Unraveling the Atomic Mechanism of the Crystalline Phase‐Dependent Structural Features and Special Spectral Design of α‐, β‐, and Ɛ‐Ga₂O₃
Bibliographic record
Abstract
Atomic-scale phase transformations profoundly influence the functional properties of Ga₂O₃ polymorphs. By combining irradiation experiments with microstructure characterization and theoretical approaches, phase-specific energy-dissipation pathways in α-, β-, and ε-Ga₂O₃ are uncovered and strategies for targeted property design are outlined. Competing antiphase boundaries (APBs) and twin domain boundaries (TDBs) promote irreversible α→ε interconversion through domain fragmentation. In β-Ga₂O₃, defect-induced stress gradients drive two distinct local transformations: surface Ga-aggregated β→δ that stabilizes transient states, and latent-track-confined β→κ phase transition with recoverable distortions via cation reordering. Under electronic excitation, β-Ga₂O₃ forms nanohillocks via robust GaO₆ octahedra (high density/strong Ga─O bonds), while α/ε-Ga₂O₃ generates nanopores from tetrahedral Ga looseness (low bonding energy), highlighting phase-dependent surface dynamics shaped by atomic packing and bonding anisotropy. Defect-regulated recombination suppresses visible photoluminescence in α/β-Ga₂O₃, whereas in ε-Ga₂O₃ bandgap narrowing of ΔE: 0.30 eV is observed, enhancing emission. Linking phase-dependent defect-carrier interactions and metastable-phase engineering in Ga₂O₃ enables property optimization for power-electronics and optoelectronics devices.
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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".