Am(III) adsorption on aluminum (hydr)oxide minerals: Surface complexation modeling

doi:10.21203/rs.3.rs-2374591/v1

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Am(III) adsorption on aluminum (hydr)oxide minerals: Surface complexation modeling

https://doi.org/10.21203/rs.3.rs-2374591/v1

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published 20 Jun, 2023

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Americium is a highly radioactive actinide element found in used nuclear fuel. Its adsorption on aluminum (hydr)oxide minerals is important to study for at least two reasons: (i) aluminum (hydr)oxide minerals are ubiquitous in the subsurface environment and (ii) bentonite clays, which are proposed engineered barriers for the geologic disposal of used nuclear fuel, have the same ≡AlOH sites as aluminum (hydr)oxide minerals. Surface complexation modeling is widely used to elucidate the adsorption of heavy metals on mineral surfaces. While americium sorption is understudied, multiple adsorption studies for europium, a chemical analog, are available. In this study we compiled data describing Eu(III) adsorption on three aluminum (hydr)oxide minerals—corundum (α-Al₂O₃), γ-alumina (γ-Al₂O₃) and gibbsite (γ-Al(OH)₃)—and developed surface complexation models for Am(III) adsorption on these minerals by employing diffuse double layer (DDL) and charge distribution multisite complexation (CD-MUSIC) electrostatic frameworks. For corundum and γ-alumina, two different adsorbed Am(III) species, one each for strong and weak sites, were found to be important regardless of which electrostatic framework was used. The formation constant of the weak site species was almost 10,000 times weaker than the formation constant for the corresponding strong site species. For gibbsite, two different adsorbed Am(III) species formed on the single available site type and were important for the DDL model, whereas the best-fit CD-MUSIC model for Am(III)-gibbsite system required only one Am(III) surface species. The corundum and γ-alumina models were validated using limited Am(III) adsorption data available from the literature. The CD-MUSIC model for corundum and both the DDL and the CD-MUSIC models for γ-alumina predicted the Am(III) adsorption data very well, whereas the DDL model for corundum overpredicted the Am(III) adsorption data. The root mean square of errors of the DDL and CD-MUSIC models developed in this study were smaller than those of previously-published models describing Am(III)-γ-alumina system, indicating the better predictive capacity of our models. Overall, our results suggest that using Eu(III) as an analog for Am(III) is practical approach for predicting Am(III) adsorption onto well-characterized minerals.

Surface Complexation Modeling

americium adsorption

aluminum (hydr)oxide minerals

corundum

gamma-alumina

gibbsite

europium adsorption

Americium is a highly radioactive actinide element (e.g., t_1/2,Am−241 = 432.6 a) that is formed in nuclear reactors and, thus, is present in used nuclear fuel as a minor actinide. Americium has also been introduced to the environment through nuclear weapons testing and aging legacy nuclear waste infrastructure. The emplacement of used nuclear fuel in deep underground repositories is being proposed as the best strategy for its long-term disposal [1–6]; commercially-available bentonite clay, which is mostly composed of the aluminum phyllosilicate mineral montmorillonite, will be used as a backfill material. Aluminum (hydr)oxide minerals like corundum (α-Al₂O₃), γ-alumina (γ-Al₂O₃), and gibbsite (γ-Al(OH)₃) are ubiquitous in the subsurface environment and may influence the fate and transport of actinides (including americium). Furthermore, the aluminol sites present in aluminum (hydr)oxide minerals are also present in bentonite [7, 8]. Therefore, adsorption of americium on aluminum (hydr)oxide minerals is an important phenomenon to study.

