Colloid Interaction Energies for Surfaces with Steric Effects and Incompressible and/or Compressible Roughness.

Colloid aggregation and retention in the presence of macromolecular coatings (e.g., adsorbed polymers, surfactants, proteins, biological exudates, and humic materials) have previously been correlated with electric double layer interactions or repulsive steric interactions, but the underlying causes are not fully resolved. An interaction energy model that accounts for double layer, van der Waals, Born, and steric interactions as well as nanoscale roughness and charge heterogeneity on both surfaces was extended, and theoretical calculations were conducted to address this gap in knowledge. Macromolecular coatings may produce steric interactions in the model, but non-uniform or incomplete surface coverage may also create compressible nanoscale roughness with a charge that is different from the underlying surface. Model results reveal that compressible nanoscale roughness reduces the energy barrier height and the magnitude of the primary minimum at separation distances exterior to the adsorbed organic layer. The depth of the primary minimum initially alters (e.g., increases or decreases) at separation distances smaller than the adsorbed organic coating because of a decrease in the compressible roughness height and an increase in the roughness fraction. However, further decreases in the separation distance create strong steric repulsion that dominates the interaction energy profile and limits the colloid approach distance. Consequently, adsorbed organic coatings on colloids can create shallow primary minimum interactions adjacent to organic coatings that can explain enhanced stability and limited amounts of aggregation and retention that have commonly been observed. The approach outlined in this manuscript provides an improved tool that can be used to design adsorbed organic coatings for specific colloid applications or interpret experimental observations.

Attachment of various-shaped polystyrene microplastics to silica surfaces: Experimental validation of the equivalent Cassini oval extended DLVO model.

Microplastics (MPs) vary in shape and surface characteristics in the environment. The attachment of MPs to surfaces can be studied using the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. However, this theory does not account for the shape MPs. Therefore, we investigated the attachment of spherical, pear-shaped, and peanut-shaped polystyrene MPs to quartz sand in NaCl and CaCl2 solutions using batch tests. The attachment of MPs to quartz sand was quantified using the attachment efficiency (alpha). Subsequently, alpha behaviors were interpreted using energy barriers (EBs) and interaction minima obtained from extended DLVO calculations, which were performed using an equivalent sphere model (ESM) and a newly developed equivalent Cassini model (ECM) to account for the shape of the MPs. The ESM failed to interpret the alpha behavior of the three MP shapes because it predicted high EBs and shallow minima. The alpha values for spherical MPs (0.62-1.00 in NaCl and 0.48-0.96 in CaCl2) were higher than those for pear- and peanut-shaped MPs (0.01-0.63 in NaCl and 0.02-0.46 in CaCl2, and 0.01-0.59 in NaCl and 0.02-0.40 in CaCl2, respectively). Conversely, the ECM could interpret the alpha behavior of pear- and peanut-shaped MPs either by changes in EBs or interaction minima as a function of orientation angles and electrolyte ionic strength. Therefore, the particle shape must be considered to improve the attachment analyses.

Prediction of attachment efficiency using machine learning on a comprehensive database and its validation.

Colloidal particles can attach to surfaces during transport, but the attachment depends on particle size, hydrodynamics, solid and water chemistry, and particulate matter. The attachment is quantified in filtration theory by measuring attachment or sticking efficiency (Alpha). A comprehensive Alpha database (2538 records) was built from experiments in the literature and used to develop a machine learning (ML) model to predict Alpha. The training (r-squared: 0.86) was performed using two random forests capable of handling missing data. A holdout dataset was used to validate the training (r-squared: 0.98), and the variable importance was explored for training and validation. Finally, an additional validation dataset was built from quartz crystal microbalance experiments using surface-modified polystyrene, poly (methyl methacrylate), and polyethylene. The experiments were performed in the absence or presence of humic acid. Full database regression (r-squared: 0.90) predicted Alpha for the additional validation with an r-squared of 0.23. Nevertheless, when the original database and the additional validation dataset were combined into a new database, both the training (r-squared: 0.95) and validation (r-squared: 0.70) increased. The developed ML model provides a data-driven prediction of Alpha over a big database and evaluates the significance of 22 input variables.

