Furthermore, these fine granular M2C carbides will transform into plate-like M6C carbides as exposure time increases. For the Si-containing alloys,only the granular M6C carbides formed at the grain boundaries during the whole exposure time. The coarsening of the grain boundary carbides occurred in both alloys with increasing exposure time. In addition, the coarsening kinetics of the grain boundary carbides for the non-silicon alloy is faster than that of the standard alloy. The tensile properties of both alloys are improved after exposure for 100 h due to the formation of nano-sized grain boundary carbides. The grain boundary carbides are coarsened more seriously for non-silicon alloys than that of Si-containing alloys, resulting in a more significant decrease in the tensile strength and elongation for the former case. Silicon additions can effectively inhibit the severe coarsening of the grain boundary carbides and thus avoid the obvious deterioration of the tensile properties after a long-term thermal exposure.展开更多
Optimal therapeutic and diagnostic efficacy is essential for healthcare's global mission of advancing oncologic drug development.Accurate diagnosis and detection are crucial prerequisites for effective risk strati...Optimal therapeutic and diagnostic efficacy is essential for healthcare's global mission of advancing oncologic drug development.Accurate diagnosis and detection are crucial prerequisites for effective risk stratification and person-alized patient care in clinical oncology.A paradigm shift is emerging with the promise of multi-receptor-targeting compounds.While existing detection and staging methods have demonstrated some success,the traditional approach of monotherapy is being reevaluated to enhance therapeutic effectiveness.Het-erodimeric site-specific agents are a versatile solution by targeting two distinct biomarkers with a single theranostic agent.This review describes the inno-vation of dual-targeting compounds,examining their design strategies,thera-peutic implications,and the promising path they present for addressing complex diseases.展开更多
Predicting fatigue life with precision requires more than isolated evaluations of mechanical properties;it requires an integrated approach that captures the interdependencies between various parameters,including elast...Predicting fatigue life with precision requires more than isolated evaluations of mechanical properties;it requires an integrated approach that captures the interdependencies between various parameters,including elastic modulus,tensile strength,yield strength,and strain-hardening exponent.Neglecting these correlations in sensitivity analyses can compromise prediction accuracy and physical interpretability.In this study,we introduce a dependency-aware sensitivity analysis framework,assisted by machine learning-based surrogate models,to evaluate the contributions of these mechanical properties to fatigue life variability.Tensile strength emerged as the most influential parameter,with significant second-order interactions,particularly between tensile and yield strength,highlighting the central role of coupled effects in fatigue mechanisms.By addressing these interdependencies,the proposed approach improves the reliability of fatigue life predictions and offers a solid foundation for the optimization of metallic components subjected to cyclic stresses.展开更多
Accurate prediction of fatigue life under multiaxial loading conditions remains challenging due to complex stress–strain interactions.In this study,we integrate machine-learning(ML)regression with variance-based sens...Accurate prediction of fatigue life under multiaxial loading conditions remains challenging due to complex stress–strain interactions.In this study,we integrate machine-learning(ML)regression with variance-based sensitivity analysis(SA)to predict multiaxial fatigue life in CuZn37 brass and to identify the dominant mechanical factors influencing fatigue damage.Several surrogate models were evaluated,with the Gaussian Process model achieving the highest accuracy(R^(2)=0.991)while maintaining robust generalization across loading paths.Gradient Boosting,Random Forest,and Penalized Spline Regression models also demonstrated strong predictive capabilities.Importantly,the SA explicitly accounted for statistical dependencies among input parameters,revealing that normal strain–stress interactions account for over 40%of the total variance in fatigue life.In contrast,shear-related parameters exhibited secondary,compensatory effects.These results highlight the importance of capturing parameter dependencies in fatigue modeling and demonstrate that ML-based surrogates can help provide both high-fidelity predictions and physical insights under complex multiaxial loading conditions.展开更多
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA02004210)
文摘Deformation characteristics and range of optimized hot working parameters of a 6.5 tons GH3535 superalloy ingot with an average columnar grain size of over 1 mm in diameter were investigated. Axial compression experiments were performed in temperature range of 900-1240 ℃ and strain rate range of 0.001-30 s;at a total strain of 0.8. The hot deformation activation energy of the experimental GH3535 alloy is calculated to be 483.22 kJ/mol. Furthermore, the deformation constitutive equation is established by the peak stresses obtained from the stress-strain curves under various conditions. The hot working window of the alloy ingot at a strain of 0.8 can be preliminarily discussed based on the deformed microstructures and processing maps. The optimized hot working window was thus determined at the strain of 0.95 for 6.5 tons GH3535 alloy ingot by the supplementary compression tests. A large-size GH3535 superalloy ring with a dimension of 03010 mm x 410 mm was ultimately manufactured.
