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Abstract Socio-technical transformations towards low-carbon energy systems are on the way in developed countries. Conversely, developing countries tend to be locked in fossil fuels and foster coal-based energy structures, emphasizing reliable and cost-effective energy provision and sidelining environmental concerns. In this study, we identified and analysed the predominant factors related to coal-based power generation in Bangladesh. We applied a mixed-method approach, initially conducting a systematic literature review and, subsequently, semi-structured expert interviews to identify and validate relevant factors. We then assessed their relative importance using an Analytical Hierarchy Process based on expert judgments. The results of this assessment reveal that socio-economic aspects and environmental issues scored highest, while technological aspects and sector regulations were considered to be less relevant for large-scale coal power implementation. We conclude that future energy policies created in Bangladesh will need to use appropriate legal instruments and address issues such as human displacement and resettlement, low levels of public acceptance, health hazards and environmental pollution. Participative policy frameworks should be deployed in coal plant projects, and active monitoring systems are necessary to reduce the negative consequences associated with increased electrification and energy consumption. To address foreseeable structural challenges, it furthermore will be crucial to explore sustainable alternatives.

C57BL/6J mice showed dose dependent devreases in locomotor activity with increasing IP doses of ethanol (0.0, 0.75, 1.50 and 2.25 g/kg), while BALB/cJ mice showed dose dependent increases in activity; both strains were equally active with saline. Whether this finding represents decreased CNS responsivity in C57BL mice to ethanol's excitatory effect or increased response to its depressant action at sub-hypnotic doses is unclear, since anesthetic doses produce anesthesia of far shorter duration in the C57BL strain than in the BALB strain. It is possible that the biphasic action of alcohol is under the control of separate and distinct mechanisms, rather than a common one, and that these two mechanisms are differentially affected by alcohol. Endogenous as well as ethanol-induced neurochemical differences in biogenic amines may also be correlated with the gentic variation in CNS responsivity towards alcohol.

We evaluated two-step hydrothermal liquefaction in a semi-continuous reactor for recovery of both nutri- ents and biocrude from the alga Coelastrum sp. in direct comparison with a one-step process. The influence of the operating temperature, pressure and water flow rate was investigated by means of a 2 k factorial experimental design and response surface methodology. The two-step process gave a higher total biocrude yield (~36 wt% (daf. basis)) and nutrient recovery level in terms of nitrogen containing compounds (~60 wt% of the protein content in the original algae as ammonium and nitrate ions and protein/polypeptides) than the single-step process. The highest biocrude yield was achieved at first-step temperature of 473 K, second-step temperature of 593 K, pressure of 200 bar and water flow rate of 0.5 mL/min.

Cryptomeria japonica (CJ) samples were heated in a thermogravimetric analyzer (TGA) with CO2 as agent to study the gasification kinetics in the isothermal process. The samples were torrefied at 250 and 350 °C for 1 h. before being tested in TGA. Three isothermal conditions of 750, 800, and 850 °C were tested in this study to estimate the kinetic parameters of the Arrhenius form with the proposed models. The homogeneous model (HM), shrinking core model (SCM), and linear model (LM) were used, and the predicted results obtained from these models were compared with experimental data. The reaction rate of gasification was enhanced as temperature was raised, and a correlation of kinetic parameters with temperature was obtained. The simulated results of the linear model (LM) fit well with experimental data. This work is useful for design gasification in downdraft gasifier.

Abstract Carbon-based counter electrodes of dye-sensitized solar cells (DSSCs) such as carbon black-graphite composite (CB/Gr) have received unprecedented interest in recent years due to their ease of fabrication, good corrosion resistance, and low cost compared to platinum (Pt) electrodes. However, the poor surface adherence between the carbon counter electrodes (CE) and FTO substrate, low surface area, and poor inter-particle connection between the carbon materials have consistently become a major challenge. In order to overcome these issues, we have fabricated CB/Gr by incorporating binders of titanium (IV) isopropoxide (TTIP), and zirconium (IV) dioxide (ZrO2) as the counter electrodes for the DSSC. The performance of the CE is characterized by four-point probe conductivity measurement, scanning electron microscopy (SEM) and energy dispersive X-ray (EDX), X-ray photoelectron spectroscopy (XPS), electrochemical impedance spectroscopy (EIS), and current density voltage (J-V) characteristics. The results revealed that incorporating binders to the CB/Gr has improved the series resistance (RS) and charge transfer resistance (RCT), which enhances the short-circuit current density (JSC), fill factor (FF), and the power conversion efficiency (PCE) of the device. The TTIP binder in CB/Gr CE exhibited superior performance in DSSC is largely attributed to the formation of TiO2 in situ with small particle size of about 100 nm, leading to enhanced intimate contact between the carbon materials, high surface area, and better surface adherence between counter electrode and the FTO substrate. Our findings, thereby, offer the possibility to engineer and optimize the energy levels of CE in an effort to develop a high-performing DSSC counter electrode.

Abstract As pyrolysis reaction is one of an important reaction applied to lignocellulosic biomass in order to transform it to be user-friendly energy form recognized as prospective alternative energy source, the reaction has been widely investigated in order to understand the mechanisms and kinetics of the pyrolysis. However, modeling pyrolysis of biomass is full of complication. As lignocellulosic biomass is not a homogeneous chemical source, chemical compositions in biomass are also uncertain and they vary even in the same biomass. The reactions of imprecise chemical compositions in biomass affects the capability of deterministic model in modeling chemical reaction since available deterministic models are designed to model homogeneous and precise chemical compositions. With this problem, it raises the idea of using model which has ability to calculate something ambiguous. Since the fuzzy logic-based model which is adaptive network-based fuzzy inference system (ANFIS) is built to calculate uncertainty, the model should be suitable to handle uncertainty which is imprecise chemical compositions in the reaction. The proposed model is built with four input variables: the reaction time, amount of cellulose component, amount of hemicellulose component, and amount of lignin component in biomass. The model is trained with tuning datasets which are the pyrolysis datasets of lignin, cellulose and Madhuca before applying to predict the pyrolysis reactions of Pongamia pinnata and Jatropha curcas . The comparative results show that the proposed model can correctly predict 91.82% and 97.29%, respectively, of the pyrolysis reactions of P. pinnata and J. curcas . As the ANFIS model gives good prediction in modeling pyrolysis of two different biomasses, the model can be applied to predict the pyrolysis reaction of other lignocellulosic biomass products.