# Effects of adding zeolite particles on the hierarchical microstructure of zeolite-geopolymer composites and their Sr²⁺ adsorption properties

Samuel Vannier, Alban Gossard, Lucile Magnier, Vanessa Proust, Thomas

David, Agnès Grandjean

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## To cite this version:

Samuel Vannier, Alban Gossard, Lucile Magnier, Vanessa Proust, Thomas David, et al. Ef- fects of adding zeolite particles on the hierarchical microstructure of zeolite-geopolymer com- posites and their Sr²⁺ adsorption properties. Materials & Design, 2024, 244, pp.113233. 10.1016/j.matdes.2024.113233. cea-04711767

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# Effects of adding zeolite particles on the hierarchical microstructure of zeolite-geopolymer composites and their Sr²⁺ adsorption properties

Samuel Vannier<sup>a, b</sup>, Alban Gossard<sup>a, *</sup>, Lucile Magnier<sup>b</sup>, Vanessa Proust<sup>a</sup>, Thomas David<sup>b</sup>, Agnès Grandjean<sup>a</sup>

## ARTICLE INFO

**Keywords:**

LTA zeolite

Geopolymer

Multiscale characterization

Hierarchical materials

Segmentation

Decontamination

## ABSTRACT

Strontium ions can be removed from wastewater in fixed-bed reactors by adsorption on hierarchical materials (different pore sizes and phases). A detailed understanding of the materials multiscale structure is therefore crucial to optimize their sorption properties. This article presents a multi-technique approach developed to characterize the relationship between microstructure and adsorption in a range of Linde-type A (LTA) zeolite-geopolymer composites,. Two-dimensional scanning electron microscopy and 3D X-ray tomography were used to image the porous and solid phases at different length scales. Advanced numerical methods involving machine learning were applied to segment the images and provide quantitative morphological values describing the porous network geometry and the location and accessibility of the zeolite particles in the geopolymer. These results were then correlated with the materials sorption properties, measured in batch and column processes. Increasing the materials zeolite content (from 0 to 27 wt%) slowed down their adsorption kinetics but increased their maximal adsorption capacities from 20 to 49 mg·g⁻¹ and Sr²⁺-selectivity at equilibrium. In breakthrough experiments, the material with highest zeolite content had a much higher breakthrough volume passing from 169 to 478 ml in the presented experimental conditions (much higher wastewater decontamination capacity), but a shallower breakthrough curve (lower column efficiency).

## 1. Introduction

Nuclear wastewater decontamination is an essential component of nuclear facility maintenance, decommissioning activities, and cleanup operations after nuclear accidents [1,2]. One of the most hazardous radionuclides to be extracted in this context is Sr-90 (Sr²⁺) [3], whose removal from wastewater is however complicated by with the presence of competing cations, such as Na⁺ or Ca²⁺. Highly selective Sr²⁺ extraction is therefore crucial [4,5]. Solid adsorbents are an attractive solution to this problem, because their microstructure can be designed to optimize their selectivity for Sr²⁺ [6,7]. Moreover, since column extraction is generally the only viable approach to treat large volumes of effluent while minimizing solid secondary waste [8,9], and because powdery materials are ill suited to column processes because they induce large pressure increases, monolithic adsorbent materials are required, with specific hierarchical properties [10,11]. For Sr extraction, the active site must be selective for Sr²⁺ over other cations; however,

Sr²⁺-selective materials, such as zeolites [12–14] and titanates [15–18], are generally synthesized as powders and must be shaped into monoliths [19,20] or grains [21] with a multiscale porous structure to optimize their mass-transport properties [20,22–24]. These shaped materials can be packed into columns, where the inter-grain voids improve hydrodynamic flow by reducing pressure loss, and the intra-grain pores insure fast adsorption kinetics and provide access to active sites in the core of the grains [25,26].

Geopolymers are amorphous aluminosilicate materials with highly interconnected mesopores [27,28] and exchangeable cations (Na⁺ or K⁺, notably) [22,29–31]. We previously presented a range of hierarchical materials designed for selective Sr²⁺ decontamination of highly saline wastewater [32]. These materials consist of Linde-type A (LTA) zeolite particles [4,14,33,34] dispersed in a multiscale porous geopolymer binder prepared by emulsion-templating [35–37]. The two phases in these LTA zeolite-geopolymer composite adsorbents (the crystalline zeolite particle phase and the amorphous geopolymer phase) can

<footnote><sup>a</sup> CEA, DES, ISEC, DMRC, Univ. Montpellier, Marcoule, France</footnote>

<footnote><sup>b</sup> Univ. Grenoble Alpes, CEA, LITEN, DTNM, LCAE, F-38000 Grenoble, France</footnote>

