Combinatorial Approach Based on Interdiffusion Experiments for the Design of Thermoelectrics: Application to the Mg<sub>2</sub>(Si,Sn) Alloys

Vivès, Solange; Bellanger, Philippe; Gorsse, Stéphane; Wei, Changdong; Zhang, Qiaofu; Zhao, Ji‐Cheng

doi:10.1021/cm502413t

Cited by 28 publications

(20 citation statements)

References 34 publications

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“…9) as a function of the composition for the solid solution Mg 2 (Si,Sn). The value of this adjustable parameter is in agreement with a previous study where a combinatorial approach based on diffusion couple was used to measure the thermal conductivity as a function of the composition of the solid solution Mg 2 (Si,Sn) [39].…”

Section: Comparison With the Experimental Results And Discussionsupporting

confidence: 88%

Effect of microstructure on the thermal conductivity of nanostructured Mg2(Si,Sn) thermoelectric alloys: An experimental and modeling approach

et al. 2015

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Section: Comparison With the Experimental Results And Discussionsupporting

confidence: 88%

Effect of microstructure on the thermal conductivity of nanostructured Mg2(Si,Sn) thermoelectric alloys: An experimental and modeling approach

et al. 2015

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“…Experimental diffusion data are used to estimate the atomic mobility. The recent combinatorial diffusion couple study by Vivés et al [70], and the indirect calculation of inter-diffusion coefficient using the forward-simulation method provides an insight about the diffusion at 600 • C. Their calculations suggest an estimated diffusion coefficient of ≈ 10 −15 ms −2 . There is no other information regarding diffusivity measurements to the best of our knowledge.…”

Section: Effect Of Parameter Uncertainties On Microstructurementioning

confidence: 99%

Uncertainty propagation in a multiscale CALPHAD-reinforced elastochemical phase-field model

et al. 2020

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ICME approaches provide decision support for materials design by establishing quantitative process-structure-property relations. Confidence in the decision support, however, must be achieved by establishing uncertainty bounds in ICME model chains. The quantification and propagation of uncertainty in computational materials science, however, remains a rather unexplored aspect of computational materials science approaches. Moreover, traditional uncertainty propagation frameworks tend to be limited in cases with computationally expensive simulations. A rather common and important model chain is that of CALPHAD-based thermodynamic models of phase stability coupled to phase field models for microstructure evolution. Propagation of uncertainty in these cases is challenging not only due to the sheer computational cost of the simulations but also because of the high dimensionality of the input space. In this work, we present a framework for the quantification and propagation of uncertainty in a CALPHADbased elasto-chemical phase field model. We motivate our work by investigating the microstructure evolution in Mg 2 (Si x Sn 1−x ) thermoelectric materials. We first carry out a Markov Chain Monte Carlo-based inference of the CALPHAD model parameters for this pseudobinary system and then use advanced sampling schemes to propagate uncertainties across a high-dimensional simulation input space. Through high-throughput phase field simulations we generate 200,000 time series of synthetic microstructures and use machine learning approaches to understand the effects of propagated uncertainties on the microstructure landscape of the system under study. The microstructure dataset has been curated in the Open Phase-field Microstructure Database (OPMD), available at http://microstructures.net.

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“…This allows for effi cient characterization of a desired compositional area, yielding insights in the structure-property relationship of a material system. [ 17 ] There are several examples of combinatorial methods being applied in the fi eld of thermoelectrics, ranging from bulk diffusion couples [ 18,19 ] over sol-gel prepared samples [ 20 ] to thin fi lms prepared by pulsed-laser deposition [ 21,22 ] and co-sputtering. [23][24][25][26][27] Compared to, e.g., the diffusion couple approach, combinatorial sputtering offers much shorter sample fabrication times.…”

Section: Introductionmentioning

confidence: 99%

Unraveling Self‐Doping Effects in Thermoelectric TiNiSn Half‐Heusler Compounds by Combined Theory and High‐Throughput Experiments

Wambach

Stern

Bhattacharya

et al. 2015

Adv Elect Materials

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The control of the carrier concentration is a key topic in the optimization of the thermoelectric power factor. It depends intricately on the defect chemistry of a host phase (here: TiNiSn) and the boundary conditions set by competing phases. The large impact of a slight off‐stoichiometry in the intermetallic half‐Heusler phase TiNiSn makes combinatorial techniques ideally suited for systematic optimization of its thermoelectric performance. In this work, computational thermochemistry, combinatorial synthesis, and high‐throughput characterization are combined to obtain a complete map of the thermoelectric power factor for the Ti–Ni–Sn system. The role of the chemical potential of the constituents in determining the detailed nonstoichiometric composition of the intermetallic half‐Heusler phase TiNiSn is elucidated. This work not only confirms the assumption of a large phase‐width in terms of Ni surplus but also demonstrates that TiNiSn phases with a relatively large Ti surplus can be produced. This can serve as a new route for achieving high carrier concentrations by self‐doping in the ternary system Ti–Ni–Sn. The defect thermochemistry calculations for the carrier concentration are in excellent agreement with the experimental results. The findings of this work suggest new ways of improving the thermoelectric performance of half‐Heusler phases such as TiNiSn.

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Combinatorial Approach Based on Interdiffusion Experiments for the Design of Thermoelectrics: Application to the Mg₂(Si,Sn) Alloys

Abstract: Thermoelectric materials are ranked by their ability to meet a criterion of excellence that is related to their conversion efficiency. This dimensionless figure of merit, zT = (σS 2 T)/κ, measures how well a

Cited by 28 publications

References 34 publications

Effect of microstructure on the thermal conductivity of nanostructured Mg2(Si,Sn) thermoelectric alloys: An experimental and modeling approach

Effect of microstructure on the thermal conductivity of nanostructured Mg2(Si,Sn) thermoelectric alloys: An experimental and modeling approach

Uncertainty propagation in a multiscale CALPHAD-reinforced elastochemical phase-field model

Unraveling Self‐Doping Effects in Thermoelectric TiNiSn Half‐Heusler Compounds by Combined Theory and High‐Throughput Experiments

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Combinatorial Approach Based on Interdiffusion Experiments for the Design of Thermoelectrics: Application to the Mg2(Si,Sn) Alloys

Abstract: Thermoelectric materials are ranked by their ability to meet a criterion of excellence that is related to their conversion efficiency. This dimensionless figure of merit, zT = (σS 2 T)/κ, measures how well a

Cited by 28 publications

References 34 publications

Effect of microstructure on the thermal conductivity of nanostructured Mg2(Si,Sn) thermoelectric alloys: An experimental and modeling approach

Effect of microstructure on the thermal conductivity of nanostructured Mg2(Si,Sn) thermoelectric alloys: An experimental and modeling approach

Uncertainty propagation in a multiscale CALPHAD-reinforced elastochemical phase-field model

Unraveling Self‐Doping Effects in Thermoelectric TiNiSn Half‐Heusler Compounds by Combined Theory and High‐Throughput Experiments

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Combinatorial Approach Based on Interdiffusion Experiments for the Design of Thermoelectrics: Application to the Mg₂(Si,Sn) Alloys