Final Report: Development of an In Vitro Test and a Prototype Model to Predict Cellular Penetration of Nanoparticles

EPA Grant Number: R833856
Title: Development of an In Vitro Test and a Prototype Model to Predict Cellular Penetration of Nanoparticles
Investigators: Chen, Yongsheng , Capco, David , Chen, Zhongfang
Institution: Arizona State University - Main Campus , Georgia Institute of Technology - Main Campus , University of Puerto Rico - Rio Piedras Campus
EPA Project Officer: Hahn, Intaek
Project Period: July 1, 2008 through June 30, 2011
Project Amount: $399,628
RFA: Exploratory Research: Nanotechnology Research Grants Investigating Fate, Transport, Transformation, and Exposure of Engineered Nanomaterials: A Joint Research Solicitation - EPA, NSF, & DOE (2007) RFA Text |  Recipients Lists
Research Category: Nanotechnology , Safer Chemicals

Objective:

Nanomaterials are defined as materials that have at least one dimension in the 1 to 100 nm range. Recently, manufactured nanomaterials have received enormous attention for their applications in commercial products such as anti-aging creams, drug delivery systems, sunscreens, toothpastes, food products, and orthopedic implants. Testing in vivo all the potential biological effects of the anticipated large number of nanoscale products is impossible because the time and expense would be considerable. Accordingly, an urgent need exists to develop both a systematic short-term in vitro test that can predict the toxicity of nanoparticles (NPs) and computational toxicological tools that can predict NP toxicity from their physicochemical properties.
 
In this study, we will focus on the penetration of NPs into and their pathological effects on epithelial cell layers because epithelial cells represent the body’s first line of defense against the introduction of NPs and other potentially harmful materials. An interdisciplinary team consisting of environmental engineers, a cell biologist and toxicologist, and a computational chemist has been assembled to systematically evaluate cellular responses to NPs with varied morphology, surface chemistry, and composition (including coatings and metals) and thus to develop the test and tools described above. The ultimate goal of this project is to comprehensively integrate such data into a structure/activity paradigm or algorithm so that NP penetration of and pathology to epithelial cells can be predicted from their physicochemical parameters.

Summary/Accomplishments (Outputs/Outcomes):

1. Aggregation Kinetics of CeO2 Nanoparticles and Modeling Analysis
The nanoscience and nanotechnology boom of recent years has demonstrated that nanotechnology will play a significant role in advancing the technologies of the 21st century in many sectors (e.g., pharmaceutical, energy, electronic and textile) [1]. Engineered nanoparticles (NPs) probably will be released into the aquatic environment through manufacturing processes, waste disposal or product uses; however, insufficient research has examined the environmental behavior of NPs [2]. There are only limited data available on the aggregation of NPs. Especially, theoretical analysis and quantitative models are insufficiently developed to quantify the environmental transport and fate of NPs [3].
 
1.1. Characterizations of CeO2 NPs
CeO2 NP was used as a model NP in our study because it has extensive commercial applications [4-6] and thus is very likely to be released into the environment. The Organization for Economic CO-operation and Development (OECD) has listed CeO2 NPs as one of priority nanomaterials for immediate testing [7]. Aggregation kinetics of CeO2 nanoparticles (NPs) was studied by time-resolved dynamic light scattering (TR-DLS) experiments and the data was interpreted with Derjaguin−Landau−Verwey−Overbeek (DLVO) theory. A TEM image of CeO2 NPs is presented in Fig. 1a. The NPs are close to spherical in shape with a relatively uniform size distribution. The inset in Figure 1 shows the PSD diagram of CeO2 NPs, which was measured by DLS. Consistent with previous studies, the DLS-measured NP size is larger than that determined by TEM [8, 9]. This is probably owing to particle aggregation and the water layer surrounding NP surface. The polydispersivity index (PDI) is quite small (~ 0.1), indicating that CeO2 NPs are relatively monodispersed in solution. Fig. 1b-c show the electrophoretic mobilities (EPMs) of CeO2 NPs under different humic acid (HA) concentrations in KCl and CaCl2 solutions. In the absence of HA, the CeO2 NPs are positively charged at pH 5.7. However, with HA present, the surface charge (potential) of CeO2 NPs shifts to the negative domain, which indicates HA adsorption onto the CeO2 NPs.
 
 
 
Figure 1. Characterizations of CeO2 NPs. (a) TEM image of CeO2 NPs. The inset is the particle size distribution of 10 mg/L CeO2 NPs. The narrow particle size distribution and small PDI value imply that the NPs are relatively monodispersed. Electrophoretic mobilities (EPMs) of CeO2 NPs under different HA concentrations in (b) KCl and (c) CaCl2 solutions. The small marks in the symbols of (b) and (c) are error bars.
 
 
1.2. Modeling aggregation kinetics under the effect of ionic strength, natural organic matter and temperature
On the basis of extended Derjaguin- Landau-Verwey-Overbeek (EDLVO) theory and von Smoluchowski's population balance equation [10], we developed a model to describe the aggregation kinetics of NPs:
 
 
where rt is the particle radius at time t, a is the primary particle radius, n0 is the initial number concentration of primary particles, µ is the solution viscosity, and dF is the fractal dimension of aggregates. W is the stability ratio, which can be expressed by Eq. (2) [11, 12]:
 
 
where u is the normalized surface-to-surface separation distance (h) between two particles (u=h/a). VA(u) is the attractive energy. VT(u) is the net energy between NPs. l(u) is the correction factor for the diffusion coefficient, which is related to the separation distance by Eq. (3) [13]:
 
 
The aggregation model was used to evaluate the effect of ionic strength, natural organic matter and temperature on the aggregation kinetics of CeO2 NPs. The interaction energy VT was computed according to EDLVO theory. The modeling results were compared with experimental observations, and representative comparisons were presented in Fig. 2. Under all ionic strengths, natural organic matter and temperatures, model predictions well agreed with experimental data. Some minor discrepancies between model predictions and experimental observations may be attributed to the deviation of surface potential of NPs and the distribution of particle size. The modeling of the NP aggregation process is expected to benefit the prediction of environmental transport, fate and biological effects of NPs and further contribute to the environmental and health risk assessment of NPs.
 
 
 
 
2. Aggregation Kinetics of Silver Nanoparticles in Open and Closed Systems
The wide range of applications of silver nanoparticles (AgNPs) in food processing, clothing and other household products provides many opportunities for their release into the environment [14-18]. There is much evidence of NP toxicity to bacteria [19, 20], aquatic organisms [21, 22], and mammalian cells [23-26], which makes it imperative to understand the likelihood of the exposure, fate, transport, and transformation of AgNPs in complex and realistic environmental matrices [27, 28].
 
In open systems, different physiochemical processes may occur concurrently with aggregation, including mass transfer of oxygen and carbon dioxide between the gas and liquid phases, oxidation of silver metals, silver ion release, speciation of silver ions, and equilibrium between precipitation and dissolution [29]. In contrast, a closed system (anoxic and anaerobic conditions) imposes a low redox potential, and thus oxidation processes may be inhibited or slowed. A considerable level of AgNPs likely enters deep soils, river sediments, and underground water, where anaerobic conditions dominate and AgNPs could avoid oxidation and reside longer than in aerobic conditions. Bacteria (especially nitrogen-fixing heterotrophic and soil-forming chemolithotrophic bacteria) provide important ecological functions and fundamental services, such as nitrogen cycling, to ecosystems. The persistence of AgNPs may adversely affect beneficial bacteria and disrupt ecological functions. In situ studies have demonstrated that silver, even in larger particle form, inhibits microbial growth at concentrations less than those of other heavy metals as well as disrupts denitrification processes [30], which means that silver has the potential to cause ecosystem-level disruption. Similarly, evidence shows that AgNPs can enter and accumulate in the gastrointestinal (GI) tract of the human body and disrupt the intestinal microbial functions, resulting in irritation or disorders of the digestive system [17, 31-33]. As mentioned earlier, aggregation is an important environmental behavior that may be linked with the fate and biological interactions of AgNPs. However, no study has investigated or compared the aggregation kinetics of AgNPs in open and closed systems yet.
 
2.1. Characterizations of AgNPs
Fig. 3a shows the morphology of AgNPs we used, which were spherical in shape with a relatively uniform size distribution of 40-65 nm and consistent with the manufacturer’s reported size. The inset of Fig. 3a is a PSD histogram of 40-nm AgNPs immediately after dispersal in DI water. The peak intensity corresponds to approximately 43 nm, which agrees with the diameter determined from AFM. The DLS system reported a Polydispersivity Index (PDI) value of 0.19 for the AgNP suspension and a PDI value between 0.1 and 0.25 indicates that the NPs are polydispersed in the suspension with a narrow particle size distribution and without significant aggregation or sedimentation. To determine the surface charge, ζ-potential was measured under different pH values. The relationships between ζ-potential and suspension pH for the three NP sizes are shown in Fig. 3b. The zero points of charge (ZPC) for all AgNP sizes were approximately pH 2, at which AgNPs are neutrally charged and highly unstable. In the aggregation experiments, the pH was approximately 5.6 (in both DI water and the Hoagland medium), and thus the AgNPs were negatively charged according to Fig. 3b. This result agrees with the reported ζ-potential of −50 mV for citrate-coated AgNPs with a mean diameter of 4.8 nm [34].
 
