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The possibility to manufacture perovskite solar cells (PSCs) at low temperatures paves the way to flexible and lightweight photovoltaic (PV) devices manufactured via high-throughput roll-to-roll processes. In order to achieve higher power conversion efficiencies, it is necessary to approach the radiative limit via suppression of non-radiative recombination losses. Herein, we performed a systematic voltage loss analysis for a typical low-temperature processed, flexible PSC in n-i-p configuration using vacuum deposited C-60 as electron transport layer (ETL) and two-step hybrid vacuum-solution deposition for CH3NH3PbI3 perovskite absorber. We identified the ETL/absorber interface as a bottleneck in relation to non-radiative recombination losses, the quasi-Fermi level splitting (QFLS) decreases from similar to 1.23 eV for the bare absorber, just similar to 90 meV below the radiative limit, to similar to 1.10 eV when C-60 is used as ETL. To effectively mitigate these voltage losses, we investigated different interfacial modifications via vacuum deposited interlayers (BCP, B4PyMPM, 3TPYMB, and LiF). An improvement in QFLS of similar to 30-40 meV is observed after interlayer deposition and confirmed by comparable improvements in the open-circuit voltage after implementation of these interfacial modifications in flexible PSCs. Further investigations on absorber/hole transport layer (HTL) interface point out the detrimental role of dopants in Spiro-OMeTAD film (widely employed HTL in the community) as recombination centers upon oxidation and light exposure. [GRAPHICS] .
Reaching the Sustainable Development Goals requires a fundamental socio-economic transformation accompanied by substantial investment in low-carbon infrastructure. Such a sustainability transition represents a non-marginal change, driven by behavioral factors and systemic interactions. However, typical economic models used to assess a sustainability transition focus on marginal changes around a local optimum, whichby constructionlead to negative effects. Thus, these models do not allow evaluating a sustainability transition that might have substantial positive effects. This paper examines which mechanisms need to be included in a standard computable general equilibrium model to overcome these limitations and to give a more comprehensive view of the effects of climate change mitigation. Simulation results show that, given an ambitious greenhouse gas emission constraint and a price of carbon, positive economic effects are possible if (1) technical progress results (partly) endogenously from the model and (2) a policy intervention triggering an increase of investment is introduced. Additionally, if (3) the investment behavior of firms is influenced by their sales expectations, the effects are amplified. The results provide suggestions for policy-makers, because the outcome indicates that investment-oriented climate policies can lead to more desirable outcomes in economic, social and environmental terms.
EDTA and NTA effectively tune the mineralization of calcium phosphate from bulk aqueous solution
(2017)
This study describes the effects of nitrilotriacetic acid (NTA) and ethylenediaminotetraacetic acid (EDTA) on themineralization of calciumphosphate from bulk aqueous solution. Mineralization was performed between pH 6 and 9 and with NTA or EDTA concentrations of 0, 5, 10, and 15 mM. X-ray diffraction and infrared spectroscopy show that at low pH, mainly brushite precipitates and at higher pH, mostly hydroxyapatite forms. Both additives alter the morphology of the precipitates. Without additive, brushite precipitates as large plates. With NTA, the morphology changes to an unusual rod-like shape. With EDTA, the edges of the particles are rounded and disk-like particles form. Conductivity and pH measurements suggest that the final products form through several intermediate steps.
Climate impacts on transocean dispersal and habitat in gray whales from the Pleistocene to 2100
(2015)
Arctic animals face dramatic habitat alteration due to ongoing climate change. Understanding how such species have responded to past glacial cycles can help us forecast their response to today's changing climate. Gray whales are among those marine species likely to be strongly affected by Arctic climate change, but a thorough analysis of past climate impacts on this species has been complicated by lack of information about an extinct population in the Atlantic. While little is known about the history of Atlantic gray whales or their relationship to the extant Pacific population, the extirpation of the Atlantic population during historical times has been attributed to whaling. We used a combination of ancient and modern DNA, radiocarbon dating and predictive habitat modelling to better understand the distribution of gray whales during the Pleistocene and Holocene. Our results reveal that dispersal between the Pacific and Atlantic was climate dependent and occurred both during the Pleistocene prior to the last glacial period and the early Holocene immediately following the opening of the Bering Strait. Genetic diversity in the Atlantic declined over an extended interval that predates the period of intensive commercial whaling, indicating this decline may have been precipitated by Holocene climate or other ecological causes. These first genetic data for Atlantic gray whales, particularly when combined with predictive habitat models for the year 2100, suggest that two recent sightings of gray whales in the Atlantic may represent the beginning of the expansion of this species' habitat beyond its currently realized range.
RainNet v1.0
(2020)
In this study, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. Its design was inspired by the U-Net and SegNet families of deep learning models, which were originally designed for binary segmentation tasks. RainNet was trained to predict continuous precipitation intensities at a lead time of 5min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900km × 900km and has a resolution of 1km in space and 5min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In order to achieve a lead time of 1h, a recursive approach was implemented by using RainNet predictions at 5min lead times as model inputs for longer lead times. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the rainymotion library and had previously been shown to outperform DWD's operational nowcasting model for the same set of verification events.
RainNet significantly outperforms the benchmark models at all lead times up to 60min for the routine verification metrics mean absolute error (MAE) and the critical success index (CSI) at intensity thresholds of 0.125, 1, and 5mm h⁻¹. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15mm h⁻¹). The limited ability of RainNet to predict heavy rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16km and below. Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5min, however, the increasing level of smoothing is a mere artifact – an analogue to numerical diffusion – that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance in terms of a binary segmentation task. Furthermore, we suggest additional input data that could help to better identify situations with imminent precipitation dynamics. The model code, pretrained weights, and training data are provided in open repositories as an input for such future studies.
