Evidence map›Paper›PMID 34577704›Full record

ArticleMicromachines2021

Influence of the Surface Material and Illumination upon the Performance of a Microelectrode/Electrolyte Interface in Optogenetics.

Junyu Shen, Yanyan Xu, Zhengwen Xiao, Yuebo Liu, Honghui Liu, Fengge Wang, Wanqing Yao, Zhaokun Yan, Minjie Zhang, Zhisheng Wu and 7 more

Abstract read
In one paragraph

Article in Micromachines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Junyu ShenSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China.
Yanyan XuSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China.
Zhengwen XiaoSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China.
Yuebo LiuSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China.
Honghui LiuSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China.
Fengge WangSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China.
Wanqing YaoSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China.
Zhaokun YanSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China.
Minjie ZhangSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China.
Zhisheng WuSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China.
Yang LiuSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China.
Sio Hang PunState Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau 999078, China.
Tim C LeiDepartment of Electrical Engineering, University of Colorado Denver, Denver, CO 80204, USA.
Mang I VaiState Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau 999078, China.
Peng Un MakDepartment of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau 999078, China.ORCID 0000-0003-3813-5800
Changhao ChenState Key Laboratory of Analog and Mixed-Signal VLSI, Institute of Microelectronics, University of Macau, Macau 999078, China.
Baijun ZhangSchool of Electronics and Information Technology, Sun Yat-sen University, Guangzhou 510275, China.

Funding

Science & Technology Plan of Guangdong Province, China 2019B010132003, 2019B010132001the National Key R&D Program of China 2020YFB1313502the National Key Research and Development Program 2016YFB0400105, 2017YFB0403001the Science and Technology Development Fund, Macau SAR 088/2016/A2, 0144/2019/A3, 0022/2020/AFJ, SKL-AMSV (FDCT-funded), SKL-AMSV-ADDITIONAL FUND, SKL-AMSV(UM)-2020-2022the University of Macau MYRG2018-00146-AMSV, MYRG2019-00056-AMSVthe Zhuhai Key Technology Laboratory of Wide Bandgap Semiconductor Power Electronics, Sun Yat-sen University, China 20167612042080001This research was funded by the joint funding of the Nature Science Foundation of China (NSFC) & the Macao Science and Technology Development Fund (FDCT) of China 62061160368 & 0022/2020/AFJ
6 · The paper itself

Abstract

Integrated optrodes for optogenetics have been becoming a significant tool in neuroscience through the combination of offering accurate stimulation to target cells and recording biological signals simultaneously. This makes it not just be widely used in neuroscience researches, but also have a great potential to be employed in future treatments in clinical neurological diseases. To optimize the integrated optrodes, this paper aimed to investigate the influence of surface material and illumination upon the performance of the microelectrode/electrolyte interface and build a corresponding evaluation system. In this work, an integrated planar optrode with a blue LED and microelectrodes was designed and fabricated. The charge transfer mechanism on the interface was theoretically modeled and experimentally verified. An evaluation system for assessing microelectrodes was also built up. Using this system, the proposed model of various biocompatible surface materials on microelectrodes was further investigated under different illumination conditions. The influence of illumination on the microelectrode/electrolyte interface was the cause of optical artifacts, which interfere the biological signal recording. It was found that surface materials had a great effect on the charge transfer capacity, electrical stability and recoverability, photostability, and especially optical artifacts. The metal with better charge transfer capacity and electrical stability is highly possible to have a better performance on the optical artifacts, regardless of its electrical recoverability and photostability under the illumination conditions of optogenetics. Among the five metals used in our investigation, iridium served as the best surface material for the proposed integrated optrodes. Thus, optimizing the surface material for optrodes could reduce optical interference, enhance the quality of the neural signal recording for optogenetics, and thus help to advance the research in neuroscience.

Indexed as

microelectrode/electrolyte interfaceoptical artifactoptogeneticsoptrodephotostabilitysurface material

Identifiers

PMID34577704
PMCPMC8471589

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.