2.2. Attachment efficiency (α) and critical coagulation concentration (CCC)
Time-resolved dynamic light scattering (TR-DLS) experiments indicated that aggregation of 20- and 40-nm AgNPs exhibited apparent reaction-limited (slow) and diffusion-limited (fast) regimes under different ionic strengths. The effect of electrolyte addition on particle aggregation kinetics was previously discussed and is mainly attributed to the compression of the outer shell of the electrical double layer and the decrease in the magnitude of the ζ-potential [35, 36]. As a result, the inter-particle repulsion from electrostatic force is reduced, and thus the particle aggregation is greatly promoted. We confirmed this by measuring ζ-potentials of the three sizes of AgNPs at different KNO3 concentrations (results are shown here). For 20- and 40- nm AgNPs, increasing KNO3 concentrations apparently led to a transition from unfavorable (low α) to favorable (high α) aggregation regimes, which is congruent with previous aggregation studies of metal NPs [29, 36-38]. Because 80-nm AgNPs did not have notable aggregation during the initial 30 min, the attachment efficiency was shown as zero in Fig. 4. The estimated CCC from Fig. 4 was approximately 100 mM (KNO3) for both 20- and 40-nm AgNPs, which did not show significant particle size dependency.
 
 
Figure 3. (a) Images of AgNPs acquired from AFM (the white scale bar at the bottom right is equal to 100 nm); the inset is a histogram of particle size distribution. (b) ζ-potentials for different sizes of AgNPs under different pH levels with 0.001 M KNO3 as the reference electrolyte.
 
 
 
Figure 4. Experimental data on attachment efficiencies (α) and the calculated inverse stability ratios (1/W) for AgNPs as a function of KNO3 concentration (pH 5.6). The critical coagulation concentration (CCC) is based on the intersection of the extrapolations of the unfavorable and favorable regimes, as marked by the black arrows. The estimated CCC for 20-, 40-, and 80-nm AgNPs were approximately 100, 75, 50 mM KNO3.
 
 
2.3. Aggregation kinetics of AgNPs in the quarter-strength Hoagland medium in open and closed systems
Fig. 5 compares the aggregation kinetics in open and closed systems for three sizes of AgNPs at two initial mass concentrations (300 and 600 µg/L). The graphs in the left and right columns are the hydrodynamic radius changes in open and closed systems, respectively. Clearly, the two systems share some common features, as well as some apparent differences.
 
 
 
 
In both systems, the hydrodynamic radii for all NP sizes increased almost linearly within the initial 4~6 h. After that, hydrodynamic radius changes became random and trendless in open systems. The effects of particle size and particle concentration on aggregation rates were similar in both systems. The slopes (drH/dt) of the hydrodynamic radius curves during the linear growth stage, as obtained by the curve fits, indicate the aggregation rates. These are shown in the insets of each graph in Fig. 5. In both open and closed systems and at both initial concentrations, 20-nm AgNPs had much steeper slopes than did 40- and 80-nm AgNPs, which is consistent with previous findings on particle size effects on the aggregation kinetics of hematite NPs [39]. Moreover, increasing the initial concentration from 300 to 600 µg/L significantly increased the aggregation rates for all three sizes of AgNPs by approximately 21%-49% in open systems and 28%-93% in closed systems, respectively.
 
Fig. 5 also shows differences in the aggregation kinetics of open and closed systems. After 50 to 150 h in an open system, the hydrodynamic radii of all sizes of AgNPs began to decline, probably because of disaggregation and oxidation of AgNPs. In contrast, the hydrodynamic radii in closed systems smoothly increased and exhibited features of salt-induced aggregation common to other metal NPs [36, 39, 40]. The most striking difference between open and closed systems is that the aggregation rates were faster in open systems by approximately 3-8 times, as indicated by the slopes shown in the insets. As a result, AgNPs persisted for a longer time in closed systems than in open systems.
 
The ionic strength (I) of the quarter-strength Hoagland medium was approximately 9.1 mM, which was determined by the equation I=0.5·ΣciZi2 (where ci is the molar concentration of one ionic species (i), and zi is the valency of the ith ion). Compared to the CCC (100 mM KNO3) in Fig. 4, the aggregation of AgNPs in the Hoagland medium should be reaction-limited during the linear stage of aggregation. One may argue that the Hoagland medium contains different divalent and monovalent cations such as Ca2+ and Mg2+, which may be more significantly effective in inducing AgNP aggregation than monovalent ions (K+) [35]. However, the total ionic strength of the Hoagland medium is relatively low and thus the effects of divalent cations on aggregation state can be negligible.
 
2.4. Implications of AgNP aggregation for aquatic and biological environments.
Previous studies have extensively studied the influences of monovalent and divalent salts as well as natural organic matters (NOM) on the aggregation kinetics, fate, and transport of nanomaterials [41-44]. Our work indicates that aerobic and anaerobic conditions also influence the aggregation behaviors of AgNPs, and our results suggest that AgNPs tend to aggregate slowly under anaerobic conditions and may appear as nanoparticles (instead of aggregates) for a long residence time. These findings are important for understanding the aggregation behaviors of AgNPs in the typical solution chemistries of aquatic environments. Moreover, anaerobic environments not only are common in natural environments (e.g., deep soils and underground water) but also are important in biological systems such as the human GI tract. Therefore, this study may lay out the ground work for understanding the potential fate and transformation of AgNPs in biological fluids or with biological interfaces. Meanwhile, it is also imperative to conduct a systematic fundamental study of aggregation behavior in biosystems and to gain insights about potential implications for human health.
 
3. Novel Attachment Efficiency Model on the Basis of Maxwell-Boltzmann Distribution
Aggregation kinetics of various engineered NP systems has been extensively studied using attachment efficiency (α),[45-51] which is commonly determined by normalizing the hydrodynamic size growth rate in initial aggregation curves to the growth rate under the favorable (or fast) aggregation condition in which the ionic strength is equal to or greater than the critical coagulation concentration (CCC). According to the Derjaguin−Landau−Verwey−Overbeek (DLVO) theory, α is equal to the inverse stability ratio (1/W), which is defined as Eq. (2) in the current document. Despite the wide applications in many colloidal systems, the attachment efficiency calculation by Eq. (2) has some issues when applied to the nanoscale aggregation problems. For example, the calculated 1/W has been reported to be steeper than the experimental value.[11] The discrepancy may arise from the assumption that van der Waals attraction is the sole driving force for particle aggregation, which could be true for colloidal particles. However, as we previously reported,[11, 52] the nanoscale transport of NPs is governed by both interaction energy and random Brownian diffusion according to interfacial force boundary layer (IFBL) theory. For small NPs, the role of interaction energy should be discounted appreciably owing to its relatively small particle size, whereas random kinetic energy plays a dominant role in the transport mechanism.
 
We derived the new equation for attachment efficiency (defined as the modified attachment efficiency or αm) by combining the Maxwell-Boltzmann distribution and DLVO theory. The new equation was employed to evaluate experimentally obtained attachment efficiencies to validate its applicability in describing the aggregation kinetics of CeO2 NPs. Finally, αm were compared with the experimental attachment efficiency results and simulations using 1/W for a variety of different NPs (i.e., ZnO, α-Fe2O3, C60, Ag, and Si) selected from the literature to further support the general applicability of αm to different NP systems.
 
3.1. New attachment efficiency equation
As indicated by the Maxwell approach, the primary and secondary energy minima could both be the deposition position for colloids.[11, 42, 53] However, the secondary energy minimum is only critical for particles greater than approximately 0.5 µm,[54] whereas NPs generally will not significantly deposit or aggregate in the secondary energy minimum but more likely in the primary energy minimum.[55, 56] Here we considered the role of Eb in aggregation kinetics and estimated the ratio of the number (ΔN) of particles with kinetic energy exceeding Eb to the total number (N) of particles with kinetic energy ranging from zero to infinity using the Maxwell-Boltzmann distribution:
 
 
where m is the molecular mass, kB is the Boltzmann constant (1.38×10-23 J/K), T is temperature (K), v is the velocity of random motion (m/s), Eb can be obtained from the DLVO theory equations, and E is the random kinetic energy (kBT) of NPs. Eq. (4) thus yields the ratio of NPs with a minimum velocity of v (or a minimum kinetic energy of Eb) over the total number of NPs. Note that the denominator is constant, and we can define the modified attachment efficiency (αm) as:
 
 
where δ physically accounts for the hydrodynamic damping effect (also called the drag effect) on the kinetic energy distribution of NPs as well as other potential discrepancies of the DLVO prediction. The Boltzmann velocity distribution applies ideally to dilute systems of non-interacting gas molecules.[44] In aqueous phase, solvent molecules should dampen (or decrease) the kinetic motion of NPs, which is called velocity relaxation for Brownian particles.[57] Both the collision efficiency and frequency should be lower than those in dilute systems (e.g., air)[58]. Moreover, the particle concentration and the medium viscosity may affect the kinetic energy distribution of NPs. To apply the Maxwell-Boltzmann distribution, the dispersed NPs are assumed to be Brownian particles (particles are moving continuously in Brownian motion with an average kinetic energy of 3kBT/2) in dilute systems.[59, 60] Since environmentally relevant concentrations of most engineered NPs in the environment are probably within the range of a few ng/L to µg/L,[61, 62] the kinetic energy of NPs in aqueous phase should fit the Maxwell-Boltzmann distribution.
 
