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Record W4392016767 · doi:10.1515/ntrev-2023-0207

Single flake homo p–n diode of MoTe <sub>2</sub> enabled by oxygen plasma doping

2024· article· en· W4392016767 on OpenAlexaff
Irsa Zulfiqar, Sania Gul, Hafiz Aamir Sohail, Iqra Rabani, Saima Gul, Malik Abdul Rehman, Saikh Mohammad Wabaidur, Muhammad Yasir, Inam Ullah, Muhammad Asghar Khan, Shania Rehman

Bibliographic record

VenueNanotechnology Reviews · 2024
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsUniversity of Alberta
FundersMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaSejong UniversityKing Saud UniversityNational Research Foundation
KeywordsNanoelectronicsDiodeDopingMaterials scienceTransistorOptoelectronicsRectificationElectron mobilityPlasmaNanotechnologyElectronicsElectrical engineeringPhysicsVoltageEngineering

Abstract

fetched live from OpenAlex

Abstract Two-dimensional (2D) materials play a crucial role as fundamental electrical components in modern electronics and optoelectronics next-generation artificial intelligent devices. This study presents a methodology for creating a laterally uniform p–n junction by using a partial oxygen plasma-mediated strategy to introduce p-type doping in single channel MoTe 2 device. The MoTe 2 field effect transistors (FETs) show high electron mobility of about ∼23.54 cm 2 V −1 s −1 and a current ON/OFF ratio of ∼10 6 while p-type FETs show hole mobility of about ∼9.25 cm 2 V −1 s −1 and current ON/OFF ratio ∼10 5 along with artificially created lateral MoTe 2 p–n junction, exhibited a rectification ratio of ∼10 2 and ideality factor of ∼1.7 which is proximity to ideal-like diode. Thus, our study showed a diversity in the development of low-power nanoelectronics of next-generation integrated circuits.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.019
GPT teacher head0.249
Teacher spread0.231 · 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 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

Citations11
Published2024
Admission routes1
Has abstractyes

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