Combustion synthesis of porous biomaterials
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".