Intrinsic Modulation Doping Enhances the Thermoelectric Performance of Monolayer GaGeTe
Bibliographic record
Abstract
Modulation doping is a well-known approach for improving the efficiency of bulk thermoelectrics, yet its application to 2D materials has remained elusive. Our thorough first-principles calculations reveal a unique intrinsic modulation doping in monolayer GaGeTe that synergistically raises its electrical transport coefficients while dwarfing its lattice heat transport, resulting in a high thermoelectric figure of merit, zT . Herein, we envision 2D GaGeTe as a chair-like germanene monolayer shrouded by two GaTe layers. The germanene layer donates electrons to the outer GaTe, creating a spatial separation between the electron-donation center and charge transport channels, a feature that suppresses free carrier scattering. Our accurate electron–phonon calculations explain that electrical transport in GaGeTe results from the metallic nature of germanene combined with the Mexican-hat shape of the GaTe valence band. The superior electrical and poor heat transport coefficients turn GaGeTe into a promising p-type thermoelectric monolayer working at moderate carrier concentrations of ≈10 13 cm –2 . The presented results put forward a distinct design approach for identifying competent thermoelectrics among 2D materials whose frail structures cannot tolerate heavy doping.
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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".