The significance of early and late stages of coupled aggregation and sedimentation in the fate of nanoparticles: measurement and modelling

Despite aggregation’s crucial role in controlling the environmental fate of nanoparticles (NP), the extent to which current models can describe the progressive stages of NP aggregation/sedimentation is still unclear. In this paper, 24 model combinations of two population-balance models (PBMs) and various collision frequency and settling velocity models are used to analyse spatiotemporal variations in the size and concentration of hydroxyapatite (HAp) NP. The impact of initial conditions and variability in attachment efficiency, α, with aggregate size are investigated. Although permeability models perform well in calculating collision frequencies, they are not appropriate for describing settling velocity because of their negative correlation or insensitivity in respect to fractal dimension. Considering both early and late stages of aggregation, both experimental and model data indicate overall mass removal peaks at an intermediate ionic strength (5 mM CaCl2) even though the mean aggregate size continued to increase through higher ionic strengths (to 10 mM CaCl2). This trend was consistent when different approaches to the initial particle size distribution (PSD) were used and when a variable or constant α was used. These results point to the importance of accurately considering different stages of aggregation in modeling NP fate within various environmental conditions

Boron Doped Graphene Quantum Structure and MoS2 Nanohybrid as Anode Materials for Highly Reversible Lithium Storage

Herein, the boron-doped graphene quantum structure (BGQS), which contains both the advantages of 0-D graphene quantum dot and 2-D reduced graphene oxide, has been fabricated by top-down hydrothermal method and then mixed with molybdenum sulfide (MoS2) to serve as an active electrode material for the enhanced electrochemical performance of lithium ion battery. Results show that 30 wt% of BGQS/MoS2 nanohybrid delivers the superior electrochemical performance in comparison with other BGQS/MoS2 and bare components. A highly reversible capacity of 3,055 mAh g−1 at a current density of 50 mA g−1 is achieved for the initial discharge and a high reversible capacity of 1,041 mAh g−1 is obtained at 100 mA g−1 after 50 cycles. The improved electrochemical performance in BGQS/MoS2 nanohybrid is attributed to the well exfoliated MoS2 structures and the presence of BGQS, which can provide the vitally nano-dimensional contact for the enhanced electrochemical performance. Results obtained in this study clearly demonstrate that BGQS/MoS2 is a promising material for lithium ion battery and can open a pathway to fabricate novel 2-D nanosheeted nanocomposites for highly reversible Li storage application

Aggregation and sedimentation of shattered graphene oxide nanoparticles (SGO) in dynamic environments: a solid-body rotational approach

Nanoparticle (NP) aggregation is typically investigated in either quiescent or turbulent mixing conditions; neither is fully representative of dynamic natural environments. In groundwater, complex interacting influences of advective-diffusive transport, pore tortuosity, and the arrival of aggregates from up-gradient pores impacts the aggregation behaviour of NPs, whereas in surface waters, continuous mixing of fresh particle and aged aggregate populations amends aggregation rates. To mimic such conditions, a cylinder reactor containing shattered graphene oxide NP (5 times in the rotating system than in the static system. Later (5-13 h) aggregates collided with extensively each other, broke, and reformed on the rotating cylinder wall giving rise to larger, denser aggregates (>1 cm). These results thus shed new light on the differences in aggregation behaviour between porous media and other natural environmental systems compared to quiescent batch experiments

N-Doped Graphene Quantum Dots/Titanium Dioxide Nanocomposites: A Study of ROS-Forming Mechanisms, Cytotoxicity and Photodynamic Therapy

