Reversible or Not? Distinguishing Agglomeration and Aggregation at

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Reversible or not? Distinguishing agglomeration and aggregation at the nanoscale Stanislav V. Sokolov, Kristina Tschulik, Christopher BatchelorMcAuley, Kerstin Jurkschat, and Richard G Compton Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.5b02639 • Publication Date (Web): 09 Sep 2015 Downloaded from http://pubs.acs.org on September 13, 2015

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Analytical Chemistry

Reversible or not? Distinguishing agglomeration and aggregation at the nanoscale Stanislav V. Sokolov1 , Kristina Tschulik1 , Christopher Batchelor-McAuley1 , Kerstin Jurkschat2 , and Richard G. Compton∗1 1 Department

of Chemistry, Physical and Theoretical Chemistry Laboratory, Oxford

University, South Parks Road, Oxford OX1 3QZ, UK 2 Department

of Materials, Oxford University, Parks Road, Oxford OX1 3PH, UK

ABSTRACT

Nanoparticles are prone to clustering either via aggregation (irreversible) or agglomeration (reversible) processes. It is exceedingly difficult to distinguish the two via conventional techniques such as dynamic light scattering (DLS), nanoparticle tracking analysis (NTA) or electron microscopy imaging (scanning electron microscopy (SEM), transmission electron microscopy (TEM)) as such techniques only generally confirm the presence of large particle clusters. Herein we develop a joint approach to tackle the issue of distinguishing between nanoparticle aggregation vs. agglomeration by characterizing a colloidal system of Ag NPs using DLS, NTA, SEM imaging and the electrochemical “nano-impacts” technique. In contrast to the conventional techniques which all reveal the presence of large clusters of particles, electrochemical “nano-impacts” provide information regarding individual nanoparticles in the solution phase and reveal the presence of small nanoparticles (< 30 nm ) even in high ionic strength (above 0.5 M KCl) and allows a more complete analysis. The detection of small nanoparticles in high ionic strength media evidences the clustering to be a reversible process. As a result it is concluded that agglomeration rather than irreversible aggregation takes place. This observation is of general importance for all colloids as it provides a feasible analysis technique for a wide range of systems with an ability to distinguish subtly different processes.

Keywords:

Nano-impacts, coulometric sizing, nanoparticle tracking analysis, silver nanoparticles,

agglomeration. Supporting information. SEM images of citrate-capped Ag NPs, a figure of the observed frequency of the impacts in KCl electrolyte.

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INTRODUCTION Nanoparticles (NPs) are ubiquitous in the modern world. Their uses span a wide range of commercial products and medical applications. 1 As the demand for nanoparticle-based products increases, methods to detect and quantify engineered nanoparticles are of the utmost importance, in particular in complex aqueous systems such as surface and waste waters as well as a variety of industrial uses as the presence of the NPs is expected to rise. 2 All such complex matrices typically are characterized by high ionic strength and as a result NPs often show a tendency to cluster. The knowledge of the aggregation/agglomeration state and the corresponding behavior is vital for the understanding of their fate and distribution in such complex systems. In the present work an IUPAC definition 3 of an agglomerate and an aggregate will be used. According to this an agglomerate corresponds to the case when the dispersed particles are held together by weak physical interactions ultimately leading to phase separation by the formation of precipitates of larger than colloidal size and the whole process is reversible. An aggregate is defined as comprising strongly bonded colloidal particles and the clustering process is irreversible. Electron microscopy has been used for the study of nanoparticles but it can provide little evidence for the aggregation/agglomeration state in the solution due to the nature of the sample preparation, which involves drying of the sample. 4 An alternative to traditional electron microscopy is cryo-TEM, a technique where frozen samples are imaged. Through use of time-series images it is possible to monitor aggregation/agglomeration process. The technique has been successfully applied to a range of systems. 5 6 While providing clear visual information regarding the aggregation/agglomeration state of the particles, the technique has the following disadvantages: complex sample preparation, high associated costs and lack of information regarding the dynamics of the system. In addition cryo-TEM requires complex analysis and does not allow distinguishing reversible agglomeration and irreversible aggregation as both processes would appear identical. Traditionally investigations of the solution phase aggregation/agglomeration were mainly based on light-scattering techniques such as dynamic light scattering (DLS) 7 8 9 where the resultant size-distribution is often skewed by the presence of the large particles with high refractive index. 10 Alternative analytical techniques such as inductively coupled plasma mass spectrometry (ICP-MS), and X-ray photoelectron spectroscopy (XPS) have also been successfully applied to nanoparticle aggregation 11 but can be time consuming and require complex analysis. Nanoparticle tracking analysis (NTA) is another light-scattering technique and it avoids some of the common problems associated with DLS as it tracks individual NPs. 12 It has yielded accurate results for a range of metallic systems and continuous improvement in the detection algorithm has lead to further improvement of accuracy, 13 even enabling the study of aggregation/agglomeration of proteins. 14 UV-vis spectroscopy has also been often employed due to the shift of the absorption 15 caused by the


