Motorizing the buckled blister for rotary actuation.

Snap-through bistability was widely exploited for rapid hopping in micro-electro-mechanical systems and soft robots. However, considerable energy input was required to trigger the transition between discrete buckling states blocked by potential wells. Here a dynamic buckling mechanism of a buckled blister constrained inside an outer ring is explored for eliciting rotary actuation via a localized change of curvature in the blister. Due to rotational invariance of the buckled blister, lower energy supply is required to initiate the snap-through of buckling compared to conventional bistable mechanism. The controllability in rotational speed and output torque of the bimetallic blister-based rotator inside a rigid stator is exhibited, and the locomotion is demonstrated with two elastic rings via localized pneumatic actuators. With broad choices of stimulus and material for rings, the findings illustrate the promising potential of two nested rings to create active motions for diverse applications including gearless motors, peristaltic pumps, and locomotive robots.

Machine learning for predicting halogen radical reactivity toward aqueous organic chemicals.

Rapid advances in machine learning (ML) provide fast, accurate, and widely applicable methods for predicting free radical-mediated organic pollutant reactivity. In this study, the rate constants (logk) of four halogen radicals were predicted using Morgan fingerprint (MF) and Mordred descriptor (MD) in combination with a series of ML models. The findings highlighted that making accurate predictions for various datasets depended on an effective combination of descriptors and algorithms. To further alleviate the challenge of limited sample size, we introduced a data combination strategy that improved prediction accuracy and mitigated overfitting by combining different datasets. The Light Gradient Boosting Machine (LightGBM) with MF and Random Forest (RF) with MD models based on the unified dataset were finally selected as the optimal models. The SHapley Additive exPlanations revealed insights: the MF-LightGBM model successfully captured the influence of electron-withdrawing/donating groups, while autocorrelation, walk count and information content descriptors in the MD-RF model were identified as key features. Furthermore, the important contribution of pH was emphasized. The results of the applicability domain analysis further supported that the developed model can make reliable predictions for query compounds across a broader range. Finally, a practical web application for logk calculations was built.

Photochemical transformation and immobilization of thallium in the presence of iron and arsenic: Mechanistic insights from the coupled formation of arsenate complexes.

Despite the occurrence of thallium (Tl) in the acidic mining-affected areas being highly positively correlated with iron (Fe) and arsenic (As), the effects of the two accompanying elements on Tl redox transformation and immobilization remain largely unknown. Here, we investigated the photochemical redox kinetics and immobilization efficiency of Tl for a wide range of As/Fe and As/Tl ratios under acidic conditions. We provided the first experimental confirmation of the complexation of Tl(III) with As(V) by the spectrophotometric method and revealed the role of Tl(III)-As(V) complexes in decreasing the photoreduction rate of Tl(III) under sunlight. Additionally, the negative impact of colloidal Fe(III)-As(V) and Fe(III)-As(III) complexes formation on decreasing photoactive Fe(III) speciation and thus the apparent quantum yield of •OH was highlighted, which consequently hindered the oxidative conversion of Tl(I) to Tl(III). We rationalize the kinetics results by developing the model which quantitatively describes the photochemistry of Tl. Furthermore, we demonstrated the colloid-facilitated immobilization of Tl(III) through the formation of Tl(III)-As(V) clusters and surface adsorption onto the complexes. This study broadens the mechanistic understanding of redox transformation and immobilization potential of Tl and aids in assessing Tl speciation as well as its coupled transformation with Fe and As species in the sunlit water environment.

The application of machine learning methods for prediction of heavy metal by activated carbons, biochars, and carbon nanotubes.

