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Record W4384408269 · doi:10.1002/adma.202303272

Optical Enhancement of Indirect Bandgap 2D Transition Metal Dichalcogenides for Multi‐Functional Optoelectronic Sensors

2023· review· en· W4384408269 on OpenAlexafffund
Riya Dutta, Arindam Bala, Anamika Sen, Michael Ross Spinazze, Heekyeong Park, Woong Choi, Youngki Yoon, Sunkook Kim

Bibliographic record

VenueAdvanced Materials · 2023
Typereview
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaNational Research Foundation of Korea
KeywordsPhotodetectionMaterials scienceOptoelectronicsBand gapHeterojunctionNanomaterialsPhotodetectorMonolayerNanotechnologyDirect and indirect band gaps

Abstract

fetched live from OpenAlex

The unique electrical and optical properties of transition metal dichalcogenides (TMDs) make them attractive nanomaterials for optoelectronic applications, especially optical sensors. However, the optical characteristics of these materials are dependent on the number of layers. Monolayer TMDs have a direct bandgap that provides higher photoresponsivity compared to multilayer TMDs with an indirect bandgap. Nevertheless, multilayer TMDs are more appropriate for various photodetection applications due to their high carrier density, broad spectral response from UV to near-infrared, and ease of large-scale synthesis. Therefore, this review focuses on the modification of the optical properties of devices based on indirect bandgap TMDs and their emerging applications. Several successful developments in optical devices are examined, including band structure engineering, device structure optimization, and heterostructures. Furthermore, it introduces cutting-edge techniques and future directions for optoelectronic devices based on multilayer TMDs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.096
GPT teacher head0.368
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations103
Published2023
Admission routes2
Has abstractyes

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