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Record W4414555915 · doi:10.1021/acs.nanolett.5c03319

Pressure-Driven Metallicity in Ångström-Thickness 2D Bismuth and Layer-Selective Ohmic Contact to MoS<sub>2</sub>

2025· article· en· W4414555915 on OpenAlexaff
Shibo Fang, Qiang Li, Yunliang Yue, Zongmeng Yang, Xiaotian Sun, Jing Lu, Chit Siong Lau, L. K. Ang, Lain‐Jong Li, Yee Sin Ang

Bibliographic record

VenueNano Letters · 2025
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsInnovation Cluster (Canada)
FundersNational Science and Technology Major ProjectNational Research Foundation SingaporeNational Natural Science Foundation of ChinaPeking UniversityMinistry of Education - SingaporeScience and Engineering Research CouncilHubei Provincial Department of Education
KeywordsOhmic contactBismuthvan der Waals forceMonolayerSchottky diodeFabricationTransition metal

Abstract

fetched live from OpenAlex

Recent fabrication of two-dimensional (2D) metallic bismuth (Bi) via van der Waals (vdW) squeezing offers a route to ultrascaling metal into ångström thickness. However, free-standing 2D Bi is typically semiconducting, which contradicts the experimentally observed metallicity in vdW-squeezed 2D Bi. Here we show that this discrepancy originates from the pressure-induced buckled-to-flat structural transition in 2D Bi, changing the electronic structures from semiconducting to semimetallic. Based on the experimentally fabricated MoS 2 -Bi-MoS 2 trilayer heterostructure, we demonstrate the concept of layer-selective Ohmic contact in which one MoS 2 layer forms an Ohmic contact to the 2D Bi while the opposite MoS 2 exhibits a Schottky barrier. The Ohmic contact can be switched between the two sandwiching MoS 2 monolayers by reversing an external gate field, thus enabling charge to be spatially injected into different MoS 2 layers. The layer-selective Ohmic contact proposed here represents a layertronic generalization of semimetal/semiconductor contact, paving the way toward layertronic device application.

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 score0.013
Threshold uncertainty score0.761

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 head0.009
GPT teacher head0.245
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

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

Citations10
Published2025
Admission routes1
Has abstractyes

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