Carbonaceous materials are commonly used as adsorbents for heavy metals. The determination of the adsorption capacity needs time and energy, and the key factors affecting the adsorption capacity have not been determined. Therefore, a new and efficient method is needed to predict the adsorption capacity and explore the decisive factors in the adsorption process. In this study, three tree-based machine learning models (i.e., random forest, gradient boosting decision tree, and extreme gradient boosting) were developed to predict the adsorption capacity of eight heavy metals (i.e., As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) on activated carbons, biochars, and carbon nanotubes using 3674 data points extracted from 151 journal articles. After a comprehensive comparison, the gradient boosting decision tree had the best performance for a combined model based on all data (R2 = 0.9707, RMSE = 0.1420). Moreover, independent models were developed for three datasets classified by the adsorbent and eight datasets classified by the heavy metals. In addition, a graphical user interface was built to predict the adsorption capacity of heavy metals. This study provides a novel strategy and convenient tool for the removal of heavy metals and can help to improve the removal efficiency of heavy metals to build a healthier world.

Prediction of antibiotic sorption in soil with machine learning and analysis of global antibiotic resistance risk.

Although the sorption of antibiotics in soil has been extensively studied, their spatial distribution patterns and sorption mechanisms still need to be clarified, which hinders the assessment of antibiotic resistance risk. In this study, machine learning was employed to develop the models for predicting the soil sorption behavior of three classes of antibiotics (sulfonamides, tetracyclines, and fluoroquinolones) in 255 soils with 2203 data points. The optimal independent models obtained an accurate predictive performance with R2 of 0.942 to 0.977 and RMSE of 0.051 to 0.210 on test sets compared to combined models. Besides, a global map of the antibiotic sorption capacity of soil predicted with the optimal models revealed that the sorption potential of fluoroquinolones was the highest, followed by tetracyclines and sulfonamides. Additionally, 14.3% of regions had higher antibiotic sorption potential, mainly in East and South Asia, Central Siberia, Western Europe, South America, and Central North America. Moreover, a risk index calculated with the antibiotic sorption capacity of soil and population density indicated that about 3.6% of soils worldwide have a high risk of resistance, especially in South and East Asia with high population densities. This work has significant implications for assessing the antibiotic contamination potential and resistance risk.

Preparing for the Next Pandemic: Predicting UV Inactivation of Coronaviruses with Machine Learning.

The epidemic of coronaviruses has posed significant public health concerns in the last two decades. An effective disinfection scheme is critical to preventing ambient virus infections and controlling the spread of further outbreaks. Ultraviolet (UV) irradiation has been a widely used approach to inactivating pathogenic viruses. However, no viable framework or model can accurately predict the UV inactivation of coronaviruses in aqueous solutions or on environmental surfaces, where viruses are commonly found and spread in public places. By conducting a systematic literature review to collect data covering a wide range of UV wavelengths and various subtypes of coronaviruses, including severe acute respiratory syndrome 2 (SARS-CoV-2), we developed machine learning models for predicting the UV inactivation effects of coronaviruses in aqueous solutions and on environmental surfaces, for which the optimal test performance was obtained with R2 = 0.927, RMSE = 0.565 and R2 = 0.888, RMSE = 0.439, respectively. Besides, the required UV doses at different wavelengths to inactivate the SARS-CoV-2 to 1 Log TCID50/mL titer from different initial titers were predicted for inactivation in protein-free water, saliva on the environmental surface, or the N95 respirator. Our models are instructive for eliminating the ongoing pandemic and controlling the spread of an emerging and unknown coronavirus outbreak.

Sunlight-Mediated Reductive Transformation of Thallium(III) in Acidic Natural Organic Matter Solutions: Mechanisms and Kinetic Modeling.