Surface complexation modeling is a predictive tool that is often used to elucidate the adsorption of metal cations on minerals [9–12]. Surface complexes are analogous to aqueous complexes; however, unlike the aqueous complexation reaction, surface electrostatic effects, which are dependent on surface potential, need to be considered for the formation of surface complexes [10, 13–16]. Different types of electrostatic model frameworks can be used to develop surface complexation models for a given metal-mineral system and vary from one another in how they treat charge distribution at the mineral surface. Diffuse double layer (DDL) and charge distribution multi-site complexation (CD-MUSIC) are widely used electrostatic modeling frameworks for surface complexation modeling [9–11, 17]. In the CD-MUSIC framework, the charge on the mineral surface is distributed in three planes, which is a more realistic depiction of the charge distribution as compared to the DDL framework, where a point charge distribution is assumed. Although the CD-MUSIC framework is more realistic than DDL framework, a larger number of parameters are required for defining the surface complexation modeling under the CD-MUSIC framework. These parameters are the inner and the outer layer capacitances and the charge distribution coefficients for the three planes (i.e., ΔZ₀, ΔZ₁, ΔZ₂). The capacitances denote the rate of change of the surface potential as a function of distance from the surface. In the DDL framework, the surface potential remains constant between the naught plane and the d-plane and then decreases exponentially beyond the d-plane. Apart from the DDL and the CD-MUSIC framework, constant capacitance model (CCM) is also used for elucidating the surface electrostatics. In CCM, the surface potential decreases linearly from its maximum value on the naught plane to zero on the d-plane, and no surface effect is present beyond the d-plane. The details of different electrostatic models and their fundamentals have been explained in literature [18].

Europium is a lanthanide metal that resembles americium in its ionic size (1.066 Å and 1.09 Å for Eu and Am, respectively, in 8-fold coordination), oxidation state, coordination number, and the properties of its coordination complexes [19, 20]. Therefore, europium is widely used as a chemical analog for americium. Surface complexation models have been developed for various metal ion sorption onto iron oxide minerals for a large and varied dataset sourced from multiple different studies which denote large variation in the input conditions [10, 21–25]. Many surface complexation models for Eu(III)-γ-alumina system have been developed since 2000 [26–28]. Rabung et al. [28] report the results of parameter optimization for Eu(III) adsorption to γ-alumina as a function of pH using a CCM electrostatic framework and two site types (strong and weak). The log K values for the same surface complex at different pH values varied, but were all close to 2.5; no global optimization was completed in order to obtain a unique log K for this system. Almost a decade later, two more surface complexation modeling studies [26, 27] were published for the Eu(III)-γ-alumina system. Kumar et al. [29] assume the presence of only a single surface site on γ-alumina and both monodentate and bidentate surface complexation of Eu(III) on the sorbent surface. The average log K values for the monodentate and the bidentate surface complexes are 2.22 ± 0.35 and − 4.99 ± 0.04, respectively. Although sorption edge data were collected as a function of Eu(III) concentration (0.1–100 µM), no global optimization was reported. This could be because of the limitations created by the use of FITEQL as the optimization tool, where only a single set of input data can be introduced for a given optimization problem. Morel et al. [27] generate a surface complexation model based off a single set of adsorption edge data. They assume the presence of only one site and surface complex for optimization. The log K of the surface complex is -1.2, which was almost six times weaker than the log K of the same surface complex optimized by Kumar et al. [26]. To the best of our knowledge, no surface complexation models have been developed for the Eu(III)-corundum and Eu(III)-gibbsite systems.

The objectives of this work are to compile all the available data for Eu(III) adsorption onto corundum, γ-alumina, and gibbsite, use DDL and CD-MUSIC electrostatic frameworks to develop surface complexation models describing Eu(III) adsorption to these minerals, and determine whether the resulting surface complexation models could also describe the corresponding Am(III)-corundum, Am(III)-γ-alumina and Am(III)-gibbsite systems.

Aqueous speciation of Am(III) and Eu(III)

The aqueous speciation of both Am(III) and Eu(III) were determined for three different carbonate conditions: (i) no carbonate, (ii) carbonate in equilibrium with atmospheric CO₂, and (iii) [CO₃²⁻]_T = 10 mM. The set of aqueous complexation reactions, corresponding log K values, and specific ion interaction theory (SIT) coefficients of all the relevant cation-anion pairs were selected from the NEA-TDB database [30], as summarized in Tables S1 and S2.

