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Record W2539429775 · doi:10.1063/1.4964922

Sensitivity measurement of a cantilever-based surface stress sensor

2016· article· en· W2539429775 on OpenAlexafffund
Ann-Lauriene Haag, Zeno Schumacher, Peter Grütter

Bibliographic record

VenueThe Journal of Chemical Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsCantileverSurface stressElectrodeMaterials scienceChlorideStress (linguistics)Sensitivity (control systems)PerchlorateAnalytical Chemistry (journal)AdsorptionIonReference electrodeElectrochemistryCurrent (fluid)Electrochemical potentialChemistryComposite materialElectronic engineeringThermodynamicsPhysicsPhysical chemistryChromatography

Abstract

fetched live from OpenAlex

A detailed analysis of the temporal surface stress evolution for potential-driven adsorption of ions is discussed. A gold-coated cantilever is used to simultaneously measure the change in surface stress as well as the current response during an applied potential step. In this electrochemical configuration, the cantilever acts as the working electrode, a platinum wire as the counter electrode, and the Ag/AgCl (sat. KCl) electrode as the reference electrode. To study the time-dependent signal and the sensitivity of the cantilever response, the frequency of the potential step applied to the cantilever is varied from 1 s to 0.1 ms. First, a comparison between a strong adsorbing (chloride Cl−) and a weak adsorbing ion (perchlorate ClO4−) in a 1 mM solution is presented. Next, the linear relationship between surface stress and charge density is measured for these fast potential steps. The slope of this fit is defined as the sensitivity of the system and is shown to increase for shorter potential pulses. Finally, the behaviour of the surface stress and current for consecutive applied potential steps is studied.

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)0.001
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 score0.040
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.021
GPT teacher head0.233
Teacher spread0.213 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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