Liquid‐Metal Fabrication of Ultrathin Gallium Oxynitride Layers with Tunable Stoichiometry
Bibliographic record
Abstract
The synthesis of nanometer‐thick (≈3 nm) gallium oxynitride (GaO x N y ) layers with a variable stoichiometry is reported. The approach primarily exploits the liquid metal chemistry (LMC) technique and promises easier integration of 2D materials onto photonic devices compared to traditional top‐down and bottom‐up methods. The fabrication follows a two‐step process, involving first liquid metal‐based printing of a nanometer‐thick layer of gallium oxide (Ga 2 O 3 ), followed a plasma‐enhanced nitridation reaction. Control over nitridation parameters (plasma power, exposure time) allows adjustment of the GaO x N y layer's composition, granting access to compounds with distinct optical properties (e.g., a 20% index variation), as demonstrated by ellipsometry and density functional theory (DFT) simulations. DFT provides a microscopic understanding of the effect of the bond polarization and crystallinity on the optical properties of GaO x N y compounds. These findings expand the knowledge of ultrathin GaO x N y alloys, which are poorly studied with respect to their gallium nitride (GaN) and Ga 2 O 3 counterparts. They also represent an essential step toward integrating such 2D materials into photonic chips and offer new opportunities to improve the performance of hybrid optoelectronic 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".