Results 221 to 230 of about 37,624 (294)

Rapid self-assembly of robust ultrathin ionogel films for high-performance bioelectronics. [PDF]

open access: yesSci Adv
Li N   +10 more
europepmc   +1 more source

High‐Performance and Environmentally Stable Organic Electrochemical Transistors Enabled by a Reprocessed Self‐Doped PEDOT Channel

open access: yesAdvanced Electronic Materials, EarlyView.
A simple reprocessing strategy based on freeze‐drying and re‐dissolution is introduced to enhance the electrical performance of self‐doped PEDOT while preserving its intrinsic environmental stability. The reprocessed S‐PEDOT enables organic electrochemical transistors with improved drain current, transconductance, and robust operation under high ...
Ruifeng Xu   +4 more
wiley   +1 more source

Material Strategies for Stimulation and Recording in Neural Biocomputing Platforms

open access: yesAdvanced Electronic Materials, EarlyView.
Material strategies enabling stimulation and recording are central to neural biocomputing systems. This review examines how electronic materials govern the encoding of inputs and decoding of outputs in living neural networks. Advances in electrical, optical, and multimodal interfaces highlight emerging design principles for biocomputing platforms ...
Sehong Kang   +5 more
wiley   +1 more source

Toward Environmentally Friendly Hydrogel‐Based Flexible Intelligent Sensor Systems

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review summarizes environmentally and biologically friendly hydrogel‐based flexible sensor systems focusing on physical, chemical, and physiological sensors. Furthermore, device concepts moving forward for the practical application are discussed about wireless integration, the interface between hydrogel and dry electronics, automatic data analysis
Sudipta Kumar Sarkar, Kuniharu Takei
wiley   +1 more source

Materials and System Design for Self-Decision Bioelectronic Systems. [PDF]

open access: yesAdv Mater
Zeng Q   +9 more
europepmc   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

open access: yesAdvanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu   +11 more
wiley   +1 more source

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