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Record W2999682115 · doi:10.1111/ijag.15006

A review of acellular immersion tests on bioactive glasses––influence of medium on ion release and apatite formation

2020· review· en· W2999682115 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 Applied Glass Science · 2020
Typereview
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsMcGill University
Fundersnot available
KeywordsApatiteSimulated body fluidMaterials scienceImmersion (mathematics)Bioactive glassBiomaterialChemical engineeringParticle sizeAqueous solutionPrecipitationIonMineralogyComposite materialNanotechnologyChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract When evaluating new bioactive glass compositions for their suitability as a biomaterial, one of the first steps is the study of their behavior in contact with aqueous solutions. Ion release, pH changes, and apatite precipitation are investigated during immersion experiments, and a wide variety of solutions is used, including simulated body fluid, Tris buffer solution, various cell culture medium formulations or deionized water. This paper reviews the different parameters used for immersion experiments on bioactive glasses. Results show that, depending on solution composition, pH, and buffering capacity, the experimental outcome is likely to vary. In addition, bioactive glass particle size and solution volume/glass surface area ratio affect the resulting ion concentration in solution, and, thus, the rate at which apatite is formed. It is, therefore, important to consider these effects when planning experiments, interpreting results or comparing the results of experiments performed in different laboratories.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.741
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.015
GPT teacher head0.279
Teacher spread0.264 · 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