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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. The solar cell service lifetime, as quantified by T80 (the time required for the power conversion efficiency to drop to 80% of its starting value), 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 it. 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 do so. 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. Efficiency losses are driven by short-circuit current and fill factor, indicating that photochemical reactions with O2 and H2O are a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy also suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition. Models are trained using a menu of features from three distinct categories: (i) measurements of the initial rates of change of device parameters, (ii) ambient conditions during operation (temperature & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of CH3NH3PbI3 is particularly valuable for prediction and was selected as the most dominant feature in the best performing models. With a dataset consisting of 45 degradation experiments with T80 values ranging over a factor of almost 30, the model predicts T80 with an average accuracy of about 40% on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes. [ABSTRACT FROM AUTHOR]

The rapid increase of the potent greenhouse gas methane in the atmosphere creates great urgency to develop and deploy technologies for methane mitigation. One approach to removing methane is to use bacteria for which methane is their carbon and energy source (methanotrophs). Such bacteria naturally convert methane to CO2 and biomass, a value-added product and a cobenefit of methane removal. Typically, methanotrophs grow best at around 5,000 to 10,000 ppm methane, but methane in the atmosphere is 1.9 ppm. Air above emission sites such as landfills, anaerobic digestor effluents, rice paddy effluents, and oil and gas wells contains elevated methane in the 500 ppm range. If such sites are targeted for methane removal, technology harnessing aerobic methanotroph metabolism has the potential to become economically and environmentally viable. The first step in developing such methane removal technology is to identify methanotrophs with enhanced ability to grow and consume methane at 500 ppm and lower. We report here that some existing methanotrophic strains grow well at 500 ppm methane, and one of them, Methylotuvimicrobium buryatense 5GB1C, consumes such low methane at enhanced rates compared to previously published values. Analyses of bioreactor-based performance and RNAseq-based transcriptomics suggest that this ability to utilize low methane is based at least in part on extremely low non-growth-associated maintenance energy and on high methane specific affinity. This bacterium is a candidate to develop technology for methane removal at emission sites. If appropriately scaled, such technology has the potential to slow global warming by 2050. [ABSTRACT FROM AUTHOR]

Halide perovskites have the potential to disrupt the photovoltaics market based on their high performance and low cost. However, the decomposition of perovskites under moisture, oxygen, and light raises concerns about service lifetime, especially because degradation mechanisms and the corresponding rate laws that fit the observed data have thus far eluded researchers. Here, we report a water-accelerated photooxidation mechanism dominating the degradation kinetics of archetypal perovskite CH 3 NH 3 PbI 3 in air under >1% relative humidity at 25 °C. From this mechanism, we develop a kinetic model that quantitatively predicts the degradation rate as a function of temperature, ambient O 2 and H 2 O levels, and illumination. Because water is a possible product of dry photooxidation, these results highlight the need for encapsulation schemes that rigorously block oxygen ingress, as product water may accumulate beneath the encapsulant and initiate the more rapid water-accelerated photooxidative decomposition.

Standard methods of monitoring the growth kinetics of anaerobic microorganisms are generally impractical when there is a protracted or indeterminate period of active growth, and when high numbers of samples or replications are required. As part of our studies of the adaptive evolution of a simple anaerobic syntrophic mutualism, requiring the characterization of many isolates and alternative syntrophic pairings, we developed a multiplexed growth monitoring system using a combination of commercially available electronics and custom designed circuitry and materials. This system automatically monitors up to 64 sealed, and as needed pressurized, culture tubes and reports the growth data in real-time through integration with a customized relational database. The utility of this system was demonstrated by resolving minor differences in growth kinetics associated with the adaptive evolution of a simple microbial community comprised of a sulfate reducing bacterium, Desulfovibrio vulgaris, grown in syntrophic association with Methanococcus maripaludis, a hydrogenotrophic methanogen., (Copyright © 2021. Published by Elsevier B.V.)