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Carbon dioxide presents many unique advantages for cooling and power cycles under supercritical or near-critical conditions, where the characterization of thermophysical properties is a daunting task. The present study proposes different deep feedforward neural network (DFNN) models for property evaluations of carbon dioxide. The all-in-one DFNN model appears acceptable for enthalpy, entropy, and thermal conductivity, but it exhibits poor performance in density, speed of sound, viscosity, and constant-pressure specific heat. The specific DFNN model presents limited improvement in the near-critical and pseudoboiling regions, where steep property gradients occur. To alleviate the situation, the sampling data are divided into easy samples and hard samples. Easy samples are data that have small-gradient norm and can be well fitted, whereas hard samples are those with large-gradient norms and are difficult to fit. The gradient-harmonizing method is proposed to solve the imbalance between hard and easy samples by rectifying their gradient contribution and assigning different weights. The resultant models show significantly improved performance as compared to the existing methods in the literature, with less than 0.4% in absolute average relative errors for the studied properties. The gradient-harmonizing algorithm can potentially be adopted to property evaluations of other substances involved in aerospace propulsion.

Developing efficient and stable organic photovoltaics (OPVs) is crucial for the technology's commercial success.

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Conspectus Over the past decade, the impressive development of metal halide perovskites (MHPs) has made them leading candidates for applications in photovoltaics (PVs), X-ray scintillators, and light-emitting diodes (LEDs). Constructing MHP nanocrystals (NCs) with promising optoelectronic properties using a low-cost approach is critical to realizing their commercial potential. Self-assembly and regrowth techniques provide a simple and powerful “bottom-up” platform for controlling the structure, shape, and dimensionality of MHP NCs. The soft ionic nature of MHP NCs, in conjunction with their low formation energy, rapid anion exchange, and ease of ion migration, enables the rearrangement of their overall appearance via self-assembly or regrowth. Because of their low formation energy and highly dynamic surface ligands, MHP NCs have a higher propensity to regrow than conventional hard-lattice NCs. Moreover, their self-assembly and regrowth can be achieved simultaneously. The self-assembly of NCs into close-packed, long-range-ordered mesostructures provides a platform for modulating their electronic properties (e.g., conductivity and carrier mobility). Moreover, assembled MHP NCs exhibit collective properties (e.g., superfluorescence, renormalized emission, longer phase coherence times, and long exciton diffusion lengths) that can translate into dramatic improvements in device performance. Further regrowth into fused MHP nanostructures with the removal of ligand barriers between NCs could facilitate charge carrier transport, eliminate surface point defects, and enhance stability against moisture, light, and electron-beam irradiation. However, the synthesis strategies, diversity and complexity of structures, and optoelectronic applications that emanate from the self-assembly and regrowth of MHPs have not yet received much attention. Consequently, a comprehensive understanding of the design principles of self-assembled and fused MHP nanostructures will fuel further advances in their optoelectronic applications. In this Account, we review the latest developments in the self-assembly and regrowth of MHP NCs. We begin with a survey of the mechanisms, driving forces, and techniques for controlling MHP NC self-assembly. We then explore the phase transition of fused MHP nanostructures at the atomic level, delving into the mechanisms of facet-directed connections and the kinetics of their shape-modulation behavior, which have been elucidated with the aid of high-resolution transmission electron microscopy (HRTEM) and first-principles density functional theory calculations of surface energies. We further outline the applications of assembled and fused nanostructures. Finally, we conclude with a perspective on current challenges and future directions in the field of MHP NCs.

