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Elements Doping Strategy for Improving the Thermoelectric Properties of g-C<sub>3</sub>N<sub>4</sub>/SWCNT Composite Films

Xiaoliang Ma, Yi Li, Can Jiang, Si Chen, Hui Liu, Yunfei Zhang, Qiao Zhang, Feipeng Du

2025ACS Applied Materials & Interfaces12 citationsDOI

Abstract

Optimizing carrier concentration and transport has been demonstrated to be a practical strategy to improve the thermoelectric efficacy of single-walled carbon nanotube (SWCNT)-based composite films, which have potential application in self-powered wearable electronics. In this study, nonmetallic heteroatoms (boron, sulfur, and phosphorus) are selected to dope g-C 3 N 4 to adjust the energy band structure for fabricating g-C 3 N 4 /SWCNT with high thermoelectric performance. Heteroatom doping of g-C 3 N 4 improves the energy band structure and mobility of g-C 3 N 4, which promotes the carrier transport between g-C 3 N 4 and SWCNT and optimizes the carrier mobility and concentration of the composite, substantially improving both the Seebeck coefficient ( S ) and the electrical conductivity (σ) of g-C 3 N 4 /SWCNT. The results show that boron doped g-C 3 N 4 /SWCNT exhibits the maximum room temperature power factor (PF) of 198.4 μW m – 1 K – 2 alongside a Seebeck coefficient of 31.4 μV K – 1 among the prepared nonmetallic heteroatom doped g-C 3 N 4 /SWCNT composite films. Furthermore, under a temperature difference of 60 K, the flexible thermoelectric device made of the composite film produces a high output power of 5.7 μW and a large open-circuit voltage of 50.3 mV. Thus, this study presents an innovative method for improving the efficacy of composite thermoelectric materials utilizing SWCNT and inorganic materials, demonstrating potential applications in flexible electronics.

Topics & Concepts

Materials scienceThermoelectric effectDopingComposite numberNanotechnologyCarbon nanotubeChemical engineeringComposite materialOptoelectronicsThermodynamicsEngineeringPhysicsAdvanced Thermoelectric Materials and DevicesAdvanced Photocatalysis TechniquesMachine Learning in Materials Science