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Record W2108973751 · doi:10.1088/0957-4484/21/7/075501

Cantilever-based sensing: the origin of surface stress and optimization strategies

2010· article· en· W2108973751 on OpenAlexafffund
Michel Godin, Vincent Tabard‐Cossa, Yoichi Miyahara, Tanya Monga, Peter J. Williams, Luc Beaulieu, R. Bruce Lennox, Peter Grütter

Bibliographic record

VenueNanotechnology · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMechanical and Optical Resonators
Canadian institutionsMemorial University of NewfoundlandMcGill UniversityAcadia UniversityUniversity of Ottawa
FundersCanadian Institutes of Health Research
KeywordsSurface stressCantileverMaterials scienceMonolayerAdsorptionSurface (topology)Intermolecular forceStress (linguistics)NanotechnologyChemical physicsSelf-assembled monolayerMoleculeComposite materialPhysical chemistrySurface energyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Many interactions drive the adsorption of molecules on surfaces, all of which can result in a measurable change in surface stress. This article compares the contributions of various possible interactions to the overall induced surface stress for cantilever-based sensing applications. The surface stress resulting from adsorption-induced changes in the electronic density of the underlying surface is up to 2-4 orders of magnitude larger than that resulting from intermolecular electrostatic or Lennard-Jones interactions. We reveal that the surface stress associated with the formation of high quality alkanethiol self-assembled monolayers on gold surfaces is independent of the molecular chain length, supporting our theoretical findings. This provides a foundation for the development of new strategies for increasing the sensitivity of cantilever-based sensors for various applications.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.240
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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

Citations137
Published2010
Admission routes2
Has abstractyes

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