Digitala Vetenskapliga Arkivet

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  • 1.
    Armgarth, Astrid
    et al.
    Linköping University, Department of Science and Technology, Laboratory of Organic Electronics. Linköping University, Faculty of Science & Engineering. RISE Res Inst Sweden AB, Sweden.
    Pantzare, Sandra
    RISE Res Inst Sweden AB, Sweden.
    Arven, Patrik
    J2 Holding AB, Sweden.
    Lassnig, Roman
    RISE Res Inst Sweden AB, Sweden.
    Jinno, Hiroaki
    RIKEN, Japan; Univ Tokyo, Japan.
    Gabrielsson, Erik
    Linköping University, Department of Science and Technology, Laboratory of Organic Electronics. Linköping University, Faculty of Science & Engineering.
    Kifle, Yonatan Habteslassie
    Linköping University, Department of Electrical Engineering, Integrated Circuits and Systems. Linköping University, Faculty of Science & Engineering.
    Cherian, Dennis
    Linköping University, Department of Science and Technology, Laboratory of Organic Electronics. Linköping University, Faculty of Science & Engineering.
    Arbring Sjöström, Theresia
    Linköping University, Department of Science and Technology, Laboratory of Organic Electronics. Linköping University, Faculty of Science & Engineering.
    Berthou, Gautier
    Res Inst Sweden AB, Sweden.
    Dowling, Jim
    Res Inst Sweden AB, Sweden; KTH Royal Inst Technol, Sweden.
    Someya, Takao
    RIKEN, Japan; Univ Tokyo, Japan.
    Wikner, Jacob
    Linköping University, Department of Electrical Engineering, Integrated Circuits and Systems. Linköping University, Faculty of Science & Engineering.
    Gustafsson, Göran
    RISE Res Inst Sweden AB, Sweden.
    Simon, Daniel
    Linköping University, Department of Science and Technology, Laboratory of Organic Electronics. Linköping University, Faculty of Science & Engineering.
    Berggren, Magnus
    Linköping University, Department of Science and Technology, Laboratory of Organic Electronics. Linköping University, Faculty of Science & Engineering.
    A digital nervous system aiming toward personalized IoT healthcare2021In: Scientific Reports, E-ISSN 2045-2322, Vol. 11, no 1, article id 7757Article in journal (Refereed)
    Abstract [en]

    Body area networks (BANs), cloud computing, and machine learning are platforms that can potentially enable advanced healthcare outside the hospital. By applying distributed sensors and drug delivery devices on/in our body and connecting to such communication and decision-making technology, a system for remote diagnostics and therapy is achieved with additional autoregulation capabilities. Challenges with such autarchic on-body healthcare schemes relate to integrity and safety, and interfacing and transduction of electronic signals into biochemical signals, and vice versa. Here, we report a BAN, comprising flexible on-body organic bioelectronic sensors and actuators utilizing two parallel pathways for communication and decision-making. Data, recorded from strain sensors detecting body motion, are both securely transferred to the cloud for machine learning and improved decision-making, and sent through the body using a secure body-coupled communication protocol to auto-actuate delivery of neurotransmitters, all within seconds. We conclude that both highly stable and accurate sensing-from multiple sensors-are needed to enable robust decision making and limit the frequency of retraining. The holistic platform resembles the self-regulatory properties of the nervous system, i.e., the ability to sense, communicate, decide, and react accordingly, thus operating as a digital nervous system.

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  • 2.
    Cherian, Dennis
    et al.
    Linköping University, Department of Science and Technology, Laboratory of Organic Electronics. Linköping University, Faculty of Science & Engineering.
    Armgarth, Astrid
    Linköping University, Department of Science and Technology, Laboratory of Organic Electronics. Linköping University, Faculty of Science & Engineering.
    Beni, Valerio
    Res Inst Sweden, Sweden.
    Linderhed, Ulrika
    Linköping University, Department of Science and Technology, Laboratory of Organic Electronics. Linköping University, Faculty of Science & Engineering. Res Inst Sweden, Sweden.
    Tybrandt, Klas
    Linköping University, Department of Science and Technology, Physics and Electronics. Linköping University, Faculty of Science & Engineering.
    Nilsson, David
    Res Inst Sweden, Sweden.
    Simon, Daniel
    Linköping University, Department of Science and Technology, Laboratory of Organic Electronics. Linköping University, Faculty of Science & Engineering.
    Berggren, Magnus
    Linköping University, Department of Science and Technology, Laboratory of Organic Electronics. Linköping University, Faculty of Science & Engineering.
    Large-area printed organic electronic ion pumps2019In: FLEXIBLE AND PRINTED ELECTRONICS, ISSN 2058-8585, Vol. 4, no 2, article id 022001Article in journal (Refereed)
    Abstract [en]

    Biological systems use a large variety of ions and molecules of different sizes for signaling. Precise electronic regulation of biological systems therefore requires an interface which translates the electronic signals into chemically specific biological signals. One technology for this purpose that has been developed during the last decade is the organic electronic ion pump (OEIP). To date, OEIPs have been fabricated by micropatterning and labor-intensive manual techniques, hindering the potential application areas of this promising technology. Here we show, for the first time, fully screen-printed OEIPs. We demonstrate a large-area printed design with manufacturing yield amp;gt;90%. Screen-printed cation- and anion-exchange membranes are both demonstrated with promising ion selectivity and performance, with transport verified for both small ions (Na+,K+,Cl-) and biologically-relevant molecules (the cationic neurotransmitter acetylcholine, and the anionic anti-inflammatory salicylic acid). These advances open the iontronics toolbox to the world of printed electronics, paving the way for a broader arena for applications.

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    fulltext
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