Structural Evolution of Millisecond Laser-Induced Metastable Crystalline GeTe
Bibliographic record
Abstract
Discovering new inorganic materials using solid-state synthesis in an accelerated fashion is difficult due their sluggish diffusion coefficients and long diffusion distances. Furthermore, high temperatures used in these reactions generally produce thermodynamically stable products, which provides limited control on the reaction and prevents access to functional metastable phases. Herein, we report the use of a millisecond laser annealing technique to regulate the crystallographic phases of germanium telluride films of varying thicknesses. After laser heating, we combine temperature-dependent synchrotron grazing incidence measurements and transmission electron microscopy to study the structural evolution of the post-laser-heated GeTe. On average, we observe that millisecond laser heating induced the transformation of amorphous GeTe samples up to a ∼40% to 60% mixture of cubic β-GeTe ( Fm 3̅ m ) and rhombohedral α-GeTe ( R 3 m ) for GeTe films (thicknesses between 100 nm and 2 μm) deposited on thermally conducting substrates (such as Si), as opposed to phase-pure α-GeTe, which is obtained on low thermal conductivity substrates (quartz). Further, a room-temperature thermoelectric power factor of 6.10 μV cm –1 K –2 was measured for a laser-heated film on quartz. These findings suggest that conformal interfaces on substrates with high thermal conductivity facilitate accelerated rates of heat extraction at the sample–substrate interface to achieve phase control. We believe our strategy opens new avenues for the development of materials that are stabilized far from their equilibrium conditions.
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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".