Thallium (Tl) redox state determines its speciation and fate in aqueous environments. Despite the high potential of natural organic matter (NOM) providing the reactive groups to complex and reduce Tl(III), the kinetics and mechanisms by which NOM influences the Tl redox transformation have remained insufficiently understood. Here, we studied the reduction kinetics of Tl(III) in acidic Suwannee River fulvic acid (SRFA) solutions under dark and solar-irradiated conditions. Our results show that the thermal Tl(III) reduction occurs by the reactive organic moieties in SRFA, with the electron-donating capacities of SRFA increased with pH and decreased with [SRFA]/[Tl(III)] ratios. Solar irradiation promoted Tl(III) reduction in SRFA solutions as a result of ligand-to-metal charge transfer (LMCT) within the photoactive Tl(III) species as well as an additional reduction process mediated by a photogenerated superoxide. We demonstrated that the formation of Tl(III)-SRFA complexes decreased the reducibility of Tl(III), with the kinetics dependent on the nature of the binding component and SRFA concentrations. A "three ligand class" kinetics model has been developed and satisfactorily describes Tl(III) reduction kinetics over a range of experimental conditions. The insights presented here should assist in understanding and predicting the NOM-mediated speciation and redox cycle of Tl in a sunlit environment.

Light- and H2O2-Mediated Redox Transformation of Thallium in Acidic Solutions Containing Iron: Kinetics and Mechanistic Insights.

The redox transformation between the oxidation states of thallium (Tl(I) and Tl(III)) is the key to influencing its toxicity, reactivity, and mobility. Dissolved iron (Fe) is widely distributed in the environment and coexists at a high level with Tl in acidic mine drainages (AMDs). While ultraviolet (UV) light and H2O2 can directly (by inducing Tl(III) reduction) and indirectly (by inducing Fe(III) to form reactive intermediates) impact the redox cycles of Tl in Fe(III)-containing solutions, the kinetics and mechanism remain largely unclear. This study is the first to investigate the UV light- and H2O2-mediated Tl redox kinetics in acidic Fe(III) solutions. The results demonstrate that UV light and H2O2 could directly reduce Tl(III) to Tl(I), with the extent of reduction dependent on the presence of Fe(III) and the solution pH. At pH 3.0, Tl(I) was completely oxidized to Tl(III), which can be ascribed to the generation of hydroxyl radicals (•OH) from the Fe(III) photoreduction or Fe(III) reaction with H2O2. The kinetics of Tl(I) oxidation were strongly affected by the Fe(III) concentration, pH, light source, and water matrix. Kinetic models incorporating Tl redox kinetics with Fe redox kinetics were developed and satisfactorily interpreted Tl(III) reduction and Tl(I) oxidation under the examined conditions. These findings emphasize the roles of the UV light- and H2O2-driven Fe cycles in influencing the redox state of Tl, with implications for determining its mobility and fate in the environment.

Kinetics and mechanism of Thallium(I) oxidation by Permanganate: Role of bromide.

The oxidation of thallium(I) (Tl (I)) to Tl (III) is referred to as an efficient means for Tl removal. Bromide (Br‾) inevitably occurs in nearly all water sources at concentrations of 0.01-67 mg/L (0.14-960 µM). The effect of Br‾ remains largely unclear but likely of critical importance on the redox fate and thus the removal potential of Tl (I) during typical oxidation treatment processes. Here, we investigate the kinetics and tackle the mechanism of Tl (I) oxidation by permanganate (KMnO4) under the influence of Br‾. The results demonstrated that Br‾ at environmental levels exhibited significant catalytic effect on Tl (I) oxidation kinetics by KMnO4 at acidic pH of 4.0-7.0, while no significant effect of Br‾ was observed for Tl(I) oxidation under alkaline conditions of pH 8.0 and 9.0. It was found that the enhanced oxidation kinetics under acidic conditions was driven by the combined effect of and autocatalysis mediated by MnO2 and a fast oxidation kinetics served by in-situ formed bromine species. Through quantifying the relative contributions of those bromine species to the homogenous oxidation of Tl(I), HOBr, Br2 and Br2O were found to play roles in catalyzing the oxidation of Tl(I) by KMnO4. The results discussed herein highlight the critical role of Br‾ on the Tl(I) complex oxidation process by KMnO4 and may have implications for evaluating the redox cycle and removal potential of Tl in bromide-containing water treatment.

