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Structural Characterization and Mechanical Evaluation of Bioactive Glass 45S5 Foams Obtained by a Powder Technology Approach

2012· article· en· W2100302144 on OpenAlexfundaboutno aff
E.A. Aguilar‐Reyes, C.A. León‐Patiño, Benito Jacinto‐Diaz, Louis‐Philippe Lefebvre

Bibliographic record

VenueJournal of the American Ceramic Society · 2012
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsnot available
FundersNational Research Council Canada
KeywordsMaterials scienceSinteringPorosityBioactive glassComposite materialSuspension (topology)Foaming agentPolymerParticle sizeParticle (ecology)Characterization (materials science)Chemical engineeringNanotechnology

Abstract

fetched live from OpenAlex

Bioactive glass 45S5 foams were produced using a powder technology process developed by The National Research Council Canada–Industrial Materials Institute. NRC – IMI 's proprietary process, combining powder technology and polymer foam technique, allows the production of materials having different structures and properties. It can be used to produce components into various forms, such as fully porous bodies or coatings on solid structures. During foaming, the foaming agent is decomposed and expands the binder‐bioactive glass suspension. Then, the binder is burnt out by heating the sample at 500°C and finally the bioactive glass particle network is sintered to consolidate the material. Foams sintered at various temperatures were characterized from a microstructural and mechanical point of view. The foam structure and properties are affected by the sintering temperature when it is varied between 950°C and 1025°C. Foams exhibited open porosity (64%–79%) and pore size (335–530 μm) optimal for bone ingrowth. In all cases, the glass crystallized during sintering and the material was mostly composed of Na 6 Ca 3 Si 6 O 18 and Na 2 Ca 4 ( PO 4 ) 2 SiO 4 phases. The mechanical strength increased from 1.7 to 5.5 MPa while the density of the material increased from 0.56 to 0.97 g/cm 3 .

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.376

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.011
GPT teacher head0.236
Teacher spread0.225 · 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 teacher head, 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

Citations40
Published2012
Admission routes2
Has abstractyes

Explore more

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