Tailoring nanostructured NbCoSn-based thermoelectric materials via crystallization of an amorphous precursor

Jung, Chanwon; Dutta, Biswanath; Dey, Poulumi; Jeon, Seong Jae; Han, Seungwoo; Lee, Hyun Mo; Park, Jin‐Seong; Yi, So Young; Choi, Pilsoon

doi:10.1016/j.nanoen.2020.105518

Cited by 26 publications

(25 citation statements)

References 63 publications

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“…In contrast to the majority spin channel, the minority spin channel shows increased DOS at the bottom of the conduction band and the top of the valence band. The changes of the DOS due to excessive Co obtained in the current work are consistent with those reported in ref . Besides, it is noteworthy that the Co antisite defects are not energetically favorable, of which the formation energy is about 2 eV higher than that of the Co interstitials …”

Section: Resultssupporting

confidence: 92%

“…The changes of the DOS due to excessive Co obtained in the current work are consistent with those reported in ref . Besides, it is noteworthy that the Co antisite defects are not energetically favorable, of which the formation energy is about 2 eV higher than that of the Co interstitials …”

Section: Resultssupporting

confidence: 92%

“…It should be mentioned that according to the EDX results shown in Table 2 the formation energy is about 2 eV higher than that of the Co interstitials. 53 The temperature dependence of the Seebeck coefficient S for Ni-x samples is shown in Figure 8a. The bipolar conduction is observed at 800 K in the pristine NbCoSn sample.…”

Section: Electrical Transport Propertiesmentioning

confidence: 99%

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Effects of Doping Ni on the Microstructures and Thermoelectric Properties of Co-Excessive NbCoSn Half-Heusler Compounds

Yan

Xie

et al. 2021

ACS Appl. Mater. Interfaces

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The half-Heusler (HH) compound NbCoSn with 18 valence electrons is a promising thermoelectric (TE) material due to its appropriate electrical properties as well as its suitable thermal and chemical stability. Nowadays, doping/substitution and tailoring of microstructures are common experimental approaches to enhance the TE performance of HH compounds. However, detailed theoretical insights into the effects of doping on the microstructures and TE properties are still missing. In this work, the microstructure of NbCoSn was tailored through precipitating the full-Heusler phases in the matrix by changing the nominal ratio of Co and Ni on the Co sites, focusing on the resulting TE properties. Further, first-principles calculations were employed to understand the relationship between the microstructure and the TE properties from the thermodynamic point of view. Detailed analysis of the electronic structure reveals that the presence of excess Co/Ni contributes to the increasing carrier concentration. Through an increase in the electrical conductivity and a reduction in the thermal conductivity, the TE performance is improved. Therefore, the present work offers a new pathway and insights to enhance the TE properties by modifying the microstructure of HH compounds via tailoring the chemical compositions.

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Section: Resultssupporting

confidence: 92%

Section: Resultssupporting

confidence: 92%

Section: Electrical Transport Propertiesmentioning

confidence: 99%

See 1 more Smart Citation

Effects of Doping Ni on the Microstructures and Thermoelectric Properties of Co-Excessive NbCoSn Half-Heusler Compounds

Yan

Xie

et al. 2021

ACS Appl. Mater. Interfaces

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“…Note that in the present study, we have chosen only one extra atom as defect at the interstitial site of the considered supercell. Choosing higher number of interstitial defects in theoretical simulations usually lead to the formation of cluster of defects, which may alter the predicted band-gap value and carrier concentration [64,104,120]. Hence, interstitial defects must be selected carefully in theoretical simulations in these and similar half-Heusler alloys in order to explain the experimental observation of band gap, carrier concentration, and type of electrical conductivity.…”

Section: B Electronic Band Structure and Density Of Statesmentioning

confidence: 99%

Decisive role of interstitial defects in half-Heusler semiconductors: An ab initio study

Dey

Dutta

2021

Phys. Rev. Materials

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Half-Heusler semiconductors satisfying 18-electron rule typically display promising characteristics for thermoelectric applications. A persistent inconsistency between the type of charge carriers in some of these alloys as obtained from experiment and theory however casts serious doubt on the computational prediction of new and efficient half-Heusler alloys. To gain insights into the origin of this disparity, we have investigated the effect of intrinsic point defects on the electronic structure of four frequently studied half-Heusler alloys of the form XY Z with Y being Ni or Co. Using state-of-the-art ab initio calculations, our study reveals that interstitial Ni and Co are energetically most stable point defects in these alloys. Remarkably, interstitial defect modifies the location of the Fermi level inside the band gap as well as the value of the band gap, thereby bringing in close agreement with the corresponding experimental result. This work thus highlights the decisive role played by interstitial defects in thermoelectric half-Heusler alloys, which may open a new avenue for deliberately utilizing these defects as a strategy for tailoring electronic structure and hence the corresponding thermoelectric properties.

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“…It is widely used in the fields of electronic communication and aerospace. Because of its small size, high reliability and non-polluting advantages, it has shown great application potentials in the fields of waste heat recovery and self-supplying [4][5][6][7].Therefore, TE materials have become a hot spot in the field of materials science and condensed matter physics.…”

Section: Introductionmentioning

confidence: 99%

Machine learning prediction of the optimal carrier concentration and band gap of quaternary thermoelectric materials via element feature descriptors

Wan

Wang

Liu

et al. 2021

Int J of Quantum Chemistry

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While the thermoelectric (TE) materials have attracted significant attention in recent years, the design and discovery of new TE materials with optimal carrier concentration and band gap remains a great challenge. Herein, we report the development of machine learning (ML) methods to predict TE materials with introducing physically meaningful simple descriptors. Specifically, we use the number of electrons, Pauling electronegativity and relative atomic mass as the basic physical variables and compute 242 descriptors in 64 categories to characterize the molecular information of a TE material. Multiple stepwise regression is employed to reduce the dimensionality in the developed ML models, and 5 and 4 important features for the band gap and carrier concentration is selected, respectively. The important features are used as input of a total number of 19 ML methods to select the optimal ML models for the prediction of band gap and carrier concentration, respectively. It is shown that the least square support vector machines method is the best model for the prediction of the band gap, while the back propagating artificial neutral network model exhibits the best performance in predicting the carrier concentration values. This work provides novel theoretical guidance for the rapid prediction properties of TE materials. The simple descriptors we defined can accurately predict the band gap and carrier concentration of quaternary TE materials.

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Tailoring nanostructured NbCoSn-based thermoelectric materials via crystallization of an amorphous precursor

Cited by 26 publications

References 63 publications

Effects of Doping Ni on the Microstructures and Thermoelectric Properties of Co-Excessive NbCoSn Half-Heusler Compounds

Effects of Doping Ni on the Microstructures and Thermoelectric Properties of Co-Excessive NbCoSn Half-Heusler Compounds

Decisive role of interstitial defects in half-Heusler semiconductors: An ab initio study

Machine learning prediction of the optimal carrier concentration and band gap of quaternary thermoelectric materials via element feature descriptors

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