Evidence map›Paper›PMID 41580905›Full record

ReviewSmall methods2026

Organic Transistor-Based Neuromorphic Electronics and Their Recent Applications.

Ziru Wang, Feng Yan

Abstract readReview
In one paragraph

Review in Small methods, 2026. 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. Review
  2. 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

2 authors.

Ziru WangDepartment of Applied Physics, Research Centre for Organic Electronics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, P. R. China.
Feng YanDepartment of Applied Physics, Research Centre for Organic Electronics, The Hong Kong Polytechnic University, Kowloon, Hong Kong, P. R. China.ORCID https://orcid.org/0000-0001-7385-6334

Funding

Hong Kong Polytechnic University 1-CE0PResearch Centre for Organic Electronics of the Hong Kong Polytechnic University
6 · The paper itself

Abstract

Neuromorphic technologies offer a promising pathway to address the escalating energy demands of artificial intelligence. At the system level, neuromorphic computing seeks to overcome the von Neumann bottleneck by integrating memory and processing, while neuromorphic sensing minimizes redundant data transfer by processing signals directly at the point of acquisition. Organic transistors have emerged as compelling candidates for emulating synaptic and neuronal behaviors owing to their low power consumption, flexibility, stretchability, and biocompatibility, making them particularly attractive for bio-related neuromorphic applications. This review provides an overview of organic transistor-based artificial synapses and neurons, with emphasis on the mechanisms underlying their neuromorphic behaviors. Subsequently, recent advances in applications, broadly categorized into neuromorphic computing and neuromorphic sensing, are summarized and representative bio-integrated demonstrations are highlighted. Finally, we outline key challenges at the material, device, and system levels, and discuss future opportunities for advancing organic neuromorphic electronics toward practical, biocompatible, and intelligent systems.

Indexed as

hardware computingorganic neuromorphic electronicsorganic transistors

Identifiers

PMID41580905
PMCPMC13054128

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.