Evidence map›Paper›PMID 40528986›Full record

ReviewFundamental research2025

Emerging optoelectronic architectures in carbon nanotube photodetector technologies.

Xiaolu Xia, Shaoyuan Zhou, Ying Wang, Zhiyong Zhang

Abstract readReview
In one paragraph

Review in Fundamental research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

4 authors.

Xiaolu XiaKey Laboratory of Luminescence & Optical Information, Ministry of Education, School of Physical Science and Engineering, Beijing Jiaotong University, Beijing 100044, China.
Shaoyuan ZhouKey Laboratory for the Physics and Chemistry of Nanodevices and Center for Carbon-Based Electronics, School of Electronics, Peking University, Beijing 100871, China.
Ying WangKey Laboratory of Luminescence & Optical Information, Ministry of Education, School of Physical Science and Engineering, Beijing Jiaotong University, Beijing 100044, China.
Zhiyong ZhangKey Laboratory for the Physics and Chemistry of Nanodevices and Center for Carbon-Based Electronics, School of Electronics, Peking University, Beijing 100871, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Photodetectors are the fundamental building blocks for many optoelectronic systems, including night vision, optical communications, biomedical imaging, security and motion detection. Carbon nanotubes (CNTs), which have a direct-bandgap structure, a broad spectral response and a large absorption coefficient, provide an ideal research platform for the exploration of high-performance infrared photodetectors. In the past twenty years, great efforts have been devoted to improve detection sensitivity via adopting high-purity CNT films, various doping strategies, optical manipulations and sensitizing nanostructures. Despite considerable strides made, challenges remain in simultaneously achieving high responsivity, low dark current and fast response. In this Review, we summarize recent advances on key device construction strategies and underlying concepts that contribute to improve performance of fabricated CNT photodetectors. The newly emerging heterojunction gated CNT transistors and their potential are highlighted to overcome trade-offs between the optical and electronic processes. Novel applications of CNT photodetectors are further summarized for advanced optoelectronic technologies.

Indexed as

Carbon nanotubesDevice architectureDiodeInfrared photodetectorsPhototransistor

Identifiers

PMID40528986
PMCPMC12167882

What OpenQuestion holds

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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.