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Machine learning algorithms can enhance the design and experimental processing of solar cells, resulting in increased conversion efficiency. In this study, we introduce a novel machine learning-based methodology for optimizing the Pareto front of four-terminal (4T) perovskite-copper indium selenide (CIS) tandem solar cells (TSCs). By training a neural network using the Bayesian regularization-backpropagation algorithm via Hammersley sampling, we achieve high prediction accuracy when testing with unseen data through random sampling. This surrogate model not only reduces computational costs but also potentially enhances device performance, increasing from 29.4% to 30.4% while simultaneously reducing material costs for fabrication by 50%. Comparing experimentally fabricated cells with the predicted optimal cells, the latter show a thinner front contact electrode, charge-carrier transport layer, and back contact electrode. Highly efficient perovskite cells identified from the Pareto front have a perovskite layer thickness ranging from 420 to 580 nm. Further analysis reveals the front contact electrode needs to be thin, while the back contact electrode can have a thickness ranging from 100 to 145 nm and still achieve high efficiency. The charge-carrier transport layers play a crucial role in minimizing interface recombination and ensuring unidirectional current flow. The optimal design space suggests thinner electron and hole transport layer thicknesses of 7 nm, down from 23 to 10 nm, respectively. It indicates a balanced charge-carrier extraction is crucial for an optimized perovskite cell. Overall, the presented methodology and optimized design parameters have the potential to enhance the performance of 4T perovskite/CIS TSC while reducing material fabrication costs. [ABSTRACT FROM AUTHOR]

Opto‐electronic models that seek to predict the performance of perovskite and tandem solar cells (PSCs/TSCs) often keep the optical and recombination parameters (ORPs) constant in subsequent studies. During fabrication of PSCs, however, these parameters can vary significantly. To account for the inherent fabrication variability, a comprehensive opto‐electronic‐electric model to predict the current–voltage characteristics of four‐terminal (4T) TSCs is developed. This model is calibrated with forty‐eight in‐house fabricated transparent PSCs with perovskite layer thickness of 420, 550, and 700 nm and corresponding median efficiencies of 20.6%, 21.1%, and 21.0%, respectively; a CIS bottom cell with stand‐alone efficiency of 17.5%; and combined 4T TSCs with a median efficiency of 29.0%. After fitting and validation, the functional forms of the ORPs are captured to estimate how they change with perovskite layer thickness. Finally, the errors with models assuming constant ORPs are demonstrated and how to improve the TSCs efficiency to more than 30% is discussed. [ABSTRACT FROM AUTHOR]

Through the analysis of scales on the drift–diffusion device model of a planar-structured perovskite solar cell, we have obtained accurate-analytical expressions that capture the recombination losses within the cell. The recombination losses are resolved into the radiative and Shockley–Read–Hall mechanisms, as well as interfacial recombination between the perovskite and electron/hole-transporting layers. After parameter calibration with the state of the art planar perovskite solar cell of 23.5% efficiency, the percentage contribution of various recombination loss channels within a planar-structured perovskite solar cell is analytically determined through derived scales and numerically verified at the condition of an open-circuit voltage and a short-circuit current, as well as gives a good prediction of a dominant recombination mechanism within the cell. On this basis, a comparison of loss analysis between the estimated scales and numeric results is carried out at the condition of an open-circuit voltage when a wide range of parameters influencing the recombination current is deviated simultaneously, and a good agreement is obtained. [ABSTRACT FROM AUTHOR]

Meso-structured perovskite solar cells (PSC), utilizing a mesoporous absorber layer consisting of mesoporous metal oxide and the perovskite material inside, are still delivering the highest solar cell efficiency for perovskite-based solar cells up to date. Their outstanding performance critically depends on the nanoscopic morphology formed inside the mesoporous absorber layer. This, however, is not accounted for in most of the perovskite device models, as they are based on an effective-medium formulation for the mesoporous absorber layer, and the details of its underlying morphology are ignored. The mesoporous absorber layer is treated as a two-phase model that describes intrinsic solar cell physics such as free charge carrier generation, carrier transport, and recombination within the two phases, as well as at the interface between the two phases. We derive a spatially smoothed device model for meso-structured PSCs based on volume-averaging of electric potential and electron and hole concentrations of the two-phase model, and this spatially smoothed formulation captures two essential morphological descriptors that are not found in existing effective-medium formulations for meso-structured PSCs, namely, surface-to-volume ratio and porosity inside the mesoporous layer. Furthermore, we determine the explicit functional forms of the effective parameters in the spatially smoothed model for the case of an ideal "Spaghetti" blend morphology. [ABSTRACT FROM AUTHOR]