Kinetics of Thallium(I) Oxidation by Free Chlorine in Bromide-Containing Waters: Insights into the Reactivity with Bromine Species.

The oxidation of thallium [Tl(I)] to Tl(III) by chlorine (HOCl) is an important process changing its removal performance in water treatment. However, the role of bromide (Br-), a common constituent in natural water, in the oxidation behavior of Tl(I) during chlorination remains unknown. Our results demonstrated that Br- was cycled and acted as a catalyst to enhance the kinetics of Tl(I) oxidation by HOCl over the pH range of 5.0-9.5. Different Tl(I) species (i.e., Tl+ and TlOH(aq)) and reactive bromine species (i.e., HOBr/BrO-, BrCl, Br2O, and BrOCl) were kinetically relevant to the enhanced oxidation of Tl(I). The oxidation by free bromine species became the dominant pathway even at a low Br- level of 50 µg/L for a chlorine dose of 2 mg of Cl2/L. It was found that the reactions of Tl+/BrCl, Tl+/BrOCl, and TlOH(aq)/HOBr dominated the kinetics of Tl(I) oxidation at pH < 6.0, pH 6.0-8.0, and pH > 8.0, respectively. The species-specific rate constants for Tl+ reacting with individual bromine species were determined and decreased in the order: BrCl > Br2 > BrOCl > Br2O > HOBr. Overall, the presented results refine our knowledge regarding the species-specific reactivity of TI(I) with bromine species and will be useful for further prediction of thallium mobility in chlorinated waters containing bromide.

Machine learning in natural and engineered water systems.

Water resources of desired quality and quantity are the foundation for human survival and sustainable development. To better protect the water environment and conserve water resources, efficient water management, purification, and transportation are of critical importance. In recent years, machine learning (ML) has exhibited its practicability, reliability, and high efficiency in numerous applications; furthermore, it has solved conventional and emerging problems in both natural and engineered water systems. For example, ML can predict various water quality indicators in situ and real-time by considering the complex interactions among water-related variables. ML approaches can also solve emerging pollution problems with proven rules or universal mechanisms summarized from the related research. Moreover, by applying image recognition technology to analyze the relationships between image information and physicochemical properties of the research object, ML can effectively identify and characterize specific contaminants. In view of the bright prospects of ML, this review comprehensively summarizes the development of ML applications in natural and engineered water systems. First, the concept and modeling steps of ML are briefly introduced, including data preparation, algorithm selection and model evaluation. In addition, comprehensive applications of ML in recent studies, including predicting water quality, mapping groundwater contaminants, classifying water resources, tracing contaminant sources, and evaluating pollutant toxicity in natural water systems, as well as modeling treatment techniques, assisting characterization analysis, purifying and distributing drinking water, and collecting and treating sewage water in engineered water systems, are summarized. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of ML algorithms for different studies, as well as prospects on the application and development of ML in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.

Impacts of carrier properties, environmental conditions and extracellular polymeric substances on biofilm formation of sieved fine particles from activated sludge.

To investigate the effect of properties of carriers, environmental conditions and extracellular polymeric substances (EPS) on the initial adhesion of biofilm formation in biofilm-based reactors, a quartz crystal microbalance with dissipation (QCM-D) was applied to monitor the deposition rates and viscoelastic properties of sieved sludge particles on model biocarriers. The results suggested that surface charge, hydrophobicity and surface coating of five representative carriers influenced deposition rates and viscoelastic properties of biofilm, whose variation with NaCl concentrations was controlled by not only the Derjaguin-Landau-Verwey-Overbeek (DLVO) interaction but also non-DLVO forces. On hydrophobic surface, the addition of cationic substances enhanced the deposition rates and the compaction of deposited layer due to strong "hydrophobizing effect". For examples, 10 mM Ca2+, 10 mM Mg2+ and 10 mg/L poly-l-lysine enhanced the deposition rates to nearly 3, 2 and 4 times, as well as reduced the softness of deposited layer to almost 35%, 60% and 35%. Conversely, 10 mg/L negatively charged alginate might cause water retainment and steric shielding, thereby reducing the deposition rates to 40% and increasing the softness of deposited film to 120%. The presence of EPS sub-fractions can modify surface properties of sludge particles, to distinct degrees, contributing to biofilm formation. Notably, compared to tightly bound EPS (TB-EPS), loosely bound EPS (LB-EPS) was more conducive to microbial attachment, but the presence of LB-EPS promoted the formation of a soft layer on a hydrophobic surface. Overall, these results provide insights into intrinsic mechanisms of the variation of deposition rates and viscoelastic properties responding to critical factors, which are meaningful to predict and regulate the initial adhesion process in biofilm-based reactors.

