A Novel Fabrication Method for Nitinol Shape Memory Alloys
Bibliographic record
Abstract
Nitinol (NiTi) shape memory alloys are widely used in a variety of biomedical applications, such as dental implants, cervical and lumbar vertebral replacements, joint replacements and stents. In this study, commercially pure Ti and Ni foils ~100 um thick were diffusion bonded in vacuum. The experimental conditions were optimized to achieve a near equiatomic composition to produce NiTi SMA thin foil of approx. 5-8 micron thick. The cross-sectional surfaces of joint were subjected to metallographic investigation using optical microscope after grinding, polishing and etching. Scanning electron microscope equipped with EDX system was utilized to characterize the bonded layer and compositional analysis. Differential scanning calorimetry (DSC) technique was employed to determine the shape memory effect. The samples were subjected to X-ray diffraction analysis in order to establish phase structures formed during the diffusion bonding stage. An ultra fast femto-second laser facility was utilized to ensure the production of complex shapes or patterns within micron scale.
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 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".