This study examines public information needs and the role of information and perceived knowledge in shaping the acceptance of solar fuels produced through artificial photosynthesis. Using a mixed-method approach that includes qualitative interviews and a quasi-experimental online survey, we identify specific information needs and assess the influence of information on solar fuel acceptance using structural equation modeling. Our findings reveal that richer informational content significantly enhances perceived knowledge. In turn, this fosters more positive affective and cognitive evaluations. Although information shows no direct effect on societal perceptions and acceptance, its significant indirect effect through perceived knowledge on perceptions and acceptance underscores its importance. Our findings contribute to an understanding of which information is prioritized regarding solar fuels by the public and how feelings of knowledgeability enhance positive perceptions. These insights provide clear recommendations for tailored communication strategies for solar fuels and related sustainable innovations. • The ecological footprint is rated the most important information for solar fuels. • Richer informational content significantly enhances perceived knowledge. • Higher perceived knowledge leads to more positive attitudes toward solar fuels. • The effect of information provision on solar fuel acceptance is indirect/mediated.

Ammonia (NH3) reactivity in a micro flow reactor with a controlled temperature profile (MFR) is reexamined through species measurements utilizing two heating sources in the MFR: an H2/air flat flame and an electric heater. The maximum wall temperatures (Tw,max) formed in the reactor vary in a range of Tw,max = 1100–1400 K. A stoichiometric NH3/air mixture is tested, and exhaust NH3 is detected by a quadrupole mass spectrometer (QMS). Unexpectedly, NH3 is completely consumed at temperatures at least 100 K lower in the H2/air flat flame case compared to the electric furnace case, despite nearly identical conditions of a MFR characteristic residence time estimated by the wall temperature profiles and the convective flow velocity. Considering the non-thermal characteristics of the two heating sources that the H2/air flat flame emits ultraviolet light, whereas infrared light as thermal radiation is emitted within the electric furnace, the possibility of NH3 photolysis in the H2/air flat flame case is discussed based on literature regarding emissions from the H2/air flames, the transmittance of the quartz tube, and the photodissociation of NH3 in the ultraviolet region. When ultraviolet light emitted from the H2/air flat flame passes through the quartz tube and decomposes NH3 into NH2 and H radicals, these produced radicals enhance the growth of OH radicals, resulting in increased NH3 reactivity. These findings suggest the possibility of photolysis-assisted ammonia combustion, which could be an additional method to overcome the low reactivity of NH3.

This study scrutinizes the effect of the heating rate obtained in two reactors of different volume (10 mL and 500 mL) on the production of bio-crude during the hydrothermal liquefaction (HTL) process applied to sludges of different nature: a municipal, a paper mill and an agricultural sludge. The effect of the reactor scale on the chemical composition of the bio-crude and the solid residue (co-product of HTL process) is also evaluated by elemental, GC–MS and FT-IR analysis. Results suggest that different heating rates along HTL exert an almost negligible influence on the bio-crude yield and limited effect on the chemical composition when the reaction time under the isothermal conditions is kept at 10 min. In fact, regardless of the heating rate, the bio-crude yields on dry basis from municipal, paper mill and agricultural sludges are equal to 21 %, 21 % and 12 %, respectively. These results could be explained with the different reactive pathway and kinetics of the sludges macro-components.

Nanoparticles are being used as additives for solid and liquid fuels owing to their high specific surface area (high reactivity) and potential ability to store energy in surfaces. The use of nanoparticles in diesels, biodiesels, and their blends is a novel area with unrealised potential owing to the higher catalytic activity of nanoparticles compared with that of micro-sized materials Nanoparticles have been shown to disperse more evenly in fuels and exhibit high stability. In addition, nanoparticles in similar media burn faster than micro-sized particles. The addition of nanoparticles into diesel, biodiesels, and their blends affect the physicochemical properties of the fuels such as kinematic viscosity, density, flash point, and cetane number. Studies have shown that nanoparticles affect the brake specific fuel consumption, brake specific energy consumption, and brake thermal efficiency, depending on the dosage and type of nanoparticles. Studies have also shown that the addition of nanoparticles affect carbon monoxide, carbon dioxide, nitrogen oxide, and unburned hydrocarbon emissions, along with smoke opacity. This review presents the application of various types of nanoparticles in diesel, biodiesels, and their blends to enhance the physicochemical properties of the fuels, combustion efficiency, and engine performance, and reduce harmful exhaust emissions. It is believed that this review will be beneficial to scholars, researchers, and industrial practitioners looking forward to improve diesel engine performance and reduce exhaust emissions by exploiting nanotechnology.

Accelerated global human population growth and the corresponding increased urbanisation and industrialisation have resulted in increased manufacturing of goods, and production and loading of domestic and industrial wastewater overwhelming conventional wastewater treatment plants (CWTPs). The net result has been the release of untreated and partially treated domestic wastewater into water systems posing human health hazards and disturbing aquatic habitat integrity. Considering the severe challenges of wastewater treatment not only in Zimbabwe but in Africa and the world in general, it is prudent to assess the microbial fuel cells (MFCs) as an alternative wastewater treatment method for the CWTPs that have failed to operate efficiently. This purposive literature scoping review aimed to: (a) Examine the concept design and operational efficacy of microbial fuel cells (MFCs), (b) Examine the MFC operational system (c) Outline in brief the evolutionary history and assess the existent prototypes and (d) Establish the drivers and barriers for the uptake of microbial fuel cells (MFCs) from a global and local, Zimbabwe, context. Few prototypes have been utilized in real-world systems; with the majority of them being laboratory-scale based. Although MFCs are effective at treating wastewater, scaling them up is still difficult due to their low power generation. Nonetheless, MFCs' simultaneous wastewater treatment and power generation, low carbon footprint, and reduced sludge production are the main drivers behind their adoption. However, capital and maintenance costs and upscaling remain the major challenges in adopting MFC technology. If MFCs are to be used in developing nations like Zimbabwe, further studies should focus on low-cost materials that guarantee maximum power generation and effective wastewater treatment. To ensure effective wastewater treatment, MFCs should be compatible, and integrated with currently utilized sustainable wastewater treatment systems.

In this paper, we develop a series of Artificial Neural Networks (ANN) using different chemical kinetic and thermodynamic input parameters to predict detonation cell sizes. The feedforward neural networks are trained and validated using available experimental data from the Caltech detonation database covering a wide variety of gaseous combustible mixtures at different initial conditions. For each combination of input parameters, a multiple-stage process is followed, which is described in detail, to first determine the best hyperparameters of the ANN (hidden layers, nodes per layer, etc.) and secondly to establish through a fitting process the optimal parameters for each specific network. The performance of the artificial neural networks with different input features is assessed using data from the same source, but that is kept independent and separate from the training and validation process of the ANN. It is found that ANN with three features can provide an accurate estimation of detonation cell size, while increasing the number of features does not improve the accuracy of the ANN. It is also found that the input parameters with the best performance relate indirectly to the stability parameter χ.