Summary Stacking perovskite solar cells onto crystalline silicon bottom cells in a monolithic tandem configuration enables power-conversion efficiencies (PCEs) well above those of their single-junction counterparts. However, state-of-the-art wide-band-gap perovskite films suffer from phase stability issues. Here, we show how carbazole as an additive to the perovskite precursor solution can not only reduce nonradiative recombination losses but, perhaps more importantly, also can suppress phase segregation under exposure to moisture and light illumination. This enables a stabilized PCE of 28.6% (independently certified at 28.2%) for a monolithic perovskite/silicon tandem solar cell over ∼1 cm2 and 27.1% over 3.8 cm2, built from a textured silicon heterojunction solar cell. The modified tandem devices retain ∼93% of their performance over 43 days in a hot and humid outdoor environment of almost 100% relative humidity over 250 h under continuous 1-sun illumination and about 87% during a 85/85 damp-heat test for 500 h, demonstrating the improved phase stability.

Lead halide perovskites (LHPs) have attracted a tremendous amount of attention because of their applications in solar cells, lighting, and optoelectronics. However, the atomistic principles underlying their decomposition processes remain in large part obscure, likely due to the lack of precise information about their local structures and composition along regions with dimensions on the angstrom scale, such as crystal interfaces. Aberration-corrected scanning transmission electron microscopy combined with X-ray energy dispersive spectroscopy (EDS) is an ideal tool, in principle, for probing such information. However, atomic-resolution EDS has not been achieved for LHPs because of their instability under electron-beam irradiation. We report the fabrication of CsPbBr

Optimal transportation plays a fundamental role in many fields in engineering and medicine, including surface parameterization in graphics, registration in computer vision, and generative models in deep learning. For quadratic distance cost, optimal transportation map is the gradient of the Brenier potential, which can be obtained by solving the Monge-Ampere equation. Furthermore, it is induced to a geometric convex optimization problem. The Monge-Ampere equation is highly non-linear, and during the solving process, the intermediate solutions have to be strictly convex. Specifically, the accuracy of the discrete solution heavily depends on the sampling pattern of the target measure. In this work, we propose a self-adaptive sampling algorithm which greatly reduces the sampling bias and improves the accuracy and robustness of the discrete solutions. Experimental results demonstrate the efficiency and efficacy of our method.

Tight multi-medium oil reservoirs are the main source of hydrocarbon resources around the world. Acid fracturing is the most effective technology to improve productivity in such reservoirs. As carbonates are primarily composed of dolomite and calcite, which are easily dissolved by hydrochloric acid, high-permeability region will be formed near the well along with the main artificial fracture when acid fracturing is implemented in tight multi-medium oil reservoirs. In this study, a comprehensive composite linear flow model was developed to simulate the transient pressure behavior of an acid fracturing vertical well in a naturally fractured vuggy carbonate reservoir. By utilizing Pedrosa’s substitution, perturbation, Laplace transformation and Stehfest numerical inversion technology, the pressure behavior results were obtained in real time domain. Furthermore, the result of this model was validated by comparing with those of previous literature. Additionally, the influences of some prevailing parameters on the type curves were analyzed. Moreover, the proposed model was applied to an acid fracturing well to evaluate the effectiveness of acid fracturing measures, to demonstrate the practicability of the proposed model.

The all-inorganic nature of CsPbI3 perovskites allows to enhance stability in perovskite devices. Research efforts have led to improved stability of the black phase in CsPbI3 films; however, these strategies-including strain and doping-are based on organic-ligand-capped perovskites, which prevent perovskites from forming the close-packed quantum dot (QD) solids necessary to achieve high charge and thermal transport. We developed an inorganic ligand exchange that leads to CsPbI3 QD films with superior phase stability and increased thermal transport. The atomic-ligand-exchanged QD films, once mechanically coupled, exhibit improved phase stability, and we link this to distributing strain across the film. Operando measurements of the temperature of the LEDs indicate that KI-exchanged QD films exhibit increased thermal transport compared to controls that rely on organic ligands. The LEDs exhibit a maximum EQE of 23 % with an electroluminescence emission centered at 640 nm (FWHM: ≈31 nm). These red LEDs provide an operating half-lifetime of 10 h (luminance of 200 cd m-2 ) and an operating stability that is 6× higher than that of control devices.

