A review of the methods of harvesting atmospheric moisture.

Moisture is an inherent constituent of air present across the world. The relative humidity varies with the change in temperature and climate specific to a region. In some regions of the world, there may be a relatively inadequate number of grains of moisture in the air in comparison with other regions. These factors widen the scope for the deployment of decentralized technology to capture water. The effectiveness in capturing moisture gains significance in these regions. Among the numerous forms of moisture, fog and dew are studied in depth. Over time, flora and fauna in different ecosystems have adapted to capture moisture as well as repel excesses of it according to their requirements. Therefore, bio-inspired studies and tailored engineering strategies have been incorporated in this review. Since efficient technologies are required at moisture-scarce locations, active moisture harvesting has also been studied. The use of innovative materials along with different energy sources to capture water is elaborated. The effects of climate change and environmental contamination on harvested moisture are therefore assessed. Community participation and economical use of harvested fog or dew influence the sustainability of moisture-capture projects. Therefore, this article also provides an insight into the services of decentralized water-harvesting projects run by diverse organizations and researchers across the globe.

Prediction of organophosphorus pesticide adsorption by biochar using ensemble learning algorithms.

Machine learning (ML) models have become a potent tool for advancing environmentally conscious research in materials science, allowing the prediction of wastewater treatment efficacy using eco-materials. In this study, we showcase the potential of an advanced decision tree-based ensemble learning algorithm to model the eviction of emerging organophosphate-based pesticidal pollutants in aqueous systems. The model is trained using laboratory-based biochar adsorption data, and it establishes the relationship between independent experimental factors and the % organophosphate pesticide adsorption efficiency as the output parameter. We classified the experimental dataset into input and output parameters to build the model. The input parameters included pyrolysis temperature, solution pH, surface area, pore volume, and initial pesticide concentration. Grid search optimization in Python was employed to train the model using sets of input-output patterns. The results indicated that the XGBoost-based ensemble ML framework provides the best forecast for pesticide adsorption on the biochar matrix, achieving high scores for the regularization coefficient (R2train = 0.998, R2test = 0.981). The concentration of the organophosphorus compound and the morphology of biochar significantly influenced the pesticide adsorption behavior. These findings demonstrate the potential of using ensemble learning algorithms for the balanced design of carbon-enriched biomaterials to remove emerging micropollutants from water effectively.

Animated concept-in-context maps as a materials science learning resource in an online flipped classroom.

Abstract: The COVID-19 global pandemic has caused a profound change in the teaching pedagogies and assessment strategies in engineering curricula worldwide. Concerning this, the article examines the role of animation-enhanced concept-in-context maps as a learning resource for the introductory materials science course in an online flipped format. The research was conducted on second-year mechanical engineering undergraduates. The methodology used two-group quasi-experimental design where the experimental group received animated concept-in-context maps as a learning resource, in contrast to the control group, which obtained static concept-in-context maps. The student's understanding of the topic was evaluated from their performance in pre-quiz and post-quiz scores. The preliminary results of the pilot study turned out to be in favor of animation-assisted mapping; further research is in process, and in-depth experimentation has been planned.