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Record W2979667979 · doi:10.1149/2.0042003jes

Review—Nanomechanical Calorimetric Infrared Spectroscopy using Bi-Material Microfluidic Cantilevers

2019· article· en· W2979667979 on OpenAlexafffund
Abdullah N. Alodhayb, Faheem Khan, Hashem Etayash, Thomas Thundat

Bibliographic record

VenueJournal of The Electrochemical Society · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsUniversity of Alberta
FundersCanada Excellence Research Chairs, Government of Canada
KeywordsAnalyteCantileverMicrofluidicsInfraredInfrared spectroscopySpectroscopyMaterials scienceBendingSelectivityAnalytical Chemistry (journal)WavelengthChemistryNanotechnologyOptoelectronicsOpticsChromatographyComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

Receptor-free, in-situ sensing of chemical and biological analytes with high selectivity and sensitivity is a highly sought-after goal. While a resonating microfluidic, hollow channel microcantilever is an ideal platform for sensing pico liters (pL) of liquid analytes based on changes in the specific gravity it does not offer any chemical selectivity. Fabricating these hollow channel cantilevers as bi-material beams allows them to be extremely sensitive to small changes in temperature as well. When a liquid confined in such a cantilever is illuminated with tunable IR radiation, it undergoes bending whenever the liquid analyte absorbs the light at a particular wavelength. Monitoring the cantilever bending as a function of illuminating wavelength provides IR spectrum of the analyte confined in the channel. This method combines the selectivity of IR spectroscopy and the sensitivity of a cantilever for molecular recognition of pL volume of liquid samples. This nanomechanical calorimetric infrared spectroscopy is an ideal technique for physical and chemical characterization of pico liter volumes of liquid analytes.

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 score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.243
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

Citations11
Published2019
Admission routes2
Has abstractyes

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