Crystal Face Distributions and Surface Site Densities of Two Synthetic Goethites: Implications for Adsorption Capacities as a Function of Particle Size

Livi, Kenneth J. T.; Villalobos, Mario; Leary, Rowan K.; Varela, M.; Barnard, J. C.; Villacís-García, Milton; Zanella, Rodolfo; Goodridge, Anna; Midgley, Paul A.

doi:10.1021/acs.langmuir.7b01814

Cited by 28 publications

(37 citation statements)

References 26 publications

(42 reference statements)

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“…However, surface processes, including sorption, desorption, and dissolution, depend on particle size and particle aggregation, which may or may not be captured well by BET-SSA measurements. 21,[78][79][80][81][82] Evidently, precise definitions of particle size and careful characterization of the mineral structure are necessary. 79,83,84 In addition, mineral characterization must be done on samples prepared under conditions relevant to those used in reactivity studies.…”

Section: Implications For Nanoparticle Reactivitymentioning

confidence: 99%

Crystal growth and aggregation in suspensions of δ-MnO₂nanoparticles: implications for surface reactivity

Marafatto

Lanson

Peña

2018

Environ. Sci.: Nano

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a Birnessite (layer-type Mn oxide) is a key reactive phase in soils and sediments and its sorption and oxidative properties render it attractive for use in technical applications. The most widely used synthetic analog of natural and biogenic birnessite in laboratory studies is nanocrystalline δ-MnO 2 . However, a wide range of physicochemical properties have been reported in the literature for δ-MnO 2 . In this study, we produced several batches of δ-MnO 2 and identified the mechanisms leading to significant variations in particle size, as probed by X-ray diffraction (3 to 7 nm), dynamic light scattering (85 to 501 nm) and N 2 Ĳg) BET specific surface area (SSA: 119 to 259 m 2 g −1) measurements. Both the coherent scattering domain (CSD) size in the ab plane and the wet-aggregate size decreased with increasing suspension pH and Na content, which is consistent with base-catalyzed oxidation and nucleation at high pH and growth by oriented attachment at low pH. The increase in the CSD size upon sample acidification but not basification provides further evidence for OA as a crystal growth mechanism. Finally, the sample SSA was not related to the crystallite size, but instead was inversely correlated to the suspension pH and Na : Mn content. The surface charge and counter-cation content of δ-MnO 2 control the aggregate structure, where low pH (low Na : Mn) favored high surface area structures and high pH (high Na : Mn) favored low surface area structures. The reversibility of SSA upon the acidification or basification of parent suspensions and the crystal growth only upon suspension acidification confirmed that the primary crystallite size and the aggregate/agglomerate size are highly sensitive to solution chemistry and surface charge and has direct implications for δ-MnO 2 nanoparticle reactivity towards organic and inorganic contaminants in environmental systems that can encompass a dynamic range of pH values and ionic compositions.

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Section: Implications For Nanoparticle Reactivitymentioning

confidence: 99%

Crystal growth and aggregation in suspensions of δ-MnO₂nanoparticles: implications for surface reactivity

Marafatto

Lanson

Peña

2018

Environ. Sci.: Nano

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“…Site densities (Ns) are also required, and constant values such as 3 sites/nm 2 or 2.3 sites/nm 2 are often used in modeling studies. Crystallographic considerations tied to microscopy tools, such as transmission electron microscopy (TEM) and atomic force microscopy (AFM), − are being increasingly employed to examine individual crystal faces, which can exhibit different site densities and binding affinities.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Modeling Uncertainties Using a Multistart Optimization Tool for Surface Complexation Equilibrium Parameters (MUSE)

Bompoti

Chrysochoou

Machesky

2018

ACS Earth Space Chem.

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The MUlti-start optimization algorithm for Surface complexation Equilibrium (MUSE) algorithm has been developed to optimize the fitting of thermodynamic constants for surface complexation modeling (SCM). Although there is a plethora of software to perform data fitting and determine intrinsic equilibrium constants, the algorithms used are highly dependent on initial values and choice of parameters. This limits their transferability to model other systems, for example, reactive transport processes. With this in mind, a hybridized optimization approach, based on a multistart algorithm combined with a local optimizer, has been developed to allow the simultaneous optimization of SCM parameters and to assess the sensitivity of these parameters to changes in the model assumptions. In this study, the CD–MUSIC formalism with a Basic Stern electrostatic model is utilized to model chromate adsorption on ferrihydrite, although the MUSE algorithm can be applied to any adsorption data set and be implemented in any model formulation. This study offers two innovative components to the inverse SCM modeling approach: (a) determination of the true global optimum by performing multiple minimizations of the mean squared error between the simulated and observed data using a large number of initial starting points and (b) quantitative simulation of spectroscopic pH-dependent profiles for two chromate surface complexes. We demonstrate that when MUSE is implemented to determine chromate log Ks, their dependence on other adjustable parameters such as specific surface area (SSA) and capacitance is relatively small (i.e., less than one unit difference for chromate log Ks on ferrihydrite) and can be accounted by mathematical functions determined through the MUSE algorithm. The robustness of the algorithm is demonstrated in the absence of the spectroscopy data as well, with traditional batch tests yielding similar thermodynamic constants as the spectroscopic profiles.

