The past few years have witnessed a rapid evolution of hybrid organic-inorganic perovskite solar cells as an unprecedented photovoltaic technology with both relatively low cost and high power conversion. The fascinating physical and chemical properties of perovskites are benefited from their unique crystal structures represented by the general chemical formula AMX3, where the A cations occupy the hollows formed by the MX3 octahedra and thus balance the charge of the entire network. Despite a vast amount of theoretical and experimental investigations have been dedicated to the structural stability, electrical, and optical properties of hybrid halide perovskite materials in relation to their applications in solar cells, the thermal transport property, another critical parameter to the design and optimization of relevant solar cell modules, receives less attention. In this paper, we evaluate the lattice thermal conductivity of a representative methylammonium lead triiodide perovskite (CH3NH3PbI3) with direct non-equilibrium ab initio molecular dynamics simulation. Resorting to full first-principle calculations, we illustrate the details of the mysterious vibration of the methylammonium cluster (CH3NH + 3 ) and present an unambiguous picture of how the organic cluster interacting with the inorganic cage and how the collective motions of the organic cluster drags the thermal transport, which provide fundamental understanding of the ultra-low thermal conductivity of CH3NH3PbI3. We also reveal the strongly localized phonons associated with the internal motions of the CH3NH + 3 cluster, which contribute little to the total thermal conductivity. The importance of the CH3NH + 3 cluster to the structural instability is also discussed in terms of the unconventional dispersion curves by freezing the partial freedoms of the organic cluster. These results provide more quantitative description of organic-inorganic interaction and coupling dynamics from accurate firstprinciples calculations, which are expected to underpin the development of emerging photovoltaic devices.
Phosphorus (P) originating from lubricant oil additives or biofuels is an emerging chemical poison in catalytic systems for automotive exhaust after-treatment. Here, we demonstrate that P-poisoning led to severe deactivation of small-pore Pd-SSZ-13 zeolites (with CHA framework) as passive NO x adsorbers (PNA) and CO oxidation catalysts for cold-start exhaust purification applications. Deactivation mechanisms of P-poisoning were unraveled by comparatively examining the P-free and P-loaded Pd-SSZ-13 zeolites using transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), nuclear magnetic resonance (NMR), temperature-programmed reduction by hydrogen (H2-TPR), CO pulse adsorption, temperature-programmed desorption using NH3 as a probe molecule (NH3-TPD), ultraviolet/visible light (UV/vis) spectroscopy, and in situ diffuse relectance infrared Fourier transform spectroscopy (DRIFTS). The loss of isolated Pd sitesnamely, [Pd(OH)]+ and Pd2+ (located in the eight- and six-membered rings of CHA framework, respectively)was revealed to be largely responsible for the deactivation of Pd-SSZ-13 in passive NO x adsorption and catalytic CO oxidation. In situ DRIFTS studies using NO or CO as a probe molecule suggest that [Pd(OH)]+ was more susceptible to P-poisoning than Pd2+. Specifically, P-poisoning led to a migration of [Pd(OH)]+ from cationic exchange sites to the zeolite surface, forming inactive metaphosphate (i.e., [Pd(OH)]+PO3 –) and bulk PdO x species at high temperatures. In contrast, P-poisoning of Pd2+ sites proceeded via a sequential transformation to [Pd(OH)]+ first, and then to [Pd(OH)]+PO3 – and bulk PdO x . This study provides a comprehensive mechanistic understanding on the deactivation of Pd-SSZ-13 by P-poisoning, and may guide the design of high-performance, phosphorus-resistant Pd-zeolite catalysts for cold-start exhaust after-treatment.
Two-dimensional (2D) GaS, GaSe, and InSe were reported to be semiconductors and have been recently fabricated with potential applications in photoelectrics, where in-depth understanding from electronic structure is necessary. In addition, the thermal transport properties play a key role as to the thermal stability and the efficient heat dissipation for device operation, which are also necessary to be addressed. In this paper, we present a systematic first-principles study on the electronic, optical, and thermal transport properties for the representative group III–VI monolayer GaS, GaSe, and InSe. Our results indicate that monolayer GaS, GaSe, and InSe are semiconductors with an indirect bandgap. The predominant influence of interband transitions due to the large bandgap causes monolayer GaSe to possess the highest absorptivity along both “in-plane” and “out-of-plane” directions compared to the other two systems. Moreover, the lattice thermal conductivities (κL) of these materials are found to be inversely proportional to their average atomic mass, but the decrease in thermal conductivity from GaS to GaSe is negligible in comparison to that of GaSe to InSe with a nearly equivalent mass difference. It is found that the underlying mechanism lies in the larger phonon relaxation time of GaSe caused by weaker anharmonicity. Our study provides a comprehensive understanding of the inherent physical properties of monolayer GaS, GaSe, and InSe, which would benefit their future applications in photoelectrics.
The thermal properties of β-Ga2O3 can significantly affect the performance and reliability of high-power electronic devices. To date, due to the absence of a reliable interatomic potential, first-principles calculations based on density functional theory (DFT) have been routinely used to probe the thermal properties of β-Ga2O3. DFT calculations can only tackle small-scale systems due to the huge computational cost, while the thermal transport processes are usually associated with large time and length scales. In this work, we develop a machine learning based Gaussian approximation potential (GAP) for accurately describing the lattice dynamics of perfect crystalline β-Ga2O3 and accelerating atomic-scale simulations. The GAP model shows excellent convergence, which can faithfully reproduce the DFT potential energy surface at a training data size of 32 000 local atomic environments. The GAP model is then used to predict ground-state lattice parameters, coefficients of thermal expansion, heat capacity, phonon dispersions at 0 K, and anisotropic thermal conductivity of β-Ga2O3, which are all in excellent agreement with either the DFT results or experiments. The accurate predictions of phonon dispersions and thermal conductivities demonstrate that the GAP model can well describe the harmonic and anharmonic interactions of phonons. Additionally, the successful application of our GAP model to the phonon density of states of a 2500-atom β-Ga2O3 structure at elevated temperature indicates the strength of machine learning potentials to tackle large-scale atomic systems in long molecular simulations, which would be almost impossible to generate with DFT-based molecular simulations at present.
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