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Thermochemical Transformation of Calcium during Biomass Burning and the Effects on Postfire Aqueous Dissolution of Macronutrients
Calcium is commonly the most abundant element in fire residues and its speciation largely determines the geochemical properties of fire residues and their effects on postfire soil chemistry. To explore the effects of biomass composition and fire conditions on ash Ca speciation, this study characterizes the speciation of Ca in charcoal and ash samples that were derived from different plant compartments and thermal conditions, using Ca K-edge X-ray absorption near edge spectroscopy. Results showed that biomass contains abundant organic Ca complexes, which were mineralized into fairchildite and calcite after heating at 450 to 600 °C and then CaO, as temperature increased to 750 °C. Apatite could be an abundant Ca species in fire residues if the Ca/P molar ratio of the biomass is small (<2). The mineralization of organic Ca to the identified Ca minerals during burning was negligibly affected by the oxygen level. Calcium speciation in prescribed fire residues resembled that of biomass ash burned at 550 °C with similar Ca/P molar ratios. Batch experiments showed that macronutrients (Ca, Mg, K, and P) were differentially released, as a result of different solubility of minerals in ashes and reprecipitation of minerals. The aqueous solubility of Ca, Mg, and P decreased as pH increased from 5 to 9, while K showed no pH dependency and was almost completely soluble. Results from this study improve our understanding of the chemistry of fire residues and their geochemical behaviors, which can help evaluate the impact of fire on postfire soil properties and macronutrient cycling.
Forcing Dicyanamide Coordination to f-Elements by Dissolution in Dicyanamide-Based Ionic Liquids
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Direct Measurement of Mineral Dissolution Enhanced by X-ray Irradiation
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Atomic Dislocations and Bond Rupture Govern Dissolution Enhancement under Acoustic Stimulation
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Drastic Modulation of Molecular Packing and Intrinsic Dissolution Rates by Meniscus-Guided Coating o
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Enhanced LiMn 2 O 4 Thin-Film Electrode Stability in Ionic Liquid Electrolyte: A Pathway to Suppress Mn Dissolution
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Effects of Phosphate, Silicate, and Bicarbonate on Arsenopyrite Dissolution and Secondary Mineral Precipitation
Abstract not provided
Interfacial Electrochemical Lithiation and Dissolution Mechanisms at a Sulfurized Polyacrylonitrile Cathode Surface
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Solvent Dynamics Are Critical to Understanding Carbon Dioxide Dissolution and Hydration in Water
Simulations of carbon dioxide (CO 2 ) in water may aid in understanding the impact of its accumulation in aquatic environments and help advance technologies for carbon capture and utilization (via, e.g., mineralization). Quantum mechanical (QM) simulations based on static molecular models with polarizable continuum solvation poorly reproduce the energetics of CO 2 hydration to form carbonic acid in water, independent of the level of QM theory employed. Only with density-functional-theory-based molecular dynamics and rare-event sampling, followed by energy corrections based on embedded correlated wavefunction theory (in conjunction with density functional embedding theory), can a close agreement between theory and experiment be achieved. Such multilevel simulations can serve as benchmarks for simpler, less costly models, giving insight into potential errors of the latter. The strong influence of sampling/averaging over dynamical solvent configurations on the energetics stems from the difference in polarity of both the transition state and product (both polar) versus the reactant (nonpolar). When a solute undergoes a change in polarity during reaction, affecting its interaction with the solvent, careful assessment of the energetic contribution of the solvent response to this change is critical. Here, we show that static models (without structural sampling) that incorporate three explicit water molecules can yield far superior results than models with more explicit water molecules because fewer water molecules yield less configurational artifacts. Static models intelligently incorporating both explicit (molecules directly participating in the reaction) and implicit solvation, along with a proper QM theory, e.g., CCSD(T) for closed-shell systems, can close the accuracy gap between static and dynamic models.