Emerging investigator series: quantifying the impact of cloud cover on solar irradiance and environmental photodegradation.

Environmental photodegradation is dependent on the solar irradiance that reaches the Earth's surface, and photodegradation half-lives of contaminants are typically estimated assuming clear sky (i.e., cloudless) conditions. In this work, the effect of cloud cover on solar irradiance was investigated. Data from the National Renewable Energy Laboratory (NREL), which spanned 3 years of observations (10/2017 to 12/2020), were used to train two machine learning models to predict irradiance based on three inputs - day of year, time of day, and percentage of the sky that was cloudy. Results showed a non-linear relationship between cloud cover and irradiance. Solar irradiance was minimally impacted up to ≈50% cloud cover but decreased by ≈67% at 100% cloud cover. Both random forest and artificial neural network models performed well with relative root mean squared errors of 26-31%, which varied depending on the source of cloud cover data and the spectral region being modeled. Daily irradiance values for a whole year were predicted for varying cloud conditions using the machine learning models; this result was approximated using a quadratic fit of y = 1 - 0.00243x - (4.24 × 10-5)x2 where y is the fraction of clear sky irradiance expected and x is the percentage of cloud cover in the sky. In addition, the model results supported that there was no wavelength dependence for the effect of cloud cover. Therefore, decreases in both direct and indirect photodegradation rates should be proportional to the decrease in irradiance, which has a non-linear dependence on cloud cover.

Scalable Chitosan-Graphene Oxide Membranes: The Effect of GO Size on Properties and Cross-Flow Filtration Performance.

Chitosan (CS)-graphene oxide (GO) composite films were fabricated, characterized, and evaluated as pressure-driven water filtration membranes. GO particles were incorporated into a chitosan polymer solution to form a suspension that was cast as a membrane via evaporative phase inversion allowing for scale-up for cross-flow testing conditions. Morphology and composition results for nano and granular GO in the CS matrix indicate that the particle size of GO impacts the internal membrane morphology as well as the structural order and the chemical composition. Performance of the membranes was evaluated with cationic and anionic organic probe molecules and revealed charge-dependent mechanisms of dye removal. The CSGO membranes had rejections of at least 95% for cationic methylene blue with mass balances obtained from measurements of the feed, concentrate, and permeate. This result suggests the dominant mechanism of removal is physical rejection for both GO particle sizes. For anionic methyl orange, the results indicate sorption as the dominant mechanism of removal, and performance is dependent on both GO particle size and time, with micrometer-scale GO removing 68-99% and nanometer-scale GO showing modest removal of 29-64%. The pure water flux for CSGO composite membranes ranged from 2-4.5 L/m2 h at a transmembrane pressure of 344 kPa (3.44 bar), with pure water permeance ranging from 5.8 × 10-3 to 0.01 L/m2 h kPa (0.58-1.3 L/m2 h bar). Based on the 41 µm membrane thickness obtained from microscopy, the hydraulic permeability ranged from 0.24-0.54 L µm/m2 h kPa (24.4-54.1 L µm/m2 h bar).