Physics

For the first time the quantum dots CsPbX_3 (X=Cl, Br, I) in the fluorophosphate glasses were prepared. The samples were precipitated by two methods:(i) through self-crystallization during cooling of the glass melt and (ii) heat treatment of the glass. Controlled formation of CsPbX_3 quantum dots was realized by adjustment of cooling rate and heat-treatment conditions. The X-ray diffraction data was confirmed CsPbCl_3(Br_3, I_3) quantum dots formation. It was shown that, CsPbX_3 (X=Cl, I) quantum dots are formed in a cubic modification, while CsPbBr_3 in orthorhombic one. The photoluminescence of quantum dots have high intensity with quantum yield 45-50% and narrow band emission. The combined introduction of two anions (Cl/Br and Br/I) led to the simultaneous formation of two types of quantum dots, and indicates the difficulty of the anion exchange.

In recent years, vanadium oxides have gained immense attention in the field of energy storage devices due to their low-cost, layered structure and multi-valency despite their limited electrical conductivity and lower structural stability. In this brief review, we have tried to focus on electrochemical properties of the stoichiometric vanadium oxides along with VO_x composites. The morphology engineering, doping with heteroatom and formation of composites with carbon-based materials and/or conducting polymers in enhancing the supercapacitive performances of the vanadium oxides are discussed in details. Finally, the potentiality and challenges of vanadium oxides nanocomposites for supercapacitor applications are discussed.

Applicability of associated plasticity for particulate materials such as soils does not yield satisfactory results when Coulomb's theory of shear strength of soils is assumed, and the yield function derived accordingly is used to define both the stress state and the direction of plastic flow. The limitation mainly stems from the fact that Coulomb's theory (and its derivatives) is a simplification that intentionally ignores deformation characteristics that manifest from the particulate nature of such materials. It is thus customary to apply a branch of plasticity called non-associated plasticity for soils and similar materials. In the non-associated plasticity framework, yield functions and plastic potential functions are different. The former defines the mobilization of the stress state while the later defines the direction of plastic flow. For soils, stress-dilatancy theories have become central in the formulation of non-associated flow rules. In this paper, cyclic stress-dilatancy relations are derived based on complementarity hypothesis of stress-dilatancy conjugates. Both loading and unloading are explicitly considered. Then, yield functions are derived based on the resulting stress-dilatancy relations. In so doing, the resulting yield functions are rendered with a quality to be used for the modelling of deformation behavior of soils subjected to monotonic and cyclic loading conditions. The newly derived yield functions are called Associated Cyclic Stress Dilatancy yield functions for which the abbreviation ACStD is used. The theoretical framework is established first for special cases of deformation modes-plane strain and axisymmetric. The framework is generalized for considering Lode angle dependency of the yield function and extending the Matusoka-Nakai criterion.

Liquid electricity generator and hydrovoltaic technology have received numerous attentions, which can be divided into horizontal movement generator and vertical movement generator. The horizontal movement generator is limited for powering the integrated and miniaturized energy chip as the current output direction is depending on the moving direction of the water droplet, which means a sustainable and continuous direct-current (DC) electricity output can be hardly achieved because of the film of limited length. On the other hand, the existing vertical movement generators include triboelectricity or humidity gradient-based liquid electricity generator, where the liquid or water resource must be sustainably supplied to ensure continuous current output. Herein, we have designed an integratable vertical generator by sandwiching water droplets with semiconductor and metal, such as graphene or aluminum. This generator, named as polarized liquid molecular generator (PLMG), directly converts the lateral kinetic energy of water droplet into vertical DC electricity with an output voltage of up to ~1.0 V from the dynamic water-semiconductor interface. The fundamental discovery of PLMG is related to the non-symmetric structure of liquid molecules, such as water and alcohols, which can be polarized under the guidance of built-in field caused by the Fermi level difference between metal and semiconductor, while the symmetric liquid molecules cannot produce any electricity on the opposite. Integratable PLMG with a large output power of ~90 nW and voltage of ~2.7 V has been demonstrated, meanwhile its small internal resistance of ~250 kilohm takes a huge advantage in resistance matching with the impedance of electron components. The PLMG shows potential application value in the Internet of Things (IoTs) after proper miniaturization and integration.

DandeLiion (available at this http URL) is a robust and extremely fast solver for the Doyle Fuller Newman (DFN) model, the standard electrochemical model for (dis)charge of a planar lithium-ion cell. DandeLiion conserves lithium, uses a second order spatial discretisation method (enabling accurate computations using relatively coarse discretisations) and is many times faster than its competitors. The code can be used `in the cloud' and does not require installation before use. The difference in compute time between DandeLiion and its commercial counterparts is roughly a factor of 100 for the moderately-sized test case of the discharge of a single cell. Its linear scaling property means that the disparity in performance is even more pronounced for bigger systems, making it particularly suitable for applications involving multiple coupled cells. The model is characterised by a number of phenomenological parameters and functions, which may either be provided by the user or chosen from DandeLiion's library. This library contains data for the most commonly used electrolyte (LiPF6) and a number of common active material chemistries including graphite, lithium iron phosphate (LFP), nickel cobalt aluminium (NCA), and a variant of nickel cobalt manganese (NMC).