3.2. Application of the modified attachment efficiency in various NP systems
To validate the applicability of Eq. (5) for describing the aggregation kinetics of NP dispersions, we compared the experimentally derived attachment efficiencies of various NP dispersions from the literature. Fig. 6 shows that the model fits of 1/W and αm were generally in a good agreement with each other except for ZnO NPs and polyvinylpyrrolidone-coated silver NPs (PVP-coated AgNPs). For CeO2 and ZnO NPs, αm exhibited a better fit with experimental results than did 1/W, whereas 1/W fitted better for C60 NPs than αm did. For hematite NPs, both 1/W and αm slightly deviated from experimental data, although they were similar to each other. For Si NPs, both αm and 1/W were not in good agreement with the experimental data. The discrepancies between the simulated fits and experimental data could be due to the intrinsic limitations of classic DLVO theory and the lack of consideration of non-DLVO forces. For instance, PVP-coated AgNPs owing to the polymer capping agent had strong steric repulsion [44, 63] and thus led to significant discrepancies between the model and experimental data.
 
 
 
4. Modeling the Size Effects of Citrate-coated Silver Nanoparticles on Their Ion Release Kinetics
Ion release is an important environmental behavior of silver nanoparticles (AgNPs), and characterization of Ag+ release is critical for understanding the environmental fate, transport and biological impacts of AgNPs. This study aims to elucidate the fundamental mechanisms of Ag+ release kinetics through experimental and modeling approaches. The ion release experiments were conducted in glass media bottles filled with quarter-strength Hoagland medium, a common hydroponic nutrient medium for growing plants (29). The electrolytic nutrients of this medium simulate relevant ionic conditions in soils (30), sediments (31), and ground water systems (32, 33), environmental matrices that AgNPs are likely to enter. In this study, we focused on the effects of primary particle size and concentration on ion release kinetics, because previous research has indicated that factors such as DO, pH, salinity, and temperature likely affect ion release from AgNPs (9, 21). To quantitatively describe the ion release kinetics, we derived for the first time a kinetic model, which provides insight into the mechanisms of ion release kinetics of AgNPs in aqueous environments.
 
4.1. Ion release model development with the Arrhenius equation
We derived the Ag+ release rate (γ Ag+) on basis of the hard sphere collision theory that has been used previously for modeling the nanoparticle dissolution kinetics (35, 36). The proposed oxidation reaction stoichiometry of AgNPs is (9, 23):
 
 
Because of their small size, AgNPs may act as soluble reactants and the oxidation reaction can be described by first-order reaction kinetics. Accordingly, γ Ag+ can be expressed by the Arrhenius equation:
 
 
where γ Ag+  is the Ag+ release rate (mol/(L·h)); k is the reaction rate constant (mol/h); [AgNPs], [O2], and [H+] are the molar concentrations (mol/L) of AgNPs, DO, and proton, respectively; f is the frequency factor for the reaction; Ea is the activation energy (J); T is temperature (298 K); kB is the Boltzmann constant (1.38×10-23 J/K); NA is Avogadro’s number (6.02×1023/mol); sAB is the collision radius (nm); sA and sB are the molecular radii of the reactants A and B (nm); mAB is the reduced mass (g/mol); and mA and mB are the molecular weights of reactants A and B (g/mol). For the silver oxidation reaction, reactant A is AgNPs and reactant B is either oxygen or protons. The radius of oxygen or protons is much smaller than that of an AgNP (r). Likewise, the molecular weights of oxygen and proton are much lower than that of AgNP.
 
 
 
4.2. Silver ion release kinetics and model fitting
Fig. 7 shows the concentrations of the released Ag+ over time in the medium. For all sizes of AgNPs, the released Ag+ concentrations increased almost linearly within the first 8-12 h, and some slight fluctuation in Ag+ concentrations was observed for 40- and 80-nm NPs after the equilibrium was reached. The initial Ag+ concentrations were nonzero and slightly different for each size, which indicates that oxidation may have occurred for each size at time=0, probably due to unintended exposure to oxygen during the experimental setup. Increasing the initial AgNP concentration from 300 to 600 µg/L significantly increased the ion release rates for each size of AgNPs, which agrees with our model prediction. Small AgNPs (e.g., 20 nm) took longer (approximately 300 h) to reach reaction equilibrium, whereas large NPs (e.g., 80 nm) reached equilibrium after 100 h. In all cases (three sizes of AgNPs at two initial concentrations), AgNPs did not completely dissolve as a result of oxidation through the entire experimental period, which was also found by Kettler et al. and Liu et al. (9, 21). Thermodynamic analysis predicts that only in the long term should AgNPs be completely dissolved in aqueous environments containing sufficient DO (9). Thus, our observed “equilibrated” state of the Ag+ release kinetics within 350 h does not correspond to the physicochemical “equilibrium” in which the released silver concentrations should be independent of the amount of AgNPs present. Obviously, Fig. 7 reveals that the “equilibrated” concentrations of Ag+ depend on the primary particle size and the initial AgNP concentration. This can be explained by the different thermodynamic properties of AgNPs as compared with their bulk phase (37, 38). Small AgNPs have higher specific surface areas and higher enthalpies of formation compared to the same amount of bulk silver (23). In addition, the faster ion release rate for smaller AgNPs agrees with previous studies on minerals that are well interpreted with the Kelvin equation (39, 40), which indicated that small particles had higher solubility than their larger counterparts. The black dashed lines in Figure 2 show the model fit using Eq. (11). The model fitted the experimental data well with correlation coefficients of 0.97~0.99, and the model usefully interpreted the dependence of ion release kinetics on the primary particle size and concentration.
 
 
The ion release behaviors of NPs may be linked to their fate, transport, and even biological impacts. Better predictive models are clearly needed to understand their environmental behaviors and biological impacts. The kinetic model is capable of interpreting the ion release kinetics as a function of reaction time, primary particle size, particle concentration, DO, pH, and temperature. These model equations potentially can predict the metal ion release kinetics of any type of metal-containing NPs as a result of chemical reactions (e.g., oxidation). This study used relatively simple water chemistries to facilitate the modeling and analysis of primary particle size and concentration effects on ion release kinetics. The present results, both modeling and experimental, suggest that the primary particle size of citrate-coated AgNPs, rather than the aggregated sizes, will dramatically govern the Ag+ release kinetics. The ion release kinetics modeling may lay the groundwork for developing appropriate models to describe the kinetic behaviors of NPs in environmentally relevant solution chemistries.
 
5. Mechanism of Photogenerated Reactive Oxygen Species and Correlation with the Antibacterial Properties of Engineered Metal-Oxide NPs
 
Oxidative stress induced by reactive oxygen species (ROS) generation in NP systems is thought to be the main mechanism of their antibacterial activity.[65-67] Three types of ROS (O2•−, •OH, and 1O2) contribute to the major oxidative stress in biological systems. To the best of our knowledge, little research has examined the role of the electronic properties (e.g., band energy edge structures) of metal-oxide NPs in ROS generation.[67] A deeper understanding in this aspect would allow us to interpret the underlying ROS generation mechanisms, potentially predict the amount of ROS generation or the antibacterial activity of newly synthesized metal- oxide NPs, and effectively reduce experimental testing costs.
 
We selected seven types of metal-oxide NPs (nTiO2, nCeO2, nZnO, nCuO, nSiO2, nAl2O3, and nFe2O3) as case studies because of their broad application in industrial products and their antibacterial properties.[68-73] On these NPs we investigated the generation of three types of ROS (1O2, •OH, and O2•−) under UV irradiation (365 nm). The ROS generation mechanism was analyzed by comparing the band energy structures of the metal oxides with the redox potentials (EH) of different ROS. To elucidate the effect of primary particle size on ROS generation, the bulk counterparts of these NPs (bTiO2, bCeO2, bZnO, bCuO, bSiO2, and bFe2O3) was also quantified. Furthermore, we systematically studied the antibacterial activity of the NPs on E. coli cells as the model bacterium, which was then correlated with the average concentration of total ROS of different NPs as an indicator of oxidative power. Overall, this work is aimed at developing a theoretical framework for predicting the oxidative stress of metal oxide NPs and providing insight into the application potential of engineered NPs as antibacterial agents.
 
