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Record W3195787063 · doi:10.21203/rs.3.rs-123802/v1

The effect of Sr and Mg substitutions on the mechanical properties and solubility of the fluorapatite ceramics for biomedical applications

2020· preprint· en· W3195787063 on OpenAlexaff
Mohammad Hossein Ghaemi, S.Yu. Sayenko, V.A. Shkuropatenko, A Zykova, Kateryna Ulybkina, Olena Bereznyak, A. Krupa, Mirosław Sawczak

Bibliographic record

VenueResearch Square (Research Square) · 2020
Typepreprint
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsNSCAD University
Fundersnot available
KeywordsFluorapatiteSolubilityCeramicMaterials scienceMineralogyChemical engineeringChemistryApatiteMetallurgyPhysical chemistryEngineering

Abstract

fetched live from OpenAlex

Abstract The ionic substitutions play important role in the modifications of the biological apatites. Recently, the attention has been focused on the co-doping effects of additives on the functional properties of apatite based biomaterials. Under a research work for which the results are presented here, the dense samples of fluorapatites: Ca 10 (PO 4 ) 6 F 2 and Ca 8 MgSr(PO 4 ) 6 F 2 were produced after sintering at a temperature of 1250 °C for 6 hours in air. The XRD, IR and Raman spectrometry results show a high crystallinity of the fluorapatite and strontium-magnesium-doped fluorapatites. The results demonstrate the stability of structural and mechanical properties of fluorapatites after immersion tests in saline and buffer solutions. The durability of mechanical properties and biocompatibility of Ca 10 (PO 4 ) 6 F 2 and Ca 8 MgSr(PO 4 ) 6 F 2 fluorapatites make these materials highly attractive for biomedical application.

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 score0.004

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 head0.095
GPT teacher head0.355
Teacher spread0.260 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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