Recycling plastic waste into triboelectric nanogenerators(TENGs)presents a sustainable approach to energy harvesting,self-powered sensing,and environmental remediation.This study investigates the recycling of polyviny...Recycling plastic waste into triboelectric nanogenerators(TENGs)presents a sustainable approach to energy harvesting,self-powered sensing,and environmental remediation.This study investigates the recycling of polyvinyl chloride(PVC)pipe waste polymers into nanofibers(NFs)optimized for TENG applications.We focused on optimizing the morphology of recycled PVC polymer to NFs and enhancing their piezoelectric properties by incorporating ZnO nanoparticles(NPs).The optimized PVC/0.5 wt%ZnO NFs were tested with Nylon-6 NFs,and copper(Cu)electrodes.The Nylon-6 NFs exhibited a power density of 726.3μWcm^(-2)—1.13 times higher than Cu and maintained 90%stability after 172800 cycles,successfully powering various colored LEDs.Additionally,a 3D-designed device was developed to harvest energy from biomechanical movements such as finger tapping,hand tapping,and foot pressing,making it suitable for wearable energy harvesting,automatic switches,and invisible sensors in surveillance systems.This study demonstrates that recycling polymers for TENG devices can effectively address energy,sensor,and environmental challenges.展开更多
Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient conditions.Among the available functional diagnos...Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient conditions.Among the available functional diagnostic methods,electrocardiography(ECG)is particularly well-known for its ability to detect MI.However,confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice.This study,therefore,proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI.In particular,the learning vector quantization(LVQ)algorithm was applied,considering the contribution of each ECG lead in the 12-channel system,which obtained an accuracy of 87%in localizing damaged myocardium.The developed model was tested on verified data from the PTB database,including 445 ECG recordings from both healthy individuals and MI-diagnosed patients.The results demonstrated that the 12-lead ECG system allows for a comprehensive understanding of cardiac activities in myocardial infarction patients,serving as an essential tool for the diagnosis of myocardial conditions and localizing their damage.A comprehensive comparison was performed,including CNN,SVM,and Logistic Regression,to evaluate the proposed LVQ model.The results demonstrate that the LVQ model achieves competitive performance in diagnostic tasks while maintaining computational efficiency,making it suitable for resource-constrained environments.This study also applies a carefully designed data pre-processing flow,including class balancing and noise removal,which improves the reliability and reproducibility of the results.These aspects highlight the potential application of the LVQ model in cardiac diagnostics,opening up prospects for its use along with more complex neural network architectures.展开更多
The rapid development of nanotechnology has significantly revolutionized wearable electronics and expanded their functionality.Through introducing innovative solutions for energy harvesting and autonomous sensing,this...The rapid development of nanotechnology has significantly revolutionized wearable electronics and expanded their functionality.Through introducing innovative solutions for energy harvesting and autonomous sensing,this research presents a cost-effective strategy to enhance the performance of triboelectric nanogenerators(TENGs).The TENG was fabricated from polyvinylidene fluoride(PVDF)and N,N'-poly(methyl methacrylate)(PMMA)blend with a porous structure via a novel optimized quenching method.The developed approach results in a highβ-phase content(85.7%)PVDF/3wt.%PMMA porous blend,known for its superior piezoelectric properties.PVDF/3wt.%PMMA modified porous TENG demonstrates remarkable electrical output,with a dielectric constant of 40 and an open-circuit voltage of approximately 600 V.The porous matrix notably increases durability,enduring over 36000 operational cycles without performance degradation.Moreover,practical applications were explored in this research,including powering LEDs and pacemakers with a maximum power output of 750mWm^(-2).Also,TENG served as a self-powered tactile sensor for robotic applications in various temperature conditions.The work highlights the potential of the PVDF/PMMA porous blend to utilize the next-generation self-powered sensors and power small electronic devices.展开更多
This paper endeavours to bridge the existing gap in muscular actuator design for ligament-skeletal-inspired robots,thereby fostering the evolution of these robotic systems.We introduce two novel compliant actuators,na...This paper endeavours to bridge the existing gap in muscular actuator design for ligament-skeletal-inspired robots,thereby fostering the evolution of these robotic systems.We introduce two novel compliant actuators,namely the Internal Torsion Spring Compliant Actuator(ICA)and the External Spring Compliant Actuator(ECA),and present a comparative analysis against the previously conceived Magnet Integrated Soft Actuator(MISA)through computational and experimental results.These actuators,employing a motor-tendon system,emulate biological muscle-like forms,enhancing artificial muscle technology.Then,applications of the proposed actuators in a robotic arm inspired by the human musculoskeletal system are presented.Experiments demonstrate satisfactory power in tasks like lifting dumbbells(peak power:36 W),playing table tennis(end-effector speed:3.2 m/s),and door opening,without compromising biomimetic aesthetics.Compared to other linear stiffness serial elastic actuators(SEAs),ECA and ICA exhibit high power-to-volume(361×10^(3)W/m^(3))and power-to-mass(111.6 W/kg)ratios respectively,endorsing the biomimetic design’s promise in robotic development.展开更多
Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,...Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and entertainment.Since wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor values.Further,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,etc.Recently-developed Artificial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR process.With this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare environment.The proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare service.In addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the out-liers.IHPTDL-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of users.Moreover,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN model.Finally,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects.The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.展开更多
Rice stands as a crucial staple food globally,with its enduring sustainability hinging on the prompt detection of rice leaf diseases.Hence,efficiently detecting diseases when they have already occurred holds paramount...Rice stands as a crucial staple food globally,with its enduring sustainability hinging on the prompt detection of rice leaf diseases.Hence,efficiently detecting diseases when they have already occurred holds paramount importance for solving the cost of manual visual identification and chemical testing.In the recent past,the identification of leaf pathologies in crops predominantly relies on manual methods using specialized equipment,which proves to be time-consuming and inefficient.This study offers a remedy by harnessing Deep Learning(DL)and transfer learning techniques to accurately identify and classify rice leaf diseases.A comprehensive dataset comprising 5932 self-generated images of rice leaves was assembled along with the benchmark datasets,categorized into 9 classes irrespective of the extent of disease spread across the leaves.These classes encompass diverse states including healthy leaves,mild and severe blight,mild and severe tungro,mild and severe blast,as well as mild and severe brown spot.Following meticulous manual labelling and dataset segmentation,which was validated by horticulture experts,data augmentation strategies were implemented to amplify the number of images.The datasets were subjected to evaluation using the proposed tailored Convolutional Neural Networks models.Their performance are scrutinized in conjunction with alternative transfer learning approaches like VGG16,Xception,ResNet50,DenseNet121,Inception ResnetV2,and Inception V3.The effectiveness of the proposed custom VGG16 model was gauged by its capacity to generalize to unseen images,yielding an exceptional accuracy of 99.94%,surpassing the benchmarks set by existing state-of-the-art models.Further,the layer wise feature extraction is also visualized as the interpretable AI.展开更多
Soft pneumatic robotic grippers have found extensive applica-tions across various engineering domains,which prompts active research due to their splendid compliance,high flex-ibility,and safe human-robot interaction o...Soft pneumatic robotic grippers have found extensive applica-tions across various engineering domains,which prompts active research due to their splendid compliance,high flex-ibility,and safe human-robot interaction over conventional stiff counterparts.Previously simplified rod-based models prin-cipally focused on clarifying overall large deformation and bending postures of soft grippers from static or quasi-static perspectives,whereas it is challenging to elaborate grasping characteristics of soft grippers without considering contact interaction and nonlinear large deformation behaviors.To address this,based on absolute nodal coordinate formulation(ANCF),comprehensively allowing for structural complexity,geometric,material and boundary nonlinearities,and incorpor-ating Coulomb’friction law with a multiple-point contact method,we put forward an effective nonlinear dynamic mod-eling approach for exploring grasping capability of soft grip-per.Moreover,we solved the established dynamic equations using Generalized-αscheme,and conducted thorough numer-ical simulation analysis on a three-jaw soft pneumatic gripper(SPG)in terms of grasping configurations,displacements and contact forces.The proposed dynamic approach can accurately both describe complicated deformed configurations along with stress distribution and provide a feasible solution to simulate grasping targets,whose effectiveness and precision were analyzed theoretically and verified experimentally,which may shed new light on devising and optimizing other multi-functional SPGs.展开更多
