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.

Algal mediated intervention for the retrieval of emerging pollutants from aqueous media.

Water is a crucial elemental contributor for all sectors; however, the agricultural sector alone accounts for 70% of the world's total water withdrawal. The anthropogenic activity from various industries including agriculture, textiles, plastics, leather, and defence has resulted in the release of contaminants into water systems, resulting harm to the ecosystem and biotic community. Algae-based organic pollutant removal uses several methods, such as biosorption, bioaccumulation, biotransformation, and biodegradation. The adsorption of methylene blue by algal species Chlamydomonas sp. showed a maximum adsorption capacity of 2744.5 mg/g with 96.13% removal efficiency; on the other hand, Isochrysis galbana demonstrated a maximum of 707 µg/g nonylphenol accumulation in the cell with 77% removal efficiency indicating the potential of algal systems as efficient retrieval system for organic contaminants. This paper is a compilation of detailed information about biosorption, bioaccumulation, biotransformation, biodegradation, and their mechanism, along with the genetic alteration of algal biomass. Where the genetic engineering and mutations on algae can be advantageously utilized for the enhancement of removal efficiency without any secondary toxicity.

Article Application of neural network in metal adsorption using biomaterials (BMs): a review.

With growing environmental consciousness, biomaterials (BMs) have garnered attention as sustainable materials for the adsorption of hazardous water contaminants. These BMs are engineered using surface treatments or physical alterations to enhance their adsorptive properties. The lab-scale methods generally employ a One Variable at a Time (OVAT) approach to analyze the impact of biomaterial modifications, their characteristics and other process variables such as pH, temperature, dosage, etc., on the removal of metals via adsorption. Although implementing the adsorption procedure using BMs seems simple, the conjugate effects of adsorbent properties and process attributes implicate complex nonlinear interactions. As a result, artificial neural networks (ANN) have gained traction in the quest to understand the complex metal adsorption processes on biomaterials, with applications in environmental remediation and water reuse. This review discusses recent progress using ANN frameworks for metal adsorption using modified biomaterials. Subsequently, the paper comprehensively evaluates the development of a hybrid-ANN system to estimate isothermal, kinetic and thermodynamic parameters in multicomponent adsorption systems.

Sustainable conversion of saturated adsorbents (SAs) from wastewater into value-added products: future prospects and challenges with toxic per- and poly-fluoroalkyl substances (PFAS).

Following circular economy principles, the reuse or recycling of saturated adsorbents (SAs or adsorbate-laden adsorbents) into a low-cost engineered product is a valuable alternative to eliminate secondary pollution after adsorption. This review evaluates the application of SAs for the generation of products that can serve as (i) antimicrobial agents or disinfectants, (ii) materials for civil construction, (iii) catalysts, (iv) fertilizers, and (v) secondary adsorbents. The importance of SAs configuration in terms of functional groups, surface area and pore morphology played a crucial role in their reutilization. The SAs-laden silver ions (Ag+) strongly inhibit (~ 99%) the growth of Escherichia coli and Staphylococcus aureus microbes found in drinking and wastewaters. The intra-solidification of SAs containing toxic metal pollutants (As3+ and F-) with cementitious materials can effectively reduce their leaching below permissible limits of USEPA standards for their utility as additives in construction work. The existence of transition metal ions (Cu2+, Cr3+/6+, Ni2+) on the surface of SAs boosted activity and selectivity towards the desired product during catalytic oxidation, degradation, and conversion processes. The thermally recycled SAs can assist in the secondary adsorption of pollutants from another waste solution due to a larger surface area (> 1000 m2g-1). However, there are chances that the SAs discussed above will contain traces of PFAS. The article summarizes the challenges, performance efficacy, and future prospects at the end of each value-added product. We also highlight critical challenges for managing PFAS-laden SAs and stimulate new perspectives to minimize PFAS in air, water, and soils.

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.