Explore the vast range of titles available.

Anthropogenic emissions of greenhouse gases into the atmosphere is inducing global warming, ocean acidification, polar ice melting, rise in sea level, droughts and hurricanes, thus threatening human health and the global economy. Therefore, there is a need to develop cost-effective technologies for CO2 capture. For instance, solution absorption is promising due to a large processing capacity, high flexibility and reliability, and rich experience in engineering applications. Nonetheless, actual commercial solutions, solvents and processes for CO2 capture suffer from slow reaction kinetics, low absorption capacity, high-energy consumption, susceptibility to corrosion, toxicity, low stability and high costs. Therefore, current research focuses on developing more economical, effective, green and sustainable technologies. Here we review 2015–2020 findings on CO2 capture using liquid absorption methods. Methods are based on various solutions, solvents and processes such as carbonate solution, ammonia solution, amine-based solution, ionic liquid, amino acid salt, phase changing absorbent, microcapsulated and membrane absorption, nanofluids and phenoxide salt solution. We discuss absorption performance, absorption mechanism, enhancement pathways and challenges. Amine- and NH3-based absorbents are widely used, yet they are limited by high regeneration energy, corrosiveness and degradation, reagent loss and secondary pollution caused by NH3 escape. Phase changing absorbents are getting more attention due to their lower cost and lower energy penalty. The incorporation of membrane and microencapsulation technologies to absorbing solvents could enhance CO2 absorption performance by reducing corrosion and increasing selectivity. Adding nanoparticles to solvents could improve CO2 absorption performance and reduce energy requirement. Besides, solvent blends and promoter-improved solvents performed better than single and non-promoted solvents because they combine the benefits of individual solvents and promoters.

CO2 emission is partly responsible for climate change induced by greenhouse effects. Carbon capture, utilization and storage is a major pathway to reduce CO2 emission. This article reviews conversion of CO2 into value-added products by photocatalytic, electrocatalytic and photoelectrocatalytic processes, which involve a catalyst, a reducing agent, an electrolyte and a reactor. An ideal catalyst should be cheap, abundant, non-toxic, less corrosive and chemically stable. Doping various catalysts can increase product yields up to 207 times. Furthermore, monolith reactors generated 23 times and 14 times higher yields than slurry and cell reactors, respectively. Photoelectrocatalytic conversion standout because it combines the advantages of photocatalytic and electrocatalytic processes such as high product yield and selectivity, no electrical energy required, cost-effectiveness, more catalysts option and no sacrificial donor.

In this work, the treatment of oily wastewater was investigated using developed cellulose acetate (CA) membranes blended with Nylon 66. Membrane characterization and permeation results in terms of oil rejection and flux were compared with a commercial CA membrane. The solution casting method was used to fabricate membranes composed of CA and Nylon 66. Scanning Electron Microscopy (SEM) analysis was done to examine the surface morphology of the membrane as well as the influence of solvent on the overall structure of the developed membranes. Mechanical and thermal properties of developed blended membranes and a commercial membrane were examined by thermogravimetric analysis (TGA) and universal (tensile) testing machine (UTM). Membrane characterizations revealed that the thermal and mechanical properties of the fabricated blended membranes better than those of the commercial membrane. Membrane fluxes and rejection of oil as a function of Nylon 66 compositions and transmembrane pressure were measured. Experimental results revealed that the synthetic membrane (composed of 2% Nylon 66 and Dimethyl Sulfoxide (DMSO) as a solvent) gave a permeate flux of 33 L/m2h and an oil rejection of around 90%, whereas the commercial membrane showed a permeate flux of 22 L/m2h and an oil rejection of 70%.

