Carrier mobility tensor in doped phosphorene due to scattering by charged impurities using the energy loss method
Bibliographic record
Abstract
We use the energy loss method (ELM) to evaluate the drift mobility tensor of doped phosphorene due to scattering of its carriers on an ensemble of charged impurities located in a substrate. The ELM makes it possible to circumvent the endeavor of numerically solving the Boltzmann transport equation for the same problem, whereby it yields an explicit expression for the mobility tensor components as a double integral, making it straightforward to survey various model layouts and parameters. This enabled us to perform a statistical analysis of the effects of spatial correlation among the impurities by means of a geometric structure factor for the hard-disk model of a planar distribution of pointlike impurities. We found that the correlation distance between impurities plays an important role at doping densities of phosphorene that are lower than the areal density of impurities. Moreover, the ELM naturally brings about the dielectric function of phosphorene, allowing us to explore the role of interband electron transitions in static screening of the impurities, in addition to the role played by the intraband transitions, which we treat in the random phase approximation. We found that the interband transitions augment the screening when impurities are close to phosphorene and when its doping density increases, thereby increasing the mobility tensor components while decreasing their asymmetry ratio.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".