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Record W2060331527 · doi:10.1039/c4tb01175a

Triethylphosphite as a network forming agent enhances in vitro biocompatibility and corrosion protection of hybrid organic–inorganic sol–gel coatings for Ti6Al4V alloys

2014· article· en· W2060331527 on OpenAlexfundno aff
Amir A. El hadad, Violeta Barranco, Antonia Jiménez‐Morales, Graham J. Hickman, J.C. Galván, Carole C. Perry

Bibliographic record

VenueJournal of Materials Chemistry B · 2014
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsnot available
FundersMinisterio de Economía y CompetitividadTrent UniversityNottingham Trent University
KeywordsBiocompatibilityCorrosionMaterials scienceSol-gelChemical engineeringCondensation reactionIn vitroCondensationMetallurgyNanotechnologyOrganic chemistryChemistryCatalysisBiochemistry

Abstract

fetched live from OpenAlex

The biocompatibility and life of metallic implants can be enhanced through improving the biocompatibility and corrosion protection characteristics of the coatings used with these materials. In this study, triethylphosphite (TEP) was used to introduce phosphorus into organic-inorganic hybrid silica based sol-gel coatings prepared using γ-methacryloxypropyltrimethoxysilane and tetramethylorthosilicate. Addition of TEP dramatically increased the rate of intermolecular condensation and resulted in materials showing greater cross-linking. Protein (fibrinogen) uptake, osteoblast in vitro biocompatibility and corrosion resistance was enhanced in coatings containing TEP. Although higher concentrations of phosphorus supported the greatest improvement in biocompatibility, a compromise in the phosphorus concentration used would be required if corrosion resistance was most desirable parameter for optimisation. Films prepared by dip coating on Ti6Al4V alloys from these sols offer a promising alternative to wholly metallic prostheses.

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 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)0.001
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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.007
GPT teacher head0.202
Teacher spread0.195 · 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 teacher head, 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

Citations17
Published2014
Admission routes1
Has abstractyes

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