Statistical analysis, machine learning modeling, and text analytics of aggregation attachment efficiency: Mono and binary particle systems.

The aggregation attachment efficiency (α) is the fraction of particle-particle collisions resulting in aggregation. Despite significant research, α predictions have not accounted for the full complexity of systems due to constraints imposed by particle types, dispersed matter, water chemistry, quantification methods, and modeling. Experimental α values are often case-specific, and simplified systems are used to rule out complexity. To address these challenges, statistical analysis was performed on α databases to identify gaps in current knowledge, and machine learning (ML) was used to predict α under various particle types and conditions. Moreover, text analytics was employed to support knowledge from statistics and ML, as well as gain insight into the ideas communicated by current literature. Most studies investigated α in mono-particle systems, but binary or higher systems require more investigation. Furthermore, our work highlights that numerous variables, interactions, and mechanisms influence α behavior, making its investigation complex and difficult for both experiments and modeling. Consequently, future research should incorporate more particle types, shapes, coatings, and surface heterogeneities, and aim to address overlooked variables and conditions. Therefore, building a comprehensive α database can enable the development of more accurate empirical models for prediction.

A CFD study of the transport and fate of airborne droplets in a ventilated office: The role of droplet-droplet interactions.

Previous studies reported that specially designed ventilation systems provide good air quality and safe environment by removing airborne droplets that contain viruses expelled by infected people. These water droplets can be stable in the environment and remain suspended in air for prolonged periods. Encounters between droplets may occur and droplet interactions should be considered. However, the previous studies focused on other physical phenomena (air flow, drag force, evaporation) for droplet transport and neglected droplet interactions. In this work, we used computational fluid dynamics (CFD) to simulate the transport and fate of airborne droplets expelled by an asymptomatic person and considered droplet interactions. Droplet drag with turbulence for prediction of transport and fate of droplets indicated that the turbulence increased the transport of 1 µm droplets, whereas it decreased the transport of 50 µm droplets. In contrast to only considering drag and turbulence, consideration of droplet interactions tended to increase both the transport and fate. Although the length scale of the office is much larger than the droplet sizes, the droplet interactions, which occurred at the initial stages of release when droplet separation distances were shorter, had a significant effect in droplet fate by considerably manipulating the final locations on surfaces where droplets adhered. Therefore, it is proposed that when an exact prediction of transport and fate is required, especially for high droplet concentrations, the effects of droplet interactions should not be ignored. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary material is available in the online version of this article at 10.1007/s11783-021-1465-8 and is accessible for authorized users.

Intensified bioleaching of chalcopyrite concentrate using adapted mesophilic culture in continuous stirred tank reactors.

The bioleaching of chalcopyrite concentrate, intensified by the adapted mesophilic culture in the continuous stirred tank reactors (CSTR) was investigated. The cumulative bioleaching efficiency of copper was found to be increased from 34.8% to 49.3% in CSTR-1, 40.3% to 71.2% in CSTR-2, and 44.3% to 73.8% in CSTR-3, while the temperature was elevated from 30 to 37 °C, respectively; whereas, the pulp density (10%, w/v), agitation speed (350 rpm), aeration (400 cc/min), and retention time (7 days across the three reactors) were also optimized to keep constant. Further, the activation energy calculated for copper dissolution under the continuous flow indicated that the surface-diffusion was the overall rate-limiting step for the bioleaching process. Instrumental analysis of solid samples could reveal the degradation pathways of chalcopyrite bioleaching as: CuFeS2 → Cu2S → Cu0.3333Fe0.6667S → H9Fe3O18S8. It follows a complex mechanism that includes the occurrence of polysulfide and cooperative mechanism along with the passivation onto mineral surfaces.