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Record W1772709247 · doi:10.3233/bme-2008-0552

Tailor-made biopolymers porous scaffold fabrication for tissue engineering: Application of radiant energy in the form of microwave under vacuum

2008· article· en· W1772709247 on OpenAlexaff
S. Jaya, T.D. Durance

Bibliographic record

VenueBio-Medical Materials and Engineering · 2008
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFabricationRadiant energyPorosityMaterials scienceScaffoldNanotechnologyTissue engineeringMicrowaveMechanical engineeringBiomedical engineeringComposite materialEngineeringOpticsRadiationPhysics

Abstract

fetched live from OpenAlex

Many methods are available for developing three-dimensional porous scaffolds using various polymeric materials for tissue-engineering applications. Each has its own advantages and disadvantages. Some of the available methods and their limitations were discussed briefly. This paper focuses on the scope of novel technology called radiant energy application under vacuum for the fabrication of three-dimensional porous scaffolds for tissue engineering applications. Radiant energy application in the form of microwave under vacuum has been shown to develop and maintain the porous structure in fruits and vegetables after dehydration, which produced the microstructure similar to the freeze dried materials. Same principle of applying radiant energy under vacuum was used on the biopolymeric gels to create tailor-made, porous scaffolds for biomedical purposes. It has many advantages over the other existing methods of scaffold fabrication. This paper also reviews the scaffolds design recently fabricated by the authors using radiant energy under vacuum.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.007
GPT teacher head0.213
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2008
Admission routes1
Has abstractyes

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