Titanium dioxide nanoparticles (TiO2 NPs) have been proven to be potential candidates in cancer therapy, particularly photodynamic therapy (PDT). However, the application of TiO2 NPs is limited due to the fast recombination rate of the electron (e−)/hole (h+) pairs attributed to their broader bandgap energy. Thus, surface modification has been explored to shift the absorption edge to a longer wavelength with lower e−/h+ recombination rates, thereby allowing penetration into deep-seated tumors. In this study, TiO2 NPs and N-doped graphene quantum dots (QDs)/titanium dioxide nanocomposites (N-GQDs/TiO2 NCs) were synthesized via microwave-assisted synthesis and the two-pot hydrothermal method, respectively. The synthesized anatase TiO2 NPs were self-doped TiO2 (Ti3+ ions), have a small crystallite size (12.2 nm) and low bandgap energy (2.93 eV). As for the N-GQDs/TiO2 NCs, the shift to a bandgap energy of 1.53 eV was prominent as the titanium (IV) tetraisopropoxide (TTIP) loading increased, while maintaining the anatase tetragonal crystal structure with a crystallite size of 11.2 nm. Besides, the cytotoxicity assay showed that the safe concentrations of the nanomaterials were from 0.01 to 0.5 mg mL−1. Upon the photo-activation of N-GQDs/TiO2 NCs with near-infrared (NIR) light, the nanocomposites generated reactive oxygen species (ROS), mainly singlet oxygen (1O2), which caused more significant cell death in MDA-MB-231 (an epithelial, human breast cancer cells) than in HS27 (human foreskin fibroblast). An increase in the N-GQDs/TiO2 NCs concentrations elevates ROS levels, which triggered mitochondria-associated apoptotic cell death in MDA-MB-231 cells. As such, titanium dioxide-based nanocomposite upon photoactivation has a good potential as a photosensitizer in PDT for breast cancer treatment

Parameterization and prediction of nanoparticle transport in porous media : a reanalysis using artificial neural network

The continuing rapid expansion of industrial and consumer processes based on nanoparticles (NP) necessitates a robust model for delineating their fate and transport in groundwater. An ability to reliably specify the full parameter set for prediction of NP transport using continuum models is crucial. In this paper we report the reanalysis of a data set of 493 published column experiment outcomes together with their continuum modeling results. Experimental properties were parameterized into 20 factors which are commonly available. They were then used to predict five key continuum model parameters as well as the effluent concentration via artificial neural network (ANN)-based correlations. The Partial Derivatives (PaD) technique and Monte Carlo method were used for the analysis of sensitivities and model-produced uncertainties, respectively. The outcomes shed light on several controversial relationships between the parameters, e.g., it was revealed that the trend of math formula with average pore water velocity was positive. The resulting correlations, despite being developed based on a “black-box” technique (ANN), were able to explain the effects of theoretical parameters such as critical deposition concentration (CDC), even though these parameters were not explicitly considered in the model. Porous media heterogeneity was considered as a parameter for the first time and showed sensitivities higher than those of dispersivity. The model performance was validated well against subsets of the experimental data and was compared with current models. The robustness of the correlation matrices was not completely satisfactory, since they failed to predict the experimental breakthrough curves (BTCs) at extreme values of ionic strengths

Comparison of a new mass-concentration, chain-reaction model with the population-balance model for early- and late-stage aggregation of shattered graphene oxide nanoparticles

Aggregation as an essential mechanism impacting nanoparticle (NP) functionality, fate, and transport in the environment is currently modelled using population-balance equation (PBE) models which are computationally expensive when combined with other continuum-scale reactive transport models. We propose a new simple mass-concentration-based, chain-reaction modelling (CRM) framework to alleviate computational expenses of PBE and potentially to facilitate combination with other fate, transport, and reaction models. Model performance is compared with analytical PBE solution and a standard numerical PBE technique (fixed pivot, FP) by fitting against experimental data (i.e., hydrodynamic diameter and derived count rate of dynamic light scattering used as a representative of mass concentration) for early- and late-stage, aggregation of shattered graphene oxide (SGO) NP across a broad range of solution chemistries. In general, the CRM approach demonstrates a better match with the experimental data with a mean Nash-Sutcliffe model efficiency (NSE) coefficient of 0.345 than the FP model with a mean NSE of 0.29. Comparing model parameters (aggregation rate constant and fractal dimension) obtained from fitting CRM and FP to the experimental data, similar trends or ranges are obtained between the two approaches. Computationally, the modified CRM is an order-of-magnitude faster than the FP technique, suggesting that it can be a promising modelling framework for efficient and accurate modelling of NP aggregation. However, in the scope of this study, reaction rate coefficients of the CRM have been linked to collision frequencies based on simplified and empirical relationships which need improvement in future studies

Continuum-based models and concepts for the transport of nanoparticles in saturated porous media: A state-of-the-science review

