.png){kind=icon}
.png){kind=icon}
Return
Isoelectronic doping-induced modulation of electro-thermal transport and ZT enhancement in monolayer WS2: a DFT and machine learning study
.png){kind=icon}
PRE
AI
A
G
R
DOI：10.1088/1402-4896/ae64bd.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Two-dimensional semiconducting tungsten disulfide (WS2) has recently gained significant focus from researchers as a thermoelectric material because of the pronounced negative correlation between thermal and electrical conductivities, unlike the conventional 3D materials. However, high thermal conductivity and limited carrier mobility still restrict its rapid employment in the energy conversion sector. In this context, we have systematically explored the potential of substitutional doping in monolayer WS2 by isoelectronic chalcogenide counterpart selenium (Se) using density functional theory (DFT) combined with the linearized Boltzmann transport equation. The results demonstrate a nearly one-and-a-half-fold improvement in hole mobility at 60% doping concentration along with a significant lowering of thermal conductivity, caused by softening of phonon modes. The thermal conductivity was found to reduce further,up to 90% at carrier concentration similar to 1012 cm-2, emphasizing substantial importance of phonon carrier interaction on heat transport. Although, Se doping slightly reduces the Seebeck coefficient, due to the higher carrier concentration arising from band gap narrowing (while pristine WS2 exhibits the highest seebeck coefficient), the overall suppression of thermal conductivity contributes to the enhanced thermoelectric figure of merit (ZT), reaching 2.4 at room temperature for 50% Se doping. Furthermore, a machine learning-based model, trained on DFT-generated data predicts a remarkably high ZT of 3.34 for 36.2% Se doping at 885 K and a carrier concentration of similar to 1013 cm-2, highlighting the crucial role of doping in optimizing the thermoelectric performance of WS2.
Keywords:
density functional theory (DFT)
machine learning
substitutional doping
thermoelectricity
transition metal dichalcogenides (TMDs)
Journal
IF:
2.6
Papers:
4.3K
Citations:
2.5W
Organization
Cited Papers