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aggregation or agglomeration, but this is an ensemble technique and lacks easily accessible information about the distribution of sizes. 16 Hence a particularly difficult analytical task is to distinguish between aggregated and agglomerated nanoparticles as conventional techniques are usually unable to separate one from the other. To measure the complex distribution of particle sizes in clustering suspension a combination of techniques is required. Recently an electrochemical “nano-impact” technique has proved to be an alternative robust method for characterization of a wide range of systems (gold, 17 silver, 18 19 20 iron oxide, 21 22 organic nanoparticles 23 24 25 26 and nickel 17 ), insensitive to the coating of the nanoparticles unlike traditional electrochemical techniques 27 and offers a feasible alternative to the more traditional sizing methods due to the relative simplicity of the sample preparation and low cost. 28 A typical experimental setup consists of a microelectrode or an array of microelectrodes, held at a high overpotential which allows full reduction or oxidation of a colliding nanoparticle, leading to an observation of a spike in a chronoamperogram. The area of each spike quantifies the charge transferred to a nanoparticle and is related to its size. Provided that a statistically meaningful number of particles are analysed then the size distribution of the sample can be obtained. In addition nano-impacts have been successfully employed in analysis of the aggregation state of the small silver nanoparticles. 29 In the following work we use a combination of light scattering techniques with electrochemical nano-impacts to analyse the effect of electrolyte on the particle aggregation/agglomeration state. Both techniques are conducted in the solution phase which allows a direct comparison of the resultant size distributions. Chloride ions are omnipresent in the environment and in order to be useful an analytical technique must be suitable for a range of potential conditions and for this purpose potassium chloride electrolyte was chosen. Thus we demonstrate that this unified approach utilizing several state of the art analytical techniques allows differentiation between agglomeration/aggregation states of nanoparticles in colloidal suspensions of high ionic strength.

1 EXPERIMENTAL 1.1 Chemicals The chemicals used were of analytical grade and were purchased from Sigma Aldrich unless stated otherwise. Nominal 50 nm radius quasi-spherical citrate-capped silver nanoparticles were purchased from nanoComposix (4878 Ronson Court, Suite K, San Diego, CA 92111 United States) as an aqueous solution. 1.2 Characterization via SEM imaging SEM images of the citrate-capped silver NPs were taken on a high-resolution SEM (LEO Geminin 1530, Zeiss, Jena, Germany).In order to determine the size of the individual non-aggregated nanoparticles, the samples were prepared by sonicating the NP suspensions for 10 s prior to being drop-cast on a TEM-grid-


