• Energy Research
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Comfort temperature and sleep quality involving 20 participants were determined in two cases: Case A (arbitrary, controlled air-conditioner setting) and Case B (adjustment of 3 °C higher than the setting of Case A with cool bed linen). Data of indoor thermal comfort and electricity consumption were collected every night throughout the measurement period. Questionnaires on thermal comfort and sleep quality were distributed twice a night for a duration of three nights for each case; the first night was for respondents’ adaptation and the following two nights were for measurement. The sleep quality of the respondents was objectively measured using a commercially available activity tracker. Results found that most respondents were thermally comfortable in both cases, with 39% lower energy consumption reported for Case B compared to Case A. The thermal conditions of Case B were found to be more tolerable than those of Case A. Most respondents reported to have a calm and satisfied sleep for both cases. Comfort temperature and Sleep Efficiency Index (SEI) were found to be maintained in both cases.

Abstract Separating the effects of environmental factors and spatial distance on microbial composition is difficult when these factors covary. We examined the composition of ectomycorrhizal (EM) fungi along elevation gradients on geographically distant mountains to clarify the effect of climate at the regional scale. Soil cores were collected from various forest types along an elevation gradient in southwestern Japan. Fungal species were identified by the internal transcribed spacer regions of the rDNA using direct sequencing. The occurrence of fungal species in this study was compared with a previous study conducted on a mountain separated by ∼550 km. In total, we recorded 454 EM fungi from 330 of 350 soil cores. Forty-seven fungal species (∼20% of the total excluding singletons) were shared between two mountains, mostly between similar forest types on both mountains. Variation partitioning in redundancy analysis revealed that climate explained the largest variance in EM fungal composition. The similarity of forest tree composition, which is usually determined by climatic conditions, was positively correlated with the similarity of the EM fungal composition. However, the lack of large host effects implied that communities of forest trees and EM fungi may be determined independently by climate. Our data provide important insights that host plants and mutualistic fungi may respond to climate change idiosyncratically, potentially altering carbon and nutrient cycles in relation to the plant–fungus associations.

Public policy requires public support, which in turn implies a need to enable the public not just to understand policy but also to be engaged in its development. Where complex science and technology issues are involved in policy making, this takes time, so it is important to identify emerging issues of this type and prepare engagement plans. In our horizon scanning exercise, we used a modified Delphi technique. A wide group of people with interests in the science and policy interface (drawn from policy makers, policy adviser, practitioners, the private sector and academics) elicited a long list of emergent policy issues in which science and technology would feature strongly and which would also necessitate public engagement as policies are developed. This was then refined to a short list of top priorities for policy makers. Thirty issues were identified within broad areas of business and technology; energy and environment; government, politics and education; health, healthcare, population and aging; information, communication, infrastructure and transport; and public safety and national security.

Facile sol–gel synthesis of Mo:BiVO4 thin films with optimized morphology results in reduced surface recombination and enhanced hole transfer efficiency.

Fullerene-based materials are widely used as acceptor and electron transport layer materials in organic and planar perovskite solar cells. Modeling of electronic properties such as band alignment and charge transport for these applications is typically done using optimized geometries. Here, we estimate the effects of nuclear motions on band structure, and electron and hole transport in two prototypical fullerenes, C60 and C70. We model the dynamics in solid fullerenes using Density Functional Tight Binding and we use the Density Functional Theory based Projection of Monomer Orbitals on Dimer Orbitals (DIPRO) approach to estimate the effects on the charge transfer integral in the Marcus approximation. We show that room-temperature molecular dynamics cause a shift and spread of frontier orbital energies on the order of 0.1 eV, which leads to an increase by more than a factor of two in the Marcus exponent, and can cause a decrease by up to orders of magnitude in the overlap integral, leading, in most cases, to an overall decrease in the charge transport rate.

Abstract The price effect of the rising share of renewable electricity, which is called ‘merit-order-effect’, leads to noticeable changes in the German power industry and debates about the electricity market design. This paper estimates the merit-order-effect induced by variable renewable energy in the German-Austrian electricity sector with a multivariate regression model. The research focus lies on the impact of the estimated effects on the marketability of variable renewable electricity generation. The results show a systematic decline of the average market revenues for wind and photovoltaic plants in the period from January 2011 to December 2013. Current market data shows a continuation of this trend into 2016. According to the German long term goals for the use of renewables, wind and solar power will play a crucial role in the future electricity generation mix. If investments in these technologies will be profitable without any regulatory remuneration mechanisms in addition to the market revenues, depends on the further cost degression and the development of the merit-order-effect.

Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling. Differential equations given by physical laws and NODEs can be combined in a single modelling framework. Here we demonstrate the use of NODEs for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis and represents the physical part of the model. The voltage drop over the resistor–capacitor circuit, including its dependency on current and state of charge, is implemented as a NODE. After training, the grey-box model shows good agreement with experimental full-cycle data and pulse tests on a lithium iron phosphate cell. We test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.