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Record W2330662354 · doi:10.1021/acs.jpclett.6b00584

Dynamics and Mechanisms of Exfoliated Black Phosphorus Sublimation

2016· article· en· W2330662354 on OpenAlexafffund
Matthieu Fortin‐Deschênes, Pierre L. Lévesque, Richard Martel, Oussama Moutanabbir

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2016
Typearticle
Languageen
FieldMaterials Science
Topic2D Materials and Applications
Canadian institutionsUniversité de MontréalPolytechnique Montréal
FundersUniversité de MontréalNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsMinistère des relations internationales et de la Francophonie
KeywordsSublimation (psychology)NucleationBlack phosphorusChemical physicsMaterials scienceKinetic Monte CarloAnnealing (glass)Monte Carlo methodCrystallographyChemistryThermodynamicsPhysicsComposite materialOptoelectronics

Abstract

fetched live from OpenAlex

We report on real time observations of the sublimation of exfoliated black phosphorus layers throughout annealing using in situ low energy electron microscopy. We found that sublimation manifests itself above 375 ± 20 °C through the nucleation and expansion of asymmetric, faceted holes with the long axis aligned along the [100] direction and sharp tips defined by edges consisting of alternating (10) and (11) steps. This thermally activated process repeats itself via successive sublimation of individual layers. Calculations and simulations using density functional theory and kinetic Monte Carlo allowed to determine the involved atomic pathways. Sublimation is found to occur via detachments of phosphorus dimers rather than single atoms. This behavior and the role of defects is described using an analytical model that captures all essential features. This work establishes an atomistic-level understanding of the thermal stability of exfoliated black phosphorus and defines the temperature window available for material and device processing.

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 score0.019
Threshold uncertainty score0.150

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 head0.006
GPT teacher head0.217
Teacher spread0.210 · 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

Citations37
Published2016
Admission routes2
Has abstractyes

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