Interactions between activated sludge extracellular polymeric substances and model carrier surfaces in WWTPs: A combination of QCM-D, AFM and XDLVO prediction.

To understand the biofilm formation of biofilm-based processes in wastewater treatment plants (WWTPs), the interaction mechanisms between extracted extracellular polymeric substances (EPS) and three model carrier surfaces (i.e., negatively charged hydrophilic silica, positively charged hydrophilic alumina, and neutral charged hydrophobic polystyrene) were investigated employing a laboratory quartz crystal microbalance with dissipation monitoring equipment (QCM-D) and an atomic force microscope (AFM). The data suggested that surface charge and hydrophobicity of both EPS and carriers played significant roles in the interaction behaviors. Moreover, increases in ionic strength could lead to the increasing zeta potential and hydrophobicity of EPS. It is worth noting that long-range DLVO forces dominated the EPS deposition on carriers in lower ionic strength while short-range Lewis acid-base (AB) interaction controlled the adhesion behaviors in higher ionic strength. Besides, the presence of calcium ions contributed to the adhesion behaviors because of strong charge neutralization and hydrophobic effect. Bound EPS (BEPS) showed higher affinity to model carriers than dissolved EPS (DEPS), which conformed to XDLVO prediction rather than classical DLVO model. Overall, these results provide insights into the influence mechanisms of carrier characteristics, ionic strength, calcium ion and EPS components on the interaction between EPS and representative carriers, contributing to predict and regulate biofilm formation in biofilm-based processes.

Thallium(I) Oxidation by Permanganate and Chlorine: Kinetics and Manganese Dioxide Catalysis.

The oxidation of the toxic heavy metal thallium(I) (Tl(I)) is an efficient way to enhance Tl removal from water and wastewater. However, few studies have focused on the kinetics of Tl(I) oxidation in water, especially at environmentally relevant pH values. Therefore, the kinetics and mechanisms of Tl(I) oxidation by the common agents KMnO4 and HOCl under environmentally relevant pH condition were explored in the present study. The results indicated that the pH-dependent oxidation of Tl(I) by KMnO4 exhibited second-order kinetics under alkaline conditions (pH 8-10) with the main active species being TlOH, while the reaction could be characterized by autocatalysis at pH 4-6, and Mn(III) might also play an essential role in the MnO2 catalysis. Furthermore, a two-electron transfer mechanism under alkaline conditions was preliminarily proposed by using linear free energy relationships and X-ray photoelectron spectroscopy (XPS) analysis. Distinctively, the reaction rate of Tl(I) oxidation by HOCl decreased with increasing pH, and protonated chlorine might be the main active species. Moreover, the Tl(I)-HOCl reaction could be regarded as first order with respect to Tl(I), but the order with respect to HOCl was variable. Significant catalysis by MnO2 could also be observed in the oxidation of Tl(I) by HOCl, mainly due to the vacancies on MnO2 as active sites for sorbing Tl. This study elucidates the oxidation characteristics of thallium and establishes a theoretical foundation for the oxidation processes in thallium removal.