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modified SEM sample holder. SEM was used instead of transmission electron microscopy (TEM) due to better contrast of the resulting images. The SEM images were taken at 5.00 kV acceleration potential, which according to the specification of the instrument provides a resolution of 3.5 nm at this potential. Additional images are shown in the supporting material. ImageJ software was used for post-processing, particle analysis and area calculations. 1.3 Dynamic light scattering DLS measurements were performed using a Malvern Zetasizer Nano ZS (Malvern, Herrenberg, Germany) equipped with a 633-nm He-Ne laser and operating at an angle of 173 degrees. The software used to analyze the collected data was the Zetasizer Software from Malvern. Five-hundred microliters of each sample was transferred to single-use polystyrene half-micro cuvettes with a specified pathlength of 10 mm. The measurements were made under thermostatted conditions at 25◦ C. For each sample, 15 runs of 10 s were performed, with three repeats. The intensity size distribution, the Z-average diameter (Z-ave) and the polydispersity index (PdI) were obtained from the autocorrelation function using the general purpose mode. In this work we have used General Purpose non-negative least squares (NNLS) analysis in order to gain the size distributions as it is widely available and commonly used for sizing purposes. 1.4 Nanoparticle tracking analysis NTA measurements were performed with a NanoSight LM10 (NanoSight, Amesbury, United Kingdom), equipped with a sample chamber with a 638-nm laser. The samples were injected in the sample chamber with sterile syringes (BsD Discardit II, New Jersey, USA) and absence of any air bubbles was ensured. All measurements were performed at room temperature. The samples were measured for 60 s with automatic settings at 30 frames per second. The software used for capturing and data analysis was the NTA 2.3. 1.5 Electrochemical Studies Electrochemical experiments were performed under thermostatted temperature conditions (25 ± 1◦ C) using a three-electrode setup with an in-house built low noise potentiostat. 30 This potentiostat comprises of three main sections; the computer interface used for signal conversion, the current amplifier circuit and the stabilized potentiostat. A Labjack U6 (Labjack corporation, Lakewood, CO USA), with a Labjack tickDAC, was used for the computer interface. Connection to the Labjack was via a standard USB but with the ground isolated from that of the PC (USB-ISO OLIMEX, Farnell, Leeds, UK). Control of the Labjack was performed through a script written in Python 2.7 and run through the IDE Canopy (Enthought, Austin, TX USA). Measurement of the current at the working electrode (running to ground) was performed using a low current-amplifier DLPCA-200 (FEMTO, Messtechnik GmbH, Germany) and the bandwidth of the output of the current amplifier was limited using a 100 Hz 2-pole passive RC filter. The resulting


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analog signal was oversampled and digitized using the Labjack at a stream rate of 4kHz. Potentiostatic control was provided by a highly stabilized (1kHz bandwidth) classic adder potentiostat. Importantly, first, for the reference buffer a high quality operational-amplifier LMC6001 (Farnell, Leeds, UK) with ultra low-input bias (25fA) was used. Second, a high quality low-noise operational-amplifier, AD797 (Farnell, Leeds, UK), provided the control of the potential at the counter electrode. A porous carbon rod was used as a counter electrode for experiments and potentials were applied relative to a saturated calomel reference electrode (SCE) (0.244 V vs standard hydrogen electrode). Aqueous KCl solutions were prepared using ultrapure water (Millipore, resistivity not less than 18.2 MΩ cm at 25◦ C). Oxidative particle impacts were performed using a random array of microelectrodes (RAM) as a working electrode. High overpotential of 0.60 V vs SCE was used to ensure complete oxidation of Ag NPs. 31 A random assembly of microelectrodes 32 is an array of randomly dispersed carbon microfibers (approximately 3200 fibres) in non-conductive epoxy. Of the 3200 fibres ca. 20-40% are connected 32 and each of the fibres has a radius of 3.5 micrometres. The fibres are separated on average by 70 micrometres from each other. The ends of the fibres act as individual microdisks connected in parallel. The RAM was polished before the experiments using microcloth supplied by Buehler and 0.3µM alumina particles to ensure a clean and reproducible surface. For the studies 20 chronoamperograms of 25s duration were recorded at a potential of 0.60 V vs SCE and a minimum of 150 spikes were recorded for each electrolyte concentration. The solution was bubbled with nitrogen in between the scans to ensure efficient re-dispersion of the particles. The analysis of the data was performed using the software Signal Counter 29 (developed by Dario Omanović of the Center for Marine and Environmental Research, Ruder Boˇsković Institute, POB 180, 10002 Zagreb, Croatia) to identify spikes, perform baseline corrections and integrate the charge per given impact. The software Origin Pro 9.0 (Origin Lab Corporation) was used for data visualization and histogram analysis.