Selection of adsorption data

Data describing Eu(III) adsorption onto corundum (α-Al₂O₃), γ-alumina (γ-Al₂O₃), and gibbsite (γ-Al(OH)₃) were sourced from the peer-reviewed literature and selected because the sorbent materials were well characterized [26–28, 31–33]. In total, 14 distinct datasets were compiled for the Eu(III)-corundum system (Table S3), 15 distinct datasets were compiled for the Eu(III)-γ-alumina system (Table S4), and 5 distinct datasets were compiled for the Eu(III)-gibbsite system (Table S5); each dataset had a unique set of input parameters (i.e., total Eu(III) concentration, total mineral loading, carbonate condition, and ionic strength). For the Eu(III)-corundum and Eu(III)-γ-alumina systems, the specific surface area of the sorbent material varied between studies. For the Eu(III)-gibbsite system, all the sorption data were sourced from a single available study and, hence, only one gibbsite mineral with a unique specific surface area was employed. The total number of datapoints for the Eu(III)-corundum, Eu(III)-γ-alumina, and Eu(III)-gibbsite systems were 233, 160, and 77, respectively, and are available as Supporting Information.

Model development

The model development approach is illustrated in Fig. 1. Surface complexation models for the Eu(III)/Am(III)-corundum, Eu(III)/Am(III)-γ-alumina, and Eu(III)/Am(III)-gibbsite systems were developed using the DDL and CD-MUSIC electrostatic frameworks. The log K values for the surface protonation/deprotonation reactions for all three aluminum (oxy)hydroxide minerals were sourced from published potentiometric titration studies [34–37]. The DDL frameworks developed in this work were 2-pK_a models with a single set of protonation and deprotonation reactions for each mineral, whereas a 1-pK_a model was used for the CD-MUSIC frameworks. Apart from protonation, outer sphere complexation with Na⁺ and Cl⁻/ClO₄⁻/NO₃⁻ and inner sphere complexation with carbonate were also assumed in the CD-MUSIC models. The log K values for these reactions were adopted from previous studies [34–36, 38]. Tables 1 and 2 include the protonation/deprotonation and background ion surface complexation reactions and log K values selected in this work.

Table 1

DDL models for Eu(III) adsorption on aluminum (hydr)oxide minerals
Surface complexation reaction	log K
Surface complexation reaction	corundum	γ-alumina	gibbsite
\(\equiv {A}{l}{O}{H}+{{H}}^{+}\leftrightarrow { \equiv {A}{l}{O}{H}}_{2}^{+}\)	6.03 ± 0.25^a	8.50 ± 0.29^a	6.78 ± 0.29^a
\(\equiv {A}{l}{O}{H}\leftrightarrow \equiv {A}{l}{{O}}^{-}+{{H}}^{+}\)	-7.47 ± 0.42^a	-9.20 ± 0.52^a	-10.10 ± 0.43^a
\(\equiv {{A}{l}}_{{s}}{O}{H}+{{E}{u}}^{+3}\leftrightarrow \equiv {{A}{l}}_{{s}}{O}{{E}{u}}^{+2}+{{H}}^{+}\)	N/A	N/A	N/A
\(\equiv {{A}{l}}_{{s}}{O}{H}+{{E}{u}}^{+3}+{{H}}_{2}{O}\leftrightarrow \equiv {{A}{l}}_{{s}}{O}{{E}{u}{O}{H}}^{+2}+{2{H}}^{+}\)	-1.53 ± 0.16	-0.49 ± 0.09	N/A
\(\equiv {{A}{l}}_{{w}}{O}{H}+{{E}{u}}^{+3}\leftrightarrow \equiv {{A}{l}}_{{w}}{O}{{E}{u}}^{+2}+{{H}}^{+}\)	N/A	N/A	3.26 ± 0.22
\(\equiv {{A}{l}}_{{w}}{O}{H}+{{E}{u}}^{+3}+{{H}}_{2}{O}\leftrightarrow \equiv {{A}{l}}_{{w}}{O}{{E}{u}{O}{H}}^{+2}+{2{H}}^{+}\)	-5.46 ± 0.12	-5.03 ± 0.07	-5.10 ± 0.23
RMSE	0.1977	0.1166	0.1799
R²	0.7806	0.9229	0.8282
Correlation coefficient	-0.004	-0.077	-0.124
^aYang et al. [37]