Lifestyle-related disorders, such as the metabolic syndrome, have become a primary risk factor for the development of liver pathologies that can progress from hepatic steatosis, hepatic insulin resistance, steatohepatitis, fibrosis and cirrhosis, to the most severe condition of hepatocellular carcinoma (HCC). While the prevalence of liver pathologies is steadily increasing in modern societies, there are currently no approved drugs other than chemotherapeutic intervention in late stage HCC. Hence, there is a pressing need to identify and investigate causative molecular pathways that can yield new therapeutic avenues. The transcription factor p53 is well established as a tumor suppressor and has recently been described as a central metabolic player both in physiological and pathological settings. Given that liver is a dynamic tissue with direct exposition to ingested nutrients, hepatic p53, by integrating cellular stress response, metabolism and cell cycle regulation, has emerged as an important regulator of liver homeostasis and dysfunction. The underlying evidence is reviewed herein, with a focus on clinical data and animal studies that highlight a direct influence of p53 activity on different stages of liver diseases. Based on current literature showing that activation of p53 signaling can either attenuate or fuel liver disease, we herein discuss the hypothesis that, while hyper-activation or loss of function can cause disease, moderate induction of hepatic p53 within physiological margins could be beneficial in the prevention and treatment of liver pathologies. Hence, stimuli that lead to a moderate and temporary p53 activation could present new therapeutic approaches through several entry points in the cascade from hepatic steatosis to HCC.
In two-dimensional reaction-diffusion systems, local curvature perturbations on traveling waves are typically damped out and vanish. However, if the inhibitor diffuses much faster than the activator, transversal instabilities can arise, leading from flat to folded, spatio-temporally modulated waves and to spreading spiral turbulence. Here, we propose a scheme to induce or inhibit these instabilities via a spatio-temporal feedback loop. In a piecewise-linear version of the FitzHugh-Nagumo model, transversal instabilities and spiral turbulence in the uncontrolled system are shown to be suppressed in the presence of control, thereby stabilizing plane wave propagation. Conversely, in numerical simulations with the modified Oregonator model for the photosensitive Belousov-Zhabotinsky reaction, which does not exhibit transversal instabilities on its own, we demonstrate the feasibility of inducing transversal instabilities and study the emerging wave patterns in a well-controlled manner.
Lanthanide-doped upconverting nanoparticles (UCNP) are being extensively studied for bioapplications due to their unique photoluminescence properties and low toxicity. Interest in RET applications involving UCNP is also increasing, but due to factors such as large sizes, ion emission distributions within the particles, and complicated energy transfer processes within the UCNP, there are still many questions to be answered. In this study, four types of core and core-shell NaYF4-based UCNP co-doped with Yb3+ and Tm3+ as sensitizer and activator, respectively, were investigated as donors for the Methyl 5-(8-decanoylbenzo[1,2-d:4,5-d ']bis([1,3]dioxole)-4-yl)-5-oxopentanoate (DBD-6) dye. The possibility of resonance energy transfer (RET) between UCNP and the DBD-6 attached to their surface was demonstrated based on the comparison of luminescence intensities, band ratios, and decay kinetics. The architecture of UCNP influenced both the luminescence properties and the energy transfer to the dye: UCNP with an inert shell were the brightest, but their RET efficiency was the lowest (17%). Nanoparticles with Tm3+ only in the shell have revealed the highest RET efficiencies (up to 51%) despite the compromised luminescence due to surface quenching.
Electrochemical methods offer the simple characterization of the synthesis of molecularly imprinted polymers (MIPs) and the readouts of target binding. The binding of electroinactive analytes can be detected indirectly by their modulating effect on the diffusional permeability of a redox marker through thin MIP films. However, this process generates an overall signal, which may include nonspecific interactions with the nonimprinted surface and adsorption at the electrode surface in addition to (specific) binding to the cavities. Redox-active low-molecular-weight targets and metalloproteins enable a more specific direct quantification of their binding to MIPs by measuring the faradaic current. The in situ characterization of enzymes, MIP-based mimics of redox enzymes or enzyme-labeled targets, is based on the indication of an electroactive product. This approach allows the determination of both the activity of the bio(mimetic) catalyst and of the substrate concentration.
Cell-level kinetic models for therapeutically relevant processes increasingly benefit the early stages of drug development. Later stages of the drug development processes, however, rely on pharmacokinetic compartment models while cell-level dynamics are typically neglected. We here present a systematic approach to integrate cell-level kinetic models and pharmacokinetic compartment models. Incorporating target dynamics into pharmacokinetic models is especially useful for the development of therapeutic antibodies because their effect and pharmacokinetics are inherently interdependent. The approach is illustrated by analysing the F(ab)-mediated inhibitory effect of therapeutic antibodies targeting the epidermal growth factor receptor. We build a multi-level model for anti-EGFR antibodies by combining a systems biology model with in vitro determined parameters and a pharmacokinetic model based on in vivo pharmacokinetic data. Using this model, we investigated in silico the impact of biochemical properties of anti-EGFR antibodies on their F(ab)-mediated inhibitory effect. The multi-level model suggests that the F(ab)-mediated inhibitory effect saturates with increasing drug-receptor affinity, thereby limiting the impact of increasing antibody affinity on improving the effect. This indicates that observed differences in the therapeutic effects of high affinity antibodies in the market and in clinical development may result mainly from Fc-mediated indirect mechanisms such as antibody-dependent cell cytotoxicity.