Evidence map›Paper›PMID 37210533›Full record

ArticleScientific reports2023

High sensitivity and wide response range artificial synapse based on polyimide with embedded graphene quantum dots.

Lijie Kou, Nan Ye, Anjam Waheed, Rahmat Zaki Auliya, Chaoxing Wu, Poh Choon Ooi, Fushan Li

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Advances and Perspectives in Graphene-Based Quantum Dots Enabled Neuromorphic Devices.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  2. 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

7 authors.

Lijie KouSchool of Computing and Information Sciences, Fuzhou Institute of Technology, Fuzhou, 350506, People's Republic of China.
Nan YeSchool of Computing and Information Sciences, Fuzhou Institute of Technology, Fuzhou, 350506, People's Republic of China.
Anjam WaheedInstitute of Microengineering and Nanoelectronics (IMEN), Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.
Rahmat Zaki AuliyaInstitute of Microengineering and Nanoelectronics (IMEN), Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.
Chaoxing WuSchool of Physics and Information Engineering, Fuzhou University, Fuzhou, 350002, People's Republic of China. chaoxing_wu@fzu.edu.cn.
Poh Choon OoiInstitute of Microengineering and Nanoelectronics (IMEN), Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia. pcooi@gmx.com.
Fushan LiSchool of Physics and Information Engineering, Fuzhou University, Fuzhou, 350002, People's Republic of China. fushanli@hotmail.com.

Funding

Fundamental Research Grant Scheme FRGS/1/2022/TK08/UKM/02/13
6 · The paper itself

Abstract

Artificial electronic synapses are commonly used to simulate biological synapses to realize various learning functions, regarded as one of the key technologies in the next generation of neurological computation. This work used a simple spin coating technique to fabricate polyimide (PI):graphene quantum dots(GQDs) memristor structure. As a result, the devices exhibit remarkably stable exponentially decaying postsynaptic suppression current over time, as interpreted in the spike-timing-dependent plasticity phenomenon. Furthermore, with the increase of the applied electrical signal over time, the conductance of the electrical synapse gradually changes, and the electronic synapse also shows plasticity dependence on the amplitude and frequency of the pulse applied. In particular, the devices with the structure of Ag/PI:GQDs/ITO prepared in this study can produce a stable response to the stimulation of electrical signals between millivolt to volt, showing not only high sensitivity but also a wide range of "feelings", which makes the electronic synapses take a step forwards to emulate biological synapses. Meanwhile, the electronic conduction mechanisms of the device are also studied and expounded in detail. The findings in this work lay a foundation for developing brain-like neuromorphic modeling in artificial intelligence.

Identifiers

PMID37210533
PMCPMC10199894

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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.