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A moisture-enabled fully printable power source inspired by electric eels.

ABSTRACT: Great efforts have been made to build integrated devices to enable future wearable electronics; however, safe, disposable, and cost-effective power sources still remain a challenge. In this paper, an all-solid-state power source was developed by using graphene materials and can be printed directly on an insulating substrate such as paper. The design of the power source was inspired by electric eels to produce programmable voltage and current by converting the chemical potential energy of the ion gradient to electric energy in the presence of moisture. An ultrahigh voltage of 192 V with 175 cells in series printed on a strip of paper was realized under ambient conditions. For the planar cell, the mathematical fractal design concept was adapted as printed patterns, improving the output power density to 2.5 mW cm-3, comparable to that of lithium thin-film batteries. A foldable three-dimensional (3D) cell was also achieved by employing an origami strategy, demonstrating a versatile design to provide green electric energy. Unlike typical batteries, this power source printed on flexible paper substrate does not require liquid electrolytes, hazardous components, or complicated fabrication processes and is highly customizable to meet the demands of wearable electronics and Internet of Things applications.

SUBMITTER: Yang L

PROVIDER: S-EPMC8072409 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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A moisture-enabled fully printable power source inspired by electric eels.

Yang Lu L Yang Feiyao F Liu Xu X Li Ke K Zhou Yaning Y Wang Yangjian Y Yu Tianhao T Zhong Mengjuan M Xu Xiaobing X Zhang Lijuan L Shen Wei W Wei Di D

Proceedings of the National Academy of Sciences of the United States of America 20210401 16

Great efforts have been made to build integrated devices to enable future wearable electronics; however, safe, disposable, and cost-effective power sources still remain a challenge. In this paper, an all-solid-state power source was developed by using graphene materials and can be printed directly on an insulating substrate such as paper. The design of the power source was inspired by electric eels to produce programmable voltage and current by converting the chemical potential energy of the ion ...[more]

PMID: 33846255

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Project description:ObjectiveResearch in the area of transcranial electrical stimulation (TES) often relies on computational models of current flow in the brain. Models are built based on magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To simulate current flow on an individual, the subject's MRI is segmented, virtual electrodes are placed on this anatomical model, the volume is tessellated into a mesh, and a finite element model (FEM) is solved numerically to estimate the current flow. Various software tools are available for each of these steps, as well as processing pipelines that connect these tools for automated or semi-automated processing. The goal of the present tool-realistic volumetric-approach to simulate transcranial electric simulation (ROAST)-is to provide an end-to-end pipeline that can automatically process individual heads with realistic volumetric anatomy leveraging open-source software and custom scripts to improve segmentation and execute electrode placement.ApproachROAST combines the segmentation algorithm of SPM12, a Matlab script for touch-up and automatic electrode placement, the finite element mesher iso2mesh and the solver getDP. We compared its performance with commercial FEM software, and SimNIBS, a well-established open-source modeling pipeline.Main resultsThe electric fields estimated with ROAST differ little from the results obtained with commercial meshing and FEM solving software. We also do not find large differences between the various automated segmentation methods used by ROAST and SimNIBS. We do find bigger differences when volumetric segmentation are converted into surfaces in SimNIBS. However, evaluation on intracranial recordings from human subjects suggests that ROAST and SimNIBS are not significantly different in predicting field distribution, provided that users have detailed knowledge of SimNIBS.SignificanceWe hope that the detailed comparisons presented here of various choices in this modeling pipeline can provide guidance for future tool development. We released ROAST as an open-source, easy-to-install and fully-automated pipeline for individualized TES modeling.

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A moisture-enabled fully printable power source inspired by electric eels.

Publications

A moisture-enabled fully printable power source inspired by electric eels.

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