<footnote>* Corresponding author.
E-mail address: alban.gossard@cea.fr (A. Gossard).</footnote>
therefore both trap $Sr^{2+}$ ions. However, while the structure of the LTA zeolite particles makes them selective for $Sr^{2+}$ in saline wastewater [38], the geopolymer, being amorphous, is poorly selective for trapping cation by ionic exchange mechanism [39]. The formulation of zeolite-geopolymer composites is therefore a common strategy to improve the geopolymer performances [40–43]. Moreover, as geopolymer and zeolites are both inorganic aluminosilicate materials, they could be directly incorporated in nuclear waste matrices, allowing an easy handling of the secondary waste induced by the decontamination operation. Nevertheless, the concentration of zeolite particles directly affects the microstructure of the adsorbent from the nanometre to the micrometre scale [32]. Therefore, although increasing the zeolite particle content improves the adsorption properties of the material, the underlying relationship between microstructure and adsorption properties remains unclear, both in batch mode (kinetic mechanism, capacity, $Sr^{2+}$ selectivity) and in continuous flow processes (shape of the breakthrough curve).

To shed light on these relationships, we conducted a multiscale characterization of these adsorbent. The porous network and multiple phases of composites with different zeolite particle contents were investigated by X-ray tomography for large fields of view and by high resolution scanning electron microscopy (SEM) on smaller scales, using numerical methods to optimize image segmentation and extract quantitative data. These data were then correlated with the results of batch and column extraction experiments with the same materials, to explain their adsorption performance on a microstructural basis.

# 2. Materials and methods

## 2.1. Materials

The geopolymer were synthesized from an aluminosilicate source (metakaolin “Metamax”, BASF) and an activation solution consisting of commercial potassium silicate (Betol® K5020T, Wöllner) and potassium hydroxide (VWR). The templating emulsions were prepared with dodecane (Sigma-Aldrich, ≥ 99 % purity) and tetradecyltrimethylammonium (TTAB, Sigma-Aldrich, 99 %). The LTA zeolite particles were prepared from $SiO_2$ (in powder form, Aerosil® 380. Evonik Industries, ≥ 99.5 %), $NaAlO_2$ (from VWR) and NaOH (Sigma-Aldrich, ≥ 97.0 %). The sorption experiments were performed using $NaNO_3$, $Sr(NO_3)_2$ and $Ca(NO_3)_2$ purchased from Sigma-Aldrich.

## 2.2. Material synthesis

The synthesis of the LTA zeolite particles is described in detail elsewhere [44]. The geopolymer/zeolite composite materials were synthesized using a recently described emulsion templating approach [32]. The geopolymer molar composition was fixed at $SiO_2$/Al$_2$O$_3$/K$_2$O/H$_2$O=3.3/1/1/16. Briefly, zeolite LTA particles were mixed with TTAB in water and placed in an ultrasonic bath for 1 h for the TTAB to be adsorbed on the zeolite particles. The amount of zeolite was chosen to synthesize materials with 0, 5, 14 and 27 wt% zeolite particles. The TTAB concentration was adjusted in proportion to keep the amount of free TTAB (i.e. not adsorbed on the zeolite particles and free to adsorb at the aqueous solution/dodecane interface) constant. The activation solution consisting of KOH and potassium silicate was prepared separately. Both solutions were mixed and an oil-in-water emulsion was obtained by adding dodecane (50 vol% of the final emulsion) dropwise under 10000 rpm mixing with a IKA Ultra Turrax T25 single rotor (S25N-18G) homogenizer for 1 min. Metakaolin was then progressively added, still while mixing, until a stable emulsion of dodecane droplets was obtained in a continuous phase consisting of geopolymer precursors containing LTA zeolite particles. After geopolymerization and solidification of the structure, dodecane was removed by Soxhlet extraction (extractor, acetone/THF, 50/50 vol%) and the materials were finally dried at 60 °C for 24 h. Powder samples for microstructural analysis were prepared by

grinding and sieving to obtain a particle size distribution centred between 300 and 500 µm. The materials are referred to hereafter as GMZx, with x representing their zeolite content (0, 5, 14 or 27 wt%).

## 2.3. Materials characterization

Nitrogen adsorption–desorption isotherms were performed at 77 K on a Quantachrome Instruments NOVAtouch LX3 analyser. Specific surface areas were calculated with the Brunauer–Emmett–Teller (BET) method, and mesopore size distributions were calculated using the Barret-Joyner-Halenda (BJH) method. X-ray diffraction (XRD) spectra were recorded at room temperature on an X’Pert PRO–PANalytical device with a Cu Kα-1 radiation (λ = 1.5406 Å). Scanning electron micrographs were obtained with a ZEISS MERLIN device (operating voltage, 3 kV; current, 1nA) equipped with an HE-SE2 secondary electron detector. The observed materials were embedded in epoxy resin, which was polished to obtain a flat surface. Large-scale 3D images were obtained using a Phoenix Nanotom high-resolution computed tomography (CT) device. Samples were glued on a thin cylindrical stand, which was fixed to the rotation platform of the tomograph and attenuation contrast images with 2 µm cubic voxels were obtained at an operational voltage of 40 kV, using Nanotom software for reconstruction. The images were cropped to parallelepipeds to avoid edge effects. The images were then numerically segmented using the pixel classification workflow (random forest classifier) in the software Ilastik [45] to highlight the different phases in the sample. Through this approach, a subset of images were used as a training set for a machine-learning algorithm, which was then applied to the whole dataset (Supplementary Information 1).

