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Record W4308767818 · doi:10.1088/1361-6528/aca1cc

A type-II NGyne/GaSe heterostructure with high carrier mobility and tunable electronic properties for photovoltaic application

2022· article· en· W4308767818 on OpenAlexaff
Liru Zeng, Siyu Zhang, Linwei Yao, Zhisong Bi, Yanni Zhang, Peng Kang, Junfeng Yan, Zhiyong Zhang, Jiangni Yun

Bibliographic record

VenueNanotechnology · 2022
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsMcGill University
FundersNational Natural Science Foundation of China
KeywordsHeterojunctionMaterials scienceElectric fieldOptoelectronicsPhotoelectric effectSemiconductorElectron mobilityBand gapCharge carrierPhotovoltaic systemPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

Abstract The two-dimensional heterostructures with type-II band alignment and super-high carrier mobility offer an updated perspective for photovoltaic devices. Here, based on the first-principles calculation, a novel vertical NGyne/GaSe heterostructure with an intrinsic type-II band alignment, super-high carrier mobility (10 4 cm 2 V −1 s −1 ), and strong visible to ultraviolet light absorption (10 4 –10 5 cm −1 ) is constructed. We investigate the electronic structure and the interfacial properties of the NGyne/GaSe heterostructure under electric field and strain. The band offsets and band gap of the NGyne/GaSe heterostructure can be regulated under applied vertical electric field and strain efficiently. Further study reveals that the photoelectric conversion efficiency of the NGyne/GaSe heterostructure is vastly improved under a negative electric field and reaches up to 25.09%. Meanwhile, near-free electron states are induced under a large applied electric field, leading to the NGyne/GaSe heterostructure transform from semiconductors to metal. Our results indicate that the NGyne/GaSe heterostructure will have extremely potential in optoelectronic devices, especially solar cells.

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 score0.039
Threshold uncertainty score0.472

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 studies0.0010.000
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 head0.006
GPT teacher head0.205
Teacher spread0.199 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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