The use of water-based chemistry in photolithography during semiconductor fabrication is desirable due to its cost-effectiveness and minimal environmental impact,especially considering the large scale of semiconductor...The use of water-based chemistry in photolithography during semiconductor fabrication is desirable due to its cost-effectiveness and minimal environmental impact,especially considering the large scale of semiconductor production.Despite these benefits,limited research has reported successful demonstrations of water-based photopatterning,particularly for intrinsically water-soluble materials such as Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(PEDOT:PSS)due to significant challenges in achieving selective dissolution during the developing process.In this paper,we propose amethod for the direct patterning of PEDOT:PSS in water by introducing an amphiphilic Poly(ethylene glycol)-block-poly(propylene glycol)-block-poly(ethylene glycol)(PEO-PPO-PEO,P123)block copolymer to the PEDOT:PSS film.The addition of the block copolymer enhances the stretchability of the composite film and reduces the hydrophilicity of the film surface,allowing for water absorption only after UV exposure through a photoinitiated reaction with benzophenone.We apply this technique to fabricate tactile and wearable biosensors,both of which benefit fromthe mechanical stretchability and transparency of PEDOT:PSS.Our method represents a promising solution for water-based photopatterning of hydrophilic materials,with potential for wider applications in semiconductor fabrication.展开更多
The demand for multifunctional neural interfaces has grown due to the need to provide a better understanding of biological mechanisms related to neurological diseases and neural networks.Direct intracerebral drug inje...The demand for multifunctional neural interfaces has grown due to the need to provide a better understanding of biological mechanisms related to neurological diseases and neural networks.Direct intracerebral drug injection using microfluidic neural interfaces is an effective way to deliver drugs to the brain,and it expands the utility of drugs by bypassing the blood-brain barrier(BBB).In addition,uses of implantable neural interfacing devices have been challenging due to inevitable acute and chronic tissue responses around the electrodes,pointing to a critical issue still to be overcome.Although neural interfaces comprised of a collection of microneedles in an array have been used for various applications,it has been challenging to integrate microfluidic channels with them due to their characteristic three-dimensional structures,which differ from two-dimensionally fabricated shank-type neural probes.Here we present a method to provide such three-dimensional needle-type arrays with chemical delivery functionality.We fabricated a microfluidic interconnection cable(pFIC)and integrated it with a flexible penetrating microelectrode array(FPMA)that has a 3-dimensional structure comprised of silicon microneedle electrodes supported by a flexible array base.We successfully demonstrated chemical delivery through the developed device by recording neural signals acutely from in vivo brains before and after KCl injection.This suggests the potential of the developed microfluidic neural interface to contribute to neuroscience research by providing simultaneous signal recording and chemical delivery capabilities.展开更多
Epidermal electronic systems feature physical properties that approximate those of the skin,to enable intimate,long-lived skin interfaces for physiological measurements,human–machine interfaces and other applications...Epidermal electronic systems feature physical properties that approximate those of the skin,to enable intimate,long-lived skin interfaces for physiological measurements,human–machine interfaces and other applications that cannot be addressed by wearable hardware that is commercially available today.A primary challenge is power supply;the physical bulk,large mass and high mechanical modulus associated with conventional battery technologies can hinder efforts to achieve epidermal characteristics,and near-field power transfer schemes offer only a limited operating distance.Here we introduce an epidermal,farfield radio frequency(RF)power harvester built using a modularized collection of ultrathin antennas,rectifiers and voltage doublers.These components,separately fabricated and tested,can be integrated together via methods involving soft contact lamination.Systematic studies of the individual components and the overall performance in various dielectric environments highlight the key operational features of these systems and strategies for their optimization.The results suggest robust capabilities for battery-free RF power,with relevance to many emerging epidermal technologies.展开更多
基金supported by the research projects AP23486880 from the Ministry of Higher EducationScience of the Republic of Kazakhstan and 111024CRP2010,20122022FD4135 from Nazarbayev University.
文摘Recycling plastic waste into triboelectric nanogenerators(TENGs)presents a sustainable approach to energy harvesting,self-powered sensing,and environmental remediation.This study investigates the recycling of polyvinyl chloride(PVC)pipe waste polymers into nanofibers(NFs)optimized for TENG applications.We focused on optimizing the morphology of recycled PVC polymer to NFs and enhancing their piezoelectric properties by incorporating ZnO nanoparticles(NPs).The optimized PVC/0.5 wt%ZnO NFs were tested with Nylon-6 NFs,and copper(Cu)electrodes.The Nylon-6 NFs exhibited a power density of 726.3μWcm^(-2)—1.13 times higher than Cu and maintained 90%stability after 172800 cycles,successfully powering various colored LEDs.Additionally,a 3D-designed device was developed to harvest energy from biomechanical movements such as finger tapping,hand tapping,and foot pressing,making it suitable for wearable energy harvesting,automatic switches,and invisible sensors in surveillance systems.This study demonstrates that recycling polymers for TENG devices can effectively address energy,sensor,and environmental challenges.