5.1. ROS Concentration Generated by Various NPs and Their Bulk Counterpart
Table 1 summarizes the concentrations of the three types of ROS (•OH, 1O2 and O2•−) produced by different metal oxides. Bulk particles other than bTiO2 and bZnO did not produce measurable ROS, whereas NPs other than nCuO generated ROS. At the same mass concentrations and UV irradiation, NPs generated more ROS than their bulk counterparts. The average concentration of total ROS followed the orders: nTiO2 > nZnO > nAl2O3> nSiO2 > nFe2O3 > nCeO2 > nCuO and bZnO > bTiO2. It was observed that:  (1) Among NPs, nZnO generated the most O2•−, followed by nFe2O3, nTiO2, and nCeO2, whereas for bulk materials, only bZnO favors O2•− generation, (2) nTiO2 generated the most •OH, which was approximately twofold and sixfold more than that generated by nZnO and nFe2O3. For bulk materials, bTiO2 generated approximately 2.5-fold more •OH than bZnO did. (3) nTiO2 generated the most 1O2, followed by nAl2O3, nZnO and nSiO2. The enhanced ROS generation power of NPs compared to their bulk counterpart is likely due to their large surface areas that provide more available reaction sites for UV absorption;[74, 75] other potentially size-dependent properties (e.g., light absorption or scattering, defect sites, and structural disorder) may also lead to the difference in photoactivity. [76, 77]
 
 
5.2. Electronic Structures of Metal Oxides and Their Relationship to ROS Generation
Production of a specific type of ROS (e.g., •OH, 1O2, or O2•−) on metal oxides under UV illumination could be related to the electronic structures of the metal oxides as well as the redox potentials (EH) of the different ROS generation reactions.[78, 79] The electronic structure of metal oxide NPs is characterized by the band gap (Eg), which is essentially the energy interval between the valence band (Ev) and the conduction band (Ec), each of which have a high density of states.
 
By aligning Ev, Ec, and EH, one can easily identify whether the ROS generation reactions are thermodynamically favorable. For example, the reducing power of the excited electrons in the conduction band plays a significant role in the formation of O2•−. Fig. 8 shows that the Ec values of nTiO2 and nCeO2 (-0.28 and -1.69 eV with respect to NHE; unless indicated, Ec and Ev values are shown with respect to NHE) are less than the EH of O2/O2•− (-0.2 eV). This indicates the potential of nTiO2 and nCeO2 to donate electrons to O2 would lead to the formation of O2•−, which agrees with the experimental results as shown in Table 1.
 
 
 
5.3. Relationship Between the Antibacterial Potency of NPs and ROS Generation
Oxidative stress from generated ROS is the predominantly governing mechanism for the antibacterial activity of engineered NPs, especially under UV illumination,[65, 74, 80] although nonoxidant paradigms, such as sorption-induced membrane disruption and the release of toxic ions, could also cause cell inhibition or injury.[81-83] Establishing a quantitative correlation between the ROS generation and bactericidal effect would be useful for predicting the antibacterial potency of nanomaterials. In line with this effort, we summed the average concentration of each type of ROS for different NPs (see Table 1) and plotted these totals against the survival rates (2-h log(Nt/N0)) of E. coli cells, as shown in Fig. 9. Apparently, there is a logarithmic correlation between the average concentration of total ROS and log(Nt/N0) with a correlation coefficient (R2) of 0.965. The logarithmic survival rate decreased as the average concentration of total ROS increased. As mentioned above, the bulk materials generally produced insignificant amounts of ROS, and thus we examined this correlation for NPs only.
 
 
 
 
6. Surface Interactions Affect the Toxicity of Engineered Metal Oxide Nanoparticles towards Paramecium
Recently, engineered metal oxide NPs have received enormous attention for their wide applications in cosmetics, sunscreens, toothpastes, food products, textiles and water treatment.[84] Large-scale discharges of these NPs into the aquatic environment could potentially threaten human and environmental health.2 Once in the environment, aquatic organisms would likely interact with and uptake those NPs.[85, 86] Thereafter, the NPs might have toxic effects on the organisms.[87] Moreover, NPs probably bioaccumulate in higher-trophic-level organisms,[88] which may affect the entire food chain and impose risks for human beings. Paramecium and other ciliates are important to maintain the balance of ecological systems, but the toxicity of NPs against these organisms has not been extensively investigated yet.
 
Various studies have explored the cytotoxicity mechanism of metal oxide NPs.[89-95] Although the exact toxicity mechanism is still unclear, it is recognized that the toxicity of metal oxide NPs to unicellular organisms (e.g. bacteria and ciliates) is ascribed, at least in part, to interactions between the NPs and the cell surface.[89] Many studies reported that direct spatial contact between NPs and cell surface is necessary for manifestation of the cytotoxicity,[89, 90, 93, 96] and their interaction is central to much of the cytotoxicity of NPs.[89, 97, 98] An apparent mechanism relies on direct damages, either physical (e.g. pitting[99]) or chemical (e.g. oxidative effect[89]), of NPs to cell surface (cell wall or cell membrane), which can result in death of the cell.[99] Prolonged contact between the cell and NPs also likely triggers the internalization of NPs into the cell, either through endocytosis[100, 101] or direct penetration;[102, 103] whereafter, these intracellular NPs may exert adverse effects on organelles (e.g., lysosomes and mitochondria)[101, 104] as well as on DNA and other biomacromolecules.[105-107]
 
We investigated the acute toxicities of seven engineered metal oxide NPs to Paramecium, and determined the 48-h LC50 for each NP. Furthermore, the interfacial interaction between each NP and the cell membrane was evaluated on the basis of the DLVO theory, and a further correlation was established between the interaction energy and NP toxicity.
 
6.1. Characterization of metal oxide NPs
By examining forty randomly selected particles of each type of NPs from TEM images, we obtained the average radius of each NP, and the statistical results were tabulated in Table 2. The primary particle radius of all NPs except nAl2O3 is < 15 nm. Number-based hydrodynamic radii of NPs in Dryl’s solution, as measured by DLS, were also presented in Table 2. Consistent with previous studies,[108, 109] the NP radius measured with DLS is remarkably larger than that determined with TEM. This is probably caused by particle aggregation and the water layer surrounding NP surface. The representative particle size distribution histograms were presented in Figure S3 in the SI, which indicated that the aggregated NPs were dominant in the total number of NPs. Table 1 also listed EPMs of metal oxide NPs and P. multimicronucleatum in Dryl‟s solution. All of these NPs and P. multimicronucleatum were negatively charged. Thus, an electrostatic repulsion force would arise between NPs and the cell surface.
 
 
 
6.2. Acute toxicity of metal oxide NPs to P. multimicronucleatum
The acute toxicities of all tested metal oxide NPs to P. multimicronucleatum were found to increase as particle concentration increased, indicating a dose dependency (Fig. 10). The 48-h LC50 values for these NPs are listed in Table 3. These NPs, except nFe2O3 and nCuO, are not highly toxic to P. multimicronucleatum; the large LC50 values (>1000 mg/L) for some NPs are consistent with a prior study, which investigated the toxicity of metal oxide NPs to E. coli.[92] The acute toxicity ranking of the tested NPs to P. multimicronucleatum has the order nFe2O3 > nCuO > nSiO2 > nZnO > nCeO2 > nTiO2 > nAl2O3.
 
 
 
 
 
 
6.3. Correlation between the interaction energy and NP toxicity
The NP-cell membrane interaction energy was calculated according to the DLVO theory, and the net interaction energy profiles were plotted in Fig. 11. Physicochemical properties of NPs, such as particle size, surface charge, and the Hamaker constant, govern the interaction energy of NPs with cell surface. The magnitude of energy barrier, obtained from Fig. 10, was then tabulated in Table 2. The magnitude of energy barrier of each NP with cell membrane increases as follows: nCuO < nFe2O3 < nCeO2 < nZnO < nSiO2 < nTiO2 < nAl2O3.
 
 
 
 
We compared the relationship between the magnitude of energy barrier and the 48-h LC50 in Fig. 12, which shows that the 48-h LC50 increased linearly (note the log-scale of the Y axis) with increasing energy barrier magnitude, as fitted with the least squares regression method. Depending on the magnitude of energy barrier, three zones can be distinctly divided in our case. Representative images of NP-treated P. multimicronucleatum cells are shown in Fig. 13. Clearly, many particles or aggregates were observed on P. multimicronucleatum after exposure to nCuO. However, on the surface of nSiO2–treated P. multimicronucleatum, less particles were observed, while there was almost no particles observed on the surface of nTiO2–treated cells. Since weakly associated-NPs were very likely washed away during the four-times washing cycles with DI water, these AFM results suggested that nCuO particles were more strongly associated with the cell surface relative to the other two NPs, which was consistent with the theoretical analysis on interaction energy barrier.
 
 
 
 
 
 
7. Cellular and subcellular impairment by exposure to NPs
Imaging and quantifying interfacial interactions between NPs and biological surfaces is critical to gain information that will benefit both applications of nanotechnology and understanding of its potential environmental impact. In this regard, we also extend our research into the biological interactions with metal oxides NPs and the following two sections briefly introduce our preliminary results on cellular and genetic level impacts from NP exposure.
 
7.1. Surface disruption of E. coli cells after exposure to hematite NPs
As we previously reported, NP exposure led to surface disruption on the human intestinal cell line (Caco-2) [110]. The results in Fig. 14 demonstrated how individual hematite NPs interacted with live E. coli cells through AFM imaging. The AFM images provided a striking visualization of the adsorption of hematite NPs onto E. coli cells and the subsequent disruption in their extracellular appendages (flagella). The surface potential of E. coli cells dropped significantly from approximately -100 mV to -600 mV, with the adsorption of hematite NPs (results are not shown here). These findings will lead to a more thorough knowledge of nano-bio interfacial interaction mechanisms and allow us to establish criteria for designing environmentally benign semiconductor nanomaterials.
 
