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Record W2833100370 · doi:10.1080/02670844.2018.1491510

Optimisation of fluorapatite coating synthesis applied to a biodegradable substrate

2018· article· en· W2833100370 on OpenAlexaff
Malcolm Caligari Conti, Gianella Xerri, Florian Peyrouzet, Pierre Schembri-Wismayer, Emmanuel Sinagra, Diego Mantovani, Daniel Vella, Joseph Buhagiar

Bibliographic record

VenueSurface Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversité Laval
FundersEuropean Social Fund
KeywordsFluorapatiteMaterials scienceCoatingSubstrate (aquarium)Fourier transform infrared spectroscopyChemical engineeringApatiteMetallurgyNuclear chemistryMineralogyComposite materialChemistry

Abstract

fetched live from OpenAlex

Fluorapatite was synthesised using the sol–gel route at three different pH values and aged for a number of days. The coating that was determined to be of optimal morphology was then applied to a pure iron substrate and to an austenitic stainless steel, the latter serving as a control. Deposited fluorapatite coatings were characterised by means of X-ray diffraction and Fourier transform infrared spectroscopy. Crystalline fluorapatite was successfully produced at temperatures as low as 250°C. Temperatures of 250°C and higher caused carbonated fluorapatite, present at 150°C, to transform into fluorapatite. Neutralisation of the sol before coating was found to give rise to a less soluble product when immersed in Hank’s solution at 37°C and 5% CO2. Coatings on pure iron and stainless steel substrates were produced by neutralising the sol to pH 7 and heat treatment at 250°C. This generated a morphology which could potentially aid in cellular attachment.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.010
GPT teacher head0.194
Teacher spread0.184 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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