Search

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]

We describe experiments that follow species dynamics and gene expression patterns in synthetic bacterial communities including species that compete for the single carbon substrate supplied, methane, and species unable to consume methane, which could only succeed through cooperative interactions. We demonstrate that these communities mostly select for two functional guilds, methanotrophs of the family Methylococcaceae and non-methanotrophic methylotrophs of the family Methylophilaceae, these taxonomic guilds outcompeting all other species included in the synthetic mix. The metatranscriptomics analysis uncovered that in both Methylococcaceae and Methylophilaceae, some of the most highly transcribed genes were the ones encoding methanol dehydrogenases (MDH). Remarkably, expression of alternative MDH genes (mxaFI versus xoxF), previously shown to be subjects to the rare Earth element switch, was found to depend on environmental conditions such as nitrogen source and methane and O2 partial pressures, and also to be species- specific. Along with the xoxF genes, genes encoding divergent cytochromes were highly expressed in both Methylophilaceae and Methylococcaceae, suggesting their function in methanol metabolism, likely encoding proteins serving as electron acceptors from XoxF enzymes. The research presented tested a synthetic community model that is much simplified compared to natural communities consuming methane, but more complex than the previously utilized two-species model. The performance of this model identifies prominent species for future synthetic ecology experiments and highlights both advantages of this approach and the challenges that it presents. [ABSTRACT FROM AUTHOR]

Background: Oral biofilm studies based on simplified experimental setups are difficult to interpret. Models are limited mostly by the number of bacterial species observed and the insufficiency of artificial media. Few studies have attempted to overcome these limitations and to cultivate native oral biofilm. Aims: This study aimed to grow oral biofilm in vivo before transfer to a biofilm reactor for ex situ incubation. The in vitro survival of this oral biofilm and the changes in bacterial composition over time were observed. Methods: Six human enamel-dentin slabs embedded buccally in dental splints were used as biofilm carriers. Fitted individually to the upper jaw of 25 non-smoking male volunteers, the splints were worn continuously for 48 h. During this time, tooth-brushing and alcohol-consumption were not permitted. The biofilm was then transferred on slabs into a biofilm reactor and incubated there for 48 h while being nourished in BHI medium. Live/dead staining and confocal laser scanning microscopy were used to observe bacterial survival over four points in time: directly after removal (T0) and after 1 (T1), 24 (T2), and 48 h (T3) of incubation. Bacterial diversity at T0 and T3 was compared with 454-pyrosequencing. Fluorescence in situ hybridization (FISH) was performed to show specific taxa. Survival curves were calculated with a specially designed MATLAB script. Acacia and QIIME 1.9.1 were used to process pyrosequencing data. SPSS 21.0 and R 3.3.1 were used for statistical analysis. Results: After initial fluctuations at T1, survival curves mostly showed approximation of the bacterial numbers to the initial level at T3. Pyrosequencing analysis resulted in 117 OTUs common to all samples. The genera Streptococcus and Veillonella (both Firmicutes) dominated at T0 and T3. They make up two thirds of the biofilm. Genera with lower relative abundance had grown significantly at T3. FISH analysis confirmed the pyrosequencing results, i.e., the predominant staining of Firmicutes. Conclusion: We demonstrate the in vitro survival of native primary oral biofilm in its natural complexity over 48 h. Our results offer a baseline for cultivation studies of native oral biofilms in (phyto-) pharmacological and dental materials research. Further investigations and validation of culturing conditions could also facilitate the study of biofilm-induced diseases. [ABSTRACT FROM AUTHOR]

Background: Two variants of Methylobacterium extorquens AM1 demonstrated a trade-off between growth rate and biomass yield. In addition, growth rate and biomass yield were also affected by supplementation of growth medium with different amounts of cobalt. The metabolism changes relating to these growth phenomena as well as the trade-off were investigated in this study. 13C metabolic flux analysis was used to generate a detailed central carbon metabolic flux map with both absolute and normalized flux values. Results: The major differences between the two variants occurred at the formate node as well as within C3-C4 inter-conversion pathways. Higher relative fluxes through formyltetrahydrofolate ligase, phosphoenolpyruvate carboxylase, and malic enzyme led to higher biomass yield, while higher relative fluxes through pyruvate kinase and pyruvate dehydrogenase led to higher growth rate. These results were then tested by phenotypic studies on three mutants (null pyk, null pck mutant and null dme mutant) in both variants, which agreed with the model prediction. Conclusions: In this study, 13C metabolic flux analysis for two strain variants of M. extorquens AM1 successfully identified metabolic pathways contributing to the trade-off between cell growth and biomass yield. Phenotypic analysis of mutants deficient in corresponding genes supported the conclusion that C3-C4 inter-conversion strategies were the major response to the trade-off. [ABSTRACT FROM AUTHOR]

In this perspective article, we question how well model organisms, the ones that are easy to cultivate in the laboratory and that show robust growth and biomass accumulation, reflect the dynamics and interactions of microbial communities observed in nature. Today's -omics toolbox allows assessing the genomic potential of microbes in natural environments in a high-throughput fashion and at a strain-level resolution. However, understanding of the details of microbial activities and of the mechanistic bases of community function still requires experimental validation in simplified and fully controlled systems such as synthetic communities. We have studied methane utilization in Lake Washington sediment for a few decades and have identified a number of species genetically equipped for this activity. We have also identified co-occurring satellite species that appear to form functional communities together with the methanotrophs. Here, we compare experimental findings from manipulation of natural communities involved in metabolism of methane in this niche with findings from manipulation of synthetic communities assembled in the laboratory of species originating from the same study site, from very simple (two-species) to rather complex (50-species) synthetic communities. We observe some common trends in community dynamics between the two types of communities, toward representation of specific functional guilds. However, we also identify strong discrepancies between the dominant methane oxidizers in synthetic communities compared to natural communities, under similar incubation conditions. These findings highlight the challenges that exist in using the synthetic community approach to modeling dynamics and species interactions in natural communities. [ABSTRACT FROM AUTHOR]