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Record W2913482940

Improved Nanoreinforced Composite Material Bonds with Potential Sensing Capabilities

2011· article· en· W2913482940 on OpenAlexaff
David STARIKOV, Michael S. FISCHER, A. Bensaoula, Farouk Attia, Thomas A. GLENN, Mounir Boukadoum

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2011
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsComposite numberMaterials scienceComposite material
DOInot available

Abstract

fetched live from OpenAlex

Dramatic increase in the bond strength of composite/adhesive interfaces of nanoreinforced composite material joints and structures has been achieved using laser-assisted fabrication of Micro-Column Arrays (MCA) on the surface of the two materials prior to bonding. Several advantages of the MCA technology resulting in drastic improvement of virtually any bond include: interlocking of the adhesive material between micro columns, about 10-fold increase of the specific surface area, inherent elasticity of the micro columns, enhanced resistance to hygrothermal failures, substantially improved wettability, and control over the surface chemistry. In order to take advantage of the unique electrical properties of both composite materials and the Carbon Nanotube (CNT) reinforced adhesive, experiments were performed to evaluate the potential of using electrical response of the bonded material to applied stress.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.088
GPT teacher head0.387
Teacher spread0.300 · 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

Citations1
Published2011
Admission routes1
Has abstractyes

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