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Record W2982015321 · doi:10.1149/2.f07193if

Photothermal Cantilever Deflection Spectroscopy

2019· article· en· W2982015321 on OpenAlexaff
Seonghwan Kim, Thomas Thundat

Bibliographic record

VenueThe Electrochemical Society Interface · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCantileverDeflection (physics)SelectivityPhotothermal therapyMaterials scienceAdsorptionMoleculeNanotechnologyChemistryOptoelectronicsOpticsComposite materialPhysical chemistryOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Chemical sensors based on adsorption-induced deflection of microfabricated cantilevers offer very high sensitivity. Cantilever deflection occurs when molecular adsorption is confined to one side of the cantilever beam. Although these cantilever sensors have very high sensitivity, they do not have any chemical selectivity. Chemical selectivity is obtained by immobilizing chemoselective interfaces or receptors on the cantilever surface, and thus this approach suffers from chemical interferences. This problem of chemical selectivity can be overcome by exploiting the high thermal sensitivity of bi-material microcantilevers. Physisorbed molecules on a bi-material cantilever under resonant excitation by infrared (IR) radiation generate heat during nonradiative de-excitation, which results in cantilever deflection. Monitoring the variations in the cantilever deflection as a function of IR wavelength shows IR absorption peaks of the molecules. This photothermal cantilever deflection spectroscopy has very high sensitivity and selectivity and offers unprecedented opportunities for chemical sensing without chemoselective interfaces or receptor molecules.

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 score0.085
Threshold uncertainty score0.996

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)0.0050.001

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 head0.004
GPT teacher head0.238
Teacher spread0.234 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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