journal article·2024-11Open access

Rational triple optimizations boost near-room-temperature thermoelectric performance of BiSe

Hui Liu, Xiao-Lei Shi, Lin Pan, Yuanqing Mao, Meng Li, Lijun Wang, Wei-Di Liu, Qingfeng Liu, Yifeng Wang, Zhi-Gang Chen

Acta Materialia· Volume 280

Abstract

As a Te-free alternative to Bi 2 Te 3 , BiSe exhibits attractive near-room-temperature thermoelectric potential as an n-type material, yet its excessive electrical conductivity limits practical applications. To enhance the overall thermoelectric performance, we devise a rational triple optimization process to boost the figure of merit, ZT , of polycrystalline BiSe up to 0.84 at 475 K, a current record-high value. The shear exfoliation process introduces increased Bi vacancies and denser grain boundaries in the sintered polycrystalline material. This modification effectively reduces excessive electron carrier concentration and carrier scattering. Consequently, this reduction in excessive electrical conductivity, which previously leads to low Seebeck coefficients and high electronic thermal conductivity, is mitigated. As a result, there is a significant increase in the Seebeck coefficient and an optimization of the power factor. Subsequent equivalent substitution by Sb doping at Bi sites (Sb Bi ) widens the bandgap, while secondary Sn doping at Bi sites (Sn Bi ) achieves acceptor doping, further reducing electrical conductivity and enhancing the Seebeck coefficient to improve the power factor. Moreover, this triple optimization process systematically introduces various crystal and lattice defects, effectively scattering phonons of different wavelengths to lower the lattice thermal conductivity, ultimately resulting in a remarkable 235 % increase in ZT . This work offers new insights into effectively enhancing the performance of layered thermoelectric materials.
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Research context

OpenAlex topics: Advanced Thermoelectric Materials and Devices · Chalcogenide Semiconductor Thin Films · Heusler alloys: electronic and magnetic properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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