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Environmental and intrinsic factors interact to determine energy requirements in vertebrates. Glucocorticoid hormones (GCs) are key mediators of this interaction, as they fluctuate with energetic demands and regulate physiological and behavioral responses to environmental challenges. While a great body of research has focused on GC variation among individuals, the mechanisms driving GC variation across species and at broad spatial scales remain largely unexplored. Here, we adopted a macrophysiological approach to investigate the environmental factors and life-history traits driving variation in baseline GCs across lizard species. We tested three hypotheses: (1) If GCs increase with body temperature to meet higher metabolic demand, we expect an association between average baseline GCs and the mean species’ body temperature in the field (GC-temperature dependence hypothesis); (2) If GCs mediate behavioral responses to avoid thermal extremes, we expect that individuals frequently exposed to extreme conditions exhibit higher baseline GC levels (Behavioral thermoregulation hypothesis); (3) If GCs increase to support higher energy demands in active foragers during their period of activity, we expect that active foraging species have higher baseline GCs than sit-and-wait foragers, and that GC levels increase in relation to the duration of daily activity windows (Activity hypothesis). We used biophysical models to calculate operative temperatures and the activity patterns of lizards in sun-exposed and shaded microenvironments. Then, we tested the association between baseline GCs, body temperature, operative temperatures, foraging mode, and activity windows across 37 lizard species, using data from HormoneBase. Our comparative analyses showed that variation in baseline GCs was primarily related to the mean field body temperature and foraging mode, with higher baseline GCs in active foragers with higher body temperatures. Our results suggest that body temperature and foraging mode drive GC variation through their effects on energy requirements across lizard species.

Abstract Amorphous and microcrystalline silicon single layers and p–i–n solar cells were produced dynamically using an inline deposition system called “line source”. A highly uniform deposition of thin-film silicon layers with layer-thickness variations of less than ±5% was achieved. Amorphous and microcrystalline silicon single junction solar cells were dynamically fabricated with initial efficiencies of 8.3% and 6.3%, respectively. The dynamic deposition rate of these solar cells is 6.75 nm m/min in case of a-Si:H and 3.3 nm m/min for μc-Si:H. In this work it will be shown that an enhancement of the deposition rate up to 15.6 nm m/min during the i-layer deposition of a-Si:H solar cells has only a weak negative influence on the initial efficiencies of the cells. Further on, the effect of substrate velocity on solar cell characteristics of a-Si:H solar cells is investigated. Finally, a productivity estimation of the line source concept is presented.

In recent years, prices for photovoltaics have fallen steadily and the demand for sustainable energy has increased. Consequentially, the assessment of roof surfaces in terms of their suitability for PV (Photovoltaic) installations has continuously gained in importance. Several types of assessment approaches have been established, ranging from sampling to complete census or aerial image analysis methodologies. Assessments of rooftop photovoltaic potential are multi-stage processes. The sub-task of examining the photovoltaic potential of individual rooftops is crucial for exact case study results. However, this step is often time-consuming and requires lots of computational effort especially when some form of intelligent classification algorithm needs to be trained. This often leads to the use of sampled rooftop utilization factors when investigating large-scale areas of interest, as data-driven approaches usually are not well-scalable. In this paper, a novel neighbourhood-based filtering approach is introduced that can analyse large amounts of irradiation data in a vectorised manner. It is tested in an application to the city of Giessen, Germany, and its surrounding area. The results show that it outperforms state-of-the-art image filtering techniques. The algorithm is able to process high-resolution data covering 1 km2 within roughly 2.5 s. It successfully classifies rooftop segments which are feasible for PV installations while omitting small, obstructed or insufficiently exposed segments. Apart from minor shortcomings, the approach presented in this work is capable of generating per-rooftop PV potential assessments at low computational cost and is well scalable to large scale areas.

