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Record W4322772671 · doi:10.3389/fmats.2023.1108077

Dislocation-driven growth of WS2/WSe2 quantum well superlattices

2023· article· en· W4322772671 on OpenAlex
Hang Yang, Zeng Li, Ziwei Huang, Tian Zhang, Shunhui Zhang, Xuyang Zhang, Zhikang Ao, Baihui Zhang

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFrontiers in Materials · 2023
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSuperlatticeCondensed matter physicsHeterojunctionMaterials scienceQuantum dotDislocationQuantum wellQuantumMonolayerClimbNanoscopic scaleOptoelectronicsNanotechnologyPhysicsOpticsQuantum mechanics

Abstract

fetched live from OpenAlex

The synthesis of two-dimensional lateral heterojunctions with nanoscale characteristic width and sharp interfaces remains challenging. The quantum confinement effects are still difficult to create on 2D materials since widths smaller than 5 nm are necessary for quantum confinement effects and quantum well applications. In this study, we demonstrated the growth of a sub-2-nm tungsten sulfide quantum well array in a monolayer of tungsten selenide, driven by the climb of mismatch dislocation in a heterointerface due to the lattice mismatch. Width-controllable 2D quantum well superlattices are theoretically formed by the mismatch dislocation-driven growth mechanism, according to our analysis. Thus, abundant photonic electronic properties can be obtained in 2D quantum well superlattices formed at varied lateral heterointerfaces, which will support the study of topological insulators and superconductors.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.001

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 head0.014
GPT teacher head0.250
Teacher spread0.236 · 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