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Record W4394011982 · doi:10.1002/elt2.30

Colloidal quantum dots and two‐dimensional material heterostructures for photodetector applications

2024· article· en· W4394011982 on OpenAlexaff
Jingying Luo, Gurpreet Singh Selopal, Xin Tong, Zhiming Wang

Bibliographic record

VenueElectron · 2024
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsDalhousie University
FundersFundamental Research Funds for the Central UniversitiesBasic Research Programs of Sichuan ProvinceNational Key Research and Development Program of ChinaHigher Education Discipline Innovation ProjectNational Natural Science Foundation of China
KeywordsPhotodetectorHeterojunctionQuantum dotMaterials scienceOptoelectronicsColloidNanotechnologyChemistry

Abstract

fetched live from OpenAlex

Abstract Photodetectors (PDs) are optoelectronic devices that convert optical signals into electrical responses. Recently, there has been a tremendous increase in research interest in PDs based on colloidal quantum dots (QDs) and two‐dimensional (2D) material heterostructures owing to the strong light‐absorption capacity and the well‐adjustable band gap of QDs and the superior charge carriers transfer ability of 2D materials. In particular, the heterojunction formed between QDs and 2D materials can effectively enhance the separation and transport of photogenerated charge carriers, which is expected to establish PDs with ultrahigh photoconductive gain, high responsivity, and detectivity. This review aimed to summarize the state‐of‐the‐art advances in the research of QDs/2D material nanohybrid PDs, including the device parameters, architectures, working mechanisms, and fabrication technologies. The progress of hybrid PDs based on the heterojunction of QDs with different 2D materials, along with their innovative applications, are comprehensively described. In the end, the challenges and feasible strategies in future research and development are briefly proposed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.273
Teacher spread0.266 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations32
Published2024
Admission routes1
Has abstractyes

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