Self-assembled monolayers (SAMs) based on Br-2PACz ([2-(3,6-dibromo-9H-carbazol-9-yl)ethyl]phosphonic acid) 2PACz ([2-(9H-Carbazol-9-yl)ethyl]phosphonic acid) and MeO-2PACz ([2-(3,6-dimethoxy-9H-carbazol-9-yl)ethyl]phosphonic acid) molecules were investigated as hole-extracting interlayers in organic photovoltaics (OPVs). The highest occupied molecular orbital (HOMO) energies of these SAMs were measured at -6.01 and -5.30 eV for Br-2PACz and MeO-2PACz, respectively, and found to induce significant changes in the work function (WF) of indium-tin-oxide (ITO) electrodes upon chemical functionalization. OPV cells based on PM6 (poly[(2,6-(4,8-bis(5-(2-ethylhexyl-3-fluoro)thiophen-2-yl)-benzo[1,2-b:4,5-b']dithiophene))-alt-(5,5-(1',3'-di-2-thienyl-5',7'-bis(2-ethylhexyl)benzo[1',2'-c:4',5'-c']dithiophene-4,8-dione)]) : BTP-eC9 : PC71 BM ([6,6]-phenyl-C71-butyric acid methyl ester) using ITO/Br-2PACz anodes exhibited a maximum power conversion efficiency (PCE) of 18.4 %, outperforming devices with ITO/MeO-2PACz (14.5 %) and ITO/poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT : PSS) (17.5 %). The higher PCE was found to originate from the much higher WF of ITO/Br-2PACz (-5.81 eV) compared to ITO/MeO-2PACz (4.58 eV) and ITO/PEDOT : PSS (4.9 eV), resulting in lower interface resistance, improved hole transport/extraction, lower trap-assisted recombination, and longer carrier lifetimes. Importantly, the ITO/Br-2PACz electrode was chemically stable, and after removal of the SAM it could be recycled and reused to construct fresh OPVs with equally impressive performance.

A low defect density in metal halide perovskite single crystals is critical to achieve high performance optoelectronic devices. Here we show the reduction of defect density in perovskite single crystals grown by a ligand-assisted solution process with 3‐(decyldimethylammonio)‐propane‐sulfonate inner salt (DPSI) as an additive. DPSI ligands anchoring with lead ions on perovskite crystal surfaces not only suppress nucleation in solution, but also regulate the addition of proper ions to the growing surface, which greatly enhances the crystal quality. The grown CH3NH3PbI3 crystals show better crystallinity and a 23-fold smaller trap density of 7 × 1010 cm−3 than the optimized control crystals. The enhanced material properties result in significantly suppressed ion migration and superior X-ray detection sensitivity of CH3NH3PbI3 detectors of (2.6 ± 0.4) × 106 µC Gy−1air cm−2 for 60 kVp X-ray and the lowest detectable dose rate reaches (5.0 ± 0.7) nGy s−1, which enables reduced radiation dose to patients in medical X-ray diagnostics., The performance of a metal halide perovskite single crystal is governed by the defect density. Here, the authors report a high quality single crystal perovskite grown by a ligand-assisted solution process with DPSI achieving 23-fold smaller trap density than that without DPSI.

Siamese networks have drawn increasing interest in the field of visual object tracking due to their balance of precision and efficiency. However, Siamese trackers use relatively shallow backbone networks, such as AlexNet, and therefore do not take full advantage of the capabilities of modern deep convolutional neural networks (CNNs). Moreover, the feature representations of the target object in a Siamese tracker are extracted through the last layer of CNNs and mainly capture semantic information, which causes the tracker's precision to be relatively low and to drift easily in the presence of similar distractors. In this paper, a new nonpadding residual unit is designed and used to stack a 22-layer deep ResNet, referred as ResNet22. After utilizing ResNet22 as the backbone network, we can build a deep Siamese network, which can greatly enhance the tracking performance. Considering that the different levels of the feature maps of the CNN represent different aspects of the target object, we aggregated different deep convolutional layers to make use of ResNet22’s multilevel feature maps, which can form hyperfeature representations of targets. The final network architecture is named DSiamLA. Experimental results show that DSiamLA has achieved significant improvement on multiple benchmark datasets.

