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Record W2024911389 · doi:10.1142/s0219581x05003176

ELECTRODEPOSITION OF NANOCOMPOSITE ORGANIC–INORGANIC COATINGS FOR BIOMEDICAL APPLICATIONS

2005· article· en· W2024911389 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Nanoscience · 2005
Typearticle
Languageen
FieldEngineering
TopicElectrophoretic Deposition in Materials Science
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceNanocompositeElectrophoretic depositionFabricationDeposition (geology)ChitosanCoatingChemical engineeringNanoparticleElectrochemistryPrecipitationNanotechnologyCorrosionElectroplatingComposite materialElectrodeLayer (electronics)

Abstract

fetched live from OpenAlex

New method has been developed for the fabrication of nanocomposite hydroxyapatite (HA)-chitosan coatings. The method is based on the electrophoretic deposition (EPD) of HA nanoparticles prepared by a chemical precipitation technique, and electrochemical deposition of chitosan macromolecules. The deposit composition can be varied by the variation of HA concentration in chitosan solutions. X-ray studies revealed preferred orientation of HA nanoparticles in the nanocomposites with c-axis parallel to the coating surface. Nanocomposite coatings were obtained on Ti and Pt foils, Ti wires and gauzes. Deposition yield can be controlled by the variation of the deposition time. Coatings of various thicknesses in the range of up to 50 μm were obtained. The method enables the formation of dense, adherent and uniform deposits on substrates of complex shape. The obtained coatings provide corrosion protection of Ti and can be utilized for the fabrication of advanced biomedical implants.

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.

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 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 score0.538
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

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 science0.0010.000
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 head0.004
GPT teacher head0.235
Teacher spread0.231 · 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