2 RESULTS AND DISCUSSION SEM images were used to size monomeric, non-aggregated nanoparticles ex-situ to provide a starting point. In order to differentiate between the aggregation or agglomeration, the size distributions of the nanoparticles in solutions of varying concentrations of electrolyte (KCl) were determined. A series of electrochemical and nanoparticle tracking experiments were performed in KCl electrolytes in a concentration range of 20 to 2500 mM. The obtained results from the two solution phase techniques were then unified to distinguish between the two modes of NP clustering and revealed that reversible agglomeration instead of aggregation takes place.


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Figure 1. SEM images and the resultant size distributions for the silver nanoparticles

2.1 SEM imaging and analysis The SEM images were analysed using the ImageJ software. 33 Automatic particle detection and particle area calculation was used. The particles are quasi-spherical and have an icosahedral shape. Analysis of 54 nanoparticles showed a size range of 45-60 nm in accordance with the manufacturer’s specification. The size distribution shown in Figure 1 has the following characteristics: a mean radius of 50 nm and a standard deviation of 8 nm, the particles are mostly monodisperse.

2.2 Nano-impacts A random assembly of microfibers (RAM) was used with a large surface area ( 1000x area of each individual disk of 3.5 micrometre radius). Based on the Stokes-Einstein equation 34 the aggregated or agglomerated nanoparticles will have a low diffusion coefficient resulting in a tiny number of observable impacts at a single microelectrode. The RAM allows detection of greater numbers of impacts leading to higher accuracy of particle size distributions. A typical experimental chronoamperogram is shown in Figure 2, where the spikes correspond to individual nanoparticle-RAM collisions (Figure 2 red transient). Spikes are not seen for blank scans in the absence of the NPs (Figure 2 blue transient). The chronoamperogram is offset by 1.5 × 10−10 A for clarity. For a given spike by integrating its area we obtain a charge of the colliding nanoparticle. We note that estimates of any capacitance charge assuming a double layer capacitance of ≈ 10µFcm−2 correspond to less than 1% of the observed charge. 35 Where agglomeration/aggregation process takes place in the solution, the concentration of the diffusing species decreases, which in turn leads to a decreased number of the observed impacts. Redispersion caused by mechanical agitation, such as bubbling an inert gas or sonication, will result in an increase in the observed number of impacts. This hypothesis was tested by recording the frequency of the impacts observed in a given scan and plotting it against the scan number. The observed behaviour (see Figure 3) provides evidence of the aggregation/agglomeration process taking place, from the start of the experiment a sharp


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Figure 2. A typical chronoamperogram observed at a potential of 0.6 V vs SCE in the presence and absence of nanoparticles, the curves have been offset by 1.5 × 10−10 A for clarity.

decline in the number of impacts is observed, bubbling or sonication (not shown) causes the increase in frequency of the impacts immediately after this redispersion with an overall decline over time, this observation is consistent with a clustering process taking place. The effect dispersion by either bubbling or sonication has on the measured nanoparticle size was investigated in 0.5 M KCl. First the experiments were performed by bubbling with nitrogen gas and then a second second set of experiments were performed using a sonic horn. The resultant size distributions are shown in Figure 4 and clearly demonstrate that the redispersion method does not have an impact on the observed size distributions and the resultant particles are of similar mean sizes. Please note that the nano-impact technique is able to detect single (un-clustered) large particles as shown in the work by Bartlett et al. 28 but in our series of experiment, such particles were rarely if ever detected. For all further experiments bubbling was used to redisperse the particles. At high ionic strength due to agglomeration/aggregation the number of impacts is expected to decrease with time. In order to validate the hypothesis five scans were performed in succession with a three minute pause between the scans and the resulting plot shown in supporting material. For 20 mM KCl virtually constant number of impacts is observed in five consecutive scans, while for higher concentrations a significant decrease in the number of impacts is observed, which is an evidence of agglomeration/aggregation taking place. The mean calculated nominal radius for a range of KCl concentrations is presented in the Table ?? and shown graphically in Figure 5. Due to icosahedral shape of the nanoparticles a correction has been applied


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Figure 3. The observed frequency of the impacts and effect of nitrogen bubbling, E = 0.6 V vs SCE.

