Effects of biaxial tensile strain on the first-principles-driven thermal conductivity of buckled arsenene and phosphorene
Bibliographic record
Abstract
Strain engineering is an effective way to tune the thermal and electrical properties of novel two-dimensional (2D) materials. In this work, first-principles density functional theory (DFT) is used to systematically investigate the strain-dependent lattice thermal conductivity and phonon properties of buckled arsenene and phosphorene, which are the 2D materials with the highest thermal conductivities among monolayers in group-VA. We implemented an iterative self-consistent solution to the Peierls-Boltzmann transport equation (PBTE). Our results showed that the thermal conductivity in both monolayers exhibits an up-and-down behavior when biaxial tensile strain is applied in the range from 0% to 9%. The peak values in the thermal conductivities occur at 5% of strain in arsenene and 3% in phosphorene, with the maximum conductivities of strained arsenene and phosphorene being 1.4 and 1.2 times higher than those of unstrained samples, respectively. We provide a rigorous description of the underlying phonon physics responsible for these thermal responses to strain, addressing the interplay between phonon group velocities, heat capacities, and relaxation times. The acoustic-optical phonon band gaps in arsenene and phosphorene were found to reduce with strain, being the reduction more significant in phosphorene. Our results also showed that the use of the single mode relaxation time approximation (SMRTA) predict substantially lower thermal conductivities for arsenene and phosphorene than those predicted by the iterative solution of the PBTE.
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 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".