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Record W3164282248 · doi:10.1021/acs.jpcc.1c02142

Improved Charge Transfer and Barrier Lowering across a Au–MoS<sub>2</sub> Interface through Insertion of a Layered Ca<sub>2</sub>N Electride

2021· article· en· W3164282248 on OpenAlexafffund
Fouad Kaadou, Jesse Maassen, Erin R. Johnson

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMXene and MAX Phase Materials
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceHeterojunctionSchottky barrierOhmic contactQuantum tunnellingMonolayervan der Waals forceFermi levelCondensed matter physicsContact resistanceDensity functional theoryOptoelectronicsCharge (physics)MetalNanotechnologyLayer (electronics)ChemistryComputational chemistryMoleculeElectronDiode

Abstract

fetched live from OpenAlex

Despite immense promise, use of transition-metal dichalcogenides (TMDCs), such as MoS2, in electronics applications is hindered by the difficulties in forming effective metal contacts with low resistance. In this work, we propose insertion of a two-dimensional (2D) electride [Ca2N]+(e–) at a metal–TMDC interface to establish proper electrical contact. As a proof of concept, we consider the Au–MoS2 interface due to the presence of a van der Waals gap, which leads to a high tunneling barrier and strong Fermi-level pinning. Density-functional theory calculations predict nearly complete charge transfer from the electride surface states, resulting in a cationic [Ca2N]+ monolayer at the interface and metalization of the negatively doped MoS2. Thus, formation of the Au–Ca2N–MoS2 heterostructure eliminates both the tunneling and Schottky barriers, indicating that inserting a single 2D electride layer at metal–TMDC interfaces is a viable strategy to achieve proper Ohmic contacts in device manufacture.

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)0.001
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 score0.002
Threshold uncertainty score0.739

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.259
Teacher spread0.248 · 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

Citations14
Published2021
Admission routes2
Has abstractyes

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