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Record W4225509908 · doi:10.1186/s40486-022-00143-w

Multiple ratiometric nanothermometry using semiconductor BiFeO3 nanowires and quantitative validation of thermal sensitivity

2022· article· en· W4225509908 on OpenAlexafffund
K. Prashanthi, K. Krishna Mohan, Željka Antić, Kaveh Ahadi, Miroslav D. Dramićanin

Bibliographic record

VenueMicro and Nano Systems Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsUniversity of Alberta
FundersMinistarstvo Prosvete, Nauke i Tehnološkog RazvojaCanada Excellence Research Chairs, Government of Canada
KeywordsNanowireMaterials scienceSensitivity (control systems)LuminescenceQuenching (fluorescence)PhotoluminescenceSemiconductorThermalAtmospheric temperature rangeOptoelectronicsMetric (unit)Analytical Chemistry (journal)NanotechnologyElectronic engineeringFluorescenceOpticsChemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Abstract Here, we report a very sensitive, non-contact, ratio-metric, and robust luminescence-based temperature sensing using a combination of conventional photoluminescence (PL) and negative thermal quenching (NTQ) mechanisms of semiconductor BiFeO 3 (BFO) nanowires. Using this approach, we have demonstrated the absolute thermal sensitivity of ~ 10 mK −1 over the 300–438 K temperature range and the relative sensitivity of 0.75% K −1 at 300 K. Further, we have validated thermal sensitivity of BFO nanowires quantitatively using linear regression and analytical hierarchy process (AHP) and found close match with the experimental results. These results indicated that BFO nanowires are excellent candidates for developing high‐performance luminescence-based temperature sensors. Graphical abstract

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 score0.002
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.212
Teacher spread0.191 · 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

Citations11
Published2022
Admission routes2
Has abstractyes

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