Figure 4. Size distributions obtained by nano-impacts for nitrogen bubbling and sonication techniques with the shape correction, potential of 0.6 V vs SCE


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Figure 5. The shape-corrected size distributions obtained by nano-impacts for the different KCl concentrations, potential of 0.6 V vs SCE

to account for the apparent reduction in the observed charge in accordance with the previous reports at low ionic strengths, where consistent sizes were found for nano-impacts and SEM. 19 The nano-impacts experiments surprisingly show a significant number of small particles and a general lack of expected large agglomerates. For increased ionic strength of electrolyte in accordance with the Derjaguin, Landau, Verwey and Overbeek (DLVO) theory increased aggregation/agglomeration is expected 36 but is not observed experimentally by the nano-impacts technique. There is some variation within the distributions for increasing electrolyte concentration with higher ionic strength leading to an apparent decrease in the observed mean size. In particular for 0.5 M and 2.5 M KCl the mean size of the nanoparticles is smaller compared to the monomeric size obtained from the SEM images. It should be considered that nano-impacts unlike light-scattering techniques are biased towards smaller NPs due to the fact that the smaller particles have a higher probability of collision with an electrode due to larger diffusion coefficient which is inversely proportional to the radius of the particle. 29;37 The diffusion coefficient of a spherical particle is given by the Stokes-Einstein Equation 1.

D=

kb T 6πηr

(1)

where D is the diffusion coefficient, kb is the Boltzmann constant, T is the temperature, η is the viscosity and r is the radius of the particle. Hence a correction factor can be introduced to account for the fact that larger particles have lower diffusion coefficients and hence have a lower probability of a collision with an electrode. Despite the lowering of the diffusion coefficient, detection of larger particles is still expected but was not observed. In order to explain the seemingly contradictory evidence of reduced frequency of impacts, indicative of clustering process taking place and stable rather than increasing mean size further light-scattering technique were employed to gain additional evidence of the process taking place.


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Figure 6. The normalised size distributions obtained for the different KCl concentrations using DLS

2.3 Dynamic light scattering data analysis Analysis yielded the size distributions shown in Figure 6 with the Z-average and a polydispersity index shown in Table 1. The intensities were normalized for ease of comparison by setting the highest intensity as 1 and adjusting other intensities relative to it. The general observed trend is that with increasing ionic strength the Z-average value increases, which suggests that aggregation takes place. There is a general increase in polydispersity index with an apparent anomaly at 20 mM KCl. DLS is known to be challenged by polydisperse samples and hence the PdI index may be skewed by the presence of the small number of aggregated or agglomerated NPs. A significant broadening of the scattering intensity is observed for 500 mM KCl indicative of significant degree of aggregation/agglomeration. 2.4 Nanoparticle tracking analysis In order to overcome the inherent limitations of DLS with respect to presence of large particles, NTA was employed. First the nanoparticles were sized in de-ionized water in order to minimize the aggregation/agglomeration and obtain information of the unperturbed NPs. The video files of the Brownian motion were analysed using the NTA 2.3 software. Figure 7 shows a typical observation of the nanoparticles and the resultant size distribution for the particles suspended in the de-ionized water. It is evident that the NPs have a high refractive index and are clearly visible. The analysis yielded the following size distributions for the samples as shown in figure 7 with distribution characteristics shown in Table ??. The experiment provides evidence of aggregation or agglomeration taking place for electrolyte concentrations above 20 mM. The mean radius obtained from NTA is in the order of 100 nm and a large standard deviation is observed for 100mM and 500mM. (Note: it was not possible to conduct the experiments at 2.5 M KCl concentration due to problems with the thin sample cell caused by


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Figure 7. The size distributions obtained for nanoparticles in de-ionised water using NTA

Figure 8. The size distributions obtained for the different KCl concentrations via NTA


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recrystallization of the salt).