Table 2

CD-MUSIC models for Eu(III) adsorption on aluminum (hydr)oxide minerals
Surface complexation reaction	ΔZ₀	ΔZ₁	ΔZ₂	log K
Surface complexation reaction	ΔZ₀	ΔZ₁	ΔZ₂	corundum	γ-alumina	gibbsite
\({\equiv {A}{l}{O}{H}}^{-0.5}+{{H}}^{+}\leftrightarrow { \equiv {A}{l}{O}{H}}_{2}^{+0.5}\)	1	0	0	9.6^a	9.0^b	9.87^c
\({\equiv {A}{l}{O}{H}}^{-0.5}+{{N}{a}}^{+}\leftrightarrow {\equiv {A}{l}{O}{H}}^{-0.5}{{N}{a}}^{+}\)	--	--	--	-0.8^a	-0.096^b	0.7^c
\({\equiv {A}{l}{O}{H}}^{-0.5}+{{H}}^{+}+ {{C}{l}}^{-}\leftrightarrow { \equiv {A}{l}{O}{H}}_{2}^{+0.5}{{C}{l}}^{-}\)	--	--	--	8.00^a	8.82^b	9.74^c
\({\equiv {A}{l}{O}{H}}^{-0.5}+{{H}}^{+}+ {{C}{{O}}_{3}}^{-2}\leftrightarrow {\equiv {A}{l}{O}{C}{O}{O}}^{-1.5}+{{H}}_{2}{O}\)	-1	0	0	-0.3^d	-0.3^d	-0.3^d
\({\equiv {{A}{l}}_{{s}}{O}{H}}^{-0.5}+{{E}{u}}^{+3}+{{H}}_{2}{O}\leftrightarrow { \equiv {{A}{l}}_{{s}}{O}{E}{u}}^{+0.5}+2{{H}}^{+}\)	1.5	1.5	0	0.09 ± 0.07	0.88 ± 0.08	N/A
\({\equiv {{A}{l}}_{{w}}{O}{H}}^{-0.5}+{{E}{u}}^{+3}+{{H}}_{2}{O}\leftrightarrow { \equiv {{A}{l}}_{{w}}{O}{E}{u}}^{+0.5}+2{{H}}^{+}\)	1.5	1.5	0	-4.10 ± 0.11	-4.63 ± 0.07	-2.99 ± 0.24
RMSE				0.1522	0.1136	0.1982
R²				0.8689	0.9260	0.7914
Correlation coefficient				-0.021	-0.009	-0.606
^aJanot et al. [34]
^bMayordomo et al. [35]
^cWeerasooriya et al. [36]
^dWijnja and Schulthess [38]

The PHREEQC-based optimization tool, PhreePlot, was used for parameter optimization. For corundum and γ-alumina, two site types—strong and weak—were chosen. The fraction of strong sites required for a good fit, based on a comparison of root mean square of errors (RMSE), were found to be 0.05% and 0.1% for corundum and γ-alumina, respectively. For gibbsite, only one site type was considered since no improvement in fitting quality was observed after initial optimization attempts with two site types. A site density of 2.31 sites·nm^− 2 was employed in all systems. The Eu(III)-aluminum (hydr)oxide surface complexation reactions were considered based on the dominant aqueous Eu(III) species at the pH range of interest (Tables 1 and 2). For the corundum and γ-alumina systems, only one Eu(III) surface complex was important for each site type. For the gibbsite system, two surface complexes for the only surface site type were considered (Tables 1 and 2). The models were optimized for a minimum residual sum of squares between the observed and the model generated values of the fraction of europium adsorbed.

