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Record W2013147704 · doi:10.1115/1.4002749

Mechanical Property Enhancement of Nanocomposite Microstructures Generated by Two Photon Polymerization

2010· article· en· W2013147704 on OpenAlexaff
M. Alubaidy, Krishnan Venkatakrishnan, Bo Tan, Abdulsalam Mahmood

Bibliographic record

VenueJournal of Nanotechnology in Engineering and Medicine · 2010
Typearticle
Languageen
FieldEngineering
TopicNonlinear Optical Materials Studies
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceNanoindentationNanocompositeNanofiberMicrostructureComposite materialFemtosecondElastic modulusVolume fractionPolymerizationPolymerLaserOptics

Abstract

fetched live from OpenAlex

The mechanical properties of nanofiber reinforced polymer microstructures were investigated in this work by means of nanoindentation. The femtosecond laser was used to generate nanofibers on selected area on the surface of the substrate and to generate microstructures. These microstructures were formed via two photon polymerization using femtosecond laser material processing by incorporation of silicon nanofibers into Ormocer matrix. Results show that the hardness and elastic modulus of the nanocomposites have improved by 25% and 75%, respectively, with the incorporation of nanofibers using the described method, which has the potential of direct fabrication of reinforced micro-/nanostructures. The volume fraction of the generated nanofibers in the nanocomposite was calculated by using nanoindentation analysis.

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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.004
GPT teacher head0.215
Teacher spread0.211 · 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

Citations4
Published2010
Admission routes1
Has abstractyes

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