Search

Multifunctional, nanostructured membranes hold immense promise for overcoming permeability-selectivity trade-offs and enhancing membrane durability in challenging molecule separations. Following the fabrication of copolymer membranes, additive manufacturing technologies can introduce reactive inks onto substrates to modify pore wall chemistries. However, large-scale implementation is hindered by a lack of systematic optimization. This study addresses this challenge by elucidating the membrane functionalization mechanisms and optimal manufacturing conditions using a copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC) "click" reaction. Leveraging a data science toolkit (e.g., nonlinear regression, uncertainty quantification, identifiability analyses, model selection, and design of experiments), we developed two mathematical models: (1) algebraic equations to predict equilibrium concentrations after preparing reactive inks by mixing copper sulfate, ascorbic acid (AA), and an alkynyl-terminated reactant; and (2) reaction-diffusion partial differential equations (PDEs) to describe the functionalization process. The ink preparation chemistry with side reactions was validated through pH and UV-vis measurements, while the diffusion and kinetic parameters in the PDE model were calibrated using time-series conversion of the azide moieties inferred from Fourier-transform infrared spectroscopy. This modeling framework avoids redundant experimental efforts and offers a functionalization protocol for scaling up designs. Ink optimization problems were proposed to reduce the use of expensive and environmentally insulting ink materials, i.e., Cu(II), while ensuring the desired chemical distributions. With optimal ink formulation Cu(II)/AA/alkyne = 1:1:2 identified, we uncovered trade-offs between Cu(II) usage and functionalization time; for example, in continuous roll-to-roll manufacturing with a conserved functionalization bath setup, our optimal operational conditions to achieve ≥90% functionalization enable at least a 20% reduction in total copper investment compared to previous experimental results. The data science-enabled ink optimization framework is extendable for on-demand multifunctional membranes in numerous future applications such as metal recovery from wastewater and brine.

Lithium ion-based batteries are ubiquitous in modern technology due to applications in personal electronics and high-capacity storage for electric vehicles. Concerns about lithium supply and battery waste have prompted interest in lithium recycling methods. The crown ether 12-crown-4 has been studied for its abilities to form stable complexes with lithium ions (Li + ). In this paper, molecular dynamics simulations are applied to examine the binding properties of a 12-crown-4-Li + system in aqueous solution. It was found that 12-crown-4 did not form stable complexes with Li + in aqueous solution due to the binding geometry which was prone to interference by surrounding water molecules. In addition, the binding properties of sodium ions (Na + ) to 12-crown-4 are examined for comparison. Subsequently, calculations were performed with the crown ethers 15-crown-5 and 18-crown-6 to study their complexation with Li + as well as Na + . It was determined that binding was unfavorable for both types of ions for all three crown ethers tested, though 15-crown-5 and 18-crown-6 showed a marginally greater affinity for Li + than 12-crown-4. Metastable minima present in the potential of mean force for Na + render binding marginally more likely there. We discuss these results in the context of membrane-based applications of crown ethers for Li + separations.

Accurate force fields are necessary for predictive molecular simulations. However, developing force fields that accurately reproduce experimental properties is challenging. Here, we present a machine learning directed, multiobjective optimization workflow for force field parametrization that evaluates millions of prospective force field parameter sets while requiring only a small fraction of them to be tested with molecular simulations. We demonstrate the generality of the approach and identify multiple low-error parameter sets for two distinct test cases: simulations of hydrofluorocarbon (HFC) vapor-liquid equilibrium (VLE) and an ammonium perchlorate (AP) crystal phase. We discuss the challenges and implications of our force field optimization workflow.