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Record W2754573910 · doi:10.1088/2053-1583/aa8d42

Charge trapping and coalescence dynamics in few layer MoS <sub>2</sub>

2017· article· en· W2754573910 on OpenAlexaff
Víctor Vega‐Mayoral, Tetiana Borzda, Daniele Vella, Matej Prijatelj, Eva A. A. Pogna, Claudia Backes, Jonathan N. Coleman, Giulio Cerullo, D. Mihailović, Christoph Gadermaier

Bibliographic record

Venue2D Materials · 2017
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsTrinity College
FundersH2020 Marie Skłodowska-Curie ActionsH2020 European Research CouncilJavna Agencija za Raziskovalno Dejavnost RS
KeywordsCoalescence (physics)ExcitonTrappingFemtosecondMaterials scienceRelaxation (psychology)ExcitationCharge carrierSpectroscopyMolecular physicsChemical physicsCondensed matter physicsOptoelectronicsChemistryOpticsPhysicsLaser

Abstract

fetched live from OpenAlex

Abstract The optoelectronic properties of a material are determined by the processes following light-matter interaction. Here we use femtosecond optical spectroscopy to systematically study photoexcited carrier relaxation in few-layer MoS 2 flakes as a function of excitation density and sample thickness. We find bimolecular coalescence of charges into indirect excitons as the dominant relaxation process in two- to three-layer flakes while thicker flakes show a much higher density of defects, which efficiently trap charges before they can coalesce.

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 score0.009
Threshold uncertainty score0.854

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 communication0.0010.000
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 head0.024
GPT teacher head0.267
Teacher spread0.243 · 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

Citations27
Published2017
Admission routes1
Has abstractyes

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