The computational cost of a three‐dimensional (3D) fluid–structure interaction (FSI) simulation of a dissected aorta has prevented researchers from investigating the effect of a wide range of the heart rate on the hemodynamic quantities in the disease. We have presented a systematic procedure to develop a zero‐dimensional (0D) model for a dissected aorta. A series of numerical experiments were used to calculate the values for the resistance, inertance, and compliance of each lumen with irregular geometries. Having validated the results from the 0D model against those from the 3D model for one heart rate, we used the 0D model to investigate the effect of the heart rate of 50–150 bpm on the flow rates and the pressures in an idealized geometry of an aortic dissection. The 0D model showed acceptable accuracy when compared with the 3D FSI simulation. For instance, at peak systole, 7.18% relative error in the flow rate in the true lumen was observed for 0D and 3D simulations. The flow rate in the true lumen showed a stronger dependency on the heart rate, that is, 300% for the true lumen and 1.5% for the false lumen. The pressure difference between the lumina increased non‐monotonically as the heart beats faster. Because of its efficiency, the reported procedure can be used for uncertainty and sensitivity analysis of the hemodynamic quantities in a diseased aorta with complex geometries such as that of the aortic dissection. [ABSTRACT FROM AUTHOR]

The multi-effect-coupling pH-electric-stimuli (MECpHe) model for polyelectrolyte hydrogels subjected to coupled changes in the solution pH and an external electric field is reported. The model provides insight into the effect of changes on the deformation of the hydrogel and distribution of ionic species, electric potential, and fixed charge density, both inside the hydrogel as well as in the surrounding solvent.

A mathematical model for meso-structured perovskite solar cells is derived and calibrated towards measured intensity dependent current-voltage characteristics. This steady-state device model describes the transport of free carriers, carrier recombination and optical generation. The optical part considers internal transmission, reflection, and absorption of light, using a transfer matrix approach. The carrier recombination in the form of radiative, Auger and Shockley-Read-Hall mechanisms is accounted for inside the perovskite capping layer, as well as interfacial recombination between the perovskite and electron/hole-transporting layers. After calibration by best-fitting the unknown parameters towards intensity dependent current-voltage measurements of an in-house fabricated meso-structured perovskite solar cell, we identify the dominant recombination mechanisms and their locations inside the cell. A subsequent loss analysis indicates that, in our fabricated solar cell, the interfacial recombination between the perovskite/mesoporous titanium dioxide within the mesoporous absorber layer constitutes the main loss channel. This interfacial recombination accounts for up to 46% of all recombination losses at maximum power, thereby exceeding the recombination inside the perovskite capping layer with 31% loss. Furthermore, the thickness of the perovskite capping layer and the mesoporous layer is varied by means of simulation between 50 and 500 nm, in order to predict the optimum device geometry for the calibrated recombination parameters. [ABSTRACT FROM AUTHOR]

The shingled module has become an attractive interconnection architecture for its higher packing density and superior power generation. However, with longer string lengths and smaller cell areas, these modules are particularly susceptible to developing hotspots from shading elements. In this paper, a framework for the design of hotspot‐ and shading‐resistant shingled modules is presented. An electrothermal model is developed and validated extensively through specially fabricated shingled modules that allow for string‐level measurement and analysis. To investigate the relative influence of cell electrical characteristics on power loss and hotspot temperature, we perform a stochastic Monte Carlo simulation which reveals a greater sensitivity to parameters associated with the shaded cell's leakage current. A further study on cells with illumination‐dependent Jleakage shows the detriment of this light‐induced effect where higher hotspot temperatures can develop. Module‐level parameters are also investigated where string length, number of parallel strings, and cell fraction are studied in relation to their impact on module power and hotspot response. Finally, these findings are condensed into a design matrix which defines the space in which module manufacturers may configure shingled modules such that hotspots will not exceed a set threshold temperature. [ABSTRACT FROM AUTHOR]

