Why nanoparticles agglomerate

It is clear that the properties of these two nanomaterials are very different even though each contains the same number of identically sized nanoparticles.

Applying shear forces to nanoparticle agglomerates can break the agglomerates into two or more pieces. However, even with the most powerful dispersion mechanisms probe sonication, microfluidization, milling , it is typically not possible to restore agglomerated particles back to a monodisperse suspension consisting only of individual nanoparticles. This is due in part to the relative strength of the van der Waals forces that bind particles together and the shear forces that can be applied on the nanoscale.

In order to separate two agglomerated nanoparticles, forces in different directions have to be applied to each nanoparticle. At very small particle sizes it is simply not possible to generate sufficient microturbulence that creates a high enough force gradient to overcome the particle binding forces.

The agglomeration state of solution-based samples can be measured using TEM analysis of dilute solutions where the number of nanoparticles in well separated clusters is counted. However, many clusters need to be counted and it is difficult to discriminate between pre-formed agglomerates and agglomerates that formed during the drying process. Alternatively, a Dynamic Light Scattering instrument can be used to measure the mean and distribution of hydrodynamic agglomerate size in solution.

If the primary particle size has been previously determined with TEM, the average number of particles per aggregate can be determined. Another method is to use a Centrifugal Particle Sizer CPS which measures the time it takes for the nanoparticles to pass through a gradient. CPS measurments are preferred for multi-modal size distributions or for samples that have a high degree of polydispersity.

For many types of nanoparticles, spectral peaks can shift due to changes in the size, shape, coating, or aggregation state of the nanoparticles. Blue-shifting refers to an electromagnetic response that is shifted towards shorter wavelengths higher frequencies, higher energies.

Red-shifting refers to a peak that shifts to longer wavelengths. For example, if the aspect ratio of a gold nanorod is decreased, the peak extinction wavelength blue shifts from nm to nm. In the past, some attempts were made to determine the influence of TiO 2 NP agglomeration on toxicity using different protocols.

Magdolenova et al. Prasad et al. Lankoff et al. Thus, within the same study, different media and protein concentrations were used to produce suspensions with different states of agglomeration, which is an evident source of potential bias [ 26 ] and probably, influenced the toxicological outcome.

Therefore, in our study, we decided to vary only the pH of the initial dispersion medium to modify the agglomeration state of the TiO 2 NPs. After stabilization of these dispersions with BSA, the pH was readjusted to obtain a pH compatible with toxicological tests. Thus, we succeeded to produce TiO 2 NPs in different agglomeration states suitable for testing our hypothesis without experimental bias. To the best of our knowledge, this is the first attempt to produce different agglomeration states of TiO 2 NPs with minimal changes in dispersion media, and thus a more reliable approach to study the influence of agglomeration on toxicity.

Both TiO 2 NPs were not cytotoxic even in their most dispersed state. Then we compared the potential of SA and LA to induce an oxidative stress, pro-inflammatory responses and DNA damage, or to disturb the epithelial barrier integrity.

Therefore, we speculate that the increased uptake of LA via phagocytosis might account for these distinct responses. This indicates that the agglomeration state of TiO 2 influences the biological responses not only depending on the cell type but also depending on the primary particle size.

When summing up these in vitro results, we can conclude that, in any case, SA were not biologically more active than LA Table 4 A.

Studies reporting the influence of agglomeration on in vivo toxicity are scarce. Therefore, we exposed mice to differently agglomerated TiO 2 NPs via two exposure routes and investigated acute toxic effects.

Inhalation studies recorded that large agglomerates of nano-TiO 2 induced a stronger pulmonary response in rats than smaller ones [ 29 , 30 ], which is in agreement with our findings.

Thus, we can conclude that TiO 2 agglomeration increases the pulmonary responses but depending on the primary particle size. In contrast, systemic DNA damage was influenced by the agglomeration state of both TiO 2 but inversely. These results suggest that the influence of TiO 2 agglomeration state on biological responses is not limited to the site of particle deposition, but could also affect systemic responses.

Size has been identified as a major determinant of NM toxicity and their distribution is an important parameter to classify NMs according the EU definition [ 31 ]. To implement regulations and guidance in practice, standardization and validation of methods for measurement of the primary particle and AA characteristics, particularly their size, are essential to examine the effect of agglomeration on toxicity.

However, not much attention has been paid to other size characterization, i. They do not measure the AA shape. As observed in our study, for particles that are agglomerated, DLS and PTA results are biased towards larger values [ 32 ]. In addition to the size differences between suspensions at the start of the exposure in vitro, evaluation of further agglomeration in cell exposure medium are crucial to determine their influence on toxicity [ 35 , 36 , 37 ].

In our study, we noticed significant agglomeration of both TiO 2 only in Caco2 cell exposure medium. Cell exposure media with different compositions were shown to influence the stability of TiO 2 NPs [ 38 ]. When comparing epithelial cell types, i. HBE and Caco2, a slight difference in the magnitude of biological responses were observed, but further agglomeration did not lead to different effects as observed in THP In vivo, it is more than likely that the size distribution of the gavaged suspensions is modified by passing through different regions of the digestive tract, where pH changes depending on the region.

However, whether these in vivo modifications can be related to the change in in vitro size distribution in Caco-2 cell exposure medium can hardly be predicted.

Recent studies recognised effective density as a potential factor affecting the sedimentation and in vitro dosimetry effective dose [ 36 , 39 ].

Therefore, we determined the effective density of TiO 2 in all suspensions and simulated delivered in vitro doses using a distorted grid DG model [ 40 ]. The agglomeration state of both TiO 2 had only a slight effect on the effective density of TiO 2 and delivered doses.

