Evidence map›Paper›PMID 36144107›Full record

ArticleMicromachines2022

Cold Laser Micro-Machining of PDMS as an Encapsulation Layer for Soft Implantable Neural Interface.

Minjie Wang, Yuan Zhang, Jianxiong Bin, Lan Niu, Jing Zhang, Lusheng Liu, Aiping Wang, Jin Tao, Jingqiu Liang, Lihua Zhang and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Micromachines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

11 authors.

Minjie WangLaboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.
Yuan ZhangLaboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.
Jianxiong BinJi Hua Laboratory, Foshan 528200, China.
Lan NiuJi Hua Laboratory, Foshan 528200, China.
Jing ZhangLaboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.
Lusheng LiuLaboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.
Aiping WangLaboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.
Jin TaoState Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
Jingqiu LiangState Key Laboratory of Applied Optics, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.
Lihua ZhangLaboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.
Xiaoyang KangLaboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI & Robotics, Ministry of Education, Shanghai Engineering Research Center of AI & Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Medical Neurobiology, Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai 200433, China.ORCID 0000-0003-4372-9531

Funding

Fudan University-CIOMP Joint Fund no.FC2019-002Ji Hua Laboratory no. X190021TB190National Key Research and Development Program of China no. 2021YFC0122702National Natural Science Foundation of China no. 61904038 and no. U1913216Opening Project of Shanghai Robot Industry R&D and Transformation Functional Platform no. KEH2310024Opening Project of Zhejiang Lab no. 2021MC0AB01Shanghai Municipal Science and Technology Commission no. 19441907600, no.19441908200, and no. 19511132000Shanghai Municipal Science and Technology Major Project no. 2021SHZDZX0103 and no. 2018SHZDZX01Shanghai Sailing Program no. 19YF1403600
6 · The paper itself

Abstract

PDMS (polydimethylsiloxane) is an important soft biocompatible material, which has various applications such as an implantable neural interface, a microfluidic chip, a wearable brain-computer interface, etc. However, the selective removal of the PDMS encapsulation layer is still a big challenge due to its chemical inertness and soft mechanical properties. Here, we use an excimer laser as a cold micro-machining tool for the precise removal of the PDMS encapsulation layer which can expose the electrode sites in an implantable neural interface. This study investigated and optimized the effect of excimer laser cutting parameters on the electrochemical impedance of a neural electrode by using orthogonal experiment design. Electrochemical impedance at the representative frequencies is discussed, which helps to construct the equivalent circuit model. Furthermore, the parameters of the equivalent circuit model are fitted, which reveals details about the electrochemical property of neural electrode using PDMS as an encapsulation layer. Our experimental findings suggest the promising application of excimer lasers in the micro-machining of implantable neural interface.

Indexed as

electrochemical impedanceequivalent circuit modelexcimer lasermicro-machiningneural electrodePDMS

Identifiers

PMID36144107
PMCPMC9504264

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.