The images were processed using the software Fiji [46], which was also used to obtain morphological parameters. The percentage of macropores was calculated after segmentation by counting the total number of pixels in this phase. The distributions of each phase inside the sample (sample isotropy) were obtained by plotting phase percentages in each slice along the propagation direction inside the sample. The connectivity of the porous network was calculated using the connected components plugin in MorphoLibJ [47]. The algorithm assigns the same label to all connected pixels from the same phase (same pixel value after segmentation), which are then considered a single object in subsequent steps, the connectivity of the pores being defined as the largest corresponding porous label. Fluid circulation through the porous network was also evaluated using Fiji. The percolation value was defined as the ratio of the length of the shortest possible path over the length of the straight-line path.

$$ \text{Percolation value} = \frac{\text{Length of the shortest possible path}}{\text{Length of the straight path}} \quad (1) $$

The length of the shortest path through the porous network was obtained using the MorphoLibJ 3D Geodesic Distance Map, as illustrated in Fig. 1 for an artificial porous network. In this example, the artificial porous network (Fig. 1a) does not vary along the z direction. The user chooses the (y, z) with the highest x coordinate as origin plan to calculate the distance map. The geodesic distance map (Fig. 1b) shows the distance of each pixel in the pore phase, from that plane. In this example, the percolation value for the straight-line path is 200/200 = 1, whereas for the porous network, the percolation value is 360/200 = 1.8.

The tortuosity of the porous network was calculated using the Rocque and Costa algorithm, based on a geodesic reconstruction of the interface between pores and the rest of the solid [48]. Tortuosity can be defined as following:

$$ \text{Tortuosity} = \frac{\text{Mean value of the length of all possible paths}}{\text{Length of the straight path}} \quad (2) $$

Finally, maps of the distance between each point in the solid phase to the nearest pore interface were also calculated.
<figure><img src="image_5.png" /><figcaption>Fig. 1. Illustration of how shortest paths through the porous network were calculated using the MorphoLibJ Geodesic Distance Map plugin. (a) Artificial porous network, (b) Geodesic distance map, from dark blue (closest) to red (furthest). (c) Extracted plane, at the bottom, representing the shortest path across the porous network to reach each point at the bottom of the volume. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

## 2.4. Batch and dynamic adsorption experiments

Adsorption experiments with the materials were performed in saline aqueous solutions, representative of nuclear effluents. The solutions contained 0.25 M $NaNO_3$ and 50 $mg·L^{-1}$ (i.e. $1.25 × 10^{-3} mol·L^{-1}$) $Ca^{2+}$ (from $Ca(NO_3)_2$) as a competing cation, to evaluate the affinity of the materials for $Sr^{2+}$.

Batch adsorption kinetics were measured by contacting $1 g·L^{-1}$ of materials with the model saline solution containing $50 mg·L^{-1}$ $Sr^{2+}$ (from $Sr(NO_3)_2$) for different time intervals (5 min to 45 h). The $Sr^{2+}$ and $Ca^{2+}$ concentrations in the solutions before and after adsorption experiments were measured by inductively coupled plasma optical emission spectrometry (ICP-OES, ThermoFisher Scientific iCAP 6000 Series), from which the adsorption capacity of the material was calculated as:

$$Q = (C_i - C_f) \frac{V}{m} (mg·g^{-1}) \quad (3)$$

where $C_i$ and $C_f$ ($mg·L^{-1}$) are respectively the initial and final concentrations of $Sr^{2+}$ in solution, $V$ (L) is the volume of the solution and $m$ (g) the mass of the material.

Batch adsorption isotherms were measured by placing $1 g·L^{-1}$ of material in saline solutions containing 0.1 to 200 $mg·L^{-1}$ in $Sr^{2+}$ for 24 h. The adsorption capacity was calculated as shown above, along with the distribution factor $K_d$ ($mL·g^{-1}$), expressed as:

$$K_d = Q/C_f \quad (4)$$

Breakthrough curves for the GMZx materials were measured in a laboratory scale glass column (diameter, 1 cm; bed height, 5 cm). Identical bed volumes were prepared for each material, after grinding, and the beds were then weighed (values listed in Table 3). A saline solution containing exactly $100 mg·L^{-1}$ $Sr^{2+}$ was passed through the column at a constant flow rate of 20 mL·h$^{-1}$ using a peristaltic pump. Samples were collected at the exit of the column and the $Sr^{2+}$ concentration was measured by ICP-OES.

