Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiment...Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiments or simulations remains a significant challenge.In this study,we focus on a double T-junction microfluidic geometry and employ a hybrid modeling approach that combines machine learning with metaheuristic optimization to address this issue.Specifically,particle swarm optimization(PSO)is used to optimize the hyperparameters of a decision tree(DT)model,and its performance is compared with that of a DT optimized through grid search(GS).The hybrid models are developed to estimate the droplet diameter based on four parameters:the main width,side width,thickness,and flow rate ratio.The dataset of more than 300 cases,generated by a three-dimensional numerical model of the double T-junction,is used for training and testing.Multiple evaluation metrics confirm the predictive accuracy of the models.The results demonstrate that the proposed DT-PSO model achieves higher accuracy,with a coefficient of determination of 0.902 on the test data,while simultaneously reducing prediction time.This methodology holds the potential to minimize design iterations and accelerate the integration of microfluidic technology into the biological sciences.展开更多
Flexible bending strain sensors emerge as promising candidates for wearable health monitoring and human-machine interaction, owing to their high stability and sensitivity. However, a critical trade-off between high se...Flexible bending strain sensors emerge as promising candidates for wearable health monitoring and human-machine interaction, owing to their high stability and sensitivity. However, a critical trade-off between high sensitivity and reliable largeangle sensing capability persists as a key bottleneck, severely hindering their practical implementation. In this study, a synergistic material-structural engineering strategy is proposed to enhance the bend-sensing performance. Specifically, two core components of this strategy involve an in-house synthesized carbon-based conductive particulate ink with favorable printability and a rationally designed sensing layer structure. By integrating the two components via electrohydrodynamic printing technology, we successfully fabricated highly robust flexible bending strain sensors. The resulting sensors exhibit exceptional electromechanical responsiveness to bending deformation, including a wide operating range(10°–150°), high sensitivity(GF = 50.74), rapid response, low hysteresis, and excellent long-term stability. Practically, they can accurately capture diverse physiological signals, ranging from subtle carotid artery pulses to large elbow flexion. Furthermore, a wearable gesture recognition system, incorporating a printed flexible bending strain sensor array, was developed to enable precise gesture recognition, thereby realizing virtual flight control of an unmanned aerial vehicle. These results indicate that the proposed printed sensor provides a promising approach to the sensitivity-angle trade-off, thereby facilitating the practical implementation of flexible electronics in human-machine interaction.展开更多
Efficient thermal management in porous media is essential for advanced engineering applications,including solar energy systems,electronic cooling,and aerospace thermal control.This study presents a comprehensive analy...Efficient thermal management in porous media is essential for advanced engineering applications,including solar energy systems,electronic cooling,and aerospace thermal control.This study presents a comprehensive analysis of ternary hybrid nanofluids,TiO_(2)-CdTe-MoS_(2) dispersed in water,flowing over a vertical stretching or shrinking surface in a Darcy-Brinkman porous medium.The investigation accounts for the combined effects of magnetohydrodynamics,thermal radiation,viscous dissipation,and internal heat generation.In contrast to previous studies that predominantly focused on single or binary nanofluids,the present work systematically examines the thermal and hydrodynamic performance of ternary hybrid nanofluids,highlighting their enhanced heat transport capabilities in porous structures.The governing momentum and energy equations are formulated in nondimensional form and solved numerically using the shifted Legendre collocation method.The results show that increasing the magnetic parameter,M=0-4,suppresses the fluid velocity by up to 28%,while stronger thermal radiation,R=0-5,raises the near-surface temperature by approximately 32%.Viscous dissipation and internal heat generation further enhance the Nusselt number,indicating improved heat transfer performance.Overall,the findings demonstrate the synergistic influence of the three nanoparticles in optimizing flow behavior and thermal characteristics,offering valuable insights for the design of high-performance thermal management systems in energy and aerospace applications.展开更多
Thiswork explores aMagnetohydrodynamic(MHD)flowin a triangular cavitywith a thermally insulated baffle.Enclosure’s inclined wall is hotter,whereas the vertical border is adiabatic and the bottom is cooler.The study a...Thiswork explores aMagnetohydrodynamic(MHD)flowin a triangular cavitywith a thermally insulated baffle.Enclosure’s inclined wall is hotter,whereas the vertical border is adiabatic and the bottom is cooler.The study aims to clarify how geometric changes affect thermal performance and offers new perspectives on how to improve heat dissipation mechanisms.A COMSOL Multiphysics version 6.2 has been used to solve numerical solutions.Streamlines and thermal distributions are examined systematically in order to understand how the unique geometry and baffle size of triangular cavities can influence the fluid flow.This influence can result in optimized flow patterns,promoting efficient heat transfer by directing the fluid to specific areas that require more cooling.In comparison with conventional designs,this optimization results in more efficient convective heat transfer,which raises cooling efficiency and lowers thermal resistance.Furthermore,by strengthening heat transfer characteristics in heat transfer systems,these geometries increase thermal efficiency,which helps several sectors,including the production of electricity,HVAC,and the automobile industry.展开更多
文摘Droplet-based microfluidics is a transformative technology with applications across diverse scientific and industrial domains.However,predicting the droplet size generated by individual microchannels before experiments or simulations remains a significant challenge.In this study,we focus on a double T-junction microfluidic geometry and employ a hybrid modeling approach that combines machine learning with metaheuristic optimization to address this issue.Specifically,particle swarm optimization(PSO)is used to optimize the hyperparameters of a decision tree(DT)model,and its performance is compared with that of a DT optimized through grid search(GS).The hybrid models are developed to estimate the droplet diameter based on four parameters:the main width,side width,thickness,and flow rate ratio.The dataset of more than 300 cases,generated by a three-dimensional numerical model of the double T-junction,is used for training and testing.Multiple evaluation metrics confirm the predictive accuracy of the models.The results demonstrate that the proposed DT-PSO model achieves higher accuracy,with a coefficient of determination of 0.902 on the test data,while simultaneously reducing prediction time.This methodology holds the potential to minimize design iterations and accelerate the integration of microfluidic technology into the biological sciences.