基金funded by the Ministry of Science and Higher Education of the Republic of Kazakhstan,grant numbers AP14969403 and AP23485820.
文摘Myocardial infarction(MI)is one of the leading causes of death globally among cardiovascular diseases,necessitating modern and accurate diagnostics for cardiac patient conditions.Among the available functional diagnostic methods,electrocardiography(ECG)is particularly well-known for its ability to detect MI.However,confirming its accuracy—particularly in identifying the localization of myocardial damage—often presents challenges in practice.This study,therefore,proposes a new approach based on machine learning models for the analysis of 12-lead ECG data to accurately identify the localization of MI.In particular,the learning vector quantization(LVQ)algorithm was applied,considering the contribution of each ECG lead in the 12-channel system,which obtained an accuracy of 87%in localizing damaged myocardium.The developed model was tested on verified data from the PTB database,including 445 ECG recordings from both healthy individuals and MI-diagnosed patients.The results demonstrated that the 12-lead ECG system allows for a comprehensive understanding of cardiac activities in myocardial infarction patients,serving as an essential tool for the diagnosis of myocardial conditions and localizing their damage.A comprehensive comparison was performed,including CNN,SVM,and Logistic Regression,to evaluate the proposed LVQ model.The results demonstrate that the LVQ model achieves competitive performance in diagnostic tasks while maintaining computational efficiency,making it suitable for resource-constrained environments.This study also applies a carefully designed data pre-processing flow,including class balancing and noise removal,which improves the reliability and reproducibility of the results.These aspects highlight the potential application of the LVQ model in cardiac diagnostics,opening up prospects for its use along with more complex neural network architectures.
基金supported by the research projects AP14869428 from the Ministry of Science and Higher Education of the Republic of Kazakhstan20122022FD4135 from Nazarbayev University.
文摘The rapid development of nanotechnology has significantly revolutionized wearable electronics and expanded their functionality.Through introducing innovative solutions for energy harvesting and autonomous sensing,this research presents a cost-effective strategy to enhance the performance of triboelectric nanogenerators(TENGs).The TENG was fabricated from polyvinylidene fluoride(PVDF)and N,N'-poly(methyl methacrylate)(PMMA)blend with a porous structure via a novel optimized quenching method.The developed approach results in a highβ-phase content(85.7%)PVDF/3wt.%PMMA porous blend,known for its superior piezoelectric properties.PVDF/3wt.%PMMA modified porous TENG demonstrates remarkable electrical output,with a dielectric constant of 40 and an open-circuit voltage of approximately 600 V.The porous matrix notably increases durability,enduring over 36000 operational cycles without performance degradation.Moreover,practical applications were explored in this research,including powering LEDs and pacemakers with a maximum power output of 750mWm^(-2).Also,TENG served as a self-powered tactile sensor for robotic applications in various temperature conditions.The work highlights the potential of the PVDF/PMMA porous blend to utilize the next-generation self-powered sensors and power small electronic devices.
基金research project funded by the National Natural Science Foundation of China(NSFC)under Grant 91948302 and Grant 52021003Research England fund at NERIC.
文摘This paper endeavours to bridge the existing gap in muscular actuator design for ligament-skeletal-inspired robots,thereby fostering the evolution of these robotic systems.We introduce two novel compliant actuators,namely the Internal Torsion Spring Compliant Actuator(ICA)and the External Spring Compliant Actuator(ECA),and present a comparative analysis against the previously conceived Magnet Integrated Soft Actuator(MISA)through computational and experimental results.These actuators,employing a motor-tendon system,emulate biological muscle-like forms,enhancing artificial muscle technology.Then,applications of the proposed actuators in a robotic arm inspired by the human musculoskeletal system are presented.Experiments demonstrate satisfactory power in tasks like lifting dumbbells(peak power:36 W),playing table tennis(end-effector speed:3.2 m/s),and door opening,without compromising biomimetic aesthetics.Compared to other linear stiffness serial elastic actuators(SEAs),ECA and ICA exhibit high power-to-volume(361×10^(3)W/m^(3))and power-to-mass(111.6 W/kg)ratios respectively,endorsing the biomimetic design’s promise in robotic development.