Most efficient perovskite solar cells are based on polycrystalline thin films; however, substantial structural disorder and defective grain boundaries place a limit on their performance. Perovskite...

Molecular doping has recently been shown to improve the operating characteristics of organic photovoltaics (OPVs). Here, we prepare neutral Diquat (DQ) and use it as n-dopant to improve the perform...

The authors acknowledge the funding support from King Abdullah University of Science and Technology (KAUST), the Ontario Research Fund−Research Excellence Program, and the Natural Sciences and Engineering Research Council of Canada (NSERC).

Metal halide perovskite (MHP) single crystals are promising candidates for photovoltaics and related optoelectronic devices. They afford optical and charge transport properties superior to those of...

Summary Solution-processed colloidal quantum dots (CQDs) are promising optoelectronic materials; however, CQD solids have, to date, exhibited either excellent transport properties but fusion among CQDs or limited transport when QDs are strongly passivated. Here, we report the growth of monolayer perovskite bridges among quantum dots and show that this enables the union of surface passivation with improved charge transport. We grow the perovskite layer after forming the CQD solid rather than introducing perovskite precursors into the quantum dot solution: the monolayer of perovskite increases interdot coupling and decreases the distance over which carriers must tunnel. As a result, we double the diffusion length relative to reference CQD solids and report solar cells that achieve a stabilized power conversion efficiency (PCE) of 13.8%, a record among Pb chalcogenide CQD solar cells.

Siamese networks have drawn increasing interest in the field of visual object tracking due to their balance of precision and efficiency. However, Siamese trackers use relatively shallow backbone networks, such as AlexNet, and therefore do not take full advantage of the capabilities of modern deep convolutional neural networks (CNNs). Moreover, the feature representations of the target object in a Siamese tracker are extracted through the last layer of CNNs and mainly capture semantic information, which causes the tracker's precision to be relatively low and to drift easily in the presence of similar distractors. In this paper, a new nonpadding residual unit (NPRU) is designed and used to stack a 22-layer deep ResNet, referred as ResNet22. After utilizing ResNet22 as the backbone network, we can build a deep Siamese network, which can greatly enhance the tracking performance. Considering that the different levels of the feature maps of the CNN represent different aspects of the target object, we aggregated different deep convolutional layers to make use of ResNet22's multilevel feature maps, which can form hyperfeature representations of targets. The designed deep hyper Siamese network is named DHSiam. Experimental results show that DHSiam has achieved significant improvement on multiple benchmark datasets.

Summary Luminescent solar concentrators (LSCs) enable large-area collection of sunlight relevant to building-integrated photovoltaics. Reduced-dimensional metal halide perovskite nanoplatelets (PNPLs) have recently emerged as candidates for low-loss large-area LSCs due to the optoelectronic properties of perovskites combined with the large Stokes shift attainable using the multiple quantum well (MQW) structure. LSCs using bromine-based PNPLs have been demonstrated; however, the band gaps of bromine-based perovskites limit the absorption range. Iodine-based PNPLs allow broader absorption, but emission can be achieved only if the chemistry of PbIx2−x precursor complexes is engineered to provide the appropriate MQW distribution. Here, by controlling the polarity and Lewis basicity of the precursor solution, we modify the solvent-Pb2+ coordination and synthesize PNPLs having a uniform MQW distribution. This improves energy funneling, enabling a film PLQY (photoluminescence quantum yield) of 56% and 10 × 10 cm LSCs with an optical conversion efficiency of 2.0%, a 1.3-fold enhancement compared to the best previously reported room-temperature-fabricated perovskite LSCs.

Blue-emitting perovskites can be easily attained by precisely tuning the halide ratio of mixed halide (Br/Cl) perovskites (MHPs). However, the adjustable halide ratio hinders the passivation of Cl ...