相关论文: Robustness of Parameter Estimation Procedures for …
Polymer solar cells are considered as very promising candidates for development of photovoltaics of the future. They are cheap and easy to fabricate, however, up to now, they possess fundamental drawback, low effectiveness. In the most…
The performance of organic solar cells strongly depends on the bulk heterojunction (BHJ) morphology of the photoactive layer. This BHJ forms during the drying of the wet-deposited solution, because of physical processes such as…
A general problem arising in computer simulations is the number of material and device parameters, which have to be determined by dedicated experiments and simulation-based parameter extraction. In this study we analyze measurements of the…
Organic heterojunction solar cells are analyzed within a minimal model that includes the essential physical features of such systems. The dynamical properties of this model, calculated using a master equation approach, account for the…
Polymer solar cells are considered as very promising candidates for development of photovoltaics of the future. They are cheap and easy to fabricate. However, up to now, they possess fundamental drawback: low effectiveness. In the most…
Machine learning has emerged as a promising approach for estimating material parameters in solar cells. Traditional methods for parameter extraction often rely on time-consuming numerical simulations that fail to capture the full complexity…
Kinetic Monte Carlo (KMC) simulations have been previously used to model and understand a wide range of behaviors in bulk heterojunction (BHJ) organic photovoltaic devices, from fundamental mechanisms to full device performance. One…
The performance of organic bulk heterojunction (BHJ) solar cells is highly sensitive to both nanomorphology and energetic disorder arising from microscopic molecular packing and structural defects. However, most models used to understand…
Electrochemical hybrid battery models have major potential to enable advanced physics-based control, diagnostic, and prognostic features for next-generation lithium-ion battery management systems. This is due to the physical significance of…
The physics of organic bulk heterojunction solar cells is studied within a six state model, which is used to analyze the factors that affect current-voltage characteristics, power-voltage properties and efficiency, and their dependence on…
Computer models are commonly used to represent a wide range of real systems, but they often involve some unknown parameters. Estimating the parameters by collecting physical data becomes essential in many scientific fields, ranging from…
This contribution presents a parameter identification methodology for the accurate and fast estimation of model parameters in a pseudo-two-dimensional (P2D) battery model. The methodology consists of three key elements. First, the data for…
Understanding the relationship between morphology and performance in organic solar cells is essential for developing devices that are both high performing and resilient to aging. This work introduces a unique method capable of calculating…
Modern photovoltaic devices are often based on a heterojunction structure where two components with different optoelectronic properties are interfaced. The properties of each side of the junction can be tuned by either utilizing different…
Bulk-heterojunction (BHJ) non-fullerene organic solar cells prepared from sequentially deposited donor and acceptor layers (sq-BHJ) have recently been promising to be highly efficient, environmentally friendly, and compatible with large…
We consider the Higgs boson decay processes and its production, and provide a parameterisation tailored for testing models of new physics beyond the Standard Model. We also compare our formalism to other existing parameterisations based on…
Physics-based battery modelling has emerged to accelerate battery materials discovery and performance assessment. Its success, however, is still hindered by difficulties in aligning models to experimental data. Bayesian approaches are a…
Machine learning enables rapid estimation of material parameters in solar cells via neural-network-based surrogate models. However, the reliability of extracted parameters depends on underlying assumptions such as the choice of…
Numerical modeling of proton exchange membrane fuel cells is at the verge of becoming predictive. A crucial requisite for this, though, is that material properties of the membrane-electrode assembly and their functional dependence on the…
Virtual garment simulation has become increasingly important with applications in garment design and virtual try-on. However, reproducing garments faithfully remains a cumbersome process. We propose an end-to-end method for estimating…