基金supported by Korea Institute for Advancement of Technology(KIAT)grant fundedthe Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)the Soonchunhyang University Research Fund.
文摘Human Activity Recognition(HAR)has been made simple in recent years,thanks to recent advancements made in Artificial Intelligence(AI)techni-ques.These techniques are applied in several areas like security,surveillance,healthcare,human-robot interaction,and entertainment.Since wearable sensor-based HAR system includes in-built sensors,human activities can be categorized based on sensor values.Further,it can also be employed in other applications such as gait diagnosis,observation of children/adult’s cognitive nature,stroke-patient hospital direction,Epilepsy and Parkinson’s disease examination,etc.Recently-developed Artificial Intelligence(AI)techniques,especially Deep Learning(DL)models can be deployed to accomplish effective outcomes on HAR process.With this motivation,the current research paper focuses on designing Intelligent Hyperparameter Tuned Deep Learning-based HAR(IHPTDL-HAR)technique in healthcare environment.The proposed IHPTDL-HAR technique aims at recogniz-ing the human actions in healthcare environment and helps the patients in mana-ging their healthcare service.In addition,the presented model makes use of Hierarchical Clustering(HC)-based outlier detection technique to remove the out-liers.IHPTDL-HAR technique incorporates DL-based Deep Belief Network(DBN)model to recognize the activities of users.Moreover,Harris Hawks Opti-mization(HHO)algorithm is used for hyperparameter tuning of DBN model.Finally,a comprehensive experimental analysis was conducted upon benchmark dataset and the results were examined under different aspects.The experimental results demonstrate that the proposed IHPTDL-HAR technique is a superior per-former compared to other recent techniques under different measures.
文摘Rice stands as a crucial staple food globally,with its enduring sustainability hinging on the prompt detection of rice leaf diseases.Hence,efficiently detecting diseases when they have already occurred holds paramount importance for solving the cost of manual visual identification and chemical testing.In the recent past,the identification of leaf pathologies in crops predominantly relies on manual methods using specialized equipment,which proves to be time-consuming and inefficient.This study offers a remedy by harnessing Deep Learning(DL)and transfer learning techniques to accurately identify and classify rice leaf diseases.A comprehensive dataset comprising 5932 self-generated images of rice leaves was assembled along with the benchmark datasets,categorized into 9 classes irrespective of the extent of disease spread across the leaves.These classes encompass diverse states including healthy leaves,mild and severe blight,mild and severe tungro,mild and severe blast,as well as mild and severe brown spot.Following meticulous manual labelling and dataset segmentation,which was validated by horticulture experts,data augmentation strategies were implemented to amplify the number of images.The datasets were subjected to evaluation using the proposed tailored Convolutional Neural Networks models.Their performance are scrutinized in conjunction with alternative transfer learning approaches like VGG16,Xception,ResNet50,DenseNet121,Inception ResnetV2,and Inception V3.The effectiveness of the proposed custom VGG16 model was gauged by its capacity to generalize to unseen images,yielding an exceptional accuracy of 99.94%,surpassing the benchmarks set by existing state-of-the-art models.Further,the layer wise feature extraction is also visualized as the interpretable AI.
基金supported by Natural Science Foundation of Zhejiang Province (Grant No.LQ22A020003)National Natural Science Foundation of China (Grant No.52075499)for which all authors are grateful.
文摘Soft pneumatic robotic grippers have found extensive applica-tions across various engineering domains,which prompts active research due to their splendid compliance,high flex-ibility,and safe human-robot interaction over conventional stiff counterparts.Previously simplified rod-based models prin-cipally focused on clarifying overall large deformation and bending postures of soft grippers from static or quasi-static perspectives,whereas it is challenging to elaborate grasping characteristics of soft grippers without considering contact interaction and nonlinear large deformation behaviors.To address this,based on absolute nodal coordinate formulation(ANCF),comprehensively allowing for structural complexity,geometric,material and boundary nonlinearities,and incorpor-ating Coulomb’friction law with a multiple-point contact method,we put forward an effective nonlinear dynamic mod-eling approach for exploring grasping capability of soft grip-per.Moreover,we solved the established dynamic equations using Generalized-αscheme,and conducted thorough numer-ical simulation analysis on a three-jaw soft pneumatic gripper(SPG)in terms of grasping configurations,displacements and contact forces.The proposed dynamic approach can accurately both describe complicated deformed configurations along with stress distribution and provide a feasible solution to simulate grasping targets,whose effectiveness and precision were analyzed theoretically and verified experimentally,which may shed new light on devising and optimizing other multi-functional SPGs.