 
 
7.2. Subcellular impairment at the genetic level
At subcellular level, our recent publication indicated that after the exposure, ultrasmall NPs (e.g., QDs) have opportunities to permeate into E. coli cells [111], probably through diffusion across cell membranes [112], endocytosis [113], and/or non-phagocytic mechanisms [114]. This study examined the binding of QDs with E. coli DNA in vitro and in vivo. With the unique function, KFM demonstrated its ability to determine the morphological and electrical changes in DNA after exposure to QDs as well as to distinguish individual QDs from DNA matrices. The results in Fig. 15 indicate that nonspecific binding with QDs led to transformation of linear DNA into pearl-like spheres. In vivo experiments showed that QDs could permeate into E. coli cells and bind to genomic DNA. To the best of our knowledge, this is the first successful demonstration of the use of KFM to detect single QD-DNA binding. KFM potentially can be used in characterization of nanomaterials and their interfacial interactions with biomolecular matrices.
 
 
 
8. Interactions of NPs with Model Cell Caco-2 Membranes
1) Transport of nanoparticles (NPs) across the epithelium:  This was tested based on the hypothesis that if NPs are transported across the epithelium, the effects of NPs can be caused by a) a disruption of intercellular junction, b) cell death which causes a ‘hole’ in the epithelium, or, c) transcytosis. Transcytosis was shown when titanium dioxide NPs were used (Koeneman et al. 2010), and also when hematite NPs were used (Kalive et al. 2012, manuscript in press). In addition disruption of cellular junctions also was shown for epithelia treated with hematite nanoparticles.
 
2) Transepithelial electrical resistance (TEER)and cell death:  The disruption of the cell junctions of the epithelium was demonstrated by monitoring TEER and by assessing for cell death. A high TEER value indicates an intact epithelium. The application of titanium dioxide NPs (Koeneman et al. 2010) and hematite NPs (Kalive et al. 2012, manuscript in press) caused a significant decrease in the TEER values thus indicating a disruption in the epithelium. Titanium dioxide NPs and hematite NPs also caused a disruption of the intracellular junctions as demonstrated by immunocytochemistry. However, significant cell death was not observed with titanium dioxide or hematite NPs treatment, indicating that damage to the epithelium was due to disruption of intracellular junctions.
 
3) Level of intracellular free calcium:  The rise of intracellular-free calcium due to a release of sequestered calcium is a mechanism by which the cell makes a response to external stimuli. This is one of the two major mechanisms of cytoplasmic signal transduction. As part of this proposal, short-term intracellular responses to titanium dioxide were examined by monitoring changes in the level of intracellular-free calcium. Application of titanium dioxide nanoparticles (40nm size) for 24 h progressively increased the level of intracellular-free calcium in the epithelial sheet (Koeneman et al. 2010). This was like to be an effector of the cell surface changes described in the next section.
 
Cell surface effects(SEM and AFM):  The cell type used in this study had numerous microvilli to increase the surface area of the cells. In the body these microvilli serve to increase nutrient uptake from the intestines. SEM was employed to examine the organization of microvilli at the apical surface of titanium dioxide and hematite NPs treated epithelia. In untreated control cells, numerous microvilli were present on the surface of the epithelium. In separate studies addition of both titanium dioxide and hematite NPs caused a change in microvillar organization (Koeneman et al. 2010; Zhang et al. 2010; Kalive et al. 2012, manuscript in press). The study with hematite NPs specifically examined the size effects of hematite NPs. Application of 17nm hematite NPs resulted in a decrease in the number of microvilli and the microvilli were no longer erect. Application of 53nm hematite however did not cause a significant decrease in the number of microvilli compared to the untreated control epithelia, and the microvilli were still erect. Application of 100nm hematite NPs caused a decrease in microvilli numbers and the microvilli exhibited a “clumping” phenomenon.
 
4) Real-time cellular responses (microarray analysis and quantitative RT-PCR):  In order to assess the cellular response to hematite NPs and to determine if different sizes of hematite NPs cause a different response, at the gene expression level, microarray analysis was performed. The up-regulation or down-regulation of genes was dependent on the size of hematite NPs. In the hematite NP treatments including 17nm and 100nm, where cell junctions were disrupted, cell junction maintenance genes such as γ-catenin, Zona occludens 1 (ZO1), Zona occludens 3 (ZO3), and villin gene showed no change or were down-regulated. In contrast, for hematite NP treatments such as 53nm that did not show epithelial disruption, these genes were up-regulated. The expression of some of the genes from microarray analysis was further confirmed by quantitative RT-PCR analysis (Kalive et al. 2012, manuscript in press). The 53nm treatment indicated a higher number of up-regulated genes than the 17nm or the 100nm treatment. When the epithelia were treated with 17nm hematite NPs for shorter time, a higher number of genes were up-regulated compared to the 17nm treatment for longer time.
 
9. To obtain physicochemical parameters by experiments and theoretical computations
As pointed out in the proposal, the first step towards to modeling nanotoxicity is to obtain physicochemical parameters of various nanomaterials of interest by experiments and theoretical computations. Chen’s group has achieved impressive progress in this direction. Support from EPA grant, “Development of a Proto-Type Model and a In Vitro Test to Predict Cellar Penetration of Nanomaterials,” enabled Dr. Chen to publish six papers from August 2010 to August 2011. These papers concentrated on studying various physical and chemical properties of available and emerging nanomaterials, which are briefly summarized below.
 
1. Structural elucidation of emerging nanoclusters
Boron clusters have generated interest for industrial applications. However, their geometric structures are not well understood. Our unbiased search by first-principles simulated annealing revealed irregular cage configurations for medium-sized Bn clusters (namely, boron fullerene) with n = 32–56, which are more stable than the previously proposed symmetric cages. The stability of these irregular cages can be understood by the three center bonds as well as the polygonal holes on the cage surface. The delocalized distribution of molecular orbitals as well as the negative nucleus-independent chemical shifts (NICS) values for each boron cage indicates strong aromaticity.i
 
In the family of graphene-based materials, graphene oxide (GO) is a focus of intensive studies partially because it is an important material to massively produce graphene. More significantly, GO itself has manifested many unique properties that may lead to technological applications in many fields, such as electronic devices, chemical sensors, optical devices, energy storage, and composite materials. Determining the atomic structure of GO is essential for us to better understand its fundamental properties and to well realize its technological applications. However, we are still in lack of the understanding of this issue. Based on the experimental observations, we constructed amorphous GO structural models, and investigated their geometric structures, thermodynamic stabilities, and electronic density of states in comparison with the previously proposed ordered GO structures.ii The thermodynamically most favorable amorphous GO models always contain some locally ordered structures in the short range, due to a compromise of the formation of hydrogen bonds, the existence of dangling bonds, and the retention of the π bonds, and possess good stability with regard to the ordered GO structures at low oxygen coverage. Varying the oxygen coverage and the ratio of epoxy and hydroxyl groups provides an efficient way to tune the electronic properties of the GOs.
Dr. Chen contributed a review article on the structures of GO and its chemical conversion between graphene, in which he summarized the most recent progress in this active field.iii
 
2. Surface engineering of graphene-enzyme nano composites for miniaturized biofuel cell
In collaboration with experimental peers, we developed a novel approach to the surface functionalization for membraneless enzymatic glucose/oxygen biofuel cell.iv The electrochemical activity of the electrodes, the power output and the biostability of the integrated biofuel cell are significantly improved.
 
3. Interactions between nanoparticles (NPs) and biorelated systems-aiming at mechanism elucidation
Understanding of the mechanism underlying the toxicity of NPs is essential and critical for future development of computational nanotoxicity. Investigating the interactions between NPs and some biorelated systems to enhance our understanding of nanotoxicity.
 
Following this direction, using neutral bare Al12X clusters as simple NP models, which was initiated last year, we investigated theoretically their interactions with adenine (A), thymine (T), guanine (G) and cytosine (C) as well as AT and GC pairs, and significantly improved the computational accuracy. We found that the Al12X clusters, and most likely also larger Al NPs, have bind tightly with DNA. These strong interactions also remarkably affect the intramolecular hydrogen bonds of the BPs. All the above effects may have potentially adverse impact on the structure and stability of DNA and thus cause its disfunction.v
 
We are also extending such studies to interactions between DNA with endohedral metallofullerenes, which have been used for medical imaging.

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[76] T. Soeborg, F. Ingerslev, B. Hallingsorensen, Chemical stability of chlortetracycline and chlortetracycline degradation products and epimers in soil interstitial water, Chemosphere, 57 (2004) 1515-1524.

[77] M. Auffan, J. Rose, M.R. Wiesner, J.-Y. Bottero, Chemical stability of metallic nanoparticles: A parameter controlling their potential cellular toxicity in vitro, Environmental Pollution, 157 (2009) 1127-1133.

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[79] C.D. Vecitis, K.R. Zodrow, S. Kang, M. Elimelech, Electronic-Structure-Dependent Bacterial Cytotoxicity of Single-Walled Carbon Nanotubes, ACS Nano, 4 (2010) 5471-5479.

[80] L. Brunet, D.Y. Lyon, E.M. Hotze, P.J.J. Alvarez, M.R. Wiesner, Comparative photoactivity and antibacterial properties of C60 fullerenes and titanium dioxide nanoparticles, Environmental science & technology, 43 (2009) 4355-4360.