The dynamics of power consumption constitutes an essential building block for planning and operating energy systems based on renewable energy supply. Whereas variations in the dynamics of renewable energy generation are reasonably well studied, a deeper understanding of short and long term variations in consumption dynamics is still missing. Here, we analyse highly resolved residential electricity consumption data of Austrian and German households and propose a generally applicable methodology for extracting both the average demand profiles and the demand fluctuations purely from time series data. The analysis reveals that demand fluctuations of individual households are skewed and consistently highly intermittent. We introduce a stochastic model to quantitatively capture such real-world fluctuations. The analysis indicates in how far the broadly used standard load profile (SLP) may be is insufficient to describe the key characteristics observed. These results offer a better understanding of demand dynamics, in particular its fluctuations, and provide general tools for disentangling mean demand and fluctuations for any given system. The insights on the demand dynamics may support planning and operating future-compliant (micro) grids in maintaining supply-demand balance.

In this paper, based on neural networks, we develop a data-driven model for extremely fast prediction of steady-state heat convection of a hot object with arbitrary complex geometry in a two-dimensional space. According to the governing equations, the steady-state heat convection is dominated by convection and thermal diffusion terms, thus the distribution of the physical fields would exhibit stronger correlations between adjacent points. Therefore, the proposed neural network model uses Convolutional Neural Network (CNN) layers as the encoder and Deconvolutional Neural Network (DCNN) layers as the decoder. Compared with fully connected (FC) network model, the CNN based model is good for capturing and reconstructing the spatial relationships of low-rank feature spaces, such as edge intersections, parallelism and symmetry. Furthermore, we applied the signed distance function (SDF) as the network input for representing the problem geometry, which contains more information compared to a binary image. For displaying the strong learning and generalization ability of the proposed network model, the training dataset only contains hot objects with simple geometries: triangles, quadrilaterals, pentagons, hexagons and dodecagons; while the validating cases use arbitrary and complex geometries. According to the study, the trained network model can accurately predict the velocity and temperature field of the problems with complex geometries which has never been seen by the network model during the model training; and the prediction speed is four orders faster than the CFD. The ability of the accurate and extremely faster prediction of the network model suggests the potentials of applying such kind of models to the applications of real-time control, optimization, and design in future.

The ultrafast polarization response to incident light and ensuing exciton/carrier generation are essential to outstanding optoelectronic properties of lead halide perovskites (LHPs). A large number of mechanistic studies in the LHP field to date have focused on contributions to polarizability from organic cations and the highly polarizable inorganic lattice. For a comprehensive understanding of the ultrafast polarization response, we must additionally account for the nearly instantaneous hyperpolarizability response to the propagating light field itself. While light propagation is pivotal to optoelectronics and photonics, little is known about this in LHPs in the vicinity of the bandgap where stimulated emission, polariton condensation, superfluorescence, and photon recycling may take place. Here we develop two-dimensional optical Kerr effect (2D-OKE) spectroscopy to energetically dissect broadband light propagation and dispersive nonlinear polarization responses in LHPs. In contrast to earlier interpretations, the below-bandgap OKE responses in both hybrid CH3NH3PbBr3 and all-inorganic CsPbBr3 perovskites are found to originate from strong hyperpolarizability and highly anisotropic dispersions. In both materials, the nonlinear mixing of anisotropically propagating light fields result in convoluted oscillatory polarization dynamics. Based on a four-wave mixing model, we quantitatively derive dispersion anisotropies, reproduce 2D-OKE frequency correlations, and establish polarization dressed light propagation in single crystal LHPs. Moreover, our findings highlight the importance of distinguishing the often-neglected anisotropic light propagation from underlying coherent quasi-particle responses in various forms of ultrafast spectroscopy.

Photoelectrochemical (PEC) water splitting offers an elegant approach for solar energy conversion into hydrogen fuel. Large-scale hydrogen production requires stable and efficient photoelectrodes and scalable PEC cells that are fitted for safe and cost-effective operation. One of the greatest challenges is the collection of hydrogen gas from millions of PEC cells distributed in the solar field. In this work, a separate-cell PEC system with decoupled hydrogen and oxygen cells was designed for centralized hydrogen production, using 100 cm2 hematite (a-Fe2O3) photoanodes and nickel hydroxide (Ni(OH)2) / oxyhydroxide (NiOOH) electrodes as redox mediators. The operating conditions of the system components and their configuration were optimized for daily cycles, and ten 8.3 h cycles were carried out under solar simulated illumination without additional bias at an average short-circuit current of 55.2 mA. These results demonstrate successful operation of a decoupled PEC water splitting system with separate hydrogen and oxygen cells.

Reconfigurable intelligent surfaces (RIS) have recently received significant attention as building blocks for smart radio environments and adaptable wireless channels. By altering the space- and time-varying electromagnetic (EM) properties, the RIS transforms the inherently stochastic nature of the wireless environment into a programmable propagation channel. Conventionally, designing RIS to yield the desired EM response requires trial-and-error by iteratively investigating a large possibility of various geometries and materials through thousands of full-wave EM simulations. In this context, deep learning (DL) techniques are proving critical in reducing the computational cost and time of RIS inverse design. Instead of explicitly solving Maxwell's equations, DL models learn physics-based relationships through supervised training data. Further, generative adversarial networks are shown to synthesize novel RIS designs not previously seen in the literature. This article provides a synopsis of DL techniques for inverse RIS design and optimization to yield targeted EM response necessary for future wireless networks.