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Record W2555735528 · doi:10.1080/21663831.2016.1256915

Toward edges-rich MoS<sub>2</sub> layers via chemical liquid exfoliation triggering distinctive magnetism

2016· article· en· W2555735528 on OpenAlexaff
Guanhui Gao, Chi Chen, Xiaobin Xie, Yantao Su, Shendong Kang, Guichi Zhu, Duyang Gao, A. Trampert, Lintao Cai

Bibliographic record

VenueMaterials Research Letters · 2016
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsUniversité de Montréal
FundersEuskal Herriko UnibertsitateaNational Natural Science Foundation of China
KeywordsFerromagnetismMaterials scienceDiamagnetismExfoliation jointMagnetismMolybdenum disulfideCondensed matter physicsX-ray photoelectron spectroscopyGrapheneTransmission electron microscopyChemical physicsCrystallographyNanotechnologyMagnetic fieldNuclear magnetic resonanceMetallurgyChemistryPhysics

Abstract

fetched live from OpenAlex

The magnetic function of layered molybdenum disulfide (MoS2) has been investigated via simulation, but few reliable experimental results have been explored. Herein, we developed edges-rich structural MoS2 nanosheets via liquid phase exfoliation approach, triggering exceptional ferromagnetism. The magnetic measurements revealed the clear ferromagnetic property of layered MoS2, compared to the pristine MoS2 in bulk exhibiting diamagnetism. The existence of ferromagnetism mostly was attributed to the presence of grain boundaries with abundant irregular edges confirmed by the transmission electron microscopy, magnetic force microscopy and X-ray photoelectron spectroscopy, which experimentally provided reliable evidences on irregular edges-rich states engineering ferromagnetism to clarify theoretical calculation.

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 score0.001
Threshold uncertainty score0.002

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)0.0010.000

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 head0.046
GPT teacher head0.300
Teacher spread0.254 · 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

Citations25
Published2016
Admission routes1
Has abstractyes

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