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Understanding the mechanisms of molecular transport in polyamide membranes is imperative to improve their solute-specific selectivity. We explored the partitioning behaviors of water and salts in polyamide membranes to elucidate the role of ion-membrane interactions in the transport. Quartz crystal microbalance (QCM) was employed to quantify the mass uptake at different temperatures and determine partition energies (Ek) for water and salts under two different pH values. Zeta potential and permeability tests were conducted to support the ionmembrane affinity trends observed with QCM and link these trends to ion-ion selectivity. Our results demonstrate a high affinity of water to the polyamide membrane (Ek < 0), with a significant swelling effect attributed to dipole interactions and hydrogen bonding. Ion partitioning revealed distinct differences between monovalent and divalent cations, as well as between kosmotropic and chaotropic anions. Specifically, divalent cations (Ca2+ and Mg2+) exhibited considerably lower partition energies (-0.99 and 0.29 kcal mol-1, respectively) and more efficient charge neutralization, indicating stronger interactions with the membrane compared to monovalent cations (~2.2 kcal mol-1). The partition energies of the chaotropic iodide and kosmotropic sulphate anions were substantially different (-5.5 and 4.0 kcal mol-1, respectively), likely due to the different tendency of these anions to shed their hydration shell and stick to the polymer. Last, our permeability tests indicate the potential existence of an intrinsic tradeoff between ion partitioning and intrapore diffusion, presumably due to the opposite effects that ion-membrane interactions have on these transport steps. Overall, our work underscores the role of ionspecific interactions in membrane transport and selectivity. 

Full Changelog: https://github.com/ponder-lab/Common-Eclipse-Refactoring-Framework/compare/v5.0.0...v5.1.0 

ABSTRACT Semi-analytic modelling furnishes an efficient avenue for characterizing dark matter haloes associated with satellites of Milky Way-like systems, as it easily accounts for uncertainties arising from halo-to-halo variance, the orbital disruption of satellites, baryonic feedback, and the stellar-to-halo mass (SMHM) relation. We use the SatGen semi-analytic satellite generator, which incorporates both empirical models of the galaxy–halo connection as well as analytic prescriptions for the orbital evolution of these satellites after accretion onto a host to create large samples of Milky Way-like systems and their satellites. By selecting satellites in the sample that match observed properties of a particular dwarf galaxy, we can infer arbitrary properties of the satellite galaxy within the cold dark matter paradigm. For the Milky Way’s classical dwarfs, we provide inferred values (with associated uncertainties) for the maximum circular velocity $$v_\text{max}$$ and the radius $$r_\text{max}$$ at which it occurs, varying over two choices of baryonic feedback model and two prescriptions for the SMHM relation. While simple empirical scaling relations can recover the median inferred value for $$v_\text{max}$$ and $$r_\text{max}$$, this approach provides realistic correlated uncertainties and aids interpretability. We also demonstrate how the internal properties of a satellite’s dark matter profile correlate with its orbit, and we show that it is difficult to reproduce observations of the Fornax dwarf without strong baryonic feedback. The technique developed in this work is flexible in its application of observational data and can leverage arbitrary information about the satellite galaxies to make inferences about their dark matter haloes and population statistics.