Characterizing nanoscale coherent double-solid-solution interfaces between non-reactive Mg and steel alloys
Bibliographic record
Abstract
Achieving coherent interface matching between immiscible Mg and Fe alloys is a significant challenge due to significant differences in their lattice constants and structures. Although the introduction of a third element into the interfacial metallurgical reaction has been explored before, it has been difficult to avoid the formation of brittle intermetallic compounds with poor mechanical properties. This study presents a groundbreaking method that, for the first time in published literature, leverages in-situ Ni alloying with a flexible laser-arc hybrid heat source to create an exceptionally high-performing nanoscale double solid solution interface between immiscible Mg and Fe alloys. This processing approach enables the high metallurgical reaction temperatures required for immiscible and nonreactive systems. The resulting lattice formation, driven by localized elemental diffusion at elevated interfacial temperatures, fosters adaptive coherent matching across the entire Mg-Fe interface. This process successfully transforms the non-coherent lattice that is generally observed at the Mg/Fe interface into a coherent double solid solution interface with the bulk matrix on both sides, significantly enhancing bonding efficiency and performance. This study provides detailed advanced characterization of the nanoscale double solid solution structures observed at the interfaces of these immiscible dissimilar metals which has been previously unexplored in the literature.
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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".