Environmental applications of nanoparticles (NP) increasingly result in widespread NP distribution within porous media where they are subject to various concurrent transport mechanisms including irreversible deposition, attachment/detachment (equilibrium or kinetic), agglomeration, physical straining, site-blocking, ripening, and size exclusion. Fundamental research in NP transport is typically conducted at small scale, and theoretical mechanistic modeling of particle transport in porous media faces challenges when considering the simultaneous effects of transport mechanisms. Continuum modeling approaches, in contrast, are scalable across various scales ranging from column experiments to aquifer. They have also been able to successfully describe the simultaneous occurrence of various transport mechanisms of NP in porous media such as blocking/straining or agglomeration/deposition/detachment. However, the diversity of model equations developed by different authors and the lack of effective approaches for their validation present obstacles to the successful robust application of these models for describing or predicting NP transport phenomena. This review aims to describe consistently all the important NP transport mechanisms along with their representative mathematical continuum models as found in the current scientific literature. Detailed characterizations of each transport phenomenon in regards to their manifestation in the column experiment outcomes, i.e., breakthrough curve (BTC) and residual concentration profile (RCP), are presented to facilitate future interpretations of BTCs and RCPs. The review highlights two NP transport mechanisms, agglomeration and size exclusion, which are potentially of great importance in controlling the fate and transport of NP in the subsurface media yet have been widely neglected in many existing modeling studies. A critical limitation of the continuum modeling approach is the number of parameters used upon application to larger scales and when a series of transport mechanisms are involved. We investigate the use of simplifying assumptions, such as the equilibrium assumption, in modeling the attachment/detachment mechanisms within a continuum modelling framework. While acknowledging criticisms about the use of this assumption for NP deposition on a mechanistic (process) basis, we found that its use as a description of dynamic deposition behavior in a continuum model yields broadly similar results to those arising from a kinetic model. Furthermore, we show that in two dimensional (2-D) continuum models the modeling efficiency based on the Akaike information criterion (AIC) is enhanced for equilibrium vs kinetic with no significant reduction in model performance. This is because fewer parameters are needed for the equilibrium model compared to the kinetic model. Two major transport regimes are identified in the transport of NP within porous media. The first regime is characterized by higher particle-surface attachment affinity than particle-particle attachment affinity, and operative transport mechanisms of physicochemical filtration, blocking, and physical retention. The second regime is characterized by the domination of particle-particle attachment tendency over particle-surface affinity. In this regime although physicochemical filtration as well as straining may still be operative, ripening is predominant together with agglomeration and further subsequent retention. In both regimes careful assessment of NP fate and transport is necessary since certain combinations of concurrent transport phenomena leading to large migration distances are possible in either case

MODELING TRANSPORT AND FATE OF CHLORINATED HYDROCARBONS GOVERNED BY BIOTIC TRANSFORMATION IN POROUS MEDIA

[[abstract]]©1998 Elsevier - A macroscopic partitioning model governed by microbial transformation was developed to elucidate the transport and fate of chlorinated hydrocarbons and microorganisms in porous media. Carbon tetrachloride (CT) and 1,1,1-trichloroethane (TCA) were selected as the target compounds. A laboratory column experiment was also conducted to validate the accuracy of the model. Results obtained from the TOC concentration and microbial activities in effluent and pore water demonstrate that the steady state of the microbial growth was reached after an incubation period of 40 days. Microorganisms tended to accumulate in the first 20 cm of the column and consumed auxiliary substrate rapidly to sustain the dechlorinating capabilities. Significant degradation of CT with the concomitant increase of chloroform, the daughter product of CT via dechlorination, was also demonstrated after 57 days. TCA is more recalcitrant than CT. Removal of 42% of the original TCA input was demonstrated in 121 days. No 1,1-dichloroethane was detected. Model simulation results indicate that the predicted concentrations are all in the range close to the experimental data. A good correlation between the simulated and measured values demonstrates that the partitioning model can describe the transport and fate of chlorinated hydrocarbons and microorganisms In the contaminated groundwaters. The distribution coefficient between free-living and attached bacteria plays a prominent role in controlling the distribution pattern of microorganisms and the fate Of chlorinated hydrocarbons. In addition, the interaction between the supplemental substrate and the active microorganisms significantly influences the dechlorination of chlorinated hydrocarbons. The valid and accurate transport model provides the basis for the accurate prediction of the transport and fate of chlorinated hydrocarbons as well as the proper selection of a remedial approach to eliminate contaminants in soil environments.[[fileno]]2060215010021[[department]]醫環