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Record W2769082292 · doi:10.1002/pssr.201700352

Surface State‐Induced Anomalous Negative Thermal Quenching of Multiferroic BiFeO<sub>3</sub> Nanowires

2017· article· en· W2769082292 on OpenAlexaff
K. Prashanthi, Željka Antić, Garima Thakur, Miroslav D. Dramićanin, Thomas Thundat

Bibliographic record

Venuephysica status solidi (RRL) - Rapid Research Letters · 2017
Typearticle
Languageen
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsUniversity of Alberta
FundersMinistarstvo Prosvete, Nauke i Tehnološkog Razvoja
KeywordsQuenching (fluorescence)Materials sciencePhotoluminescenceLuminescenceNanowireMultiferroicsSemiconductorCondensed matter physicsSurface statesThermalOptoelectronicsNanotechnologySurface (topology)OpticsFluorescenceThermodynamicsFerroelectricityPhysics

Abstract

fetched live from OpenAlex

Wide‐bandgap semiconductor nanowires with surface defect emission centers have the potential to be used as sensitive thermometers and optical probes. Here, we show that the green luminescence of multiferroic BiFeO3 (BFO) nanowires shows an anomalous negative thermal quenching (NTQ) with increasing temperatures. The release of trapped carriers from localized surface defect states is suggested as the possible mechanism for the increased green luminescence which was experimentally observed at elevated temperatures. A reasonable interpretation of the photoluminescence (PL) processes in BFO nanowires is achieved, and the activation energies of the PL quenching and thermal hopping are deduced. Negative thermal quenching of BFO nanowires provides a new strategy for optical thermometry at higher temperatures.

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)0.0000.000

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 head0.060
GPT teacher head0.339
Teacher spread0.279 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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