Interpreting the role of NO3-, SO42-, and extracellular polymeric substances on aggregation kinetics of CeO2 nanoparticles: Measurement and modeling.

The early stage of aggregation of cerium oxide nanoparticles (CeO2 NPs) in anion solutions was inspected in the absence and presence of extracellular polymeric substance (EPS) with a help of time-resolved dynamic light scattering (DLS). The aggregation kinetics and attachment efficiencies were calculated according to measured hydrodynamic diameter across a range of 1-500 mM NaNO3 and 0.01-100. mM Na2SO4. The aggregation of CeO2 NPs in both NaNO3 and Na2SO4 solution conformed with the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. In NaNO3 solution, the critical coagulation concentrations (CCC) of CeO2 NPs was calculated to be about 47 mM; in Na2SO4 solution, CeO2 NPs showed a re-stabilization process and thus there was no CCC value. SO42- had intenser effects on CeO2 NPs aggregation than NO3- might because of the distinction between their polarization, consisting in Hofmeister series. The presence of bound EPS (B-EPS), tightly bound EPS (TB-EPS) and loosely bound EPS (LB-EPS) in NaNO3 solutions all lead to significant decrease in CeO2 NPs aggregation. Steric repulsive force produced by absorbed EPS on CeO2 NPs might take main responsibility in stabilizing CeO2 NPs. Besides, Extended Derjaguin-Landau-Verwey-Overbeek (EDLVO) model successfully predicted the energy barrier between CeO2 NPs with B-EPS, TB-EPS and LB-EPS as a function of NaNO3 concentration. Furthermore, the difference in impeding the CeO2 NPs aggregation with B-EPS, TB-EPS and LB-EPS may be caused by the divergence in molecular weight and component mass fraction especially protein content. These results might subserve the assessment on the fate and transport behaviors of CeO2 NPs released in wastewater treatment plants.

Release of deposited MnO2 nanoparticles from aqueous surfaces.

Changes in solution chemistry and transport conditions can lead to the release of deposited MnO2 nanoparticles from a solid interface, allowing them to re-enter the aqueous environment. Understanding the release behavior of MnO2 nanoparticles from naturally occurring surfaces is critical for better prediction of the transport potential and environmental fate of MnO2 nanoparticles. In this study, the release of MnO2 nanoparticles was investigated using a quartz crystal microbalance with dissipation monitoring (QCM-D), and different environmental surface types, solution pH values and representative macromolecular organics were considered. MnO2 nanoparticles were first deposited on crystal sensors at elevated NaNO3 concentrations before being rinsed with double-deionized water to induce their remobilization. The results reveal that the release rate of MnO2 depends on the surface type, in the decreasing order: SiO2 > Fe3O4 > Al2O3, resulting from electrostatic interactions between the surface and particles. Moreover, differences in solution pH can lead to variance in the release behavior of MnO2 nanoparticles. The release rate from surfaces was significantly higher at pH 9.8 that at 4.5, indicating that alkaline conditions were more favorable for the mobilization of MnO2 in the aquatic environment. In the presence of macromolecular organics, bovine serum albumin (BSA) can inhibit the release of MnO2 from the surfaces due to attractive forces. In presence of humic acid (HA) and sodium alginate (SA), the MnO2 nanoparticles were more likely to be mobile, which may be associated with a large repulsive barrier imparted by steric effects.

Deposition Kinetics of Colloidal Manganese Dioxide onto Representative Surfaces in Aquatic Environments: The Role of Humic Acid and Biomacromolecules.

