Avoiding heating interference and guided thermal conduction in stretchable devices using thermal conductive composite islands

The miniaturization and high integration of devices demand significant thermal management materials.Current technologies for the thermal management of electronics show some limitations in the case of multiple chip arrays.A device in multiple chip array is affected by heat from adjacent devices,along with thermal conductive composite.To address this problem,we present a nano composite of aligned boron nitride(BN)nanosheet islands with porous polydimethylsiloxane(PDMS)foam to have mechanical stability and non-thermal interference.The islands of tetrahedrally-structured BN in the composite have a high thermal conductivity of 1.219 W·m^(-1)·K^(-1) in the through-plane direction(11.234W·m^(-1)·K^(-1)in the in-plane direction)with 16 wt.%loading of BN.On the other hand,porous PDMS foam has a low thermal conductivity of 0.0328W·m^(-1)·K^(-1) in the through-plane direction at 70%porosity.Heat pathways are then formed only in the structured BN islands of the composite.The porous PDMS foam can be applied as a thermal barrier between structured BN islands to inhibit thermal interference in multiple device arrays.Furthermore,this composite can maintain selective thermal dissipation performance with 70%tensile strain.Another beauty of the work is that it could have guided heat dissipation by assembling of multiple layers which have high vertical thermal conductive islands,while inhibiting thermal interference.The selective heat dissipating composite can be applied as a heatsink for multiple chip arrays electronics.

Fibrillary gelation and dedoping of PEDOT:PSS fibers for interdigitated organic electrochemical transistors and circuits

As one of conducting polymers,PEDOT:PSS,is commonly used in organic electronics,especially for bioelectronics due to its advantages such as high electrical and ionic conductivity,solution-processability and biocompatibility.Creating bioelectronics with the PEDOT:PSS requires advanced techniques to obtain physical/chemical modification of the PEDOT:PSS for improved performance and various applications.To satisfy these demands,fibrillary gelation of PEDOT:PSS by injection to choline acetate,an ionic liquid,with a constant flow rate was used in this study to make a conductive fiber and improve characteristics of PEDOT:PSS.Conductive fibers by fibrillary gelation showed enhanced electrical conductivity of about 400 S cm^(-1) and volumetric capacitance of about 154 F cm^(−3) which would be strongly beneficial to be utilized for organic electrochemical transistors(OECTs),resulting in a high transconductance of 19 mS in a depletion-mode.Moreover,dedoping of the conductive fibers by PEI(polyethyleneimine)enabled the creation of enhancement-mode OECTs.Interdigitated inverters were then fabricated by connecting depletion and enhancement-mode OECTs.These results demonstrate that these conductive fibers and electronic-textiles are suitable candidates for applications in bio-integrated electronics.

Epidermal piezoresistive structure with deep learning-assisted data translation

Continued research on the epidermal electronic sensor aims to develop sophisticated platforms that reproduce key multimodal responses in human skin,with the ability to sense various external stimuli,such as pressure,shear,torsion,and touch.The development of such applications utilizes algorithmic interpretations to analyze the complex stimulus shape,magnitude,and various moduli of the epidermis,requiring multiple complex equations for the attached sensor.In this experiment,we integrate silicon piezoresistors with a customized deep learning data process to facilitate in the precise evaluation and assessment of various stimuli without the need for such complexities.With the ability to surpass conventional vanilla deep regression models,the customized regression and classification model is capable of predicting the magnitude of the external force,epidermal hardness and object shape with an average mean absolute percentage error and accuracy of<15 and 96.9%,respectively.The technical ability of the deep learning-aided sensor and the consequent accurate data process provide important foundations for the future sensory electronic system.

Wearable EEG electronics for a Brain–AI Closed-Loop System to enhance autonomous machine decision-making

Human nonverbal communication tools are very ambiguous and difficult to transfer to machines or artificial intelligence(AI).If the AI understands the mental state behind a user’s decision,it can learn more appropriate decisions even in unclear situations.We introduce the Brain-AI Closed-Loop System(BACLoS),a wireless interaction platform that enables human brain wave analysis and transfers results to AI to verify and enhance AI decision-making.We developed a wireless earbud-like electroencephalography(EEG)measurement device,combined with tattoo-like electrodes and connectors,which enables continuous recording of high-quality EEG signals,especially the error-related potential(ErrP).The sensor measures the ErrP signals,which reflects the human cognitive consequences of an unpredicted machine response.The AI corrects or reinforces decisions depending on the presence or absence of the ErrP signals,which is determined by deep learning classification of the received EEG data.We demonstrate the BACLoS for AIbased machines,including autonomous driving vehicles,maze solvers,and assistant interfaces.