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Furthermore, a two-dimensional finite element method simulation of this model system was performed to quantitively analyze the concentration profiles of the electrogenerated species around the tip of the DCE and predict the concentrations of [Ru(bpy)3]2+* with various gap distances. The simulation results also proved that the higher concentrations of [Ru(bpy)3]2+* could be achieved with a smaller distance with a possible amplification factor of 6 (compared with the concentration when the gap distance is greater than 300 nm). This work provides an experimental model for further improvement of ECL efficiency and broadens the availability for annihilation ECL applications in small confined spaces.This research presents two new policy-level performance indicators for measuring hydraulic fracturing chemical transparency to address the limitations of existing metrics and provide additional perspectives to stakeholders. Existing indicators do not capture the change in proportions of hydraulic fracturing wells with publicly available chemical information or percent of ingredient mass withheld on chemical disclosure forms. Based on the new indicators, state-level policy changes and the FracFocus register have increased hydraulic fracturing chemical transparency over the past decade and continue to drive measurable improvements. The percent of wells with publicly disclosed ingredients increased from ∼0 to 95% (2010-2019), and the average percent of hydraulic fracturing fluid mass withheld on chemical disclosure forms decreased ∼46.8% (2013-2019). The percent ingredient mass withholding was used to compare the two current regulatory chemical disclosure form approaches (system and traditional). In 2019, the average percent of hydraulic fracturing fluid mass withheld on system approach chemical disclosure forms (0.044%) was 66.3% less than the traditional forms (0.132%). This research improves our capabilities to understand, evaluate, and communicate the effect of chemical transparency policy decisions and corporate practices. Recent lessons learnt from the oil and gas industry should be used to study broader chemical transparency policies, information systems, and communication strategies.The electron transport layers (ETLs) are one of the crucial factors for realizing the high performance of inverted organic solar cells (OSCs). In inverted OSCs, zinc oxide (ZnO) is a widely used n-type semiconductor as the ETL material. However, when exposed to ultraviolet (UV) light, ZnO induces decomposition of organic materials. Tin dioxide (SnO2) has higher conductivity, higher electron mobility, wider bandgap, and weaker absorption of UV light, which is thought to be one of the promising ETLs. Unfortunately, a SnO2 ETL is suffering from high work function (WF), which greatly decreases the ability of charge transport and collection. Here, we induce a facile strategy to reduce the WF of SnO2 by Co2+ tuning. The Co2+-tuned SnO2 exhibits a low WF of 3.64 eV, holding high transmittance and high conductivity. The OSCs based on PM6Y6 with a Co2+-SnO2 ETL show a notable power conversion efficiency of 15.3%, which is superior to those of the OSCs with ZnO and SnO2 ETLs. The OSCs with a Co2+-SnO2 ETL under continuous UV light and light-emitting diode irradiation exhibit a more robust photostability relative to OSCs with pristine SnO2 ETLs. The trap densities of Co2+-SnO2 films are lower than that of the SnO2 film, which may contribute to enhanced stability of OSCs.Using a multiscale computational scheme, we study the trends in distribution and composition of the surface functional groups -O, -OH, and -F on two-dimensional (2D) transition metal carbides and nitrides (MXenes). We consider Ti2N, Ti4N3, Nb2C, Nb4C3, Ti2C, and Ti3C2 to explore MXenes with different chemistry and different number of atomic layers. Using a combination of cluster expansion, Monte Carlo, and density functional theory methods, we study the distribution and composition of functional groups at experimentally relevant conditions. We show that mixtures of functional groups are favorable on all studied MXene surfaces. The distribution of functional groups appears to be largely independent of the type of metal, carbon, or nitrogen species and/or number of atomic layers in the MXene. We further show that some properties (e.g., the work function) strongly depend on the surface composition, while others, for example, the electric conductivity, exhibit only a weak dependence.The heterogeneity associated with glycosylation of the 66 N-glycan sites on the protein trimer making up the spike (S) region of the SARS-CoV-2 virus has been assessed by charge detection mass spectrometry (CDMS). CDMS allows simultaneous measurement of the mass-to-charge ratio and charge of individual ions, so that mass distributions can be determined for highly heterogeneous proteins such as the heavily glycosylated S protein trimer. The CDMS results are compared to recent glycoproteomics studies of the structure and abundance of glycans at specific sites. Interestingly, average glycan masses determined by “top-down” CDMS measurements are 35-47% larger than those obtained from the “bottom-up” glycoproteomics studies, suggesting that the glycoproteomic measurements underestimated the abundances of larger, more-complex glycans. Moreover, the distribution of glycan masses determined by CDMS is much broader than the distribution expected from the glycoproteomics studies, assuming that glycan processing on each trimer is not correlated. The breadth of the glycan mass distribution therefore indicates heterogeneity in the extent of glycan processing of the S protein trimers, with some trimers being much more heavily processed than others. This heterogeneity may have evolved as a way of further confounding the host’s immune system.The first objective of this study is to assess the predictive capability of the ALBA (ALgae-BActeria) model for a pilot-scale (3.8 m2) high-rate algae-bacteria pond treating agricultural digestate. The model, previously calibrated and validated on a one-year data set from a demonstrative-scale raceway (56 m2), successfully predicted data from a six-month monitoring campaign with a different wastewater (urban wastewater) under different climatic conditions. Without changing any parameter value from the previous calibration, the model accurately predicted both online monitored variables (dissolved oxygen, pH, temperature) and off-line measurements (nitrogen compounds, algal biomass, total and volatile suspended solids, chemical oxygen demand). selleck kinase inhibitor Supported by the universal character of the model, different scenarios under variable weather conditions were tested, to investigate the effect of key operating parameters (hydraulic retention time, pH regulation, kLa) on algae biomass productivity and nutrient removal efficiency.