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Interfacial resistance at electrode‐high Li+ conductive solid electrolytes must be reduced well to develop high‐power all‐solid‐state batteries using oxide‐based solid electrolytes (Ox‐SSBs). Herein, crystalline electrode films of LiCoO2 (LCO) are formed on a high Li+ conductive crystalline‐glass solid electrolyte sheet, Li1.3Al0.3Ti2(PO4)3 (LATP) (σ25 °C = 1 × 10−4 S cm−1), at room temperature by aerosol deposition (AD), and the effects of the annealing temperature on the interfacial resistivities (Rint) at the LCO/LATP are investigated. The Rint visibly increases by annealing over 500 °C with the growth of Co3O4 as a reactant. In contrast, Rint is reduced to ≈100 Ω cm2 by low‐temperature annealing at 250–350 °C due to superior contact through the structural rearrangement of an artificial metastable interface formed by the AD. These results are applied to bulk‐type Ox‐SSB, Li/Li7La3Zr2O12(LLZ)/LCO–LATP, and our best Ox‐SSB delivers a discharge capacity of 100 mA cm−2 at 100 °C.

This paper presents a multi-objective optimization model for a long-term generation mix in Indonesia. The objective of this work is to assess the economic, environment, and adequacy of local energy sources. The model includes two competing objective functions to seek the lowest cost of generation and the lowest CO2 emissions while considering technology diffusion. The scenarios include the use of fossil reserves with or without the constraints of the reserve to production ratio and exports. The results indicate that Indonesia should develop all renewable energy and requires imported coal and natural gas. If all fossil resources were upgraded to reserves, electricity demand in 2050 could be met by domestic energy sources. The maximum share of renewable energy that can be achieved in 2050 is 33% with and 80% without technology diffusion. The least cost optimization produces lower generation costs than the least CO2 emissions, as well as the combined scenario. Total CO2 emissions in 2050 are five to six times as large as current emissions. The least CO2 emissions scenario can reduce almost half of the CO2 emissions of the least cost scenario by 2050. The proposed multi-objective optimization model leads some optimal solutions for a more sustainable electricity system.

Abstract Light wells in the centers of high-rise apartment buildings in Japan are called ‘Voids’. Gas water-heaters built into Voids discharge exhaust gas so a large enough opening has to be designed at the bottom of a Void to keep the indoor air quality (IAQ) acceptable. In order to secure the IAQ in the Void from contamination, a simple calculation method of the ventilation rate induced by wind force and thermal buoyancy through openings at the bottom, along with heat sources such as water-heaters, is presented. The accuracy of this calculation method was examined by wind tunnel testing. As a result, it turned out that the simple calculation methods introduced in this study were valid for predicting the vertical temperature distribution and ventilation rates in Voids.

Abstract Recharging infrastructure (RI) deployment plays a vital role in improving the public recharging availability for transport electrification. Decarbonizing transportation using low-emission electricity requires massive RI network. Even though the consumers are reluctant to purchase electric vehicles (EVs) until RIs are sufficiently placed, the investors are not willing to invest in RIs due to recharging demand uncertainties. Therefore, a scientific planning framework is needed to ensure the sustainable deployment of EV-RIs in complex networks. In this study, a lifecycle thinking-based multi-period infrastructure-planning framework is proposed to develop sustainable public EV-RIs in an urban context. This framework consists of a temporal model to find the dynamic EV-RI demands, a stochastic model to obtain travel distances, and a multi-objective optimization model to select the best desirable capacities and locations for potential EV-RIs. A case study of a typical medium-scale municipality in Canada was assessed using the proposed framework and validated using conventional infrastructure planning scenarios. The geo-processing data, regional travel behaviors, and recharging characteristics were used as model inputs. The results of the case study showed that the proposed framework can be used to estimate multi-period public recharging demands, minimize lifecycle costs, maximize service coverage and infrastructure utilization, and ensure reasonable paybacks compared to conventional planning approaches. Moreover, this framework can be used to compare different investment assistances, which are required in the early stages of the RI deployment process to encourage investors. Furthermore, government and private institutions can use this framework to identify recharging demands, permitting, and developing RIs in the long-run.

Abstract The residential sector accounts for most of energy-consumption in developing countries in the form of traditional energy. The use of commercial energy is nominal and confined mostly to urban areas where fuelwood is already monetized. A model, based on an end-use/process analysis approach, is developed on a spreadsheet, which is capable of simulating scenarios to address issues of increasing traditional energy-demand caused by population growth, sustainable supply capacity of the existing energy resources, potential for development of new and renewable energy resources, technology. This paper is divided into two parts: general energy issues and the modelling approach, and the application of this approach to Nepal in the context of fuelwood-supply sustainability.

