A PV installation framework concerning electricity variable rates

This paper assesses building integrated photovoltaic (BIPV) installation parameters based on the profit generated by a photovoltaic system. It takes into consideration a home building case study and it investigates its monthly energy demand based on a specific location and a typical occupancy. The capability of a photovoltaic (PV) system to generate more profit occurs when solar intensity is maximum while the electric energy price is at its highest rate. The paper traces a framework that encompasses different aspects such as energy demand, energy price, and solar intensity. This framework identifies profit alternatives according to different installation parameters. A tool that predicts a PV installation hourly electric energy production is developed. The profit generated is simulated for home buildings located in Beirut (Lebanon) and Xihua (China), both at 33.8° latitude north. The paper highlights a new approach for BIPV installations, taking into account weather conditions, energy demand, and electric energy utility rates.     

Revealing Electrical-Poling-Induced Polarization Potential in Hybrid Perovskite Photodetectors

Despite recent rapid advances in metal halide perovskites for use in optoelectronics, the fundamental understanding of the electrical-poling-induced ion migration, accounting for many unusual attributes and thus performance in perovskite-based devices, remain comparatively elusive. Herein, the electrical-poling-promoted polarization potential is reported for rendering hybrid organic–inorganic perovskite photodetectors with high photocurrent and fast response time, displaying a tenfold enhancement in the photocurrent and a twofold decrease in the response time after an external electric field poling. First, a robust meniscus-assisted solution-printing strategy is employed to facilitate the oriented perovskite crystals over a large area. Subsequently, the electrical poling invokes the ion migration within perovskite crystals, thus inducing a polarization potential, as substantiated by the surface potential change assessed by Kelvin probe force microscopy. Such electrical-poling-induced polarization potential is responsible for the markedly enhanced photocurrent and largely shortened response time. This work presents new insights into the electrical-poling-triggered ion migration and, in turn, polarization potential as well as into the implication of the latter for optoelectronic devices with greater performance. As such, the utilization of ion-migration-produced polarization potential may represent an important endeavor toward a wide range of high-performance perovskite-based photodetectors, solar cells, transistors, scintillators, etc.     

应变纤锌矿ZnO/MgxZn1-xO量子点中离子施主束缚激子的光学性质

Within the framework of the effective-mass approximation and the dipole approximation, considering the three-dimensional confinement of the electron and hole and the strong built-in electric field(BEF) in strained wurtzite Zn O/Mg0:25Zn0:75O quantum dots(QDs), the optical properties of ionized donor-bound excitons(D+, X)are investigated theoretically using a variational method. The computations are performed in the case of finite band offset. Numerical results indicate that the optical properties of(D+, X) complexes sensitively depend on the donor position, the QD size and the BEF. The binding energy of(D+, X) complexes is larger when the donor is located in the vicinity of the left interface of the QDs, and it decreases with increasing QD size. The oscillator strength reduces with an increase in the dot height and increases with an increase in the dot radius. Furthermore, when the QD size decreases, the absorption peak intensity shows a marked increment, and the absorption coefficient peak has a blueshift. The strong BEF causes a redshift of the absorption coefficient peak and causes the absorption peak intensity to decrease remarkably. The physical reasons for these relationships have been analyzed in depth.     

Coil‐Type Asymmetric Supercapacitor Electrical Cables

Cable‐shaped supercapacitors (SCs) have recently aroused significant attention due to their attractive properties such as small size, lightweight, and bendability. Current cable‐shaped SCs have symmetric device configuration. However, if an asymmetric design is used in cable‐shaped supercapacitors, they would become more attractive due to broader cell operation voltages, which results in higher energy densities. Here, a novel coil‐type asymmetric supercapacitor electrical cable (CASEC) is reported with enhanced cell operation voltage and extraordinary mechanical‐electrochemical stability. The CASECs show excellent charge–discharge profiles, extraordinary rate capability (95.4%), high energy density (0.85 mWh cm⁻³), remarkable flexibility and bendability, and superior bending cycle stability (≈93.0% after 4000 cycles at different bending states). In addition, the CASECs not only exhibit the capability to store energy but also to transmit electricity simultaneously and independently. The integrated electrical conduction and storage capability of CASECS offer many potential applications in solar energy storage and electronic gadgets.     