Abstract Solutions containing hydrofluoric acid (HF), hydrochloric acid (HCl), and hydrogen peroxide (H2O2) were investigated as novel acidic, NOx-free etching mixtures for texturing of monocrystalline silicon wafers. High etch rates of up to 13.3 nm s−1 were observed at room temperature, which are comparable to the etch rates of KOH-IPA solutions. The silicon surface was investigated by scanning electron microscopy (SEM) and confocal laser scanning microscopy (CLSM), indicating pyramidal textures for diamond wire and SiC-slurry sawn as well as saw-damage etched (polished) wafers. Non-stirred baths generate random pyramidal structures while constantly stirred solutions generate novel random inverted pyramidal surface structures. The light trapping efficiency of wafers etched by the HF-HCl-H2O2 solutions was compared by UV/vis-reflectivity measurements to KOH/i-propanol specimens indicating lower reflectivities for the HF-HCl-H2O2-treated samples. Using the ‘wafer ray tracer’ (pvlighthouse.com) the light absorption properties of monomodal and random inverted pyramid structures were simulated and compared to well-known random and monomodal textures for PERC solar cells, clearly indicating the best performance for random inverted pyramids. Besides, simulation of a PERC solar cell on a roof top at our university was performed, indicating improved performance, especially for random inverted pyramid textures.

Smart charging of Electric Vehicles (EVs) reduces operating cost, allows more sustainable battery usage, and promotes the rise of electric mobility. In addition, bidirectional charging and improved connectivity enable efficient power grid support. Today, however, uncoordinated charging, e.g., governed by users’ habits, is still the norm. Thus, the impact of upcoming smart charging applications is mostly unexplored. We aim to estimate the expenses inherent with smart charging, e.g., battery aging costs, and give suggestions for further research. Using typical onboard sensor data we concisely model and validate an EV battery. We then integrate the battery model into a realistic smart charging use case and compare it with measurements of real EV charging. The results show that i) the temperature dependence of battery aging calls for precise thermal models for charging power greater than 7 kW, ii) disregarding battery aging underestimates EVs’ operating cost by approx. 30%, and iii) the profitability of Vehicle-to-Grid (V2G) services based on bidirectional power flow, e.g., energy arbitrage , depends on battery aging costs and the electricity price spread.

Abstract The impact of a twisted turbulator in a parabolic solar collector on the improvement of thermal–hydraulic performance (THP), as well as energy and exergy Efficiency of MgO-Cu/water hybrid nanofluid (HNF) is numerically evaluated. The main goal of this study is to use a twisted turbulator with pitch ratios (γ) of 1, 1.5, 2, and 2.5 for Reynolds numbers (Re) of 8000 to 32,000 and volume fractions (ϕ) of 1, 2, and 3% of MgO and Cu nanoparticles. Numerical simulation findings are reported in the form of graphs of average Nusselt number (Nuave), pressure drop (Δp), THP, energy Efficiency ( η c ) , and exergy Efficiency ( η ex ) . It can be concluded that Nuave and Δp depends on ϕ and Re and augments linearly with their values. Moreover, Nuave and Δp increases significantly by increasing the pitch ratio of the turbulator. The results revealed that the maximum values of augmentation of η c and η ex are respectively 23.79% and 21.15% by raising Re from 8000 to 32000. In a SC with a turbulator with γ = 2 and ϕ = 3%, η c is raised by 24.16% by increasing Re from 8000 to 32000; while, in the case of γ = 2.5 and ϕ = 3%, η c is enhanced by 18.2%.

Virtually developing of automotive power systems is currently experiencing increasing significance due to elevated vehicular electrification and automation of driving functions. The analysis of system-inherent disturbances requires accurate simulation models for both short-term transients or long-term driving cycles. The electrical behavior of electromechanical actuators such as brake or steering highly depends on the mechanical environmental interaction being dependent many variables. This contribution presents a methodology to extract abstracted models of machine driven actuators on the basis of an electric steering. The dynamics of a complex electromechanical model are investigated using pulse response analysis for differentiated operation points. The obtained model reduction is implemented in a frequency-dependent equivalent circuit.