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AbstractConcrete is a valuable construction material with high mechanical strength and durability, used extensively in the construction industry. It is produced by mixing sand, stones, cement, and water in different proportions depending on the desired quality of the final product. Water reducers are additional chemical ingredients used in concrete to reduce the quantity of water required in the concrete mixture. When added to concrete, water reducers increase the workability and flowability of concrete in the freshly mixed state and improve the mechanical strength and durability of the final hardened product. This review paper describes the different types and applications of concrete water reducers used in the construction industry including their working mechanisms and fluidity effects on concrete properties. It discusses the production of synthetic and bio‐based concrete water reducers and reviews the present challenges involved in the preparation of bio‐based concrete water reducers from renewable resources. © 2023 Society of Industrial Chemistry and John Wiley & Sons Ltd.

Novel N-methyl-4-piperidinol (MPDL)-based solvents have been considered as high potential solvents for post combustion carbon capture, especially for power generation industry. To comprehensively investigate the CO2 absorption-regeneration performance of MPDL-based solvents, transport properties (i.e., density, viscosity, and physical CO2 diffusivity) are required. These data are reported in the literature and can be estimated by conventional predictive correlations. However, the conventional correlation is applicable for an individual solvent at various blended ratios and temperatures. Thus, artificial neural network (ANN) was then applied for prediction of the transport properties of MPDL-based solvents, including aqueous solutions of MPDL, MPDL-monoethanolamine (MEA), MPDL-2-amino-2-methyl-1-propanol (AMP), and MPDL-piperazine (PZ). Three learning algorithms of (i) Levenberg–Marquardt (LM), (ii) Bayesian Regularization (BR), and (iii) Scaled Conjugate Gradient (SCG) were applied to develop the predictive ANN models with various hidden neurons. As a result, 6 hidden neurons BR-ANN model was the most convincible single prediction platform for the three transport properties. The develop model can be very beneficial for further applications associated with the novel MPDL-based solvents.

In the present study, Tween 80, a non-ionic surfactant, has been used for enhanced hydrogen production by crude glycerol bioconversion using co-culture of Enterobacter aerogenes and Clostridium butyricum. The purpose of introducing the surfactant was to decrease the crude glycerol viscosity, so that apparent solubility and bioavailability of glycerol could be improved at the expenses of pretreatment steps. Experiments were planned using central composite design (CCD); crude glycerol and Tween 80 concentrations were optimized whereas, hydrogen production, glycerol utilization and viscosity of the media were considered as responses. The response surface for quadratic model showed, Tween 80 concentration had significant effect (p < 0.05) on all the three responses. Using the optimized conditions at 17.5 g/L crude glycerol and 15 mg/L Tween 80, hydrogen production reached a maximum of 32.1 ± 0.03 mmol/L of medium. The increase in hydrogen production was around 1.25-fold in presence of Tween 80 in comparison to its absence with 25.56 ± 0.91 mmol/L production. Selected optimum conditions were also validated against absence of crude glycerol (4.69 ± 0.76), with pretreated crude glycerol (20.06 ± 0.51) and across mono-culture system (15.43 ± 0.79 to 22.14 ± 0.94). Introduction of Tween 80 to the fermentation medium improved the glycerol utilization rate, resulting in increased hydrogen production and eliminated pretreatment steps.

Abstract Bitumen recovery by steam-solvent coinjection involves the coupled thermal/compositional mechanisms for reduction of bitumen viscosity. Reliable design of such processes requires reservoir flow simulation based on a proper phase-behavior model so that the oleic-phase viscosity near the steam-chamber edge can be modeled reliably. However, the effect of bitumen characterization (e.g., the number of pseudo components used) on steam-solvent coinjection simulation has not been studied in detail, and can be realized only after running multiple reservoir simulations, which is time consuming. There are two main objectives in this paper. One is to develop a reliable method for bitumen characterization by improving the fluid characterization method that was recently developed based on perturbation from n-alkanes (PnA). The other is to develop a novel analytical method for assessing the sensitivity of a particular coinjection simulation to bitumen characterization without having to perform reservoir simulations. A simulation case study is given to validate this analytical method. A proper number of pseudo components for bitumen characterization cannot be determined without considering the effect of phase behavior on the oleic-phase viscosity at chamber-edge conditions in steam-solvent coinjection simulation. Results show that the analytical method developed in this research can detect the sensitivity of recovery simulation to bitumen characterization without performing multiple flow simulations using different sets of fluid models. The PnA-based method developed for bitumen characterization gives reliable predictions of phase behavior for bitumen/solvent mixtures with a small amount of experimental data.

