Importance of a Fully Anharmonic Treatment of Equilibrium Isotope Fractionation Properties of Dissolved Ionic Species As Evidenced by Li+(aq)

Probing surface Earth reactive silica cycling using stable Si isotopes: Mass balance, fluxes, and deep time implications

Geological reservoir δ30Si values have increasingly been applied as paleoclimate and paleoproductivity proxies. Many of these applications rely on the assumption that the surface Earth Si isotope budget is in mass balance with bulk silicate Earth, such that trends in δ30Si over time can be attributed to changes in flux to or from key silica reservoirs. We compiled δ30Si data from modern reservoirs representing the major sources and sinks of surface Earth reactive silica, to which we applied an inverse model to test assumptions about mass balance. We found that δ30Si values of reverse weathering products must closely match those of diatoms, conflicting with previous assumptions. Model results also revealed that of the 19 to 21 teramoles per year Si released during silicate mineral weathering, ~10 to 18 teramoles per year is stored in terrestrial silica sinks, consistent with assumptions of incongruent weathering reactions. Our results demonstrated that the modern silica cycle summary is in isotopic mass balance.

Applying Machine Learning to investigate metal isotope variations at the watershed scale: A case study with lithium isotopes across the Yukon River Basin

Constraining the multiple climatic, lithological, topographic, and geochemical variables controlling isotope variations in large rivers is often challenging with standard statistical methods. Machine learning (ML) is an efficient method for analyzing multidimensional datasets, resolving correlated processes, and exploring relationships between variables simultaneously. We tested four ML algorithms to elucidate the controls of riverine δ7Li variations across the Yukon River Basin (YRB). We compiled (n = 102) and analyzed new samples (n = 21), producing a dataset of 123 river water samples collected across the basin during the summer including δ7Li and extracted environmental, climatological, and geological characteristics of the drainage area for each sample from open-access geospatial databases. The ML models were trained, tuned, and tested under multiple scenarios to avoid issues such as overfitting. Random Forests (RF) performed best at predicting δ7Li across the basin, with the median model explaining 62 % of the variance. The most important variables controlling δ7Li across the basin are elevation, lithology, and past glacial coverage, which ultimately influence weathering congruence. Riverine δ7Li has a negative dependence on elevation. This reflects congruent weathering in kinetically-limited mountain zones with short residence times. The consistent ranking of lithology, specifically igneous and metamorphic rock cover, as a top feature controlling riverine δ7Li modeled by the RFs is unexpected. Further study is required to validate this finding. Rivers draining areas that were extensively covered during the last glacial maximum tend to have lower δ7Li due to immature weathering profiles resulting in short residence times, less secondary mineral formation and therefore more congruent weathering. We demonstrate that ML provides a fast, simple, visualizable, and interpretable approach for disentangling key controls of isotope variations in river water. We assert that ML should become a routine tool, and present a framework for applying ML to analyze spatial metal isotope data at the catchment scale.

Comparison of Implicit and Explicit Solvent Approaches in Ab Initio Evaluation of Thermochemistry in Solution: Application in Studying Boron Isotope Fractionation in Water

Evaluation of thermochemistry in solution plays a key role in numerous fields. For this task, the solvent effects are commonly included in theoretical computations based on either implicit or explicit solvent approaches. In the present study, we evaluate and compare the performance of some of the most widely applied methods based on these two approaches. For studying the solvent effect on thermochemistry with an explicit solvent, we demonstrate that partial normal mode analysis with frozen geometry of solvent molecules for multiple solute-solvent configurations can yield quite accurate and reliable results for a drastically reduced computational cost. As a case study, we consider the evaluation of the equilibrium constant for the boron isotope exchange between boric acid and borate (k_3-4) in pure and saline water which is of high geochemical importance. Employing three different rigorous and high-precision theoretical approaches, we provide a reliable estimation of k_3-4 which is a value within 1.028 to 1.030 for both pure and saline water which is in excellent agreement with experimental data.

Importance of Nuclear Quantum Effects on Aqueous Electrolyte Transport under Confinement in Ti3C2 MXenes

Protons display a high chemical activity and strongly affect the charge storage capability in confined interlayer spaces of two-dimensional (2D) materials. As such, an accurate representation of proton dynamics under confinement is important for understanding and predicting charge storage dynamics in these materials. While often ignored in atomistic-scale simulations, nuclear quantum effects (NQEs), e.g., tunneling, can be significant under confinement even at room temperature. Using the thermostatted ring polymer molecular dynamics implementation of path integral molecular dynamics (PIMD) in conjunction with the ReaxFF force field, density functional tight binding (DFTB), and NequIP neural network potential simulations, we investigate the role of NQEs on proton and water transport in bulk water and aqueous electrolytes under confinement in Ti₃C₂ MXenes. Although overall NQEs are relatively small, especially in bulk, we find that they can alter both quantitative values and qualitative trends on both proton transport and water self-diffusion under confinement relative to classical MD predictions. Therefore, our results suggest the need for NQEs to be considered to simulate aqueous systems under confinement for both qualitative and quantitative accuracy.

DFT and MD simulation supplemented experiments for isotopic fractionation of zinc compounds using a macrocyclic crown ether appended polymeric resin

Isotope effect is a quantum mechanical phenomenon and thus poses a challenge for the separation of isotopes of an element of interest, especially for heavy elements. Isotopic fractionation of zinc is also quite difficult and challenging but is necessitated due to various applications of its isotopes ranging from nuclear medicine to nuclear power reactors. Here, we developed the dibenzo-18-crown-6 (DB18C6) ether-functionalized poly(methacrylic acid) (PMA) resin by exploiting the ion and isotope recognition ability of the crown ether using DFT/MD simulations followed by experiments for isotopic fractionation of zinc. The PMADB18C6 adsorbent was prepared and suitably characterized. Both computational and experimental findings demonstrate that the adsorption and isotope separation of zinc with PMADB18C6 are due to the molecular recognition effect of the "O" dipole of the crown ether. Furthermore, both MD simulations and experiments suggest Langmuir type adsorption isotherms. The adsorption of Zn2+ ions on the PMA resin is predicted to be endothermic, whereas it is exothermic on the PMADB18C6 resin, as revealed from the experimentally observed enthalpy change. A small scale fixed bed column study was demonstrated to test the scale-up application. The values of the experimental separation factors: 1.0013 for 66/64 and 1.0027 for 68/64 confirm the computationally predicted results of 1.00088 and 1.0010, respectively, thus establishing the combined strength of the theory and experiments for the identification of efficient fractionating agents for a complex quantum isotope effect which eventually helps in planning further experiments in view of medicinal and technological applications of zinc isotopes.

Modulation of the reactivity of nitrogen mustards by metal complexation: approaches to modify their therapeutic properties

Metal complexation of nitrogen mustards shows promise with an ability to control the mustards’ reactivity, perform selective hypoxia activation, overcome resistance, and control GSH deactivation.