Gate-tunable and thickness-dependent electronic and thermoelectric transport in few-layer MoS2
Bibliographic record
Abstract
Over the past few years, there has been a growing interest in layered transition metal dichalcogenides such as molybdenum disulfide (MoS2). Most studies so far have focused on the electronic and optoelectronic properties of single-layer MoS2, whose band structure features a direct bandgap, in sharp contrast to the indirect bandgap of thicker MoS2. In this paper, we present a systematic study of the thickness-dependent electrical and thermoelectric properties of few-layer MoS2. We observe that the electrical conductivity (σ) increases as we reduce the thickness of MoS2 and peaks at about two layers, with six-times larger conductivity than our thickest sample (23-layer MoS2). Using a back-gate voltage, we modulate the Fermi energy (EF) of the sample where an increase in the Seebeck coefficient (S) is observed with decreasing gate voltage (EF) towards the subthreshold (OFF state) of the device, reaching as large as 500 μV/K in a four-layer MoS2. While previous reports have focused on a single-layer MoS2 and measured Seebeck coefficient in the OFF state, which has vanishing electrical conductivity and thermoelectric power factor (PF=S2σ), we show that MoS2-based devices in their ON state can have PF as large as >50μWcm K2 in the two-layer sample. The PF increases with decreasing thickness and then drops abruptly from double-layer to single-layer MoS2, a feature we suggest as due to a change in the energy dependence of the electron mean-free-path according to our theoretical calculation. Moreover, we show that care must be taken in thermoelectric measurements in the OFF state to avoid obtaining erroneously large Seebeck coefficients when the channel resistance is very high. Our study paves the way towards a more comprehensive examination of the thermoelectric performance of two-dimensional (2D) semiconductors.
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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 | 0.000 |
| 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".