Evolution of hierarchical microstructures in Cu–Fe immiscible alloy driven by liquid-state mixing
Bibliographic record
Abstract
Copper–iron (Cu–Fe) immiscible alloys are known for their potential to form hierarchical microstructures with superior mechanical properties under rapid solidification conditions. However, the formation of these microstructures during Cu/Fe melting and mixing—typically occurring in processes such as arc- and laser-induced melting‒remains poorly understood, despite its relevance to the integration of structural materials across various industries. This study showed that hierarchical and homogeneous microstructures in Cu–Fe alloys can be tailored in situ with two distinct regimes governed by the degree of Fe dilution through non-equilibrium solidification. In the high-Fe content sample, phase separation during the liquid state, followed by Marangoni-driven motion, led to the formation of a hierarchical structure comprising DO 3 -ordered Fe-rich particles with embedded Cu-rich grains, along with uniformly distributed L1 2 nanoparticles. In contrast, the low-Fe sample exhibited more uniformly dispersed, smaller DO 3 -ordered Fe-rich particles with a lower number density, along with dispersed L1 2 nanoprecipitates. The formation of such microstructures, including Cu/DO 3 Fe-rich particles and L1 2 nanoprecipitates, was primarily governed by surface energy–driven mechanisms and solute trapping under rapid cooling. These microstructures enhanced the local hardness and elastic modulus, primarily due to the increased number density of Fe-rich particles, highlighting their dominant role over size or morphology in strengthening Cu–Fe alloys. This study provides new insights into the microstructural evolution of immiscible alloy systems. The findings offer a foundation for microstructural tailoring to enhance mechanical performance and expand the potential applications of Cu–Fe alloy systems in advanced engineering technologies.
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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".