基金supported by the Korean government(the Ministry of Science and ICT,the Ministry of Trade,Industry,and Energy,the Ministry of Health&Welfare,and the Ministry of Food and Drug Safety).(Nos.2022R1C1C101007112,RS-2023-00221295,HR22C183201,RS-2020-KD000093,RS-2023-00234581,23-SENS-01).
文摘The use of water-based chemistry in photolithography during semiconductor fabrication is desirable due to its cost-effectiveness and minimal environmental impact,especially considering the large scale of semiconductor production.Despite these benefits,limited research has reported successful demonstrations of water-based photopatterning,particularly for intrinsically water-soluble materials such as Poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate)(PEDOT:PSS)due to significant challenges in achieving selective dissolution during the developing process.In this paper,we propose amethod for the direct patterning of PEDOT:PSS in water by introducing an amphiphilic Poly(ethylene glycol)-block-poly(propylene glycol)-block-poly(ethylene glycol)(PEO-PPO-PEO,P123)block copolymer to the PEDOT:PSS film.The addition of the block copolymer enhances the stretchability of the composite film and reduces the hydrophilicity of the film surface,allowing for water absorption only after UV exposure through a photoinitiated reaction with benzophenone.We apply this technique to fabricate tactile and wearable biosensors,both of which benefit fromthe mechanical stretchability and transparency of PEDOT:PSS.Our method represents a promising solution for water-based photopatterning of hydrophilic materials,with potential for wider applications in semiconductor fabrication.
基金supported by the Brain Research Program under Grant No.NRF-2018M3C7A1022309 through the National Research Foundation of Korea.
文摘The demand for multifunctional neural interfaces has grown due to the need to provide a better understanding of biological mechanisms related to neurological diseases and neural networks.Direct intracerebral drug injection using microfluidic neural interfaces is an effective way to deliver drugs to the brain,and it expands the utility of drugs by bypassing the blood-brain barrier(BBB).In addition,uses of implantable neural interfacing devices have been challenging due to inevitable acute and chronic tissue responses around the electrodes,pointing to a critical issue still to be overcome.Although neural interfaces comprised of a collection of microneedles in an array have been used for various applications,it has been challenging to integrate microfluidic channels with them due to their characteristic three-dimensional structures,which differ from two-dimensionally fabricated shank-type neural probes.Here we present a method to provide such three-dimensional needle-type arrays with chemical delivery functionality.We fabricated a microfluidic interconnection cable(pFIC)and integrated it with a flexible penetrating microelectrode array(FPMA)that has a 3-dimensional structure comprised of silicon microneedle electrodes supported by a flexible array base.We successfully demonstrated chemical delivery through the developed device by recording neural signals acutely from in vivo brains before and after KCl injection.This suggests the potential of the developed microfluidic neural interface to contribute to neuroscience research by providing simultaneous signal recording and chemical delivery capabilities.
基金XF and YM acknowledge the support from the National Basic Research Program of China(Grant No.2015CB351900)the National Natural Science Foundation of China(Grant Nos.11402135 and 11320101001).
文摘Epidermal electronic systems feature physical properties that approximate those of the skin,to enable intimate,long-lived skin interfaces for physiological measurements,human–machine interfaces and other applications that cannot be addressed by wearable hardware that is commercially available today.A primary challenge is power supply;the physical bulk,large mass and high mechanical modulus associated with conventional battery technologies can hinder efforts to achieve epidermal characteristics,and near-field power transfer schemes offer only a limited operating distance.Here we introduce an epidermal,farfield radio frequency(RF)power harvester built using a modularized collection of ultrathin antennas,rectifiers and voltage doublers.These components,separately fabricated and tested,can be integrated together via methods involving soft contact lamination.Systematic studies of the individual components and the overall performance in various dielectric environments highlight the key operational features of these systems and strategies for their optimization.The results suggest robust capabilities for battery-free RF power,with relevance to many emerging epidermal technologies.