Fused filament fabrication (FFF) 3D printing was used to fabricate continuous wire polymer composite (CWPC) heat flux sensors; the integrated wires acted as resistive sensing elements. Sensor configurations consisting of polylactic acid (PLA) with copper wire (Cu), PLA with nickel (Ni) wire, and thermoplastic polyurethane (TPU) with Cu wire were investigated. For each composition, samples with different numbers of layers were 3D printed to investigate the effect of sensor thickness on performance. Element spacing, polymer conductivity, and wire temperature coefficient of resistances were quantified. Performance testing of the 3D-printed CWPC as a heat flux sensor showed promising results for all compositions and demonstrated their ability to be used as heat flux sensors for low temperature and low heat flux applications. Measurement errors were less than 17% for all configurations and less than 10% for 2-layer samples at the higher heat fluxes. A case study demonstrated the use of a 3D-printed flexible CWPC heat flux sensor to estimate heat loss from an insulated system with good accuracy. This sensor fabrication approach can potentially be employed in a wide range of applications because it allows for custom geometries and can use different types of polymers and sensing elements.

The global challenge of sustainable and affordable wastewater treatment technology looms large as water pollution escalates steadily with the rapid pace of industrialization and population growth. The photocatalytic wastewater treatment is a cutting-edge and environmentally friendly technology that uses photons from light source to degrade and remove organic and inorganic contaminants from water. Thus, utilizing solar energy for photocatalytic wastewater treatment holds great promise as a renewable solution to alleviate pressures on the global water crisis. Employing solar concentrators to intensify sunlight for photocatalysis represents a promising avenue for future applications of a low-cost and rapid sustainable wastewater purification process. This groundbreaking approach will unveil fresh technological avenues for a cost-effective, sustainable, and swift wastewater purification process utilizing sunlight. This review article explores diverse solar concentrating systems and their potential applications in the wastewater treatment process.

A key determinant and outcome of successful environmental education is ‘pro-environmental behavior’, i.e., behavior that involves conscious action to mitigate adverse environmental impacts at personal or community level, e.g., reducing resource consumption and waste generation, avoiding toxic substances, and organizing community awareness initiatives. However, some theorists have sought to move away from rationalist models of behavioral modification, towards holistic pedagogical initiatives that seek to develop action competence. In light of the global push towards achievement of the Sustainable Development Goals (SDGs), emerging evidence suggests that education initiatives should foster action competence so students may be equipped to contribute to sustainable development as part of their education. The UNESCO Education for Sustainable Development (ESD) Roadmap 2030 has also identified key priority areas to strengthen ESD in formal curricula. This article reports two informal environmental education initiatives for promoting action competence and pro-environmental behaviors in school-aged children. The authors recommend that formal education settings (e.g., schools) should incorporate self-directed, free-choice project-based learning to augment environmental education programs and promote students’ action competence for contribution to attainment of SDGs. To this end, we propose a Free-Choice Project-based Learning for Action Competence in Sustainable Development (ACiSD) Curriculum, comprising six implementation dimensions, namely: (1) project duration and teaming arrangements, (2) topic selection, (3) student support, (4) teacher support, (5) learning environments, and (6) digital access and equity. For each implementation dimension, we recommend action steps to help educators implement this curriculum in their own educational settings, with the aid of an illustrative worked example.

Methane hydrate was formed in water occupying the interstitial spaces of a cylindrical bed of silica sand particles. The sand particles have an average diameter equal to 329 μm. The amount of methane consumed during the experiment (methane gas uptake) was determined through pressure and temperature measurements and mass balance calculations. Three different sized beds of silica sand particles were used. Water conversion to hydrates in the range of 73−84% was achieved for all of the formation experiments. Hydrate formation was followed by decomposition at 4.0 °C driven by depressurization at 3.1 MPa (nine experiments) and 2.3 MPa (one experiment). Methane recovery measurement curves were determined for each experiment. The initial rate of recovery was found to be strongly dependent on the silica sand bed size. The rate of recovery was found to depend weakly on the size during the second stage, and after 1.25 h, it was constant. During decomposition at 2.3 MPa, the temperature in some locations inside the b...

Environmental factors may operate differently when relations are measured across different geographical locations, a phenomenon known as spatial non-stationarity. This study investigates the spatial non-stationarity effect of unhealthy food environments and green spaces on the T2DM prevalence rate at the neighborhood level in Toronto. This study also compares how the results vary between age groups, classified as all adults (20 and above), young adults (from 20 to 44), middle adulthood (from 45 to 64), and seniors (65 and above). The geographically weighted regression model is utilized to explore the impacts of spatial non-stationarity effects on the research results, which may lead to biased conclusions, which have often been ignored in past studies. The results from this study reveal that environmental variables dissimilarly affect T2DM prevalence rates among different age groups and neighborhoods in Toronto after controlling for socioeconomic factors. For example, the green space density yields positive associations with diabetes prevalence rates for elder generations but negative relationships for younger age groups in twenty-two and four neighborhoods, respectively, around Toronto East. The observed associations will provide beneficial suggestions to support government and public health authorities in designing education, prevention, and intervention programs targeting different neighborhoods to control the burden of diabetes.