Abstract Communication between vehicles and road infrastructure can enable more efficient use of the road network and hence reduce congestion in urban areas. This improvement can be enhanced by distributed control due to its lighter computational load and higher reliability. Despite favourable impacts on traffic, little is known about the effects of such systems on near-road air quality. In this study, an End-To-End (E2E) dynamic distributed routing algorithm in Connected and Automated Vehicles (CAVs) was applied in downtown Toronto, to identify whether benefits to network throughput were associated with lower near-road NO2 concentrations. We observe significant reductions in the emissions of Greenhouse Gases (GHGs) with increased penetration of CAVs. Nonetheless, at times, the emissions of nitrogen oxides (NOx) increased with higher CAVs. Besides, a higher frequency and severity of NO2 hot-spots were observed under a 100% CAV scenario. Impacts of the proposed system on electric energy consumption in a full electric vehicle network were also investigated, indicating that the addition of CAVs that are electric did not contribute to high energy savings. We propose that such new transformative technologies in transportation should be designed with air pollution and public health goals.

Abstract Conversion of carbon contained in the solid residues (tars + biochar) derived from urban biomass gasification named herein TC would allow enhancing the yield of carbon species (CO/CO2) in synthetic gas. For this purpose, three low cost materials have been tested as possible catalysts: iron species (reduced Fe), bone meal (BM), and ashes (ash) recovered from biochar complete oxidation. The parametric study used the following as variables: air GHSV, onset of reaction temperature, reaction time to optimize CO/CO2 molar ratio and tar content in the produced gas. Results showed an autocatalytic effect of biochar leading to the catalytic conversion of approximately 78% of tars by the native metals contained in TC. The catalytic effect was further enhanced by adding Fe, BM, and extra ash. Addition of Fe catalyst resulted in significant heat generation (temperature increase of ca. 500 °C) and a twofold decrease in reaction time to consume all the carbon. Use of ash and BM as catalysts exhibit heat generation comparable to Fe, along with an improved reaction time, complete tars conversion and a CO/CO2 molar ratio to above 1.3 in the produced gas.

Active pharmaceutical ingredients are composed of single-component or multicomponent crystals. Multicomponent crystals include salts, co-crystals, and solvates. Indinavir sulfate is the ethanol solvate form of indinavir that is known to deliquesce through moisture absorption. However, the detailed behavior of solvent molecules in the crystal has not been investigated. In this study, we studied the desolvation mechanism of indinavir sulfate ethanol and investigated the behavior of solvent molecules in the solid from. Indinavir sulfate ethanol contained 1.7 molecules of ethanol, 0.7 of which desolvated at room temperature. They were originally two ethanol solvent molecules; one molecule of ethanol desolvated at room temperature, and the conformation of the remaining ethanol and t-butyl groups changed in conjunction with the removal of one ethanol molecule. Desolvation could hardly be detected by powder X-ray diffraction; however, it was detected using terahertz spectroscopy. Terahertz measurement of desolvation showed a high correlation with thermogravimetry data, suggesting that desolvation could be observed non-destructively using terahertz spectroscopy. We concluded that indinavir sulfate 1 ethanol deliquesced at 60% relative humidity, and it turned into an amorphous solid after drying.

AbstractAutotrophs play an essential role in the cycling of carbon and nutrients, yet disease‐ecosystem relationships are often overlooked in these dynamics. Importantly, the availability of elemental nutrients like nitrogen and phosphorus impacts infectious disease in autotrophs, and disease can induce reciprocal effects on ecosystem nutrient dynamics. Relationships linking infectious disease with ecosystem nutrient dynamics are bidirectional, though the interdependence of these processes has received little attention. We introduce disease‐mediated nutrient dynamics (DND) as a framework to describe the multiple, concurrent pathways linking elemental cycles with infectious disease. We illustrate the impact of disease–ecosystem feedback loops on both disease and ecosystem nutrient dynamics using a simple mathematical model, combining approaches from classical ecological (logistic and Droop growth) and epidemiological (susceptible and infected compartments) theory. Our model incorporates the effects of nutrient availability on the growth rates of susceptible and infected autotroph hosts and tracks the return of nutrients to the environment following host death. While focused on autotroph hosts here, the DND framework is generalizable to higher trophic levels. Our results illustrate the surprisingly complex dynamics of host populations, infection patterns, and ecosystem nutrient cycling that can arise from even a relatively simple feedback between disease and nutrients. Feedback loops in disease‐mediated nutrient dynamics arise via effects of infection and nutrient supply on host stoichiometry and population size. Our model illustrates how host growth rate, defense, and tissue chemistry can impact the dynamics of disease–ecosystem relationships. We use the model to motivate a review of empirical examples from autotroph–pathogen systems in aquatic and terrestrial environments, demonstrating the key role of nutrient–disease and disease–nutrient relationships in real systems. By assessing existing evidence and uncovering data gaps and apparent mismatches between model predictions and the dynamics of empirical systems, we highlight priorities for future research intended to narrow the persistent disciplinary gap between disease and ecosystem ecology. Future empirical and theoretical work explicitly examining the dynamic linkages between disease and ecosystem ecology will inform fundamental understanding for each discipline and will better position the field of ecology to predict the dynamics of disease and elemental cycles in the context of global change.