Aqueous Speciation of Am(III) and Eu(III)

The aqueous speciation trends for Am(III) were found to be similar to those of Eu(III) for the three different carbonate conditions we examined in this work: (i) no carbonate, (ii) carbonate in equilibrium with atmospheric CO₂, and (iii) [CO₃²⁻]_T = 10 mM (see Figure S1). In the absence of carbonate, Eu³⁺/Am³⁺ were the most stable aqueous species at pH < 7, whereas hydrolysis species (e.g., EuOH²⁺, Eu(OH)₂⁺, and Eu(OH)₃(aq)) dominated the aqueous speciation at pH > 7. For systems ii and iii, which contained dissolved inorganic carbon (DIC), Am^+ 3/Eu^+ 3 was still the most dominant species at low pH (< 6), but both americium and europium formed strong aqueous carbonate complexes (e.g., EuCO₃⁺, Eu(CO₃)₂⁻ Eu(CO₃)₃³⁻) at pH > 6. For systems in equilibrium with atmospheric CO₂, the Am(III)/Eu(III)-carbonate complexes remained dominant for the entire pH range above 6. For the closed system with a fixed amount of DIC (10 mM), the Am(III)/Eu(III)-carbonate complexes remained dominant only in the near-neutral to alkaline pH range (~ 6 to ~ 11). At a highly alkaline pH (> 11), Am(III)/Eu(III)-hydroxyl complexes (e.g., Eu(OH)₂⁺ and Eu(OH)₃) were the dominant species.

Surface complexation models describing Eu(III) adsorption to corundum

The surface complexation models developed for Eu(III)-corundum system performed well for the large range of datasets that were used for model development (see Tables 1 and 2). The overall root mean square of errors (RMSEs) were 0.1977 and 0.1522 for the DDL and the CD-MUSIC electrostatic frameworks, respectively. This indicates overall better performance of the CD-MUSIC model as compared to the DDL model. However, for different individual datasets, the relative performance of the two different types of models varied (Figure S2).

The DDL model predictions were either close to or below the observed experimental values for most input conditions (Fig. 2). The only exceptions were input conditions 2e (66 µM total Eu(III), 0.1 M ionic strength, 6 g L^− 1 corundum, no carbonate) and 3e (10 µM total Eu(III), 0.01 M ionic strength, 1.04 g L^− 1 corundum, 10^− 3.4 atm CO_2(g)), where the DDL model overpredicted Eu(III) adsorption. This could be because of the overestimation of the adsorption sites. On the other hand, the CD-MUSIC model did not overpredict Eu(III) adsorption by a large margin for any input condition. The estimation of the adsorption sites could be done better in the CD-MUSIC framework as compared to the DDL framework because the effect of the counter cation on the surface charge was also accounted in the CD-MUSIC framework. The adsorption predictions of both the DDL and the CD-MUSIC model for input condition 3d (10 µM total Eu(III), 0.01 M ionic strength, 0.52 g L^− 1 corundum, 10^− 3.4 atm CO_2(g)) can be explained by the low sorption site concentration relative to the total Eu(III) concentration.

Surface complexation models describing Eu(III) adsorption to γ-alumina

The overall RMSEs of the DDL and the CD-MUSIC models for Eu(III)-γ-alumina systems developed in this study (Tables 1 and 2) were 0.1166 and 0.1136, respectively, indicating only marginally better performance of the CD-MUSIC model as compared to the DDL model. The relative performance of the DDL and the CD-MUSIC models for the individual datasets did not show much variation (Figure S2).

