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Record W4413100924 · doi:10.1002/adma.202505101

Overcoming Material Incompatibility via 2D Free‐Surface Engineering

2025· article· en· W4413100924 on OpenAlexaff
Youcef A. Bioud, Meriem Bouchilaoun, Waldemar Schreiber, Redouane Amrar, G. Patriarche, Tao Ma, Jens Ohlmann, A. Soltani, David Lackner, S. Janz

Bibliographic record

VenueAdvanced Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicNanowire Synthesis and Applications
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
Fundersnot available
KeywordsMaterials scienceMicroelectronicsHeterojunctionDislocationWaferEpitaxyOptoelectronicsPhotoluminescenceNanotechnologyFree surfaceComposite material

Abstract

fetched live from OpenAlex

Heteroepitaxy has been pivotal in advancing both optoelectronics and microelectronics, driving the development of faster, more efficient devices across diverse applications. However, achieving high material quality remains challenging due to lattice mismatches. Strain induced by variations in lattice parameters and thermal properties provides additional degrees of freedom for material tailoring but often leads to dislocation generation, wafer bowing, and cracking. These issues are addressed through a scalable post-epitaxial approach that strategically targets the misfit dislocation network, leading to the creation of a sub-nanometric 2D free surface (2DFS). This interface effectively decouples the epilayer from the substrate, significantly reducing strain-related defects. Scalable heterostructures exhibited pronounced defect annihilation, as demonstrated by electron microscopy, defect etching, and photoluminescence analysis-an effect attributed to the surrounding free surfaces. This method strikes an optimal balance between bulk-quality characteristics and high surface integrity, offering a new paradigm for achieving heteroepitaxial bulk-class materials.

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.

How this classification was reachedexpand

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 score0.014
Threshold uncertainty score0.850

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 head0.004
GPT teacher head0.215
Teacher spread0.211 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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