Temperature effects on the anisotropic mobility of doped phosphorene due to carrier scattering on charged impurities evaluated with energy loss method
Bibliographic record
Abstract
We present a comprehensive theoretical investigation into the finite-temperature mobility tensor of monolayer black phosphorus (phosphorene), leveraging the energy loss method (ELM) for charge-carrier scattering on charged impurities. Building upon our previous zero-temperature analysis, we extend the ELM framework to systematically examine temperature effects, impurity placement, dielectric encapsulation, and spatial correlation among charged impurities. Our approach is benchmarked against recent results from the Boltzmann transport equation framework, demonstrating quantitative and qualitative agreement and confirming the ELM as a computationally efficient yet accurate methodology. Our detailed analysis reveals distinct behaviors of mobility and anisotropy in phosphorene with respect to temperature, carrier density, impurity proximity, and dielectric environment. We find that placing impurities closer to the phosphorene channel significantly enhances mobility anisotropy, with increased temperature gradually reducing this effect due to enhanced screening. Notably, encapsulation using high-κ gate dielectrics substantially elevates overall mobility and reduces its temperature dependence, particularly at lower carrier densities, attributed to stronger impurity screening effects. Furthermore, spatial correlation among impurities strongly influences mobility at low temperatures, but its impact diminishes notably with rising temperature, converging toward the behavior observed for uncorrelated impurities. Our analytical approach, expressed in dimensionless variables, explicitly highlights how various physical parameters influence mobility in a general anisotropic two-dimensional (semi-)conductor. This study provides new insights that can guide optimized design and engineering of future phosphorene-based nanoelectronic 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 | 0.001 |
| 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 | 0.000 |
| 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".