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Record W3114073932 · doi:10.1002/anbr.202000055

A Review of Phosphate and Borate Sol–Gel Glasses for Biomedical Applications

2020· review· en· W3114073932 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvanced NanoBiomed Research · 2020
Typereview
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsNanotechnologySol-gelBoronMaterials scienceChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The sol–gel processing method revolutionizes the biomedical materials field, allowing for the customized creation of nano‐ and porous materials to help treat the toughest challenges in human health. However, this process and the subsequently produced materials have mostly been based on silica, particularly in terms of biomedical glasses. Yet, within the last two decades, there has been increased interest in silica‐free glass chemistries, such as those based on borate or phosphate. Attributable to their distinct properties which allows for linear and complete degradation, these glass compositions have shown great promise for both hard and soft tissue engineering applications, albeit with only a limited number of studies on glasses created through the sol–gel process. Therefore, this review provides an overview of the advancement of nonsilica sol–gel glasses, by focusing on borate and phosphate chemistries, for biomedical applications. A comprehensive review of these materials, including the challenges in processing as well as the current uses and future potential, is discussed.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.934
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.002
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 head0.067
GPT teacher head0.408
Teacher spread0.341 · 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