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Thermally activated diffusion and lattice relaxation in (Si)GeSn materials

2020· article· en· W2986601554 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysical Review Materials · 2020
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsPolytechnique Montréal
FundersBundesministerium für Bildung und ForschungDeutsche Forschungsgemeinschaft
KeywordsMaterials scienceMetastabilityAnnealing (glass)Ternary operationGermaniumTinBand gapChemical physicsCondensed matter physicsOptoelectronicsSiliconMetallurgy

Abstract

fetched live from OpenAlex

Germanium-tin (GeSn) alloys have emerged as a promising material for future optoelectronics, energy harvesting, and nanoelectronics owing to their direct band gap and compatibility with existing Si-based electronics. Yet, their metastability poses significant challenges calling for in-depth investigations of their thermal behavior. With this perspective, this work addresses the interdiffusion processes throughout thermal annealing of pseudomorphic GeSn binary and SiGeSn ternary alloys. In both systems, the initially pseudomorphic layers are relaxed upon annealing exclusively via thermally induced diffusional mass transfer of Sn. Systematic postgrowth annealing experiments reveal enhanced Sn and Si diffusion regimes that manifest at temperatures below 650 \ifmmode^\circ\else\textdegree\fi{}C. The amplified low-temperature diffusion and the observation of only subtle differences between binary and ternary hint at the unique metastability of the Si-Ge-Sn material system as the most important driving force for phase separation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.507

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.256
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it