## 3. Results and discussion

### 3.1. Effects of increasing zeolite content on the macroporous network

The synthesized materials are amorphous geopolymers with dispersed crystalline zeolite particles. The geopolymer contains both macropores several microns across, produced by emulsion templating, and naturally present nanometre-scale mesopores. Fig. 2 compares X-ray tomography images of the materials. The solid phase consists of mesoporous geopolymer with different concentrations of LTA zeolite particles, which are not individually distinguishable at this resolution (≈ 2 µm/voxel). The macroporous networks inside the samples are visible as the darker (lower density) areas in the raw tomography images (Fig. 2a–d, left), and are more sharply defined in the segmented images (Fig. 2a–d, right). The grey areas in the segmented images of the composite materials (GMZ5, GMZ14 and GMZ27; Fig. 2c–d, right), are additional features in the solid phase of these materials which cannot be identified at this resolution. Connected component labelling of the segmented images revealed that the interconnectivity of the macroporous network was very high (above 99 %) in all the materials, indicating that this entire network in these samples is accessible to fluids.

As synthesized, the GMZ0 sample contains 50 vol% of solid geopolymer and 50 vol% of macropores from emulsion templating. The porosity of the composite samples should be lower because the zeolite particles automatically increase the proportion of solid phase. The porosity values predicted from the nominal content of the samples are similar to those obtained from the segmented images (Table 2) (Supplementary Information 2), supporting the accuracy of the segmentation process. Furthermore, the fact that the percentage of solid phase varied little (±5%) along the propagation direction indicates that the analysed volumes are representative of the material as a whole. The small discrepancies between the image-derived and the composition-based porosity values are therefore most likely due to local inhomogeneities in the studied volumes. Large, dense (light) areas are indeed visible in Fig. 2, particularly for the materials with higher zeolite contents.

Fig. 3 illustrates how the method outlined in Fig. 1 was applied to calculate the percolation values of the macroporous networks in the studied samples.

The percolation values calculated in this way for the four studied
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** X-ray tomography slices (left) and corresponding segmented images (right) of the studied geopolymer/zeolite composites with (a) 0, (b) 5, (c) 14 and (d) 27 wt% LTA zeolite particles (GMZ0, GMZ5, GMZ14, and GMZ27).</figcaption></figure>

samples are listed in Table 1 as well as tortuosity values obtained by Roque and Costa algorithm [48].

The percolation and tortuosity values vary little between the samples. The tortuosity values are lower than reported by Chen et al. for other alkali-activated materials with different pore size distributions [49], probably because the large macropores in our materials and their higher porosity facilitate overall fluid circulation.

### 3.2. Effects of increasing zeolite content on the solid phase

Although the bulk morphological parameters (tortuosity and percolation) of the macroporous network do not vary with the zeolite particle content of the materials, the X-ray tomography data indicate that large dense zones appear in the solid phase (grey areas in Fig. 2b–d, right).

X-ray diffraction experiments confirmed the presence of a geopolymer phase and of crystalline LTA zeolite particles in the composites (Supplementary Information 3). The nitrogen adsorption–desorption isotherms measured for the materials (Fig. 4a, type IV) are typical of mesoporous materials, leading to high specific surface areas (see Table 2). Note that the micropores inside the LTA zeolite particles are too small (0.41 nm) for nitrogen to enter [21], and were therefore not accounted for in this analysis. Fig. 4b shows that the mesopore size distribution shifts to smaller sizes on addition of zeolite particles (GMZ5

vs GMZ0), but that all the composites have similar average pore sizes. This can be explained by the hydrophilic nature of the zeolite particles, which by adsorbing some of the water used during synthesis, reduce the amount of water involved in geopolymerization, and thus the average size of the mesopores in the geopolymer phase [50]. Fig. 4b also shows that the total porosity of the materials, determined at $P/P_0 = 1$, decreases as the zeolite content increases. This is consistent with the decrease in average mesopore size and the increased concentration of (non-mesoporous) zeolite particles, such that the relative proportion of mesoporous geopolymer phase decreases. These data nevertheless confirm the presence of mesopores in all the studied materials, which is a crucial feature of adsorbents designed for fixed-bed processes [25].

The resolution of the X-ray tomography reconstructions (Fig. 5a) is too low to observe single zeolite particles dispersed in the geopolymer phase. The dense areas ~ 10 to 100 µm across in the X-ray tomography images (Fig. 2, Fig. 5a), presumably zeolite particle aggregates not well dispersed during the synthesis, were further investigated by SEM in the GMZ14 material. One of these large structures is clearly visible at the top of the tomographic view in Fig. 5b, and the corresponding SEM images confirm that this is a zeolite cluster (Fig. 5c,d).

