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A Novel Fabrication Method for Nitinol Shape Memory Alloys

2010· article· en· W2005607628 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueKey engineering materials · 2010
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMaterials scienceNickel titaniumShape-memory alloyScanning electron microscopePolishingDiffusion bondingComposite materialDifferential scanning calorimetryFabricationOptical microscopeTitaniumFOIL methodDiffusion weldingMetallurgyWelding

Abstract

fetched live from OpenAlex

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.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.043
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0020.000

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 head0.018
GPT teacher head0.262
Teacher spread0.244 · 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