Rethinking Enterprise Architecture for Sustainable Energy System Development

The development of a sustainable energy system throughout an enterprise is a complex task, which requires an agile holistic approach. Such an approach needs to include a variety of objectives including energy strategy formation and strategic decision-making, which are directly related to the analysis and management of the main areas of sustainable development:The economic, technological, environmental, and social. These multidimensional requirements of sustainability are often difficult to achieve within the enterprise, because these aspects are interrelated and influenced by various internal and external environment factors. This paper first reviews the main challenges for an energy system, and then demonstrates how a strategic agile enterprise architecture driven approach could effectively guide the sustainable energy system development. The study presented in this paper provides a holistic approach that contributes to the advancement and usage of literature dealing with issues of sustainable energy system development and agile enterprise architecture, which has not been discussed before to any great extent.     

Broad-Spectral-Range Sustainability and Controllable Excitation of Hyperbolic Phonon Polaritons in α-MoO3

Hyperbolic phonon polaritons (HPhPs) in orthorhombic-phase molybdenum trioxide (α-MoO₃) show in-plane hyperbolicity, great wavelength compression, and ultralong lifetime, therefore holding great potential in nanophotonic applications. However, its polaritonic response in the far-infrared (FIR) range remains unexplored due to challenges in experimental characterization. Here, monochromated electron energy loss spectroscopy (EELS) in a scanning transmission electron microscope (STEM) is used to probe HPhPs in α-MoO₃ in both mid-infrared (MIR) and FIR frequencies and correlate their behaviors with microstructures and orientations. It is found that low structural symmetry leads to various phonon modes and multiple Reststrahlen bands (RBs) over a broad spectral range (over 70 meV) and in different directions (55–63 meV and 119–125 meV along the b-axis, 68–106 meV along the c-axis, and 101–121 meV along the a-axis). These HPhPs can be selectively excited by controlling the direction of swift electrons. These findings provide new opportunities in nanophotonic and optoelectronic applications, such as directed light propagation, hyperlenses, and heat transfer.     

Coupling in situ experiments and modeling – Opportunities for data fusion,machine learning,and discovery of emergent behavior

This paper reviews recent studies, that not only includes both experiments and modeling components, but celebrates a close coupling between these techniques, in order to provide insights into the plasticity and failure of polycrystalline metals. Examples are provided of studies across multiple-scales, including, but not limited to, density functional theory combined with atom probe tomography, molecular dynamics combined with in situ transmission electron miscopy, discrete dislocation dynamics combined with nanopillars experiments, crystal plasticity combined with digital image correlation, and crystal plasticity combined with in situ high energy X-ray diffraction. The close synergy between in situ experiments and modeling provides new opportunities for model calibration, verification, and validation, by providing direct means of comparison, thus removing aspects of epistemic uncertainty in the approach. Further, data fusion between in situ experimental and model-based data, along with data driven approaches, provides a paradigm shift for determining the emergent behavior of deformation and failure, which is the foundation that underpins the mechanical behavior of polycrystalline materials.     

Charge transport and recombination in heterostructure organic light emitting transistors

Light-emitting field effect transistors (LEFETs) are a class of organic optoelectronic device capable of simultaneously delivering the electrical switching characteristics of a transistor and the light emission of a diode. We report on the temperature dependence of the charge transport and emissive properties in a model organic heterostructure LEFET system from 300 K to 135 K. We study parameters such as carrier mobility, brightness, and external quantum efficiency (EQE), and observe clear thermally activated behaviour for transport and injection. Overall, the EQE increases with decreasing temperature and conversely the brightness decreases. These contrary effects can be explained by a higher recombination efficiency occurring at lower temperatures, and this insight delivers new knowledge concerning the optimisation of both the transport and emissive properties in LEFETs.     

Distributed learning consensus control based on neural networks for heterogeneous nonlinear multiagent systems

This paper considers a novel distributed iterative learning consensus control algorithm based on neural networks for the control of heterogeneous nonlinear multiagent systems. The system's unknown nonlinear function is approximated by suitable neural networks; the approximation error is countered by a robust term in the control. Two types of control algorithms, both of which utilize distributed learning laws, are provided to achieve consensus. In the provided control algorithms, the desired reference is considered to be an unknown factor and then estimated using the associated learning laws. The consensus convergence is proven by the composite energy function method. A numerical simulation is ultimately presented to demonstrate the efficacy of the proposed control schemes.