These clusters were observed in all three composites by X-ray tomography and SEM. The dispersed zeolite particles, only observable by SEM, were found to be widely distributed throughout the sample and
<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Calculation of percolation parameters for the pure geopolymer material (GMZ0). (a) Segmented volume, (b) geodesic distance map, and (c) colour map representing the shortest path from each point in the plane to the bottom of the sample (from sepia, closest, to white, furthest; the solid phase is shown in black), from which the percolation value of the sample was calculated.</figcaption></figure>

<table><caption>**Table 1**
Percolation and tortuosity values of the studied geopolymer/zeolite composites.</caption><thead><tr><th>Sample</th><th>GMZ0</th><th>GMZ5</th><th>GMZ14</th><th>GMZ27</th></tr></thead><tbody><tr><td>Percolation</td><td>1.03 ± 0.02</td><td>1.02 ± 0.02</td><td>1.03 ± 0.02</td><td>1.04 ± 0.02</td></tr><tr><td>Tortuosity</td><td>1.18 ± 0.02</td><td>1.20 ± 0.02</td><td>1.25 ± 0.03</td><td>1.23 ± 0.03</td></tr></tbody></table>

Results are presented as mean ± standard deviation of the values calculated along the x, y and z directions.

<table><caption>**Table 2**
Morphological parameters of zeolite clusters in the studied samples.</caption><thead><tr><th></th><th>GMZ0</th><th>GMZ5</th><th>GMZ14</th><th>GMZ27</th></tr></thead><tbody><tr><td>Macroporous percentage</td><td>48.9</td><td>48.2</td><td>41.1</td><td>40.5</td></tr><tr><td>Specific surface areas</td><td>127 m<sup>2</sup>·g<sup>-1</sup></td><td>116 m<sup>2</sup>·g<sup>-1</sup></td><td>71 m<sup>2</sup>·g<sup>-1</sup></td><td>49 m<sup>2</sup>·g<sup>-1</sup></td></tr><tr><td>Zeolite particle density inside clusters</td><td>/</td><td>58.5 ± 3.1 %</td><td>60.4 ± 3.7 %</td><td>60.1 ± 2.7 %</td></tr><tr><td>Mean diameter of biggest zeolite clusters</td><td>/</td><td>102.7 ± 19.4 µm</td><td>129.9 ± 18.8 µm</td><td>136.8 ± 10.6 µm</td></tr></tbody></table>

Results are reported as mean ± standard deviation

<table><caption>**Table 3**
Experimental and breakthrough curve parameters for the studied geopolymer/zeolite composites.</caption><thead><tr><th>Sample</th><th>[Sr]<sub>0</sub> (mg·L<sup>-1</sup>)</th><th>Mass (g)</th><th>V<sub>b</sub> (mL)</th><th>V<sub>r</sub> (mL)</th><th>V<sub>b</sub>/V<sub>r</sub></th></tr></thead><tbody><tr><td>GMZ0</td><td>91.0</td><td>1.06</td><td>169</td><td>258</td><td>65</td></tr><tr><td>GMZ5</td><td>85.2</td><td>1.09</td><td>235</td><td>339</td><td>69</td></tr><tr><td>GMZ14</td><td>85.2</td><td>1.34</td><td>316</td><td>503</td><td>63</td></tr><tr><td>GMZ27</td><td>93.4</td><td>1.81</td><td>478</td><td>1039</td><td>46</td></tr></tbody></table>

GMZx, geopolymer/zeolite composite with x wt% LTA zeolite particles.

well dispersed in the geopolymer phase. Fig. 6 compares high resolution images of dispersed (Fig. 6a) and clustered (Fig. 6b) zeolite particles in the GMZ27 composite. Similar images were obtained for the other composite materials. Note that the resin in which the samples were embedded for SEM observations filled all visible mesopores, indicating that the mesoporous network was completely interconnected in all the composites. Moreover, the size of the mesopores on SEM observation

was similar to the values derived from the nitrogen adsorption-desorption experiments. Finally, although Fig. 6 shows that the mesoporous structure of the geopolymer is the same inside and outside the particle clusters, the mesopore/zeolite interfacial surface area inside the clusters is smaller than in another part of the sample, suggesting that particles in clusters are less accessible to fluids.

The zeolite surface density inside the particle clusters, defined as the ratio of the surface areas occupied by zeolite particles over the total surface area of the clusters, was obtained from the SEM images after segmentation analysis. The parts of the clusters not categorized as zeolite in the images were assumed to consist of mesoporous geopolymer (Fig. 6b). The mean diameter of all clusters is obtained from X-ray tomography data, after segmentation, labelling and shape analysis. In order to illustrate the evolution of the maximum size of the clusters, mean diameter are presented for the two largest deciles. The values obtained for the three composites are listed in Table 2.

The particle density inside the clusters was about 60 % in all three composites (Table 2). As expected however, the mean diameter of zeolite clusters increased with the zeolite content of the composites. Thus, increasing the zeolite content clearly affects the phase distribution of these composites, with the formation of larger clusters, although the structure of the clusters themselves does not seem to change.