基金supported by the Guangdong University Featured Innovation Program Project (Grant No.2024KTSCX043)the Guangdong Basic and Applied Basic Research Foundation (Grant Nos.2025A1515010967,2022A1515110621)+1 种基金the Innovation and Strong School Engineering Fund of Guangdong Province (Grant No.2025KCXTD047)the Guangdong Engineering Technology Research Center (Grant No.2021J020)。
文摘Flexible bending strain sensors emerge as promising candidates for wearable health monitoring and human-machine interaction, owing to their high stability and sensitivity. However, a critical trade-off between high sensitivity and reliable largeangle sensing capability persists as a key bottleneck, severely hindering their practical implementation. In this study, a synergistic material-structural engineering strategy is proposed to enhance the bend-sensing performance. Specifically, two core components of this strategy involve an in-house synthesized carbon-based conductive particulate ink with favorable printability and a rationally designed sensing layer structure. By integrating the two components via electrohydrodynamic printing technology, we successfully fabricated highly robust flexible bending strain sensors. The resulting sensors exhibit exceptional electromechanical responsiveness to bending deformation, including a wide operating range(10°–150°), high sensitivity(GF = 50.74), rapid response, low hysteresis, and excellent long-term stability. Practically, they can accurately capture diverse physiological signals, ranging from subtle carotid artery pulses to large elbow flexion. Furthermore, a wearable gesture recognition system, incorporating a printed flexible bending strain sensor array, was developed to enable precise gesture recognition, thereby realizing virtual flight control of an unmanned aerial vehicle. These results indicate that the proposed printed sensor provides a promising approach to the sensitivity-angle trade-off, thereby facilitating the practical implementation of flexible electronics in human-machine interaction.
文摘Efficient thermal management in porous media is essential for advanced engineering applications,including solar energy systems,electronic cooling,and aerospace thermal control.This study presents a comprehensive analysis of ternary hybrid nanofluids,TiO_(2)-CdTe-MoS_(2) dispersed in water,flowing over a vertical stretching or shrinking surface in a Darcy-Brinkman porous medium.The investigation accounts for the combined effects of magnetohydrodynamics,thermal radiation,viscous dissipation,and internal heat generation.In contrast to previous studies that predominantly focused on single or binary nanofluids,the present work systematically examines the thermal and hydrodynamic performance of ternary hybrid nanofluids,highlighting their enhanced heat transport capabilities in porous structures.The governing momentum and energy equations are formulated in nondimensional form and solved numerically using the shifted Legendre collocation method.The results show that increasing the magnetic parameter,M=0-4,suppresses the fluid velocity by up to 28%,while stronger thermal radiation,R=0-5,raises the near-surface temperature by approximately 32%.Viscous dissipation and internal heat generation further enhance the Nusselt number,indicating improved heat transfer performance.Overall,the findings demonstrate the synergistic influence of the three nanoparticles in optimizing flow behavior and thermal characteristics,offering valuable insights for the design of high-performance thermal management systems in energy and aerospace applications.
文摘Thiswork explores aMagnetohydrodynamic(MHD)flowin a triangular cavitywith a thermally insulated baffle.Enclosure’s inclined wall is hotter,whereas the vertical border is adiabatic and the bottom is cooler.The study aims to clarify how geometric changes affect thermal performance and offers new perspectives on how to improve heat dissipation mechanisms.A COMSOL Multiphysics version 6.2 has been used to solve numerical solutions.Streamlines and thermal distributions are examined systematically in order to understand how the unique geometry and baffle size of triangular cavities can influence the fluid flow.This influence can result in optimized flow patterns,promoting efficient heat transfer by directing the fluid to specific areas that require more cooling.In comparison with conventional designs,this optimization results in more efficient convective heat transfer,which raises cooling efficiency and lowers thermal resistance.Furthermore,by strengthening heat transfer characteristics in heat transfer systems,these geometries increase thermal efficiency,which helps several sectors,including the production of electricity,HVAC,and the automobile industry.
