Engineering topics

Lin, Fen

Publications and source records attributed to Lin, Fen.

3 records

DOE OSTI2023

Roadmap on commercialization of metal halide perovskite photovoltaics

Perovskite solar cells (PSCs) represent one of the most promising emerging photovoltaic technologies due to their high power conversion efficiency. However, despite the huge progress made not only in terms of the efficiency achieved, but also fundamental understanding of the relevant physics of the devices and issues which affect their efficiency and stability, there are still unresolved problems and obstacles on the path toward commercialization of this promising technology. In this roadmap, we aim to provide a concise and up to date summary of outstanding issues and challenges, and the progress made toward addressing these issues. While the format of this article is not meant to be a comprehensive review of the topic, it provides a collection of the viewpoints of the experts in the field, which covers a broad range of topics related to PSC commercialization, including those relevant for manufacturing (scaling up, different types of devices), operation and stability (various factors), and environmental issues (in particular the use of lead). We hope that the article will provide a useful resource for researchers in the field and that it will facilitate discussions and move forward toward addressing the outstanding challenges in this fast-developing field.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Optimizing the Internal Cell Structure of the Radon Monitor Based on Electrostatic Collection Method

Abstract As Rn-222 decays, an alpha particle is emitted and the residual polonium nucleus recoils in the opposite direction. At the end of the recoil path, 88% of the polonium atoms have a positive charge and 12% are neutral. The electric potential distribution in the 60 ml hemispherical internal cell of the radon monitor based on electrostatic collection is studied for reducing the combined probability of the positively charged Po-218 and the OH− produced by the ionization of water vapour in the air. The COMSOL software is used to simulate the electric potential distribution in the internal cell of the radon monitor based on the electrostatic collection method at 27°C, a pressure of 0.1 Mpa. For improving the collection efficiency of Po-218 ions, the average collection time along vertical and oblique lines is calculated when the upper surface of the internal cell is plastic, uncharged metal and charged metal, respectively. Assuming that the gas in the internal cell is uniformly distributed, the results show that if the upper surface of the hemispherical internal cell is plastic or uncharged metal, the electric potential formed in the internal cell is more uniform, and it is beneficial to reduce the total collection time of the positively charged Po-218 ions, thereby improving collection efficiency. The simulation results can be used as an effective reference for optimizing the design of the internal cell structure of the radon monitor based on electrostatic collection method.

Fan, Zhongkai (ORCID:0000000181247459)↗

DOE OSTI2020

Embedding physics domain knowledge into a Bayesian network enables layer-by-layer process innovation for photovoltaics

Process optimization of photovoltaic devices is a time-intensive, trial-and-error endeavor, which lacks full transparency of the underlying physics and relies on user-imposed constraints that may or may not lead to a global optimum. Herein, we demonstrate that embedding physics domain knowledge into a Bayesian network enables an optimization approach for gallium arsenide (GaAs) solar cells that identifies the root cause(s) of underperformance with layer-by-layer resolution and reveals alternative optimal process windows beyond traditional black-box optimization. Our Bayesian network approach links a key GaAs process variable (growth temperature) to material descriptors (bulk and interface properties, e.g., bulk lifetime, doping, and surface recombination) and device performance parameters (e.g., cell efficiency). For this purpose, we combine a Bayesian inference framework with a neural network surrogate device-physics model that is 100× faster than numerical solvers. With the trained surrogate model and only a small number of experimental samples, our approach reduces significantly the time-consuming intervention and characterization required by the experimentalist. As a demonstration of our method, in only five metal organic chemical vapor depositions, we identify a superior growth temperature profile for the window, bulk, and back surface field layer of a GaAs solar cell, without any secondary measurements, and demonstrate a 6.5% relative AM1.5G efficiency improvement above traditional grid search methods.

14 SOLAR ENERGY↗