Based on these results, we can conclude that, the delivered in vitro doses were not a confounder in assessing the differential in vitro responses observed between LA and SA suspensions.

Each type of TiO 2 NP is characterized by different physico-chemical properties and it exhibits different biological activities [ 41 , 42 ]. Therefore, the results of this study may only be applicable to the TiO 2 examined here. However, the approach and the dispersion methodology developed can be applied to investigate the differential toxicity of other types of NMs with regard to their agglomeration status.

According to the nanotoxicity paradigm, we hypothesized that small agglomerates would induce stronger toxicity than larger ones. Somewhat contra intuitively, we noted that, in most cases, no difference was found between agglomeration states and if any difference was found, large agglomerates mostly induced a stronger effect. In in vitro assays , the major differences were found in THP-1 cells, which is of interest in view of differential responses of innate immune cells. Also in in vivo assays , differential responses were noted both after respiratory and oral exposure.

Thus, we conclude that agglomeration state of TiO 2 NPs can influence their toxicity and that large agglomerates do not appear less active than small agglomerates. These results are, most probably, material and primary particle size specific, rather than agglomeration specific. A method based on a protocol developed by Guiot and Spalla [ 22 ] was used to generate two differently agglomerated suspensions of the same TiO 2 NPs. The Zeta potential varies as a function of pH, which in turn determines the electrostatic interaction of particles with other particles and with the surrounding medium.

Based on this principle, the agglomeration state over a range of pH 2—12 was analysed for each particle type using TEM, and pH conditions at which particles existed in different agglomeration states were selected.

Additional file 1 : Figure S2 shows the schematic diagram of the modified Guiot and Spalla protocol to prepare our ad-hoc stock suspensions. To ensure the delivery of the targeted energy and reproducibility of suspensions, probe sonicator was calorimetrically calibrated using the NANOREG protocol [ 44 ]. The stock suspensions were 2. The settings were 2. The selected dispersant was water refractive index of 1. The mean hydrodynamic diameter Z-average and the polydispersity index PDI were measured with version 7.

Equivalent circle ECD and Feret minimum diameter Feret min analyses were performed as described in [ 45 ]. Nr: was purchased from Sigma-Aldrich Belgium. All cell culture supplements were purchased from Invitrogen Belgium unless otherwise stated. Cells from passage 4 to 10 were used for the experiments. We used serum-free exposure media culture medium without FBS for in vitro conditions to avoid the influence of serum proteins on particle characteristics and biological responses.

On the day of exposure, freshly prepared stock suspensions were diluted in BSA 0. BSA 0. Whole lungs were then perfused with NaCl 0. Left lobes were placed in 3. Ti recovery and the effect of the biological matrix were determined in preliminary experiments data not shown.

After subtracting the blank OD values from the sample OD values, results were expressed as percentage of control untreated cells. Cell viability was assessed by cellular leakage of LDH using a kinetic assay [ 46 ]. At the end of exposure, supernatants were transferred to a new plate and cells were incubated with triton 0. Slope was calculated according to the standard curve. Cell viability was calculated as. Total glutathione GSH is a cellular antioxidant, which is depleted when excessive reactive oxygen species ROS are produced.

For in vivo, a part of the lung and liver was sliced and weighted. GSH was normalized to the total protein content in vitro and the results were expressed as percentage of control untreated cells. For in vivo , GSH was normalized to the mass of lung or liver tissue. Results were normalized to the total protein content and expressed as a ratio to control untreated cells. Results are expressed as percentage of control untreated cells.

In earlier studies, cellular and in some cases nuclear uptake of TiO 2 has been shown [ 48 , 49 , 50 , 51 ]. For in vivo experiments , comet assay was performed on blood and BAL cells collected from animals. The mean percentage of tail DNA was calculated from the median of three independent experiments. The safety of nanostructured synthetic amorphous silica SAS as a food additive E Arch Toxicol.

A systematic review of reported exposure to engineered nanomaterials. Ann Occup Hyg. Occupational exposure limits for manufactured nanomaterials, a systematic review. Nanoparticle exposure at nanotechnology workplaces: a review. Part Fibre Toxicol. Article Google Scholar. Off J Eur Union.

Accordingly, controlling the size and density of nanoparticles are challenging in nanocomposites to create the best properties. Figure 4 illustrates the effects of R and t on B interfacial parameter and tensile strength by Pukanszky model Eq.

Based on Fig. Also, B decreases to below 3 when the size of nanoparticles grows to about 40 nm and the interphase thickness decreases to less than 10 nm. Therefore, the sizes of nanoparticles and interphase play dissimilar roles in B parameter. Also, it should be noted that the small nanoparticles without formation of a strong interphase cannot give a high B in polymer nanocomposites.

As a result, both nanoparticle and interphase dimensions are important to obtain a high level of B in nanocomposites. Figure 4b also shows the effects of R and t parameters on the tensile strength of nanocomposites by Pukanszky model. It is observed that small nanoparticles and thick interphase improve the strength of nanocomposites.

However, a poor strength is observed by big particles and thin interphase. Therefore, both R and t parameters affect the tensile strength of nanocomposites. Accordingly, it is essential to isolate and disperse the nanoparticles in polymer matrix at small size to achieve the best performances. This occurrence shows the significant role of nanoparticle size in the formation of interphase regions. It should be mentioned that the interphase regions may overlap in the systems containing high filler concentration.

Figure 5b also shows the effects of R and t levels on a interphase parameter. This evidence reveals that a depends on both R and t parameters. The small size and low density cause significant levels for number, surface area, stiffening efficiency, and specific surface area of nanoparticles. Small nanoparticles and thick interphase present the high levels for B parameter, tensile strength, interphase volume fraction, and a interphase parameter.

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