Evidence map›Paper›PMID 41179224›Full record

ArticleACS omega2025

Artificial Synapse Based on Black Phosphorus/SnS

Wenxing Lv, Yuxuan Zeng, Xiaobo Wang, Weiming Lv, Xiaojing Wu, Hongmei Tian, Baoshun Zhang, Li Huang, Zhongming Zeng

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ACS omega, 2025. 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

9 authors.

Wenxing LvPhysics Laboratory, Industrial Training Center, Shenzhen Polytechnic University, Shenzhen 518055, Guangdong, People's Republic of China.ORCID https://orcid.org/0009-0009-9084-9297
Yuxuan ZengKey Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, Jiangsu, People's Republic of China.
Xiaobo WangPhysics Laboratory, Industrial Training Center, Shenzhen Polytechnic University, Shenzhen 518055, Guangdong, People's Republic of China.
Weiming LvKey Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, Jiangsu, People's Republic of China.
Xiaojing WuPhysics Laboratory, Industrial Training Center, Shenzhen Polytechnic University, Shenzhen 518055, Guangdong, People's Republic of China.
Hongmei TianPhysics Laboratory, Industrial Training Center, Shenzhen Polytechnic University, Shenzhen 518055, Guangdong, People's Republic of China.
Baoshun ZhangKey Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, Jiangsu, People's Republic of China.
Li HuangDepartment of Physics, State Key Laboratory of Quantum Functional Materials, and Guangdong Basic Research Center of Excellence for Quantum Science, Southern University of Science and Technology (SUSTech), Shenzhen 518055, Guangdong, People's Republic of China.ORCID https://orcid.org/0000-0003-0741-4903
Zhongming ZengKey Laboratory of Multifunctional Nanomaterials and Smart Systems, Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, Jiangsu, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial synaptic devices have garnered significant attention for overcoming the limitations of conventional von Neumann architecture in neuromorphic computing. However, the reported nonlinear weight update of artificial synapses prevents a hardware neural network from achieving the same high training and learning accuracy levels as those attained by a soft neural work. Despite the large-area fabrication constraints, two-dimensional (2D) materials that have tunable electronic band alignment and optoelectronic properties have emerged as promising candidates for artificial synapses. Here, we demonstrate an artificial synaptic device based on a black phosphorus (BP)/SnS

Identifiers

PMID41179224
PMCPMC12573176

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.