Evidence map›Paper›PMID 35902599›Full record

ArticleNature communications2022

Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing.

Dmitry Kireev, Samuel Liu, Harrison Jin, T Patrick Xiao, Christopher H Bennett, Deji Akinwande, Jean Anne C Incorvia

Abstract read
In one paragraph

Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

19 citing papers in PubMed.

  1. Article
  2. Battery-Inspired Electrochemical Synapses for Neuromorphic Applications.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  3. Review
  4. Review
  5. Article
  6. Article
  7. Electric Field-Driven Conformational Changes in Molecular Memristor and Synaptic Behavior.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Article
  8. Article
  9. Article
  10. Atomically thin bioelectronics.Nature reviews. Materials · 2024
    Article
  11. Article
  12. Article
  13. Review
  14. Review
  15. Review
  16. Article
  17. Emerging electrolyte-gated transistors for neuromorphic perception.Science and technology of advanced materials · 2023
    Review
  18. Graphene-based RRAM devices for neural computing.Frontiers in neuroscience · 2023
    Review
  19. Graphene-based cardiac sensors and actuators.Frontiers in bioengineering and biotechnology · 2023
    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.

Dmitry Kireev *Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.ORCID 0000-0003-1499-5435
Samuel Liu *Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.ORCID 0000-0002-1864-5734
Harrison JinDepartment of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.ORCID 0000-0001-6566-3145
T Patrick XiaoSandia National Laboratories, Albuquerque, NM, 87123, USA.
Christopher H BennettSandia National Laboratories, Albuquerque, NM, 87123, USA.
Deji AkinwandeDepartment of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA.ORCID 0000-0001-7133-5586
Jean Anne C IncorviaDepartment of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, 78712, USA. incorvia@austin.utexas.edu.ORCID 0000-0002-4805-2112

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm

Indexed as

GraphiteElectronicsHumansLong-Term PotentiationNeural Networks, ComputerSynapsesTransistors, ElectronicGraphite

Identifiers

PMID35902599
PMCPMC9334620

What OpenQuestion holds

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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.