Fig. 7 shows the distribution of distances from each point in the solid phase to the nearest interface with the macroporous network in the four GMZx materials. As expected, given the high specific surface of this material coming from the mesoporosity of the geopolymer (from 49 to 127 m<sup>2</sup>·g<sup>-1</sup>), most particles are very close to the nearest interface in all samples (the distance density is highest at small distances). However, the proportion of larger distances increases with the zeolite content of the material, nearly doubling between GMZ5 and GMZ27 and the overall distribution shifts towards higher distances. This analysis clearly shows that the presence of zeolite clusters creates remote areas in the solid phase, which are presumably more difficult to reach for Sr<sup>2+</sup> ions.

### 3.3. Effects of increasing the zeolite content on decontamination properties

#### 3.3.1. Relationship between material microstructure and adsorption kinetics

Fig. 8 plots the normalized adsorption capacity of the GMZx
<figure><img src="image_8.png" /><figcaption>Fig. 4. (a) Nitrogen adsorption–desorption isotherms and (b) mesopore size distributions of the studied geopolymer/zeolite composites with 0, 5, 14 and 27 wt% LTA zeolite particles.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 5. (a, b) X-ray tomography and (c, d) scanning electron micrographs of the geopolymer/zeolite composite with 14 wt% LTA zeolite particles (GMZ14), with zeolite particle clusters highlighted. The images in parts (b) and (c) are 2D views of the top of the 3D image shown in part (a). The image in part (d) is an expanded view of the area highlighted in part (c).</figcaption></figure>

materials as a function of the square root of the contact time with a model saline solution containing 50 mg·L⁻¹ Sr²⁺.

The different steps of the adsorption process were identified by fitting the data using the Weber-Morris model [51,52] and the kinetic curves obtained were compared between the different materials. This representation is useful to better highlight each diffusive limited steps: the first stage is related to fast diffusion through pores and then controlled by the diffusion inside the porous network and at the interfaces between the solid phase and the solution. The second stage is then the bulk diffusion of the contaminant through the bulk solid phase, and the third stage is an equilibrium phase. The three steps are highlighted in Fig. 8 by different line styles: solid lines for step I, dashed lines for step II, and dotted lines for step III.

Here, during the first stage of the adsorption process (step I in Fig. 8), the ionic exchange is controlled by the diffusion of Sr²⁺ into the porous network and through the solution/solid interface. By assuming that the concentration at the interface layer quickly reaches the capacity, the value of Q/Qe at the end of the first stage will increase with the increase of the pore volume fraction and the specific surface area. Moreover, the step I of the adsorption process (solid lines in Fig. 8) ends at the same time for the four materials, but the percentage of the equilibrium capacity reached at the end of this step decreases with their zeolite content, particularly between GMZ5 and GMZ14 and between GMZ14 and GMZ27. The smaller difference between the GMZ0 and GMZ5 materials
<figure><img src="image_10.png" /><figcaption>Fig. 6. Scanning electron micrographs of dispersed and clustered zeolite particles in the geopolymer/zeolite composite with 27 wt% LTA zeolite particles (GMZ27). Large view (a) and zoom on the limit of the cluster (b). The macropores appear in black, the mesoporous geopolymer in dark grey, and zeolite particles in light grey. Mesopores are visible throughout the solid phase, inside particle clusters.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 7. Distribution of distances from each pixel in the solid phase to the nearest solid/macropore interface in the studied geopolymer/zeolite composites with 0, 5, 14 and 27 wt% LTA zeolite particles (GMZ0, black squares; GMZ5, red squares; GMZ14, blue squares; and GMZ27, green squares). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

suggests that adding a small amount of zeolite particles does not significantly alter the solid phase/solution interface, consistent with the similarly high porosities (Fig. 4) and specific surface areas of these materials (127 m²·g⁻¹ for GMZ0 and 116 m²·g⁻¹ for GMZ5). However, increasing the zeolite particle content to 14 and 27 wt% leads to a decrease in porosity (Fig. 4) and in specific surface area (71 m²·g⁻¹ for GMZ14 and 49 m²·g⁻¹ for GMZ27). This decrease consequently limits the first diffusion stage of the adsorption process and, as highlighted in Fig. 8 furthermore, the Sr²⁺ ions diffuse more slowly into the porous network.

Step II of the adsorption process corresponds to the diffusion of Sr²⁺ ions through the solid phase, i.e. either into the geopolymer walls or the zeolite particles, with different diffusion mechanisms in each case. The non-linearity of the curves in this stage reflects the multiplicity of mechanisms involved. Here, diffusion into the solid phase includes the diffusion of Sr²⁺ into the geopolymer amorphous phase and the slower diffusion in the crystalline phase of the zeolite. Thus, comparing the dashed lines in Fig. 8 shows that increasing the zeolite content of the materials slows down the adsorption kinetics in step II and prolongs this

<figure><img src="image_12.png" /><figcaption>Fig. 8. (a) Normalized Sr²⁺ adsorption capacities of the studied geopolymer/zeolite composites with 0, 5, 14 and 27 wt% LTA zeolite particles versus the square root of the contact time with a model saline solution containing 50 mg·L⁻¹ Sr²⁺.</figcaption></figure>

stage of the adsorption process, particularly for GMZ14 and GMZ27. This can be explained by Sr²⁺ diffusion being slower inside the crystalline zeolite particles than in an amorphous phase.