[81] W. Zhang, M. Kalive, D.G. Capco, Y. Chen, Adsorption of hematite nanoparticles onto Caco-2 cells and the cellular impairments: effect of particle size, Nanotechnology, 21 (2010) 355103.

[82] W. Zhang, Y. Yao, N. Sullivan, Y. Chen, Modeling the Primary Size Effects of Citrate-Coated Silver Nanoparticles on Their Ion Release Kinetics, Environmental science & technology, 45 (2011) 4422-4428.

[83] A. Thill, O. Zeyons, O. Spalla, F. Chauvat, J. Rose, M. Auffan, A.M. Flank, Cytotoxicity of CeO2 nanoparticles for Escherichia coli. Physico-chemical insight of the cytotoxicity mechanism, Environmental science & technology, 40 (2006) 6151-6156.

[84] A.M. Schrand, M.F. Rahman, S.M. Hussain, J.J. Schlager, D.A. Smith, A.F. Syed, Metal-based nanoparticles and their toxicity assessment, Wiley Interdiscip Rev Nanomed Nanobiotechnol, 2 (2010) 544-568.

[85] X.S. Zhu, Y. Chang, Y.S. Chen, Toxicity and bioaccumulation of TiO(2) nanoparticle aggregates in Daphnia magna, Chemosphere, 78 (2010) V-215.

[86] E. Navarro, A. Baun, R. Behra, N.B. Hartmann, J. Filser, A.J. Miao, A. Quigg, P.H. Santschi, L. Sigg, Environmental behavior and ecotoxicity of engineered nanoparticles to algae, plants, and fungi, Ecotoxicology, 17 (2008) 372-386.

[87] X.S. Zhu, L. Zhu, Y.S. Chen, S.Y. Tian, Acute toxicities of six manufactured nanomaterial suspensions to Daphnia magna, J. Nanopart. Res., 11 (2009) 67-75.

[88] Y. Chang, X.S. Zhu, J.X. Wang, X.Z. Zhang, Y.S. Chen, Trophic transfer of TiO2 nanoparticles from daphnia to zebrafish in a simplified freshwater food chain, Chemosphere, 79 (2010) 928-933.

[89] K. Feris, C. Otto, J. Tinker, D. Wingett, A. Punnoose, A. Thurber, M. Kongara, M. Sabetian, B. Quinn, C. Hanna, D. Pink, Electrostatic Interactions Affect Nanoparticle-Mediated Toxicity to Gram-Negative Bacterium Pseudomonas aeruginosa PAO1, Langmuir, 26 (2010) 4429-4436.

[90] C. Pagnout, S. Jomini, M. Dadhwal, C. Caillet, F. Thomas, P. Bauda, Role of electrostatic interactions in the toxicity of titanium dioxide nanoparticles toward Escherichia coli, Colloids and Surfaces B: Biointerfaces, 92 (2012) 315-321.

[91] Y. Wang, W. Aker, H.M. Hwang, C. Yedjou, H. Yu, P.B. Tchounwou, A study of the mechanism of in vitro cytotoxicity of metal oxide nanoparticles using catfish primary hepatocytes and human HepG2 cells, Sci. Total Environ., 409 (2011) 4753-4762.

[92] X.K. Hu, S. Cook, P. Wang, H.M. Hwang, In vitro evaluation of cytotoxicity of engineered metal oxide nanoparticles, Sci. Total Environ., 407 (2009) 3070-3072.

[93] A. Thill, O. Zeyons, O. Spalla, F. Chauvat, J. Rose, M. Auffan, A.M. Flank, Cytotoxicity of CeO2 nanoparticles for Escherichia coli. Physico-chemical insight of the cytotoxicity mechanism, Environ. Sci. Technol., 40 (2006) 6151-6156.

[94] T. Xia, M. Kovochich, M. Liong, L. Madler, B. Gilbert, H.B. Shi, J.I. Yeh, J.I. Zink, A.E. Nel, Comparison of the Mechanism of Toxicity of Zinc Oxide and Cerium Oxide Nanoparticles Based on Dissolution and Oxidative Stress Properties, ACS Nano, 2 (2008) 2121-2134.

[95] W. Jiang, H. Mashayekhi, B.S. Xing, Bacterial toxicity comparison between nano- and micro-scaled oxide particles, Environ. Pollut., 157 (2009) 1619-1625.

[96] P.K. Stoimenov, R.L. Klinger, G.L. Marchin, K.J. Klabunde, Metal oxide nanoparticles as bactericidal agents, Langmuir, 18 (2002) 6679-6686.

[97] A.M. El Badawy, R.G. Silva, B. Morris, K.G. Scheckel, M.T. Suidan, T.M. Tolaymat, Surface Charge-Dependent Toxicity of Silver Nanoparticles, Environ. Sci. Technol., 45 (2011) 283-287.

[98] C.M. Goodman, C.D. McCusker, T. Yilmaz, V.M. Rotello, Toxicity of gold nanoparticles functionalized with cationic and anionic side chains, Bioconjugate Chem., 15 (2004) 897-900.

[99] I. Sondi, B. Salopek-Sondi, Silver nanoparticles as antimicrobial agent: a case study on E-coli as a model forGram-negative bacteria, J. Colloid Interface Sci., 275 (2004) 177-182.

[100] B.D. Chithrani, W.C.W. Chan, Elucidating the mechanism of cellular uptake and removal of protein-coated gold nanoparticles of different sizes and shapes, Nano Lett., 7 (2007) 1542-1550.

[101] A.E. Nel, L. Madler, D. Velegol, T. Xia, E.M.V. Hoek, P. Somasundaran, F. Klaessig, V. Castranova, M. Thompson, Understanding biophysicochemical interactions at the nano-bio interface, Nat. Mater., 8 (2009) 543-557.

[102] J.Q. Lin, H.W. Zhang, Z. Chen, Y.G. Zheng, Penetration of Lipid Membranes by Gold Nanoparticles: Insights into Cellular Uptake, Cytotoxicity, and Their Relationship, ACS Nano, 4 (2010) 5421-5429.

[103] A. Verma, O. Uzun, Y.H. Hu, Y. Hu, H.S. Han, N. Watson, S.L. Chen, D.J. Irvine, F. Stellacci, Surface-structure-regulated cell-membrane penetration by monolayer-protected nanoparticles, Nat. Mater., 7 (2008) 588-595.

[104] T. Xia, M. Kovochich, M. Liong, J.I. Zink, A.E. Nel, Cationic polystyrene nanosphere toxicity depends on cell-specific endocytic and mitochondrial injury pathways, ACS Nano, 2 (2008) 85-96.

[105] T. Cedervall, I. Lynch, S. Lindman, T. Berggard, E. Thulin, H. Nilsson, K.A. Dawson, S. Linse, Understanding the nanoparticle-protein corona using methods to quantify exchange rates and affinities of proteins for nanoparticles, Proc. Natl. Acad. Sci. U. S. A., 104 (2007) 2050-2055.

[106] M. Green, E. Howman, Semiconductor quantum dots and free radical induced DNA nicking, Chem. Commun. (Cambridge, U. K.), (2005) 121-123.

[107] G. Bhabra, A. Sood, B. Fisher, L. Cartwright, M. Saunders, W.H. Evans, A. Surprenant, G. Lopez-Castejon, S. Mann, S.A. Davis, L.A. Hails, E. Ingham, P. Verkade, J. Lane, K. Heesom, R. Newson, C.P. Case, Nanoparticles can cause DNA damage across a cellular barrier, Nat. Nanotechnol., 4 (2009) 876-883.

[108] K.G. Li, Y.S. Chen, Effect of natural organic matter on the aggregation kinetics of CeO2 nanoparticles in KCl and CaCl2 solutions: measurements and modeling, J. Hazard. Mater., (2012) in press. DOI: 10.1016/j.jhazmat.2012.1001.1013.

[109] R.C. Murdock, L. Braydich-Stolle, A.M. Schrand, J.J. Schlager, S.M. Hussain, Characterization of nanomaterial dispersion in solution prior to In vitro exposure using dynamic light scattering technique, Toxicol. Sci., 101 (2008) 239-253.

[110] W. Zhang, M. Kalive, D.G. Capco, Y. Chen, Adsorption of hematite nanoparticles onto Caco-2 cells and the cellular impairments: effect of particle size Nanotechnology, 21 (2010) 355103.

[111] W. Zhang, Y. Yao, Y. Chen, Imaging and Quantifying the Morphology and Nanoelectrical Properties of Quantum Dot Nanoparticles Interacting with DNA, J. Phys. Chem. C, (2010) DOI:10.1021/jp107676h.

[112] S.J. Lin, G. Keskar, Y.N. Wu, X. Wang, A.S. Mount, S.J. Klaine, J.M. Moore, A.M. Rao, P.C. Ke, Detection of phospholipid-carbon nanotube translocation using fluorescence energy transfer, Appl. Phys. Lett., 89 (2006) 143118-143121.

[113] J.K. Lee, Toxicity and tissue distribution of magnetic nanoparticles in mice, Toxicol. Sci., 90 (2006) 267-267.

[114] M. Geiser, B. Rothen-Rutishauser, N. Kapp, S. Schurch, W. Kreyling, H. Schulz, M. Semmler, V.I. Hof, J. Heyder, P. Gehr, Ultrafine particles cross cellular membranes by nonphagocytic mechanisms in lungs and in cultured cells, Environ. Health Perspect., 113 (2005) 1555-1560.


