Hu Y*, Zhang S, Zhou Z, Cao Z.
Heterogeneous Coprecipitation of Nanocrystals with Metals on Substrates. Accounts of Chemical Research (Cover Article) [Internet]. 2024;57(9):1254-1263.
LinkAbstractThe heterogeneous coprecipitation of nanocrystals with metals on substrates plays a significant role in both natural and engineered systems. Due to the small dimensions and thereby the large specific surface area, nanocrystal coprecipitation with metals, which is ubiquitous in natural settings, exerts drastic effects on the biogeochemical cycling of metals on the earth’s crust. Meanwhile, the controlled synthesis of nanocrystals with metal doping to achieve tunable size/composition enables their broad applications as adsorbents and catalysts in many engineered settings. Despite their importance, complex interactions among aqueous ions/polymers, nanocrystals, substrates, and metals are far from being well-understood, leaving the controlling mechanisms for nanocrystal formation with metals on substrates uncovered.
In this Account, we discuss our systematic investigation over the past 10 years of the heterogeneous formation of representative nanocrystals with metals on typical substrates. We chose Fe(OH)3 and BaSO4 as representative nanocrystals. Mechanisms for varied metal coprecipitation were also investigated for both types of nanocrystals (i.e., Fe, Al, Cr, Cu, and Pb)(OH)3 and (Ba, Sr)(SO4, SeO4, and SeO3)). Bare SiO2 and Al2O3, as well as those coated with varied organics, were selected as geologically or synthetically representative substrates. Through the integration of state-of-the-art nanoscale interfacial characterization techniques with theoretical calculations, the complex interactions during nanocrystal formation at interfaces were probed and the controlling mechanisms were identified.
For BaSO4 and Fe(OH)3 formation on substrates, the local supersaturation levels near substrates were controlled by Ba2+ adsorption and the electrostatic attraction of Fe(OH)3 monomer/polymer to substrates, respectively. Meanwhile, substrate hydrophobicity controlled the interfacial energy for the nucleation of both nanocrystals on (in)organic substrates. Metal ions’ (i.e., Cr/Al/Cu/Pb) hydrolysis constants and substrates’ dielectric constants controlled metal ion adsorption onto substrates, which altered the surface charges of substrates, thus controlling heterogeneous Fe(OH)3 nanocrystal formation on substrates by electrostatic interactions. The sizes and compositions of heterogeneous (Fe, Cr)(OH)3 and (Ba, Sr)(SO4, SeO4, SeO3) formed on substrates were found to be distinct from those of homogeneous precipitates formed in solution. The substrate (de)protonation could alter the local solution’s pH and the substrates’ surface charge; substrates could also adsorb cations, affecting local Fe/Cr/Ba/Sr ion concentrations at solid–water interfaces, thus controlling the amount/size/composition of nanocrystals by tuning their nucleation/growth/deposition on substrates. From slightly supersaturated solution, homogeneous coprecipitates of microsized (Ba, Sr)(SO4, SeO4, SeO3) formed through growth, with little Sr/Se(VI) incorporation due to higher solubilities of SrSO4 and BaSeO4 over BaSO4. While cation enrichment near substrates made the local solution highly supersaturated, nanosized coprecipitates formed on substrates through nucleation, with more Sr/Se(VI) incorporation due to lower interfacial energies of SrSO4 and BaSeO4 over BaSO4. The new insights gained advanced our understanding of the biogeochemical cycling of varied elements at solid–water interfaces and of the controlled synthesis of functional nanocrystals.
Hu Y*, Jiang X, Zhang S, Cai D, Zhou Z, Liu C, Zuo X, Lee SS.
Coprecipitation of Fe/Cr Hydroxides at Organic–Water Interfaces: Functional Group Richness and (De)protonation Control Amounts and Compositions of Coprecipitates. Environmental Science & Technology [Internet]. 2024;58(19):8501-8509.
LinkAbstractron/chromium hydroxide coprecipitation controls the fate and transport of toxic chromium (Cr) in many natural and engineered systems. Organic coatings on soil and engineered surfaces are ubiquitous; however, mechanistic controls of these organic coatings over Fe/Cr hydroxide coprecipitation are poorly understood. Here, Fe/Cr hydroxide coprecipitation was conducted on model organic coatings of humic acid (HA), sodium alginate (SA), and bovine serum albumin (BSA). The organics bonded with SiO2 through ligand exchange with carboxyl (–COOH), and the adsorbed amounts and pKa values of –COOH controlled surface charges of coatings. The adsorbed organic films also had different complexation capacities with Fe/Cr ions and Fe/Cr hydroxide particles, resulting in significant differences in both the amount (on HA > SA(–COOH) ≫ BSA(–NH2)) and composition (Cr/Fe molar ratio: on BSA(–NH2) ≫ HA > SA(–COOH)) of heterogeneous precipitates. Negatively charged –COOH attracted more Fe ions and oligomers of hydrolyzed Fe/Cr species and subsequently promoted heterogeneous precipitation of Fe/Cr hydroxide nanoparticles. Organic coatings containing –NH2 were positively charged at acidic pH because of the high pKa value of the functional group, limiting cation adsorption and formation of coprecipitates. Meanwhile, the higher local pH near the –NH2 coatings promoted the formation of Cr(OH)3. This study advances fundamental understanding of heterogeneous Fe/Cr hydroxide coprecipitation on organics, which is essential for successful Cr remediation and removal in both natural and engineered settings, as well as the synthesis of Cr-doped iron (oxy)hydroxides for material applications.
Cao Z, Hu Y*, Zhang P*.
Predicting sulfate mineral scale solubility with machine learning. Journal of Cleaner Production [Internet]. 2024.
LinkAbstractMineral scale refers to the hard inorganic solids nucleated on substrates or deposited from the aqueous phase. The formation and deposition of barium sulfate and strontium sulfate in various industries, such as water treatment and oilfield operations, can significantly impact facility operations, posing serious threats. Machine learning (ML) approaches have been adopted recently in scale threat predictions to address the limitations of conventional scaling prediction models. However, there are few reports on collecting sulfate mineral scaling data, employing ML methods for data analysis, and evaluating the modeling results to gain deeper insights of sulfate mineral scaling process and to improve the accuracy of sulfate scaling threat prediction. Despite comprehensive experimental studies, the literature does not provide adequate guidance for identifying the influence on the solubility of barium sulfate and strontium sulfate under different aqueous environments and actual operating conditions. To this end, this study collected 1600 experimental datasets of barium/strontium sulfate from the literature to construct and evaluate the reliability and versatility of a ML-based model for sulfate solubility calculations. Single neural networks, hybrid neural networks, and optimization algorithms were employed to build solubility prediction models for barium sulfate and strontium sulfate across a wide range of temperatures, pressures, and different ions. The model's applicability in predicting sulfate scaling threats in various actual operating environments demonstrated its broad usability, consistent with its actual performance. This study marks the first stride towards constructing a reliable model for identifying the scaling trends of barium sulfate and strontium sulfate across various operating conditions, underscoring the importance of developing robust and accurate prediction models to address challenges in various industrial systems.