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“…Surfaces for which the precise morphology (in terms of exposed crystal planes and their contributions to the overall surface area of a particle) is not available or which are expected to show other types of heterogeneity, do not allow a detailed treatment. Instead, one has to resort to a simpler model, ideally to the simplest one possible that allows one to describe the available data [45,46]. A basic Stern model is used for interfacial electrostatics of the system, which includes ion-specific effects via ion-pair formation constants, (as does Pitzer via ion-interaction parameters).…”

Section: Speciation Calculations and Surface Complexation Modellingmentioning

confidence: 99%

Adsorption of Strontium onto Synthetic Iron(III) Oxide up to High Ionic Strength Systems

Garcia

Lützenkirchen

Huguenel³

et al. 2021

Minerals

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In this work, the adsorption behavior of Sr onto a synthetic iron(III) oxide (hematite with traces of goethite) has been studied. This solid, which might be considered a representative of Fe3+ solid phases (iron corrosion products), was characterized by X-Ray Diffraction (XRD) and X-Ray Photoelectron Spectroscopy (XPS), and its specific surface area was determined. Both XRD and XPS data are consistent with a mixed solid containing more than 90% hematite and 10% goethite. The solid was further characterized by fast acid-base titrations at different NaCl concentrations (from 0.1 to 5 M). Subsequently, for each background NaCl concentration used for the acid-base titrations, Sr-uptake experiments were carried out involving two different levels of Sr concentration (1·10−5 and 5·10−5 M, respectively) at constant solid concentration (7.3 g/L) as a function of −log([H+]/M). A Surface Complexation Model (SCM) was fitted to the experimental data, following a coupled Pitzer/surface complexation approach. The Pitzer model was applied to aqueous species. A Basic Stern Model was used for interfacial electrostatics of the system, which includes ion-specific effects via ion-specific pair-formation constants, whereas the Pitzer-approach involves ion-interaction parameters that enter the model through activity coefficients for aqueous species. A simple 1-pK model was applied (generic surface species, denoted as >XOH−1/2). Parameter fitting was carried out using the general parameter estimation software UCODE, coupled to a modified version of FITEQL2. The combined approach describes the full set of data reasonably well and involves two Sr-surface complexes, one of them including chloride. Monodentate and bidentate models were tested and were found to perform equally well. The SCM is particularly able to account for the incomplete uptake of Sr at higher salt levels, supporting the idea that adsorption models conventionally used in salt concentrations below 1 M are applicable to high salt concentrations if the correct activity corrections for the aqueous species are applied. This generates a self-consistent model framework involving a practical approach for semi-mechanistic SCMs. The model framework of coupling conventional electrostatic double layer models for the surface with a Pitzer approach for the bulk solution earlier tested with strongly adsorbing solutes is here shown to be successful for more weakly adsorbing solutes.

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Crystal Face Distributions and Surface Site Densities of Two Synthetic Goethites: Implications for Adsorption Capacities as a Function of Particle Size

Cited by 28 publications

References 26 publications

Crystal growth and aggregation in suspensions of δ-MnO₂nanoparticles: implications for surface reactivity

Crystal growth and aggregation in suspensions of δ-MnO₂nanoparticles: implications for surface reactivity

Assessment of Modeling Uncertainties Using a Multistart Optimization Tool for Surface Complexation Equilibrium Parameters (MUSE)

Adsorption of Strontium onto Synthetic Iron(III) Oxide up to High Ionic Strength Systems

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Crystal Face Distributions and Surface Site Densities of Two Synthetic Goethites: Implications for Adsorption Capacities as a Function of Particle Size

Cited by 28 publications

References 26 publications

Crystal growth and aggregation in suspensions of δ-MnO2nanoparticles: implications for surface reactivity

Crystal growth and aggregation in suspensions of δ-MnO2nanoparticles: implications for surface reactivity

Assessment of Modeling Uncertainties Using a Multistart Optimization Tool for Surface Complexation Equilibrium Parameters (MUSE)

Adsorption of Strontium onto Synthetic Iron(III) Oxide up to High Ionic Strength Systems

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Crystal growth and aggregation in suspensions of δ-MnO₂nanoparticles: implications for surface reactivity

Crystal growth and aggregation in suspensions of δ-MnO₂nanoparticles: implications for surface reactivity