Like the Eu(III)-corundum system, no adsorption was predicted for pH < 4 and adsorption increased to ~ 100% between pH 4 and 7 and the adsorption edge shifted to higher pH with increasing Eu_T (Fig. 3). However, unlike the Eu(III)-corundum system, no prominent carbonate effect was observed

above pH 7. This is because, at a given pH, the adsorption affinity of the positively charged Eu(III) species will be higher on γ-alumina as compared to that on corundum due to the higher pH_pzc of γ-alumina as compared to that of corundum. Neither electrostatic model highly overpredicted adsorption for any input conditions over any pH range. This shows that both the electrostatic frameworks were equally helpful in developing a more robust surface complexation model for Eu(III)-γ-alumina system.

Surface complexation models describing Eu(III) adsorption to gibbsite

The overall RMSEs of the DDL and the CD-MUSIC models for Eu(III)-gibbsite system developed in this study (Tables 1 and 2) were 0.1799 and 0.1982, respectively, indicating better performance of the DDL model as compared to the CD-MUSIC model. The variation of RMSEs for each individual dataset can be seen in Figure S2.

Unlike the datasets employed in model development for the Eu(III)-corundum and the Eu(III)-γ-alumina systems, the datasets used in developing the models for the Eu(III)-gibbsite system were sourced from one single study. As with the Eu(III)-corundum and Eu(III)-γ-alumina systems, the adsorption edge is observed between pH 4 and 7 for most conditions (Fig. 4). The only exception is condition 1d (10 µM total Eu(III), 0.01 M ionic strength, 0.04 g L^− 1 gibbsite, 10^− 3.4 atm CO_2(g)), where the highest predicted adsorption is only ~ 50%. This could be because of the low sorption site concentration relative to the total Eu(III) concentration. For pH < 6, both models underpredicted Eu(III) adsorption to gibbsite, indicating that the gibbsite used in these studies had an impurity (e.g., clays) with a cation exchange capacity that we did not account for during the model development. For pH > 6, the adsorption predictions for both the DDL and the CD-MUSIC models varied according to the total Eu(III) concentration. For a low total Eu(III) concentration (~ 10 nM), adsorption predictions were close to the observed values. However, no good adsorption predictions were observed for a high total Eu(III) concentration.

Performance and validation of the surface complexation models

We were interested in knowing whether the models developed to describe Eu(III) adsorption to aluminum (hydr)oxide minerals could also be used to describe Am(III) adsorption. Model simulations were generated for the experimental conditions described in Moulin et al. [40] for americium adsorption to corundum (i.e., Am_T = 0.01 µM; I = 0.01 M or 0.1 M NaClO₄; Sol. = 10 g L^− 1 corundum; p_C = 10^− 3.4 atm CO_2(g)) using the log K values in Tables 1 and 2. At I = 0.01 M, the RMSEs for the DDL and the CD-MUSIC models were 0.1596 and 0.0869, respectively, whereas at I = 0.1 M, the RMSEs for the DDL and the CD-MUSIC models were 0.3264 and 0.1337, respectively. These results indicate better performance of the CD-MUSIC framework as compared to the DDL framework.

Figure 5 compares the model simulations to the experimental data. At pH < 5, both the DDL and CD-MUSIC models underpredicted adsorption. In addition, a higher fraction of Am(III) adsorption to corundum was observed at I = 0.01 M than at I = 0.1 M. As with the Eu(III)-gibbsite system, these observations suggest that the corundum used by Moulin et al. [39] had an impurity (e.g., clays) with a cation exchange capacity that we did not account for during model development. Visually, it is clear that the DDL model overpredicted Am(III) adsorption to corundum for the pH range 5 to 8, which is where the experimentally-observed adsorption edge occurs, whereas the CD-MUSIC model predicted values close to those observed experimentally. Better performance of the CD-MUSIC model emphasizes the importance of the outer-sphere complexation of Na⁺ with the mineral surface, which affects its charge behavior and, hence, the adsorption of Am(III).

