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Record W2066575046 · doi:10.1149/06401.0019ecst

Miniaturization of Photothermal Cantilever Deflection Spectroscopy with an Electrical Readout

2014· article· en· W2066575046 on OpenAlexafffund
Seonghwan Kim, Dongkyu Lee, Thomas Thundat

Bibliographic record

VenueECS Transactions · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsUniversity of AlbertaUniversity of Calgary
FundersCanada Excellence Research Chairs, Government of CanadaCanada Research ChairsUniversity of Calgary
KeywordsCantileverMiniaturizationPiezoresistive effectPhotothermal therapyDeflection (physics)Materials scienceOptoelectronicsNanotechnologySpectroscopyPhotothermal effectInfraredOpticsPhysics

Abstract

fetched live from OpenAlex

The chemical selectivity challenge of microcantilever sensors can be overcome by photothermal cantilever deflection spectroscopy (PCDS). A bi-material cantilever responds to heat generated by non-radiative decay process when the adsorbed molecules are resonantly excited with infrared (IR) light. The variation in cantilever deflection amplitude as a function of illuminating IR wavelength corresponds to the conventional IR absorption spectrum of the adsorbed molecules. In addition, the mass of the adsorbed molecules can be determined by measuring the resonance frequency shift of the cantilever for the quantitative analysis. This technique offers unprecedented opportunities for highly selective as well as sensitive chemical sensing without relying on chemoselective interfaces. However, conventional optical signal readout schemes employed for microcantilever sensors pose challenges on miniaturization and system level integration. Here, we demonstrate PCDS using a piezoresistive cantilever which can be easily miniaturized and integrated into a portable sensor platform.

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 categoriesInsufficient payload (model declined to judge)
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 score0.319
Threshold uncertainty score0.999

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)0.0010.000

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 head0.241
Teacher spread0.235 · 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.

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

Citations0
Published2014
Admission routes2
Has abstractyes

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