Fabrication methods and applications of microstructured gallium based liquid metal alloys
Bibliographic record
Abstract
This review contains a comparative study of reported fabrication techniques of gallium based liquid metal alloys embedded in elastomers such as polydimethylsiloxane or other rubbers as well as the primary challenges associated with their use. The eutectic gallium–indium binary alloy (EGaIn) and gallium–indium–tin ternary alloy (galinstan) are the most common non-toxic liquid metals in use today. Due to their deformability, non-toxicity and superior electrical conductivity, these alloys have become very popular among researchers for flexible and reconfigurable electronics applications. All the available manufacturing techniques have been grouped into four major classes. Among them, casting by needle injection is the most widely used technique as it is capable of producing features as small as 150 nm width by high-pressure infiltration. One particular fabrication challenge with gallium based liquid metals is that an oxide skin is rapidly formed on the entire exposed surface. This oxide skin increases wettability on many surfaces, which is excellent for keeping patterned metal in position, but is a drawback in applications like reconfigurable circuits, where the position of liquid metal needs to be altered and controlled accurately. The major challenges involved in many applications of liquid metal alloys have also been discussed thoroughly in this article.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".