Surfactant Modified Nickel-Manganese-Gallium Powder and Silicone Composites
Bibliographic record
Abstract
Power supplies are often the limiting factor for operation of many portable electronic devices. Batteries contribute significantly to their weight and impose limitations on operational longevity. Harvesting vibratory energy from the environment for conversion to electrical energy has been proposed as a means to address these problems. Previously, DRDC Atlantic has shown that single crystals of nickel-manganese-gallium (NiMnGa) can produce large reversible stressinduced and magnetic field-induced strains of up to 10%. It has been proposed that NiMnGa magnetic shape memory alloys may be an ideal alloy to harvest mechanical energy. The drawback to monolithic NiMnGa crystals is that they are susceptible to intergranular fracture. To address this issue, a composite of the alloy in a polymer matrix has been examined in the hopes of improving toughness and formability. Good bonding between the polymer and the powder is needed to facilitate optimum transfer of force between the two components. The objective of this study was to understand and optimize the polymer-alloy interactions. The polymer matrix chosen was Dow Corning Sylgard 186. It was determined that the Sylgard 186 prepolymer base interacts with the oxidized surface of the NiMnGa particles. Silane coupling agents were also investigated to examine their effect on the interfacial interactions. No change in properties were observed.
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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".