The third step finally leads to the system at equilibrium, when a plateau is reached. These results show that a 24 h contact time is enough to perform isotherm studies at equilibrium for the different materials.

### 3.3.2. Sr²⁺ adsorption isotherms in a model saline solution

Fig. 9 compares the adsorption capacities and distribution coefficients of the studied materials as a function of the equilibrium Sr²⁺ concentration. The most appropriate model to fit adsorption isotherms depends on the underlying adsorption mechanism [53]. The parameters extracted by fitting the measured data using Langmuir and Freundlich models are given in Supporting Information 4. The data for the GMZ0 and GMZ5 materials are better fitted by the Freundlich model as,

$$Q_e = K(C_f)^n \qquad (5)$$

where Qe is the equilibrium capacity of the material and C<sub>f</sub> the Sr²⁺ concentration of the solution at equilibrium, K is the Freundlich constant, and n is an exponent linked to the adsorption properties of the material. Freundlich-type isotherms are indicative of heterogeneous
<figure><img src="image_13.png" /><figcaption>**Fig. 9.** Sr²⁺ adsorption isotherms: (a) adsorption capacity and (b) distribution coefficient as a function of the equilibrium Sr²⁺ concentration of the studied geo-polymer/zeolite composites with 0, 5, 14 and 27 wt% LTA zeolite particles.</figcaption></figure>

(multisite) adsorption, which is consistent with the complex structure of the geopolymer [32,54].

For the GMZ14 and GMZ27 materials in contrast, the data are best fitted by the Langmuir model as,

$$Q_e = Q_{max} \frac{LC_f}{1 + LC_f} \qquad (6)$$

where L is the Langmuir parameter, Qmax is the maximum adsorption capacity, and the isotherms become more Langmuir-like the greater the zeolite content of the composites is (Supporting Information 4). The main adsorption mechanism in the GMZ14 and GMZ27 materials is therefore ionic exchange [32] and the fact that the maximum sorption capacity increases between 14 and 27 wt% zeolite indicates that Sr²⁺ exchange occurs mainly in the LTA zeolite phase.

The distribution coefficient (Kd) of a material represents the concentration distribution of the ions between the solid and liquid phases. The dashed lines in Fig. 9b indicate how the distribution coefficients at very low Sr²⁺ concentrations were estimated from the measured data. At this plateau, this value can be used to compare the selectivity of different materials used under the same conditions. Indeed, even if the selectivity strictly corresponds to the ratio between the distribution coefficients (at the plateau) of the different competing ions and as the materials have been tested in the same conditions (solid/solution ratio, effluent composition), it can be reasonably assumed that the comparison of the Kd values can give an indirect tendency of the evolution of the materials selectivity for Sr²⁺.

The distribution coefficients of the materials increase with their zeolite content, from about 1160 mL·g⁻¹ in GMZ0 to ~ 1300 mL·g⁻¹ in GMZ5, and ~ 1600 mL·g⁻¹ and ~ 2150 mL·g⁻¹ in GMZ14 and GMZ27, respectively (Fig. 9b), indicating that the zeolite particles improve the selectivity of the materials for Sr²⁺. The theoretical distribution coefficients of the materials calculated by assigning different Kd values to the zeolite particles and to the geopolymer, (Supporting Information 5) are slightly higher than measured experimentally, with the discrepancy increasing with the zeolite concentration. This can be explained by the presence of more zeolite clusters at higher zeolite contents, and by the fact that zeolite particles inside clusters are likely less accessible to Sr²⁺ ions than those that are dispersed in the solid phase.

### 3.3.3. Sr²⁺ adsorption in fixed-bed processes

The breakthrough curves in Fig. 10 represents the normalized Sr²⁺ concentration at the column outlet as a function of the volume of effluent passed through the column. They highlight the efficiency of the materials for dynamic Sr²⁺ extraction in fixed-bed processes. The breakthrough volume (Vb) and half-breakthrough volume (Vr) are defined as the volumes of treated solution at which the outlet Sr concentration reaches respectively 1 % and 50 % of the initial solution concentration. For nuclear wastewater decontamination, a high Vb and a

<figure><img src="image_14.png" /><figcaption>**Fig. 10.** Breakthrough curves measured for the studied geopolymer/zeolite composites with 0, 5, 14 and 27 wt% LTA zeolite particles.</figcaption></figure>

steep breakthrough front are desirable, while a short interval between breakthrough and saturation indicates that the material inside the column adsorbs efficiently right up until it is saturated.