Summary: The operation of electrochemical capacitors depends not only on extrinsic operating and design parameters, but also intrinsic physical, material, and electrochemical parameters. Fluctuations in these stochastic parameters can significantly influence the performance and may lead to quicker degradation of the electrochemical capacitors, thereby affecting their durability and reliability. Thus, it is important to quantify the sensitivities of these extrinsic and intrinsic parameters and correlate them with the performance, to delay the inevitable performance degradation. To achieve this, we perform Monte Carlo simulations (MCS) followed by sensitivity analysis under high and low charge/discharge current (load) conditions. The MCS is statistically performed by varying all the stochastic parameters simultaneously. We then identify the critical parameters that affect the performance under different load conditions, which provides insights into optimal operation of electrochemical capacitors. The thickness of positive electrode and the radius of active material were identified as the most significant parameters under low and high load conditions, respectively. Furthermore, we derive reduced order surrogate models with at least 95% accuracy using supervised machine learning techniques to predict the performance without solving the full physics‐based model. [ABSTRACT FROM AUTHOR]

Through the analysis of scales and simplification of the drift-diffusion device model, we have obtained a quantitative description of the mechanisms underlying the current-voltage (j-V) characteristics of organic bulk-heterojunction solar cells. The mechanisms have been resolved into the competition between the photogeneration, recombination, and extraction/injection rates, which determines the bulk charge carrier concentration; and the combined effect of the built-in field and the boundary layers in shaping the electric potential distribution, which determines the bulk field. The relationships between the j-V characteristics and standard model parameters have been captured with analytical expressions and verified through 1-D numerical simulations. We have determined that while the charge carrier generation rate can be reliably extracted with the device model from j-V measurements alone, the effective density of states and built-in potential, and the mobility and recombination prefactor are clustered pairs that can only be decoupled through other characterization techniques. [ABSTRACT FROM AUTHOR]

Isoporous membranes consist of well‐defined micro and nanoscale pore architecture comprising uniform pore sizes with straight pore channels. In contrast to traditional random porous membranes with tortuous flow paths, isoporous membranes offer the opportunity to achieve a high degree of membrane customization and low pressure drop. Here, a physics‐based machine learning methodology that enables the predictive design of a single‐layer isoporous membrane in terms of the pressure drop is reported. In short, the methodology consists of a hybrid approach that includes experimental data on the variability of the pore architecture and the resulting pressure drop, training of a neural network with data from validated physics‐based simulations of laminar flow through the membrane, and Monte Carlo simulation (MCS) to stochastically account for the inherent variabilities of the pore architecture of fabricated isoporous membranes. Overall, the neural network and MCS predict the range of Δp for a given single‐layer membrane well. Experimental values fall within 90% of the minimum and maximum predicted Δp values. In addition, a sensitivity analysis with MCS is carried out to quantify how design and operating parameters affect the overall pressure drop. The methodology can be extended to membranes comprising multiple layers and to account for filtration efficiency. [ABSTRACT FROM AUTHOR]

Cardiovascular disease is a chronic disease that leads to impaired cardiac function and requires long-term management to control its progression. Despite the importance of hydrogels for therapeutic applications, a contradiction between the size of a hydrogel and the amount of loaded drug has been encountered when using conventional fabrication methods. In this study, biocompatible reservoir microcapsules (diameter ∼100 μm) with a large liquid core and polymeric shell were fabricated via a one-step phase separation of poly(ethylene glycol)diacrylate (PEGDA) and dextran within pre-gel droplets through microfluidics. By controlling the process of phase separation, high drug-loading efficiency (∼80%) for long-term release (30 days) of vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF) was achieved. Drug molecules were dispersed within the liquid core at a concentration above saturation solubility for sustained delivery via regulation of the shells. Effective therapeutic enhancement of human umbilical vein endothelial cell (HUVEC) and umbilical artery smooth muscle cell (SMC) proliferation and tube formation in vitro promoted rapid cell proliferation and increased the number of migrated cells by ∼1.7 times. Moreover, in vivo blood vessel regeneration for cardiovascular control induced by sustained dual-drug (VEGF and PDGF) delivery to the rat heart was achieved, showing the effectiveness of long-term protein delivery in improving cardiac function and significantly reducing ventricular wall thickness and fibrosis of the infarct region. The ratio of heart tissue scarring was reduced to 11.2% after microcapsule treatment compared with 21.4% after saline treatment in the rat model. By using these reservoir microcapsules, similar sustained delivery of proteins, mRNAs and biologic drugs could be developed for the treatment of a range of long-term chronic diseases and regenerative medicine. [ABSTRACT FROM AUTHOR]