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Record W2396889572 · doi:10.1021/acs.nanolett.5b02557

Photothermal Electrical Resonance Spectroscopy of Physisorbed Molecules on a Nanowire Resonator

2015· article· en· W2396889572 on OpenAlexafffund
K. Prashanthi, Arindam Phani, Thomas Thundat

Bibliographic record

VenueNano Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsUniversity of Alberta
FundersCanada Excellence Research Chairs, Government of CanadaCanada Research Chairs
KeywordsPhotothermal therapyNanowireResonance (particle physics)SemiconductorSpectroscopyMaterials scienceOptoelectronicsInfraredPhotothermal spectroscopyChemistryBand gapMoleculeNanotechnologyAnalytical Chemistry (journal)OpticsAtomic physics

Abstract

fetched live from OpenAlex

Mid-infrared (IR) photothermal spectroscopy of adsorbed molecules is an ideal technique for molecular recognition in miniature sensors with very small thermal mass. Here, we report on combining the photothermal spectroscopy with electrical resonance of a semiconductor nanowire for enhanced sensitivity, selectivity, and simplified readout. Wide band gap semiconductor bismuth ferrite nanowire, by virtue of its very low thermal mass and abundance of surface states in the band gap, facilitates thermally induced charge carrier trapping in the surface states, which affects its electrical resonance response. Electrical resonance response of the nanowire varies significantly depending on the photothermal spectrum of the adsorbed molecules. We demonstrate highly selective detection of mid-IR photothermal spectral signatures of femtogram level molecules physisorbed on a nanowire by monitoring internal dissipation response at its electrical resonance.

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 score0.006
Threshold uncertainty score0.766

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 head0.010
GPT teacher head0.203
Teacher spread0.193 · 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

Citations23
Published2015
Admission routes2
Has abstractyes

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