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Register a new userSpatial modeling of the 3D morphology of hybrid polymer-ZnO solar cells, based on electron tomography data
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A spatial stochastic model is developed which describes the 3D nanomorphology of composite materials, being blends of two different (organic and inorganic) solid phases. Such materials are used, for example, in photoactive layers of hybrid polymer zinc oxide solar cells. The model is based on ideas from stochastic geometry and spatial statistics. Its parameters are fitted to image data gained by electron tomography (ET), where adaptive thresholding and stochastic segmentation have been used to represent morphological features of the considered ET data by unions of overlapping spheres. Their midpoints are modeled by a stack of 2D point processes with a suitably chosen correlation structure, whereas a moving-average procedure is used to add the radii of spheres. The model is validated by comparing physically relevant characteristics of real and simulated data, like the efficiency of exciton quenching, which is important for the generation of charges and their transport toward the electrodes.
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Solution-processed intrinsic ZnO and Al doped ZnO (ZnO:Al) were spin coated on textured n-type c-Si wafer to replace the phosphorus doped amorphous silicon as the electron selective transport layer (ESTL) of the Si heterojunction (SHJ) solar cells. Besides the function of electron selective transportation, the non-doped ZnO was found to possess certain passivation effect on c-Si wafer. The SHJ solar cells with different combinations of passivation layer (intrinsic a-Si:H, SiOx and non-doped ZnO) and electron transport layer (non-doped ZnO and ZnO:Al ) were fabricated and compared. An efficiency up to 18.46% was achieved on a SHJ solar cell with an a-Si:H/ZnO:Al double layer back structure. And, the all solution-processed non-doped ZnO/ZnO:Al combination layer presents fairly good electron selective transportation property for SHJ solar cell, resulting in an efficiency of 17.13%. The carrier transport based on energy band diagrams of the rear side of the solar cells has been discussed related to the performance of the SHJ solar cells.
In this perspective, we explore the insights into the device physics of perovskite solar cells gained from modeling and simulation of these devices. We discuss a range of factors that influence the modeling of perovskite solar cells, including the role of ions, dielectric constant, density of states, and spatial distribution of recombination losses. By focusing on the effect of non-ideal energetic alignment in perovskite photovoltaic devices, we demonstrate a unique feature in low recombination perovskite materials - the formation of an interfacial, primarily electronic, self-induced dipole that results in a significant increase in the built-in potential and device open-circuit voltage. Finally, we discuss the future directions of device modeling in the field of perovskite photovoltaics, describing some of the outstanding open questions in which device simulations can serve as a particularly powerful tool for future advancements in the field.
The honeycomb connection of carbon atoms by covalent bonds in a macroscopic two-dimensional scale leads to fascinating graphene and solar cell based on graphene/silicon Schottky diode has been widely studied. For solar cell applications, GaAs is superior to silicon as it has a direct band gap of 1.42 eV and its electron mobility is six times of that of silicon. However, graphene/GaAs solar cell has been rarely explored. Herein, we report graphene/GaAs solar cells with conversion efficiency (Eta) of 10.4% and 15.5% without and with anti-reflection layer on graphene, respectively. The Eta of 15.5% is higher than the state of art efficiency for graphene/Si system (14.5%). Furthermore, our calculation points out Eta of 25.8% can be reached by reasonably optimizing the open circuit voltage, junction ideality factor, resistance of graphene and metal/graphene contact. This research strongly support graphene/GaAs hetero-structure solar cell have great potential for practical applications.
Recent technology breakthrough in spatial molecular profiling has enabled the comprehensive molecular characterizations of single cells while preserving spatial information. It provides new opportunities to delineate how cells from different origins form tissues with distinctive structures and functions. One immediate question in analysis of spatial molecular profiling data is how to identify spatially variable genes. Most of the current methods build upon the geostatistical model with a Gaussian process that relies on selecting ad hoc kernels to account for spatial expression patterns. To overcome this potential challenge and capture more types of spatial patterns, we introduce a Bayesian approach to identify spatially variable genes via Ising model. The key idea is to use the energy interaction parameter of the Ising model to characterize spatial expression patterns. We use auxiliary variable Markov chain Monte Carlo algorithms to sample from the posterior distribution with an intractable normalizing constant in the Ising model. Simulation results show that our energy-based modeling approach led to higher accuracy in detecting spatially variable genes than those kernel-based methods. Applying our method to two real spatial transcriptomics datasets, we discovered novel spatial patterns that shed light on the biological mechanisms. The proposed method presents a new perspective for analyzing spatial transcriptomics data.
A quite general device analysis method that allows the direct evaluation of optical and recombination losses in crystalline silicon (c-Si)-based solar cells has been developed. By applying this technique, the optical and physical limiting factors of the state-of-the-art solar cells with ~20% efficiencies have been revealed. In the established method, the carrier loss mechanisms are characterized from the external quantum efficiency (EQE) analysis with very low computational cost. In particular, the EQE analyses of textured c-Si solar cells are implemented by employing the experimental reflectance spectra obtained directly from the actual devices while using flat optical models without any fitting parameters. We find that the developed method provides almost perfect fitting to EQE spectra reported for various textured c-Si solar cells, including c-Si heterojunction solar cells, a dopant-free c-Si solar cell with a MoOx layer, and an n-type passivated emitter with rear locally diffused (PERL) solar cell. The modeling of the recombination loss further allows the extraction of the minority carrier diffusion length and surface recombination velocity from the EQE analysis. Based on the EQE analysis results, the carrier loss mechanisms in different types of c-Si solar cells are discussed.
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