Evidence map›Paper›PMID 40817593›Full record

ArticleAdvanced materials (Deerfield Beach, Fla.)2025

Steep-Slope CuInP

Sungpyo Baek, Young Kwon Kim, Sang-Min Lee, HaeJu Choi, Ji-Sang Park, Byung Chul Jang, Sungjoo Lee

Abstract read
In one paragraph

Article in Advanced materials (Deerfield Beach, Fla.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Van Der Waals Ferroionic CuInPMaterials (Basel, Switzerland) · 2026
    Review
  3. Review
  4. Devices, Functions, and Applications of Artificial Neuromorphic Visual Systems.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  5. Steep-Slope CuInPAdvanced materials (Deerfield Beach, Fla.) · 2025
    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

7 authors.

Sungpyo BaekSKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 440-746, Republic of Korea.
Young Kwon KimSchool of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea.
Sang-Min LeeSKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 440-746, Republic of Korea.
HaeJu ChoiSKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 440-746, Republic of Korea.
Ji-Sang ParkSKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 440-746, Republic of Korea.
Byung Chul JangSchool of Electronic and Electrical Engineering, Kyungpook National University, Daegu, 41566, Republic of Korea.
Sungjoo LeeSKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University, Suwon, 440-746, Republic of Korea.ORCID https://orcid.org/0000-0003-1284-3593

Funding

National Research Foundation of Korea Grant Nos. RS-2023-00281048 2022R1A2C3003068 RS-2024-00355248
6 · The paper itself

Abstract

Spiking neural networks (SNNs) have garnered considerable attention as energy-efficient and biologically inspired computing paradigms. However, despite the growing interest, the development of hardware-based SNNs has remained limited, primarily because of insufficient research on hardware-based spiking neuron devices. In this study, a CuInP

Indexed as

artificial spiking neuroncopper indium thiophosphateferroionicspiking neural networksthreshold switching

Identifiers

PMID40817593
PMCPMC12592917

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.