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      Biocompatible Biphasic Iontronics Enable Neuron-Like Ionic Signal Transmission

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      Author(s): , * , , * ,
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      Journal: Research
      Publisher: AAAS

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          Abstract

          Biocompatible connections between external artificial devices and living organisms show promise for future neuroprosthetics and therapeutics. The study in Science by Zhao and colleagues introduces a cascade-heterogated biphasic gel (HBG) iontronic device, which facilitates electronic-to-multi-ionic signal transduction for abiotic–biotic interfaces. Inspired by neuron signaling, the HBG device demonstrated its biocompatibility by regulating neural activity in biological tissue, paving the way for wearable and implantable devices, including brain–computer interfaces.

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          Aptamer–field-effect transistors overcome Debye length limitations for small-molecule sensing

          Chuanzhen Zhao, Bowen Zhu, You Seung Rim … (2018)
          Detection of analytes with field-effect transistors bearing ligand-specific receptors is fundamentally limited by the shielding created by the electrical double layer (the "Debye length" limitation). We detected small molecules under physiological high ionic-strength conditions by modifying printed ultrathin metal-oxide field-effect transistor arrays with deoxyribonucleotide aptamers selected to bind their targets adaptively. Target-induced conformational changes of negatively charged aptamer phosphodiester backbones in close proximity to semiconductor channels gated conductance in physiological buffers, resulting in highly sensitive detection. Sensing of charged and electroneutral targets (serotonin, dopamine, glucose, and sphinghosine-1-phosphate) was enabled by specifically isolated aptameric stem-loop receptors.
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            Biomimetic potassium-selective nanopores

            Elif Turker Acar, Steven F. Buchsbaum, Cody Combs … (2019)
            A nanopore decorated with crown ether and DNA is selective to potassium ions over sodium ions at concentrations up to 1 M.
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              Progress in Brain Computer Interface: Challenges and Opportunities

              Simanto Saha, Khondaker Mamun, Khawza Ahmed … (2021)
              Brain computer interfaces (BCI) provide a direct communication link between the brain and a computer or other external devices. They offer an extended degree of freedom either by strengthening or by substituting human peripheral working capacity and have potential applications in various fields such as rehabilitation, affective computing, robotics, gaming, and neuroscience. Significant research efforts on a global scale have delivered common platforms for technology standardization and help tackle highly complex and non-linear brain dynamics and related feature extraction and classification challenges. Time-variant psycho-neurophysiological fluctuations and their impact on brain signals impose another challenge for BCI researchers to transform the technology from laboratory experiments to plug-and-play daily life. This review summarizes state-of-the-art progress in the BCI field over the last decades and highlights critical challenges.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Research (Wash D C)
                Journal ID (iso-abbrev): Research (Wash D C)
                Journal ID (publisher-id): RESEARCH
                Title: Research
                Publisher: AAAS
                ISSN (Electronic): 2639-5274
                Publication date (Electronic, pub): 30 January 2024
                Publication date (Electronic, collection): 2024
                Volume: 7
                Electronic Location Identifier: 0294
                Affiliations
                [1]Department of Chemistry, Imperial College London , Molecular Sciences Research Hub, London W12 0BZ, UK.
                Author notes
                [*] [* ]Address correspondence to: Joshua.edel@ 123456imperial.ac.uk (J.B.E.); alex.ivanov@ 123456imperial.ac.uk (A.P.I.)
                Article
                Publisher ID: 0294
                DOI: 10.34133/research.0294
                PMC ID: 10826849
                PubMed ID: 38292443
                SO-VID: a12c1ee0-4819-4d57-a33d-130b6ff29bed
                Copyright © Copyright © 2024 Xiaoyi Wang et al.
                License:

                Exclusive licensee Science and Technology Review Publishing House. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY 4.0).

                History
                Date received : 27 November 2023
                Date accepted : 08 December 2023
                Date: 30 January 2024
                Page count
                Figures: 1, Tables: 0, References: 8, Pages: 0
                Categories
                Subject: Perspective

                Data availability:

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