Effect of static liquid Galinstan on common metals and non-metals at temperatures up to 200 °C
Bibliographic record
Abstract
Liquid metal Galinstan (GaInSn) is corrosive in nature against other solid metals as its base component is gallium. This study experimentally investigated the compatibility of GaInSn with eight common metals at temperatures up to 200 °C for 2000 h, including aluminum, copper, brass, ferritic and austenitic stainless steels (E-brite, SS304L, SS316L), and nickel-chromium alloys (Inconel and Hastelloy). This assessment aims to assist in design and material selection of a liquid metal magnetohydrodynamics system that houses Galinstan for power generation by low temperature natural heat sources or industrial waste heat. Design and fabrication of this renewable power system required assurance of material compatibility with common construction and instrumentation materials. The most severe corrosion effects of GaInSn on the metal alloys were observed on aluminum, copper, and brass, which confirms the results of previously conducted studies. No obvious corrosion on stainless steel or nickel–chromium alloys were observed by this study, which reveals that stainless steel has a good resistance to attack by GaInSn up to 200 °C. Six non-metals were also evaluated, including acronitrile butadiene styrene (ABS), acrylic, nitrile rubber (Buna N), nylon, polyvinyl chloride (PVC), and Teflon, which were deemed to be compatible with GaInSn up to the temperatures tested.
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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 | 0.001 |
| 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) | 0.002 | 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".