This physiochemical feature was selected as the first or second most dominant feature in the best performing models. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure more » the T80. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. Production of electricity without fuel from the environmental changes and fluxes requires a durable infrastructure for a more » cost-effective utilization of the "semi-perpetual" energy resources = , Environmental changes are under-used in energy production, although people are exposed to daily and seasonal changes of the environment, which include changing air temperature, pressure, humidity, and composition, tidal changes of the gravitational field, and less periodic changes of the geo-magnetic and electric fields. Flux examples are mechanical motions of air and water, surface waves and tides, heat fluxes due to a temperature gradient, solar light, and a chemical flux (such as humidity propagation in the atmospheric air or diffusion of salt in water due to a gradient of salinity at the mouth of a fresh-water river flowing into an ocean). One can produce electricity from mechanical, chemical, thermal, electromagnetic (light), or another physical flux, or from a change of temperature, chemical composition, or a physical field: gravitational, magnetic, electric, mechanical stress and strain, etc. In general, electric energy can be harvested from a flux in the environment or from a change of the environment itself. Photovoltaic solar panels convert an electromagnetic flux of light into direct current (DC). Most power plants produce electricity by converting a mechanical motion into alternating current (AC).
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |