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Record W2040802559 · doi:10.1177/0021998308105432

Preparation and Physical Characterization of Gelatin—Starch/Hydroxyapatite Porous Composite Scaffold Fabricated Using Novel Microwave Energy under Vacuum Technique

2009· article· en· W2040802559 on OpenAlexafffund
S. Jaya, T.D. Durance, R. Wang

Bibliographic record

VenueJournal of Composite Materials · 2009
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceGelatinComposite numberScaffoldPorosityComposite materialMicrowaveStarchPolymerFourier transform infrared spectroscopyChemical engineeringBiomedical engineeringOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

This work was initiated to explore the possibility of preparing porous scaffold, using microwave energy under vacuum technique. The hypothesis was that microwave energy under vacuum may promote effective cross-linking of the biopolymers during drying as well as to lead desirable physical characteristics of composites for hard tissue-like bone regeneration. Three different percentages of hydroxyapatite (HA) was reinforced with gelatin—starch polymer network to prepare porous scaffolds. EDS result of the prepared scaffold composite showed that Ca/P ratio of the HA phase was the same for all the HA percentages, 1.7, which is slightly higher than the standard value of 1.67. FTIR results showed the existence of a carbonate group along with the peaks of phosphate groups and hydroxyls, the functional group of HA. The scaffold composite obtained by microwave energy under vacuum technique had good mechanical and structural properties, which showed a promising potential for bone-substitution applications.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.139
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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.

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

Citations17
Published2009
Admission routes2
Has abstractyes

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