The breakthrough data were fitted to the Boltzmann-sigmoid model described by Motshekga et al. [55];

$$[Sr]_{\text{outlet}} = [Sr]_{0} - \frac{[Sr]_{0}}{1 + \exp\left(\frac{V - V_{r}}{dV}\right)} \qquad (7)$$

where dV is a fitting parameter controlling the width of the sigmoid (R² > 0.99 for all fits, Supplementary Information 6). The half-breakthrough volumes (Vr) were obtained from the fits and the breakthrough volumes, were then calculated using Vb as:

$$V_b = V_r + dV * \ln\left(\frac{100}{99} - 1\right) \qquad (8)$$

The results obtained in this way for the studied materials are listed in Table 3. The Vb/Vr ratio is a good measure of column efficiency, with Vb/Vr = 1 corresponding to ideal behaviour.

The breakthrough volume (Vb) increases with the zeolite content of the materials (Table 3), indicating that the zeolite particles increase the capacity of the column bed and the amount of adsorbed Sr²⁺, as observed in the batch experiments. However, the breakthrough transition also becomes more gradual (less steep) as the zeolite content
increases (Fig. 10), indicating that $Sr^{2+}$ extraction continues in the composites after $V_b$ is reached. This is consistent with the increasing presence of zeolite particle clusters in the solid phase in these materials as the zeolite content is increased (Fig. 6 and Table 2). The breakthrough curves of the GMZ0 and GMZ5 materials are very similar, as are their microstructures, GMZ5 being the composite with the lowest cluster size (Table 2). Adding a small amount of zeolite particles therefore barely affects the column behaviour of the material. The breakthrough curve of the GMZ14 material remains sharp (Fig. 10), but is shifted to a higher $V_b$. The average distance between zeolite clusters is lower than their average size in this material, and its column efficiency ($V_b/V_r$) is similar to that of the GMZ5 and GMZ0 materials (Table 3). The fixed bed efficiency of the GMZ27 material is markedly lower however (Table 3), as evidenced also by its shallower breakthrough curve. In this sample, some of the clusters are several hundreds of micrometers across, and are therefore on a similar size scale as the material grains themselves (300–500 µm). These results suggest that there is a threshold zeolite cluster size above which the breakthrough curves cease to be sharp.

## 4. Conclusions

The results of this study show that increasing the amount of LTA zeolite particles in a geopolymer/zeolite composite strongly affects their microstructure at different length scales, and their batch and fixed-bed adsorption performance for the capture of $Sr^{2+}$ ions. Whereas adding zeolite particles does not affect the macroporous network of the composites, which remains fully interconnected with a relatively low tortuosity, the porosity on the mesoscale, the specific surface area, and the diameter of the mesopores all decrease with the zeolite content. X-ray tomography and SEM images of the materials also show that the higher the zeolite concentration of the material is, the biggest the zeolite particle clusters formed.

These microstructural modifications are reflected in the $Sr^{2+}$ adsorption properties of the materials, both in static (batch) and dynamic (column) processes. The adsorption kinetics of the materials with higher zeolite contents are slower because increasing the zeolite concentration reduces contact between the solution and the solid phase (lower porosity and specific surface area); however, the $Sr^{2+}$ adsorption capacity and selectivity of the material at equilibrium are improved. The results of our fixed bed experiments show meanwhile that all the materials have suitably fast $Sr^{2+}$ adsorption kinetics; and that their adsorption capacity increases with the zeolite content, with breakthrough occurring at larger treated volumes. Nevertheless, in the material with the highest zeolite content studied here (27 wt%), the presence of larger zeolite clusters is associated with reduced column efficiency.

Overall, this work provides a better understanding of the relationship between the multiscale microstructure of a hierarchical adsorbent and its adsorption properties, which should hopefully stimulate progress in nuclear effluent decontamination and wastewater treatment in general. Then, to go further, a straight performance comparison between adsorbent of different natures should have necessarily to be done on wastewater having the same exact compositions. Particularly, to be able to have a realistic comparison, an entire study should be performed on wastewater having different salinities, to better understand the influence of the wastewater composition on the materials performances.

### CRediT authorship contribution statement

**Samuel Vannier:** Writing – original draft, Investigation, Formal analysis, Data curation, Conceptualization. **Alban Gossard:** Writing – review & editing, Writing – original draft, Validation, Supervision, Methodology, Formal analysis, Data curation, Conceptualization. **Lucile Magnier:** Writing – review & editing, Validation, Methodology, Data curation. **Vanessa Proust:** Methodology, Conceptualization. **Thomas David:** Writing – review & editing, Validation, Methodology, Investigation, Formal analysis, Conceptualization. **Agnès Grandjean:** Writing – review & editing, Validation, Methodology, Conceptualization.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Data availability

Data will be made available on request.

### Acknowledgments

This work was supported the CEA energy division's cross-cutting basic research programme (RTA Program). The authors thank Benoît Meilleray for help analysing materials adsorption performances.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.matdes.2024.113233.

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