2.5 Reconciling the data Nano-impacts consistently yielded a mean particle size of 27-40 nm without showing the presence agglomerates, while NTA and DLS provided evidence for the aggregation of the sample (50-180 nm). In order to understand the apparent difference in results the basis of the two techniques are evaluated. Nano-impacts sizing rely on a direct collision of a nanoparticle with an electrode, which allows subsequent oxidation to take place. The size distributions shown in Figure 5 shows a prevalence of nanoparticle of 27-40 nm radius. At 20mM KCl concentration lower radius is observed, a potential explanation is incomplete oxidation of the large silver particles due to lack of supporting electrolyte. While previous reports shown that 20 mM KCl is sufficient for oxidation of smaller particles this may not be the case for large particles. At higher concentrations (above 100 mM KCl) the lower particle size can only be attributed due to agglomeration processes taking place as the conditions of the experiment are fully supported. On the other hand the NTA demonstrates an apparent presence of a large number of aggregates or agglomerates with the whole size distribution skewed towards such particles. The fact that agitation of the electrolyte increases the frequency of the observed impacts and the resultant size distribution is not affected by the technique involved suggests that the underlying process is reversible and thus has to be agglomeration instead of aggregation. Agglomeration is a dynamic process and a potential explanation for the observations the simplified generalized reversible process shown in Equation 2

NMn (N − 1)Mn + Mn (N − 2)Mn + 2Mn f urther dissociation

(2)

where M is any metallic nanoparticle, and involves equilibrium of agglomerates of N non-agglomerated nanoparticles (containing n M atoms each) at the electrode interface. Large agglomerates have low diffusion coefficients and as a result are rarely observed in the nano-impacts experiment. In contrast the smaller particles with higher diffusion coefficient have a higher probability of collision and as a result dominate the observed distribution. At the electrode surface the concentration of nanoparticle monomers is thus depleted most and as a result the equilibrium is shifted in favour of the smaller particles. This bias to detecting smaller nanoparticles is further amplified by the complexity of the above mechanism. Light scattering techniques do not perturb such equilibrium and the solution is dominated by the presence of large clusters. The position of such equilibrium will be dependent on the ionic strength of the electrolyte, size and coating of the nanoparticles.


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Electrolyte Concentration (mM) 0 20 100 500 2500

Nano-impacts Mean radius Standard Deviaton n/a n/a 44 9 49 10 36 9 30 11

Mean radius 50 52 103 123

NTA Standard Deviaton 32 38 46 60 N/A

DLS (Z-ave)/2 (nm) 54 66.4 69.7 181.65

PdI 0.18 0.27 0.149 0.322

Mean radius 50 n/a n/a n/a

SEM Standard Deviation 8 n/a n/a n/a

Table 1. Mean radius and standard deviation of the size distributions obtained using nano-impacts, NTA, DLS and SEM.

3 CONCLUSIONS In order to correctly differentiate between agglomeration and aggregation of nanoparticles in solution of high ionic strengths, electron microscopy imaging and light scattering techniques are insufficient due to the inherent limitations. Without corresponding size-distribution information provided by nano-impacts the characterization is incomplete. Hence it is evidenced that the light scattering techniques such as DLS and NTA can be ideally complemented with the electrochemical coulometric sizing technique to gain hitherto non-accessible information on the agglomeration/aggregation state. While demonstrated here for Ag nanoparticles, this new analytical approach can be extended to other systems. The joint use of these techniques allowed to clearly identify presence of reversible clusters, that is agglomeration, instead of aggregation occurs in the system shown. Analysis performed in the work can be extended to other common nanoparticle systems such as gold and iron-oxide NPs which are found in a wide range of applications, which may be of particular importance for biological and biomedical systems which rely on accurate description of the in-situ agglomeration/aggregation state.

ACKNOWLEDGMENTS The research leading to these results has received partial funding from the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement no. [320403]. K.T. was supported by a Marie Curie Intra European Fellowship within the 7th European Community Framework Programme Grant Agreement no. [327706]. We thank Dr. Enno Kaetelhöen for helping design the TOC image.

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Figure 9. for TOC only


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Reversible or Not? Distinguishing Agglomeration and Aggregation at

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