To undertake a reliable analysis of injury severity in road traffic accidents, a complete understanding of important attributes is essential. As a result of the shift from traditional statistical parametric procedures to computer-aided methods, machine learning approaches have become an important aspect in predicting the severity of road traffic injuries. The paper presents a hybrid feature selection-based machine learning classification approach for detecting significant attributes and predicting injury severity in single and multiple-vehicle accidents. To begin, we employed a Random Forests (RF) classifier in conjunction with an intrinsic wrapper-based feature selection approach called the Boruta Algorithm (BA) to find the relevant important attributes that determine injury severity. The influential attributes were then fed into a set of four classifiers to accurately predict injury severity (Naive Bayes (NB), K-Nearest Neighbor (K-NN), Binary Logistic Regression (BLR), and Extreme Gradient Boosting (XGBoost)). According to BA’s experimental investigation, the vehicle type was the most influential factor, followed by the month of the year, the driver’s age, and the alignment of the road segment. The driver’s gender, the presence of a median, and the presence of a shoulder were all found to be unimportant. According to classifier performance measures, XGBoost surpasses the other classifiers in terms of prediction performance. Using the specified attributes, the accuracy, Cohen’s Kappa, F1-Measure, and AUC-ROC values of the XGBoost were 82.10%, 0.607, 0.776, and 0.880 for single vehicle accidents and 79.52%, 0.569, 0.752, and 0.86 for multiple-vehicle accidents, respectively.

A numeric quantity that characterizes the whole structure of a network is called a topological index. In the studies of QSAR and QSPR, the topological indices are utilized to predict the physical features related to the bioactivities and chemical reactivity in certain networks. Materials for 2D nanotubes have extraordinary chemical, mechanical, and physical capabilities. They are extremely thin nanomaterials with excellent chemical functionality and anisotropy. Since, 2D materials have the largest surface area and are the thinnest of all known materials, they are ideal for all applications that call for intense surface interactions on a small scale. In this paper, we derived closed formulae for some important neighborhood based irregular topological indices of the 2D nanotubes. Based on the obtained numerical values, a comparative analysis of these computed indices is also performed.

Most of the studies conducted on the rehabilitation of reinforced concrete (RC) beam-column joints are on pre-1970 structures. Recently, it was reported that seismically designed beam-column joints might also suffer damage under lateral loading. On the other hand, there is an increasing interest among researchers to study the effectiveness of geopolymer as an alternative repair material. To date, no study has been conducted to examine the performance of geopolymer for the rehabilitation of seismically detailed beamcolumn joints following the removal and replacement method under cyclic loading. In the present investigation, two groups of exterior beam-column joints with different flexural strength ratios were rehabilitated with geopolymer mortar. For comparison, another set of beam-column joints (one from each group) were rehabilitated with cement mortar following the same rehabilitation technique and testing. Test results indicated that geopolymer rehabilitated specimens exhibited 20 to 21% higher initial stiffness, 19 to 22% higher displacement ductility, 24 to 37% higher cumulative energy dissipation, 14 to 17% higher initial equivalent viscous damping ratio, 21 to 26% higher ultimate equivalent viscous damping ratio at failure, and 10 to 14% lower damage index compared to specimens rehabilitated with cement mortar. However, irrespective of repair material, removal and replacement technique was only able to partially restore the cyclic performance of rehabilitated specimens. Keywords: beam-column joint; cyclic loading; geopolymer; removal and replacement method; seismically detailed.

A ground-breaking and soft nanomaterial, namely graphitic carbon nitride (g-C3N4) has gained importance as two-dimensional filler in polymeric membranes. In this research, g-C3N4 was synthesized by “thermal oxidation etching process”, employing melamine as a precursor. The porous nanosheets were characterized by SEM, XRD and FTIR. g-C3N4 nanosheets showed remarkable thermal stability up to 620 °C. The PVA starch nanocomposite membranes were fabricated with varying amounts of g-C3N4. Owing to strong interactions between g-C3N4, and polymers, the composite membranes showed exceptional thermal and mechanical stability and resist to degrade in various mediums including water, saline and blood. The hybrid membranes showed remarkable swelling abilities up to 96 h. Moreover, g-C3N4 enhanced the hydrophilicity, consequently, moisture retention capability and water vapor transmission were improved. XRD and SEM results revealed the proper dispersion of g-C3N4 into the polymeric matrix. The results suggested that prepared hybrid PVA/St/g-C3N4 membranes could be used as wound dressings.Graphic Abstract

