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Record W2475138629 · doi:10.3390/jfb7030018

Effect of Nanoparticle Incorporation and Surface Coating on Mechanical Properties of Bone Scaffolds: A Brief Review

2016· review· en· W2475138629 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

VenueJournal of Functional Biomaterials · 2016
Typereview
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
KeywordsMaterials scienceNanodiamondScaffoldNanoparticleCoatingShearing (physics)NanotechnologyComposite materialBiomedical engineeringDiamond

Abstract

fetched live from OpenAlex

Mechanical properties of a scaffold play an important role in its in vivo performance in bone tissue engineering, due to the fact that implanted scaffolds are typically subjected to stress including compression, tension, torsion, and shearing. Unfortunately, not all the materials used to fabricate scaffolds are strong enough to mimic native bones. Extensive research has been conducted in order to increase scaffold strength and mechanical performance by incorporating nanoparticles and/or coatings. An incredible improvement has been achieved; and some outstanding examples are the usage of nanodiamond, hydroxyapatite, bioactive glass particles, SiO₂, MgO, and silver nanoparticles. This review paper aims to present the results, to summarize significant findings, and to give perspective for future work, which could be beneficial to future bone tissue engineering.

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)0.002
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: Review · Consensus signal: Review
Teacher disagreement score0.073
Threshold uncertainty score0.839

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.029
GPT teacher head0.259
Teacher spread0.230 · 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