Journal Articles on this Report : 66 Displayed | Download in RIS Format

Other project views: All 88 publications 66 publications in selected types All 66 journal articles
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Journal Article Chen W, Li Y, Yu G, Zhou Z, Chen Z. Electronic structure and reactivity of boron nitride nanoribbons with Stone-Wales defects. Journal of Chemical Theory and Computation 2009;5(11):3088-3095. R833856 (Final)
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  • Journal Article Chen W, Li Y, Yu G, Li C-Z, Zhang SB, Zhou Z, Chen Z. Hydrogenation: a simple approach to realize semiconductor-half-metal-metal transition in boron nitride nanoribbons. Journal of the American Chemical Society 2010;132(5):1699-1705. R833856 (Final)
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  • Journal Article Chivers T, Hilts RW, Jin P, Chen Z, Lu X. Synthesis, properties, and bishomoaromaticity of the first tetrahalogenated derivative of a 1, 5-diphosphadithiatetrazocine: a combined experimental and computational investigation. Inorganic Chemistry 2010;49(8):3810-3815. R833856 (Final)
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  • Journal Article Faust JJ, Zhang W, Chen Y, Capco DG. Alpha-Fe(2)O(3) elicits diameter-dependent effects during exposure to an in vitro model of the human placenta. Cell Biology and Toxicology 2014;30(1):31-53. R833856 (Final)
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  • Journal Article Gao X, Ishimura K, Nagase S, Chen Z. Dichlorocarbene addition to C60 from the trichloromethyl anion: carbene mechanism or Bingel mechanism? Journal of Physical Chemistry A 2009;113(15):3673-3676. R833856 (Final)
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  • Journal Article Gao X, Wang L, Ohtsuka Y, Jiang D-E, Zhao Y, Nagase S, Chen Z. Oxidation unzipping of stable nanographenes into joint spin-rich fragments. Journal of the American Chemical Society 2009;131(28):9663-9669. R833856 (Final)
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  • Journal Article Gao X, Jian D-E, Zhao Y, Nagase S, Zhang S, Chen Z. Theoretical insights into the structures of graphene oxide and its chemical conversions between graphene. Journal of Computational and Theoretical Nanoscience 2011;8(12):2406-2422. R833856 (Final)
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  • Journal Article Jiang D-E, Chen W, Whetten RL, Chen Z. What protects the core when the thiolated Au cluster is extremely small? Journal of Physical Chemistry C 2009;113(39):16983-16987. R833856 (Final)
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  • Journal Article Jin P, Hao C, Gao Z, Zhang SB, Chen Z. Endohedral metalloborofullerenes La2@B80 and Sc3N@B80: a density functional theory prediction. Journal of Physical Chemistry A 2009;113(43):11613-11618. R833856 (Final)
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  • Journal Article Jin P, Li FY, Riley K, Lenoir D, Schleyer PVR, Chen ZF. What is the preferred structure of the Meisenheimer-Wheland complex between sym-triaminobenzene and 4,6-dinitrobenzofuroxan? Journal of Organic Chemistry 2010;75(11):3761-3765. R833856 (Final)
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  • Journal Article Jin P, Zhou Z, Hao C, Gao Z, Tan K, Lu X, Chen Z. NC unit trapped by fullerenes: a density functional theory study on Sc3NC@C2n (2n = 68, 78 and 80). Physical Chemistry Chemical Physics 2010;12(39):12442-12449. R833856 (Final)
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  • Journal Article Jin P, Chen Y, Zhang SB, Chen Z. Interactions between Al12X (X = Al, C, N and P) nanoparticles and DNA nucleobases/base pairs: implications for nanotoxicity. Journal of Molecular Modeling 2012;18(2):559-568. R833856 (Final)
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  • Journal Article Kalive M, Zhang W, Chen Y, Capco DG. Human intestinal epithelial cells exhibit a cellular response indicating a potential toxicity upon exposure to hematite nanoparticles. Cell Biology and Toxicology 2012;28(5):343-368. R833856 (Final)
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  • Journal Article Lee K, Kim Y-H, Sun YY, West D, Zhao Y, Chen Z, Zhang SB. Hole-mediated hydrogen spillover mechanism in metal-organic frameworks. Physical Review Letters 2010;104(23):236101. R833856 (Final)
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  • Journal Article Li B, Shu CY, Lu X, Dunsch L, Chen ZF, Dennis TJS, Shi ZQ, Jiang L, Wang TS, Xu W, Wang CR. Addition of carbene to the equator of C70 to produce the most stable C71H2 isomer: 2 aH-2(12)a-homo(C70-D5h(6))[5,6]fullerene. Angewandte Chemie International Edition 2010;49(5):962-966. R833856 (Final)
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  • Journal Article Li F, Zhao J, Chen Z. Hydrogen storage behavior of one-dimensional TiBx chains. Nanotechnology 2010;21(13):134006. R833856 (Final)
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  • Journal Article Li K, Zhang W, Huang Y, Chen Y. Aggregation kinetics of CeO2 nanoparticles in KCl and CaCl2 solutions: measurements and modeling. Journal of Nanoparticle Research 2011;13(12):6483-6491. R833856 (Final)
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  • Journal Article Li K, Chen Y, Zhang W, Pu Z, Jiang L, Chen Y. Surface interactions affect the toxicity of engineered metal oxide nanoparticles toward Paramecium. Chemical Research in Toxicology 2012;25(8):1675-1681. R833856 (Final)
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  • Journal Article Li K, Chen Y. Evaluation of DLVO interaction between a sphere and a cylinder. Colloids and Surfaces A: Physicochemical and Engineering Aspects 2012;415:218-229. R833856 (Final)
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  • Journal Article Li K, Chen Y. Effect of natural organic matter on the aggregation kinetics of CeO2 nanoparticles in KCl and CaCl2 solutions: measurements and modeling. Journal of Hazardous Materials 2012;209-210:264-270. R833856 (Final)
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  • Journal Article Li K, Zhang W, Chen Y. Quantum dot binding to DNA: single-molecule imaging with atomic force microscopy. Biotechnology Journal 2013;8(1):110-116. R833856 (Final)
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  • Journal Article Li K, Zhao X, Hammer BK, Du S, Chen Y. Nanoparticles inhibit DNA replication by binding to DNA: modeling and experimental validation. ACS Nano 2013;7(11):9664-9674. R833856 (Final)
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  • Journal Article Li K, Chen Y. Examination of nanoparticle-DNA binding characteristics using single-molecule imaging atomic force microscopy. Journal of Physical Chemistry C 2014;118(25):13876-13882. R833856 (Final)
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  • Journal Article Li M, Li Y, Zhou Z, Shen P, Chen Z. Ca-coated boron fullerenes and nanotubes as superior hydrogen storage materials. Nano Letters 2009;9(5):1944-1948. R833856 (Final)
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  • Journal Article Li Y, Zhou Z, Shen P, Chen Z. Spin gapless semiconductor-metal-half-metal properties in nitrogen-doped zigzag graphene nanoribbons. ACS Nano 2009;3(7):1952-1958. R833856 (Final)
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  • Journal Article Li Y, Zhou Z, Shen P, Zhang SB, Chen Z. Computational studies on hydrogen storage in aluminum nitride nanowires/tubes. Nanotechnology 2009;20(21):215701. R833856 (Final)
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  • Journal Article Li Y, Zhou Z, Chen Y, Chen Z. Do all wurtzite nanotubes prefer faceted ones? Journal of Chemical Physics 2009;130(20):204706. R833856 (Final)
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  • Journal Article Li Y, Zhou Z, Shen P, Chen Z. Two-dimensional polyphenylene: experimentally available porous graphene as hydrogen purification membrane. Chemical Communications 2010;46(21):3672-3674. R833856 (Final)
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  • Journal Article Li Y, Zhou Z, Zhang S, Chen Z. MoS2 nanoribbons: high stability and unusual electronic and magnetic properties. Journal of the American Chemical Society 2008;130(49):16739-16744. R833856 (Final)
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  • Journal Article Li Y, Zhou Z, Shen P, Chen Z. Structural and electronic properties of graphane nanoribbons. Journal of Physical Chemistry C 2009;113(33):15043-15045. R833856 (Final)
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  • Journal Article Li Y, Zhou Z, Yu G, Chen W, Chen Z. CO catalytic oxidation on iron-embedded graphene: computational quest for low-cost nanocatalysts. Journal of Physical Chemistry C 2010;114(14):6250-6254. R833856 (Final)
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  • Journal Article Li Y, Zhou Z, Jin P, Chen Y, Zhang SB, Chen Z. Achieving ferromagnetism in single-crystalline ZnS wurtzite nanowires via chromium doping. Journal of Physical Chemistry C 2010;114(28):12099-12103. R833856 (Final)
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  • Journal Article Li Y, Zhang W, Niu JF, Chen Y. Mechanism of photogenerated reactive oxygen species and correlation with the antibacterial properties of engineered metal-oxide nanoparticles. ACS Nano 2012;6(6):5164-5173. R833856 (Final)
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  • Journal Article Li Y, Zhang W, Li K, Yao Y, Niu J, Chen Y. Oxidative dissolution of polymer-coated CdSe/ZnS quantum dots under UV irradiation: mechanisms and kinetics. Environmental Pollution 2012;164:259-266. R833856 (Final)
