Enhancing thermal stability and mechanical properties in fully-amorphous ZrCu nanolaminates
Bibliographic record
Abstract
The synthesis of thermally stable metallic glasses (MGs) which are mechanically robust and tunable at elevated temperatures remains a key research challenge. While most strategies focus on complex MG compositions with enhanced resistance to devitrification, we demonstrate that a simple microstructural approach – based on the fabrication of fully amorphous nanolaminates (NLs) – can significantly extend thermal stability even for binary ZrCu system, while retaining strong resistance to deformation. Moreover, we investigated annealing-induced atomic structural modifications (relaxation or partial crystallization). Annealing treatments up to 330 °C ( T < T g , the glass transition temperature) maintain the amorphous structure, while inducing atomic structural relaxation and free volume annihilation, as revealed by HRTEM and the reduction in corrugations on fracture surfaces. Moreover, we report strong layer intermixing with chemical interdiffusion, delaying the onset of crystallization and resulting in superior thermal stability with a fully amorphous structure up to 420 °C, significantly above the crystallization temperature of the individual layers (respectively, 320 and 360 °C for Zr 24 Cu 76 /Zr 61 Cu 39 ). Annealing at T > T g (∼420 °C) results in a completely homogenous structure without NL features with partial crystallization, forming Cu-Zr-based intermetallic and Zr-oxide phases. We show an increase in elastic modulus and hardness for higher temperatures due to structural relaxation and nanocrystal formation, with a maximum hardness (7.6 ± 0.2 GPa) obtained at 420 °C for 60 mins annealing, ∼20% above the deposited condition. These results highlight the potential of microstructural tailoring of MGs to enhance thermal stability and tailor the mechanical properties, which can be beneficial for advanced material applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".