The initial deposition kinetics of colloidal MnO2 on three representative surfaces in aquatic systems (i.e., silica, magnetite, and alumina) in NaNO3 solution were investigated in the presence of model constituents, including humic acid (HA), a polysaccharide (alginate), and a protein (bovine serum albumin (BSA), using laboratory quartz crystal microbalance with dissipation monitoring equipment (QCM-D). The results indicated that the deposition behaviors of MnO2 colloids on three surfaces were in good agreement with classical Derjaguin-Landau-Verwey-Overbeek (DLVO) theory. Critical deposition concentrations (CDC) were determined to be 15.5 mM NaNO3 and 9.0 mM NaNO3 when colloidal MnO2 was deposited onto silica and magnetite, respectively. Both HA and alginate could largely retard the deposition of MnO2 colloids onto three selected surfaces due to steric repulsion, and HA was more effective in decreasing the deposition rate relative to alginate. However, the presence of BSA can provide more attractive deposition site and thus lead to greater deposition behavior of MnO2 colloids onto surfaces. The dissipative properties of the deposited layer were also influenced by surface type, electrolyte concentration, and organic matter characteristics. Overall, these results provide insights into the deposition behavior of MnO2 colloids on environmental surfaces and have significant implications for predicting the transport potential of common MnO2 colloids in natural environments and engineered systems.

A review on the interactions between engineered nanoparticles with extracellular and intracellular polymeric substances from wastewater treatment aggregates.

Engineered nanoparticles (ENPs) will inevitably enter wastewater treatment plants (WWTPs) due to their widespread application; thus, it is necessary to study the migration and transformation of nanoparticles in sewage treatment systems. Extracellular polymeric substances (EPSs) such as polysaccharides, proteins, nucleic acids, humic acids and other polymers are polymers released by microorganisms under certain conditions. Intracellular polymeric substances (IPSs) are microbial substances contained in the body with compositions similar to those of extracellular polymers. In this review, we summarize the characteristics of EPSs and IPSs from sewage-collecting microbial aggregates containing pure bacteria, activated sludge, granular sludge and biofilms. We also further investigate the dissolution, adsorption, aggregation, deposition, oxidation and other chemical transformation processes of nanoparticles, such as metals, metal oxides, and nonmetallic oxides. In particular, the review deeply analyzes the migration and transformation mechanisms of nanoparticles in EPS and IPS matrices, including physical, chemical, biological interactions mechanisms. Moreover, various factors, such as ionic strength, ionic valence, pH, light, oxidation-reduction potential and dissolved oxygen, influencing the interaction mechanisms are discussed. In recent years, studies on the interactions between EPSs/IPSs and nanoparticles have gradually increased, but the mechanisms of these interactions are seldom explored. Therefore, developing a systematic understanding of the migration and transformation mechanisms of ENPs is significant.

Deposition of engineered nanoparticles (ENPs) on surfaces in aquatic systems: a review of interaction forces, experimental approaches, and influencing factors.

The growing development of nanotechnology has promoted the wide application of engineered nanomaterials, raising immense concern over the toxicological impacts of nanoparticles on the ecological environment during their transport processes. Nanoparticles in aquatic systems may undergo deposition onto environmental surfaces, which affects the corresponding interactions of engineered nanoparticles (ENPs) with other contaminants and their environmental fate to a certain extent. In this review, the most common ENPs, i.e., carbonaceous, metallic, and nonmetallic nanoparticles, and their potential ecotoxicological impacts on the environment are summarized. Colloidal interactions, including Derjaguin-Landau-Verwey-Overbeek (DLVO) and non-DLVO forces, involved in governing the depositional behavior of these nanoparticles in aquatic systems are outlined in this work. Moreover, laboratory approaches for examining the deposition of ENPs on collector surfaces, such as the packed-bed column and quartz crystal microbalance (QCM) method, and the limitations of their applications are outlined. In addition, the deposition kinetics of nanoparticles on different types of surfaces are critically discussed as well, with emphasis on other influencing factors, including particle-specific properties, particle aggregation, ionic strength, pH, and natural organic matter. Finally, the future outlook and challenges of estimating the environmental transport of ENPs are presented. This review will be helpful for better understanding the effects and transport fate of ENPs in aquatic systems. Graphical abstract ᅟ.