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Record W1967160381 · doi:10.1002/jbm.a.31017

Combustion synthesis of porous biomaterials

2006· article· en· W1967160381 on OpenAlexaff
Reed Ayers, Douglas E. Burkes, Guglielmo Gottoli, Hu‐Chun Yi, Fouad Zhim, John J. Moore

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2006
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsMaterials sciencePorosityCombustionReactive materialGraphiteNanotechnologyRaw materialChemical engineeringProcess engineeringComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

This article discusses the unique material manufacturing process of self-propagating high temperature synthesis (SHS) as applied to the making of porous biomaterials. Porous materials have long been considered as the first step toward in-vivo bone tissue engineering and the creation of patient life-time implants. The authors have approached this challenge by utilizing combustion synthesis, to create novel materials such as NiTi + TiC as well as porous forms of materials that are commonly accepted for biomedical applications such as tricalcium phosphate and hydroxyapatite. In the SHS product, physico-chemical properties are controlled by, but not limited to, reactant stoichiometry; green density; particle size of the reactant mix; use or presence of a gasifying agent; heating rate of the reactants and gravity. By balancing these parameters, the energy of the reaction is controlled to create the desired product stoichiometry, porosity, and mechanical properties. SHS provides a means to rapidly manufacture materials, saving time and production costs as well as enabling the synthesis of custom devices through the use of individual molds. Mold materials can range from graphite to paper or paper machete. Combustion synthesis offers a method for the rapid manufacture of affordable, individual biomedical devices that will reduce patient recovery time.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.028
GPT teacher head0.296
Teacher spread0.268 · 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

Citations28
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Biomedical Materials Research Part ASame topicBone Tissue Engineering MaterialsFrench-language works237,207