Thermal transport in monolayer zinc-sulfide: effects of length, temperature and vacancy defects
Bibliographic record
Abstract
Abstract Of late, atomically thin two-dimensional zinc-sulfide (2D-ZnS) shows great potential for advanced nanodevices and as a substitute to graphene and transition metal di-chalcogenides owing to its exceptional optical and electronic properties. However, the functional performance of nanodevices significantly depends on the effective heat management of the system. In this paper, we explored the thermal transport properties of 2D-ZnS through molecular dynamics simulations. The impact of length, temperature, and vacancy defects on the thermal properties of 2D-ZnS are systematically investigated. We found that the thermal conductivity (TC) rises monotonically with increasing sheet length, and the bulk TC of ∼30.67 W mK −1 is explored for an infinite length ZnS. Beyond room temperature (300 K), the TC differs from the usual 1/ T rule and displays an abnormal, slowly declining behavior. The point vacancy (PV) shows the largest decrease in TC compared to the bi vacancy (BV) defects. We calculated phonon modes for various lengths, temperatures, and vacancies to elucidate the TC variation. Conversely, quantum corrections are used to avoid phonon modes’ icing effects on the TC at low temperatures. The obtained phonon density of states (PDOS) shows a softening and shrinking nature with increasing temperature, which is responsible for the anomaly in the TC at high temperatures. Owing to the increase of vacancy concentration, the PDOS peaks exhibit a decrease for both types of defects. Moreover, the variation of the specific heat capacity and entropy with BV and PV signify our findings of 2D-ZnS TC at diverse concentrations along with the different forms of vacancies. The results elucidated in this study will be a guide for efficient heat management of ZnS-based optoelectronic and nano-electronic devices.
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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 | 0.000 | 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 | 0.001 | 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".