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Record W2060411827 · doi:10.1002/jbm.a.32666

Dissolution behavior of calcium phosphate nanocrystals deposited on titanium alloy surfaces

2010· article· en· W2060411827 on OpenAlexaff
Padina Pezeshki, Stanley Lugowski, John E. Davies

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2010
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceCalciumDissolutionTitaniumIn vivoSimulated body fluidScanning electron microscopeNanocrystalPhosphateElutionChemical engineeringTitanium alloyImplantAlloyNuclear chemistryAnalytical Chemistry (journal)MetallurgyNanotechnologyComposite materialChromatographyChemistryBiochemistry

Abstract

fetched live from OpenAlex

We have recently shown that a new implant surface design, achieved by the deposition of discrete nanocrystals of calcium phosphate on microtopographically complex titanium-based substrates, accelerates osteoconduction and also renders the implant surface bone bonding. Thus, we wished to examine the elution behavior of these calcium phosphate nanocrystals and their modulation in vivo. We first compared the total amount of calcium phosphate on these implants with that of plasma-sprayed implants, by measuring the eluted calcium using atomic absorption spectrophotometry. We then plotted their dissolution behavior in vitro as a function of pH relevant to physiological conditions. To assess their structural stability in vivo for periods of up to 1 month, we placed samples in diffusion chambers, implanted them in the abdominal cavity of rats, and examined their surfaces by scanning electron microscopy following retrieval. Our results show that these nanocrystals are stable at normal pH but, as expected, dissolve at acidic pH, and that they remain unchanged when exposed to body fluid in vivo for up to 1 month.

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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.034
GPT teacher head0.323
Teacher spread0.288 · 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

Citations14
Published2010
Admission routes1
Has abstractyes

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Same venueJournal of Biomedical Materials Research Part ASame topicBone Tissue Engineering MaterialsFrench-language works237,207