Unraveling Surface Scattering Mechanisms in Hydrogen-Terminated Diamond/MoO<sub>3</sub> Heterostructures: A Theoretical Investigation
Bibliographic record
Abstract
Surface transfer doping addresses diamond’s doping challenges by enabling a two-dimensional hole gas (2DHG) in hydrogen-terminated diamond (H-diamond) through electron transfer to high-electron-affinity acceptors, such as molybdenum trioxide (MoO 3 ). While prior studies optimized 2DHG density, the mechanisms limiting carrier mobility remain unresolved. Here, we present a theoretical framework to quantify the impact of acoustic phonon, surface roughness, and surface impurity scattering on hole mobility in H-diamond/MoO 3 heterostructure (∼10 13 cm –2 ). First-principles calculations demonstrate that surface roughness dominates mobility degradation, reducing it from 10 3 to 10 2 cm 2 /(V s) compared to phonon-only cases, while surface impurities further suppress mobility (MoO 3 and HCO 3 yield the highest and lowest mobilities of ∼220 and 166 cm 2 /(V s), respectively). Total calculated hole mobility (∼200 cm 2 /(V s)) aligns with experimental ranges, with roughness heights (Δ = 0.18–0.35 nm) and impurity densities ( n imp 2 D = 4.32 × 10 13 cm − 2 ) identified as critical factors. These findings establish that mitigating interfacial surface roughness and suppressing adsorbates are essential for optimizing diamond-based devices, providing actionable strategies to enhance high-power diamond electronic performance.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".