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  1. Abstract

    An emerging chalcogenide perovskite, CaZrSe3, holds promise for energy conversion applications given its notable optical and electrical properties. However, knowledge of its thermal properties is extremely important, e.g. for potential thermoelectric applications, and has not been previously reported in detail. In this work, we examine and explain the lattice thermal transport mechanisms in CaZrSe3using density functional theory and Boltzmann transport calculations. We find the mean relaxation time to be extremely short corroborating an enhanced phonon–phonon scattering that annihilates phonon modes, and lowers thermal conductivity. In addition, strong anharmonicity in the perovskite crystal represented by the Grüneisen parameter predictions, and low phonon number density for the acoustic modes, results in the lattice thermal conductivity to be limited to 1.17 W m−1 K−1. The average phonon mean free path in the bulk CaZrSe3sample (N → ∞) is 138.1 nm and nanostructuring CaZrSe3sample to ~10 nm diminishes the thermal conductivity to 0.23 W m−1 K−1. We also find that p-type doping yields higher predictions of thermoelectric figure of merit than n-type doping, and values ofZT~0.95–1 are found for hole concentrations in the range 1016–1017 cm−3and temperature between 600 and 700 K.

     
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  2. Abstract

    The potential of an environmentally friendly and emerging chalcogenide perovskite CaZrSe3for thermoelectric applications is examined. The orthorhombic phase of CaZrSe3has an optimum band gap (1.35–1.40 eV) for single‐junction photovoltaic applications. The predictions reveal that CaZrSe3possesses an absorption coefficient of ≈4 × 105cm−1at photon energies of 2.5 eV with an early onset of optical absorption (≈0.2 eV) well below the optimum band gap. Seebeck coefficient,S, is inversely proportional to the carrier mobility as the calculated average effective mass for electrons is higher than for holes;p‐type doping enhances the electrical conductivity, σ. The electronic thermal conductivityκeremains low at all temperatures. The upper limit of the thermoelectric figure of merit (ZTe) attains ≈1.0 when doped at specific chemical potentials, while a high Seebeck coefficient contributes to the ZTe = 1.95 at 50 K forp‐type doping with 1018cm−3carrier concentration, demonstrating high thermoelectric efficiency.

     
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  3. null (Ed.)
    Orthorhombic BaZrS 3 is a potential optoelectronic material with prospective applications in photovoltaic and thermoelectric devices. While efforts exist on understanding the effects of elemental substitution and material stability, fundamental knowledge on the electronic transport properties are sparse. We employ first principles calculations to examine the electronic band structure and optical band gap and interrogate the effect of electron transport on electrical and thermal conductivities, and Seebeck coefficient, as a function of temperature and chemical potential. Our results reveal that BaZrS 3 has a band gap of 1.79 eV in proximity of the optimal 1.35 eV recommended for single junction photovoltaics. An absorption coefficient of 3 × 10 5 cm −1 at photon energies of 3 eV is coupled with an early onset to optical absorption at 0.5 eV, significantly below the optical band gap. The carrier effective mass being lower for electrons than holes, we find the Seebeck coefficient to be higher for holes than electrons. A notable (≈1.0 at 300 K) upper limit to the thermoelectric figure of merit, obtained due to high Seebeck coefficient (3000 μV K −1 ) and ultra-low electron thermal conductivity, builds promise for BaZrS 3 as a thermoelectric. 
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  4. null (Ed.)
    Abstract In this study, we propose a scalable batch sampling scheme for optimization of simulation models with spatially varying noise. The proposed scheme has two primary advantages: (i) reduced simulation cost by recommending batches of samples at carefully selected spatial locations and (ii) improved scalability by actively considering replicating at previously observed sampling locations. Replication improves the scalability of the proposed sampling scheme as the computational cost of adaptive sampling schemes grow cubicly with the number of unique sampling locations. Our main consideration for the allocation of computational resources is the minimization of the uncertainty in the optimal design. We analytically derive the relationship between the “exploration versus replication decision” and the posterior variance of the spatial random process used to approximate the simulation model’s mean response. Leveraging this reformulation in a novel objective-driven adaptive sampling scheme, we show that we can identify batches of samples that minimize the prediction uncertainty only in the regions of the design space expected to contain the global optimum. Finally, the proposed sampling scheme adopts a modified preposterior analysis that uses a zeroth-order interpolation of the spatially varying simulation noise to identify sampling batches. Through the optimization of three numerical test functions and one engineering problem, we demonstrate (i) the efficacy and of the proposed sampling scheme to deal with a wide array of stochastic functions, (ii) the superior performance of the proposed method on all test functions compared to existing methods, (iii) the empirical validity of using a zeroth-order approximation for the allocation of sampling batches, and (iv) its applicability to molecular dynamics simulations by optimizing the performance of an organic photovoltaic cell as a function of its processing settings. 
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