AgBiSe2 is a promising thermoelectric (TE) candidate because of its intrinsically low thermal conductivity (κ = 0.4–0.5 W K−1 m−1 at ∼770 K) and optimal n-type carrier concentration (5.85 × 1018 cm−3 at 300 K). However, its TE figure of merit (ZT) is still low (0.3 at ∼770 K). Therefore, it is necessary to further improve its ZT. In this work, the solid solutions (AgBiSe2)1−x(Ag2Te)x (x = 0–0.125) have been designed through simple alloying Ag2Te inspired by the entropy engineering concept, and the TE performance has been further regulated. The analyses show that the exothermic effects related to α/β and β/γ phase transitions weaken, and the transition temperature of β/γ decreases as the Ag2Te content increases, which indicates the stabilization of the cubic γ-phase at high temperatures. Aside from that, the power factor (PF) enhances from 2.91 μW/cm K2 (x = 0) to 3.49 μW/cm K2 (x = 0.075), and at the same time, the lattice thermal conductivity reduces from 0.3 W K−1 m−1 to 0.1 W K−1 m−1 at ∼760 K. This directly improves the TE performance with the highest ZT value of 1.0, which is almost double that of the pristine AgBiSe2. The result suggests that the entropy engineering is a very effective screening method in thermoelectrics.
In this work, we have improved the thermoelectric performance of n-type AgBiSe 2 by engineering its chemical compositions. This engineering is realized by doping Ag 2 Se at first and then In and S in succession. Finally, the lattice thermal conductivity (κ L ) reduces from 0.39 W K −1 m −1 to 0.15 W K −1 m −1 , and the power factor (PF) enhances from 2.80 μW/(cm K 2 ) to 4.84 μW/(cm K 2 ) at ∼770 K. The reduction in κ L is ascribed to the mass/size mismatch and/or structure anharmonicity, while the enhancement in PF is caused by the improved electrical conductivity via the creation of In and S impurity levels within the gap. As a result, the highest ZT value reaches 0.94 (using measured C p ). This value stands among the highest in the n-type AgBiSe 2 system, which proves that the composition engineering is a practical route to improve the TE performance in this system.
Thermoelectric (TE) materials are among very few sustainable yet feasible energy solutions of present time. This huge promise of energy harvesting is contingent on identifying/designing materials having higher efficiency than presently available ones. However, due to the vastness of the chemical space of materials, only its small fraction was scanned experimentally and/or computationally so far. Employing a compressed-sensing based symbolic regression in an active-learning framework, we have not only identified a trend in materials’ compositions for superior TE performance, but have also predicted and experimentally synthesized several extremely high performing novel TE materials. Among these, we found polycrystalline p-type Cu0.45Ag0.55GaTe2 to possess an experimental figure of merit as high as ~2.8 at 827 K. This is a breakthrough in the field, because all previously known thermoelectric materials with a comparable figure of merit are either unstable or much more difficult to synthesize, rendering them unusable in large-scale applications. The presented methodology demonstrates the importance and tremendous potential of physically informed descriptors in material science, in particular for relatively small data sets typically available from experiments at well-controlled conditions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.