基金financially supported by the program of International S&T Cooperation, ANSTO-SINAP (No. 2014DFG60230)the National Natural Science Foundation of China (No. 51371188)+1 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences (No. XDA02004210)Science and Technology Commission of Shanghai Municipality (No. 11JC1414900)
文摘The effect of long-term thermal exposure on the grain boundary carbides and the tensile behavior of two kinds of Ni–Mo–Cr superalloys with different silicon contents（0 and 0.46 wt%） was investigated. Experimental results showed granular M2C carbides formed at the grain boundaries after exposure for 100 h for the non-silicon alloy. Furthermore, these fine granular M2C carbides will transform into plate-like M6C carbides as exposure time increases. For the Si-containing alloys,only the granular M6C carbides formed at the grain boundaries during the whole exposure time. The coarsening of the grain boundary carbides occurred in both alloys with increasing exposure time. In addition, the coarsening kinetics of the grain boundary carbides for the non-silicon alloy is faster than that of the standard alloy. The tensile properties of both alloys are improved after exposure for 100 h due to the formation of nano-sized grain boundary carbides. The grain boundary carbides are coarsened more seriously for non-silicon alloys than that of Si-containing alloys, resulting in a more significant decrease in the tensile strength and elongation for the former case. Silicon additions can effectively inhibit the severe coarsening of the grain boundary carbides and thus avoid the obvious deterioration of the tensile properties after a long-term thermal exposure.
基金NIH Research Evaluation and Commercilization Hub,Grant/Award Number:1U01HL152410USVA Medical Research Service,Grant/Award Number:I01 BX00096409National Institutes of Health,Grant/Award Numbers:R01CA269221,R01CA225837。
文摘Optimal therapeutic and diagnostic efficacy is essential for healthcare's global mission of advancing oncologic drug development.Accurate diagnosis and detection are crucial prerequisites for effective risk stratification and person-alized patient care in clinical oncology.A paradigm shift is emerging with the promise of multi-receptor-targeting compounds.While existing detection and staging methods have demonstrated some success,the traditional approach of monotherapy is being reevaluated to enhance therapeutic effectiveness.Het-erodimeric site-specific agents are a versatile solution by targeting two distinct biomarkers with a single theranostic agent.This review describes the inno-vation of dual-targeting compounds,examining their design strategies,thera-peutic implications,and the promising path they present for addressing complex diseases.
基金We acknowledge Ho Chi Minh City University of Technology(HCMUT),VNU-HCM for supporting this study.
文摘Predicting fatigue life with precision requires more than isolated evaluations of mechanical properties;it requires an integrated approach that captures the interdependencies between various parameters,including elastic modulus,tensile strength,yield strength,and strain-hardening exponent.Neglecting these correlations in sensitivity analyses can compromise prediction accuracy and physical interpretability.In this study,we introduce a dependency-aware sensitivity analysis framework,assisted by machine learning-based surrogate models,to evaluate the contributions of these mechanical properties to fatigue life variability.Tensile strength emerged as the most influential parameter,with significant second-order interactions,particularly between tensile and yield strength,highlighting the central role of coupled effects in fatigue mechanisms.By addressing these interdependencies,the proposed approach improves the reliability of fatigue life predictions and offers a solid foundation for the optimization of metallic components subjected to cyclic stresses.
文摘Accurate prediction of fatigue life under multiaxial loading conditions remains challenging due to complex stress–strain interactions.In this study,we integrate machine-learning(ML)regression with variance-based sensitivity analysis(SA)to predict multiaxial fatigue life in CuZn37 brass and to identify the dominant mechanical factors influencing fatigue damage.Several surrogate models were evaluated,with the Gaussian Process model achieving the highest accuracy(R^(2)=0.991)while maintaining robust generalization across loading paths.Gradient Boosting,Random Forest,and Penalized Spline Regression models also demonstrated strong predictive capabilities.Importantly,the SA explicitly accounted for statistical dependencies among input parameters,revealing that normal strain–stress interactions account for over 40%of the total variance in fatigue life.In contrast,shear-related parameters exhibited secondary,compensatory effects.These results highlight the importance of capturing parameter dependencies in fatigue modeling and demonstrate that ML-based surrogates can help provide both high-fidelity predictions and physical insights under complex multiaxial loading conditions.
