Fabrication of Ti/HDPE composites for medical implants by using titanium hydride
Bibliographic record
Abstract
An effective approach for minimizing the stress-shielding effect of orthopedic implants is to use implant materials with bone-matching modulus. Ti/polymer composites formed by infiltration of a polymer into porous Ti preforms are considered promising for such applications due to their tailorable elastic modulus, good corrosion resistance and potentially high mechanical performance. In this work, a fabrication process has been developed to produce porous Ti preforms from titanium hydride powders. The sintering kinetics studies showed that titanium obtained from titanium hydride had a lower sintering activation energy of 85± kJ/mol, as compared to 93±3 kJ/mol for normal titanium powders. The lower sintering activation energy resulting from the fresh and clean (non-oxide covered) particle surfaces after dehydration has led to faster sintering (lower sintering temperature) for titanium powders derived from titanium hydride. The fabrication process using titanium hydride offers a low production cost and low interstitial contaminants (oxygen, nitrogen, carbon, and hydrogen). High density polyethylene (HDPE) was infiltrated into the porous titanium preforms produced from titanium hydride to fabricate Ti/HDPE composites. The relation of flexural modulus and flexural strength of the composites with the HDPE volume fraction was established. It was found that Ti/HDPE composites with 30-38% volume of HDPE showed a similar flexural modulus to the of the human bone, while the bending strength is higher than the of human bone.
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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 | 0.000 |
| 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".