The validation of the models for the γ-alumina system were performed by generating adsorption edge simulations for the experimental conditions described in Righetto et al. [41] (i.e., Am_T = 0.5 nM; I = 0.1 M; Sol. = 10 g L^− 1 γ-alumina; p_C = 10^− 3.4 atm CO_2(g)). Both the DDL and the CD-MUSIC models developed in this study predict Am(III) adsorption onto γ-alumina closely and represent a better fit to the experimental data than previous models [26–28] (Fig. 6). Unlike the models developed in this study, previous models [26–28] (Table S6) were developed by employing Eu(III) adsorption data sourced from a single study. Models that employ a constant capacitance modeling (CCM) framework [26–27] overpredict Am(III) adsorption to γ-alumina over the pH range 4–6 and the DDL model developed by Morel et al. [27] significantly underpredicts Am(III) adsorption to γ-alumina over the pH range 5–7. The latter results from an equilibrium constant (log K₁ = -1.2) for the \(\equiv AlOH+{Am}^{+3}\leftrightarrow {\equiv AlOAm}^{+2}+{H}^{+}\) reaction that is almost 3,000 times weaker than the equilibrium constant of the corresponding surface complexation reaction in the CCM models (log K₁ = 2.58 [26]; log K₁ = 2.21 [27]). In the DDL model developed in this work, different speciation of Am(III), Al_sOAmOH⁺², and Al_wOAmOH⁺², were found to be important.

Surface complexation models were developed using data from the literature describing Eu(III) adsorption to three different aluminum (hydr)oxide minerals—corundum, γ-alumina, and gibbsite—and applying diffuse double layer (DDL) and charge distributed multisite complexation (CD-MUSIC) electrostatic frameworks. The DDL and CD-MUSIC models showed varying degrees of effectiveness in describing Eu(III) adsorption to corundum and gibbsite. However, both models predicted Eu(III) adsorption to γ-alumina very well. The models developed for corundum and γ-alumina were validated by generating adsorption simulations describing Am(III) adsorption to these minerals. The CD-MUSIC model for corundum and both the DDL and the CD-MUSIC models for γ-alumina predicted the observed Am(III) data very well, whereas the DDL model for corundum overpredicted the Am(III) adsorption data. The performance of the models developed for Am(III)-γ-alumina system were compared with three models available in literature. Our models performed better than all three previous models when predicting Am(III) adsorption on γ-alumina. This work helps us better predict the adsorption trends of Am(III) onto common alumina (hydr)oxide minerals under varied input conditions, and thus, towards a better understanding of the fate and transport of Am(III) in subsurface environment.

CCM constant capacitance model

CD-MUSIC charge distribution multisite complexation

DDL diffuse double layer

NEA-TDB Nuclear Energy Agency Thermochemical Database

RMSE root mean square of errors

SIT specific ion interaction theory

Availability of data and materials

The datasets supporting the conclusions of this article are included within the article (and its additional files).

Competing interests

The authors declare that they have no competing interests.

Ethical Approval

Not applicable

Funding

This project was funded by the National Science Foundation program in Environmental Chemical Sciences under award number CHE-1847939.

Authors’ contributions

A. Satpathy – conceptualization, methodology, software, validation, analysis, writing; A. Hixon – conceptualization, review and editing, supervision, funding acquisition

Acknowledgements

We acknowledge the contributions of Dr. Nicole A. DiBlasi for helping with the introduction and training of PHREEQC and PhreePlot software during the early stage of this work.

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No competing interests reported.

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Am(III) adsorption on aluminum (hydr)oxide minerals: Surface complexation modeling

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Journal Publication

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Abstract

Figures

Introduction

Methods

Aqueous speciation of Am(III) and Eu(III)

Selection of adsorption data

Model development

Results And Discussion

Aqueous Speciation of Am(III) and Eu(III)

Surface complexation models describing Eu(III) adsorption to corundum

Surface complexation models describing Eu(III) adsorption to γ-alumina

Surface complexation models describing Eu(III) adsorption to gibbsite

Performance and validation of the surface complexation models

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1