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  • Journal Article Li Y, Zhang W, Niu J, Chen Y. Surface-coating-dependent dissolution, aggregation, and reactive oxygen species (ROS) generation of silver nanoparticles under different irradiation conditions. Environmental Science & Technology 2013;47(18):10293-10301. R833856 (Final)
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  • Journal Article Liu C, Alwarappan S, Chen Z, Kong X, Li C-Z. Membraneless enzymatic biofuel cells based on graphene nanosheets. Biosensors & Bioelectronics 2010;25(7):1829-1833. R833856 (Final)
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  • Journal Article Liu C, Chen Z, Li C-Z. Surface engineering of graphene-enzyme nanocomposites for miniaturized biofuel cell. IEEE Transactions on Nanotechnology 2011;10(1):59-62. R833856 (Final)
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  • Journal Article Liu L, Wang L, Gao J, Zhao J, Gao X, Chen Z. Amorphous structural models for graphene oxides. Carbon 2012;50(4):1690-1698. R833856 (Final)
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  • Journal Article Sun L, Li Y, Li Z, Li Q, Zhou Z, Chen Z, Yang J, Hou JG. Electronic structures of SiC nanoribbons. Journal of Chemical Physics 2008;129(17):174114. R833856 (Final)
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  • Journal Article Sun YY, Lee K, Wang L, Kim Y-H, Chen W, Chen Z, Zhang SB. Accuracy of density functional theory methods for weakly bonded systems: the case of dihydrogen binding on metal centers. Physical Review B 2010;82(7):073401. R833856 (Final)
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  • Journal Article Tang Q, Li Y, Zhou Z, Chen Y, Chen Z. Tuning electronic and magnetic properties of wurtzite ZnO nanosheets by surface hydrogenation. ACS Applied Materials & Interfaces 2010;2(8):2442-2447. R833856 (Final)
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  • Journal Article Wang L, Zhao J, Zhou Z, Zhang SB, Chen Z. First-principles study of molecular hydrogen dissociation on doped Al12X (X = B, Al, C, Si, P, Mg, and Ca) clusters. Journal of Computational Chemistry 2009;30(15):2509-2514. R833856 (Final)
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  • Journal Article Wang L, Lee K, Sun Y-Y, Lucking M, Chen Z, Zhao JJ, Zhang SB. Graphene oxide as an ideal substrate for hydrogen storage. ACS Nano 2009;3(10):2995-3000. R833856 (Final)
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  • Journal Article Wang L, Zhao J, Li F, Chen Z. Boron fullerenes with 32-56 atoms: irregular cage configurations and electronic properties. Chemical Physics Letters 2010;501(1-3):16-19. R833856 (Final)
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  • Journal Article Wang L, Sun YY, Lee K, West D, Chen ZF, Zhao JJ, Zhang SB. Stability of graphene oxide phases from first-principles calculations. Physical Review B 2010;82(16):161406. R833856 (Final)
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  • Journal Article Wu Y-B, Jiang J-L, Li H, Chen Z, Wang Z-X. A bifunctional strategy towards experimentally (synthetically) attainable molecules with planar tetracoordinate carbons. Physical Chemistry Chemical Physics 2010;12(1):58-61. R833856 (Final)
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  • Journal Article Zhang C-G, Zhang R, Wang Z-X, Zhou Z, Zhang SB, Chen Z. Ti-substituted boranes as hydrogen storage materials: a computational quest for the ideal combination of stable electronic structure and optimal hydrogen uptake. Chemistry-A European Journal 2009;15(24):5910-5919. R833856 (Final)
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  • Journal Article Zhang G, Zhang W, Wang P, Minakata D, Chen Y, Crittenden J. Stability of an H(2)-producing photocatalyst (Ru/(CuAg)(0.15)In(0.3)Zn(1.4)S(2)) in aqueous solution under visible light irradiation. International Journal of Hydrogen Energy 2013;38(3):1286-1296. R833856 (Final)
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  • Journal Article Zhang G, Zhang W, Minakata D, Chen Y, Crittenden J, Wang P. The pH effects on H(2) evolution kinetics for visible light water splitting over the Ru/(CuAg)(0.15)In(0.3)Zn(1.4)S(2) photocatalyst. International Journal of Hydrogen Energy 2013;38(27):11727-11736. R833856 (Final)
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  • Journal Article Zhang G, Zhang W, Crittenden J, Minakata D, Chen Y, Wang P. Effects of inorganic electron donors in photocatalytic hydrogen production over Ru/(CuAg)(0.15)In(0.3)Zn(1.4)S(2) under visible light irradiation. Journal of Renewable and Sustainable Energy 2014;6(3):033131. R833856 (Final)
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  • Journal Article Zhang G, Zhang W, Minakata D, Wang P, Chen Y, Crittenden J. Efficient photocatalytic H(2) production using visible-light irradiation and (CuAg)(x)In(2x)Zn(2(1-2x))S(2) photocatalysts with tunable band gaps. International Journal of Energy Research 2014;38(12):1513-1521. R833856 (Final)
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  • Journal Article Zhang Q, Yue S, Lu X, Chen Z, Huang R, Zheng L, von Rague Schleyer P. Homoconjugation/homoaromaticity in main group inorganic molecules. Journal of the American Chemical Society 2009;131(28):9789-9799. R833856 (Final)
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  • Journal Article Zhang W, Kalive M, Capco DG, Chen Y. Adsorption of hematite nanoparticles onto Caco-2 cells and the cellular impairments: effect of particle size. Nanotechnology 2010;21(35):355103. R833856 (Final)
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  • Journal Article Zhang W, Yao Y, Li K, Huang Y, Chen Y. Influence of dissolved oxygen on aggregation kinetics of citrate-coated silver nanoparticles. Environmental Pollution 2011;159(12):3757-3762. R833856 (Final)
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  • Journal Article Zhang W, Stack AG, Chen Y. Interaction force measurement between E. coli cells and nanoparticles immobilized surfaces by using AFM. Colloids and Surfaces B: Biointerfaces 2011;82(2):316-324. R833856 (Final)
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  • Journal Article Zhang W, Yao Y, Chen Y. Imaging and quantifying the morphology and nanoelectrical properties of quantum dot nanoparticles interacting with DNA. Journal of Physical Chemistry C 2011;115(3):599-606. R833856 (Final)
    R831713 (Final)
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  • Journal Article Zhang W, Rittman B, Chen Y. Size effects on adsorption of hematite nanoparticles on E. coli cells. Environmental Science & Technology 2011;45(6):2172-2178. R833856 (Final)
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  • Journal Article Zhang W, Yao Y, Sullivan N, Chen Y. Modeling the primary size effects of citrate-coated silver nanoparticles on their ion release kinetics. Environmental Science & Technology 2011;45(10):4422-4428. R833856 (Final)
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  • Journal Article Zhang W, Crittenden J, Li K, Chen Y. Attachment efficiency of nanoparticle aggregation in aqueous dispersions: modeling and experimental validation. Environmental Science & Technology 2012;46(13):7054-7062. R833856 (Final)
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  • Journal Article Zhang W, Hughes J, Chen Y. Impacts of hematite nanoparticle exposure on biomechanical, adhesive, and surface electrical properties of Escherichia coli cells. Applied and Environmental Microbiology 2012;78(11):3905-3915. R833856 (Final)
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  • Journal Article Zhang W, Li Y, Niu J, Chen Y. Photogeneration of reactive oxygen species on uncoated silver, gold, nickel, and silicon nanoparticles and their antibacterial effects. Langmuir 2013;29(15):4647-4651. R833856 (Final)
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  • Journal Article Zhang W, Chen Y. Experimental determination of conduction and valence bands of semiconductor nanoparticles using Kelvin probe force microscopy. Journal of Nanoparticle Research 2013;15(1):1334 (4 pp.). R833856 (Final)
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  • Journal Article Zhang Y, Chen Y, Westerhoff P, Crittenden J. Impact of natural organic matter and divalent cations on the stability of aqueous nanoparticles. Water Research 2009;43(17):4249-4257. R833856 (Final)
    R831713 (Final)
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  • Journal Article Zhao J, Wang L, Li F, Chen Z. B80 and other medium-sized boron clusters: core-shell structures, not hollow cages. Journal of Physical Chemistry A 2010;114(37):9969-9972. R833856 (Final)
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  • Journal Article Zhao J, Chen Z. A special issue on structures, properties, and applications of nanomaterials: a computational exploration. Journal of Computational and Theoretical Nanoscience 2011;8(12):2395-2397. R833856 (Final)
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  • Journal Article Zhou Z, Li Y, Liu L, Chen Y, Zhang SB, Chen Z. Size- and surface-dependent stability, electronic properties, and potential as chemical sensors: computational studies on one-dimensional ZnO nanostructures. Journal of Physical Chemistry C 2008;112(36):13926-13931. R833856 (Final)
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  • Supplemental Keywords:

    metal oxides, nanoparticles, cell, biological fate, toxicity, quantum calculation, model, Quantitative Structure Activity Relationships (QSARs)

    Progress and Final Reports:

    Original Abstract
  • 2009
  • 2010