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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".