Introduction: To mitigate the spread of the pandemic coronavirus infection (COVID-19), governments across the world have adopted \"lockdowns\" which have confined many individuals to their homes. This disrupts normal life routines, elements of which are important circadian cues. The pandemic is also associated with new stressors, altered roles, and uncertainties about health and economic security, which are also likely to affect sleep. The current study is an online survey of sleep experience, routines, physical activity, and symptoms of anxiety and depression, to study the alterations associated with the lockdown. Materials and Methods: The survey was conducted in early May 2020 using a questionnaire circulated through social media platforms. Questions related to demographic characteristics, current and previous sleep schedules, routine, and working patterns. Insomnia (Insomnia Severity Index - 4), Stress (Perceived Stress Scale - 4), anxiety and depressive symptoms (Patient Health Questionnaire - 4) and physical activity (International Physical Activities Questionnaire) were assessed using standardized instruments. Results: A total of 958 valid responses were received. Compared to the prelockdown period, there was a shift to a later bedtime and waking time, with a reduction in night-time sleep and an increase in day-time napping. These effects were visible across occupational groups, but mostly affected working individuals except health professionals. Sleep quality deteriorated across groups. Reductions in sleep duration were associated with depressive symptoms. Conclusions: The COVID-19 lockdown is associated with changes in sleep schedule and in the quantity and quality of night-time sleep. Although these changes are associated with elevated rates of emotional symptoms, it is unclear from these cross-sectional results, whether sleep deterioration produces psychological distress, or vice versa.

The longevity and safety of asphalt pavements, which form the foundation of our transportation infrastructure, are directly impacted by their performance. Pavement performance has traditionally been measured using the Marshall Mix Design method, which is a time- and resource-intensive laboratory procedure. Machine learning algorithms (MLAs) are increasingly popular today and are being utilized in various fields. Their performances vary; therefore, evaluating different MLAs and comparing them is important. The potential of various machine learning (ML) algorithms to predict Marshall Stability (MS) and Marshall Flow (MF) was investigated in this work. We collected data from published studies in the literature encompassing 732 data points to train and evaluate ML models. Eight key input parameters were considered for modeling. We used three feature importance analysis techniques (Random Forest, Permutation Importance, and Lasso Regression) to determine which parameters were the most significant. Linear regression (LR), Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), Gradient Boosting Machines (GBMs), and Artificial Neural Networks (ANNs) were the six MLAs that were assessed. Robust statistical measures such as MSE, MAE, R2, and RMSE were employed to evaluate each model’s performance. Our results indicate that the RF algorithm had the best performance for both MS and MF parameter prediction, followed by ANN and DT. The predicted and actual values showed a strong correlation, which was evidenced by the high R2 and the lowest values in other error metrics, indicating good performance. This highlights the significance of selecting an optimal machine learning algorithm for a particular predictive task.

Significant advances in the field of composite structures continue to be made on a variety of fronts, including theoretical studies based on advances in structural theory kinematics and computer models of structural elements employing advanced theories and unique formulations. Plate vibration is a persistently interesting subject owing to its wider usage as a structural component in the industry. The current study was carried out using the Co continuous eight-noded quadrilateral shear-flexible element having five nodal degrees of freedom, which is ground on first-order shear deformation theory (FSDT). For small strain and sufficiently large deformation, the geometric nonlinearity is integrated using the Von Kármán assumption. The governing equations in the time domain are solved employing the modified shooting technique along with an arc-length and pseudo-arc-length continuation strategy. This work explored the effect of fiber angle on the steady-state nonlinear forced vibration response. To explain hardening nonlinearity, the strain and stress fluctuation throughout the thickness for a rectangular laminated composite plate is determined. The cyclic fluctuation of the steady-state nonlinear normal stress during a time period at the centre of the top/bottom surfaces is also provided at the forcing frequency ratio of peak amplitude in a nonlinear response. Because of the variation in restoring forces, the frequency spectra for all fiber angle orientations show significantly enhanced harmonic participation in addition to the fundamental harmonic.