William A. Phillip

Analytical techniques such as Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) are widely used to measure ion concentrations, but are time-consuming, laborintensive, and costly. The required sample collection, preparation, and offline, energyintensive analysis render ICP-OES inadequate for real-time monitoring in membrane processes. Conductivity sensors provide rapid, in situ measurements of the electrical conductivity of ionic solutions, offering an attractive alternative for real-time tracking of ionic species during membrane processes; however, converting conductivity measurements into ion concentrations in multicomponent systems remains a significant challenge. This work addresses this limitation by developing soft sensors that estimate ion concentrations in real time using conductivity data from inexpensive sensors and mathematical models that map conductivity measurements to ion concentrations. Specifically, the Shedlovsky and mean spherical approximation (MSA) models are deployed, and their conductivity predictions are validated using experimental data representative of electronic waste and lithium-ion battery leachate. By capturing key physical phenomena in ion transport in the presence of the electric field generated by the conductivity sensor, such as electrophoretic and relaxation effects, these models accurately predict the conductivity of single, binary, and ternary salt feed solutions. Furthermore, Shedlovsky and MSA-based soft sensors are applied to estimate real-time ion concentrations in the permeate solution of a membrane-based separation, and the estimates are validated against ICP-OES analysis, demonstrating good agreement (within 5%). Overall, this study demonstrates that the development of scientifically rigorous and generalizable soft sensors is essential for enabling rapid, reliable, and efficient measurement of ion concentrations in complex ionic environments.

Membranes that selectively enhance target solute permeation while rejecting competing species are essential for precision separations. This study introduces charge-patterned mosaic membranes (CMMs) that selectively transport divalent asymmetric salts by leveraging a net-neutral membrane–solution interface. This mechanism, dictated by the charge ratio of positive and negative domains on the membrane surface and the balance of cations and anions in the salt, is supported by analytical, numerical, and experimental results. Analytical solutions identified cationic domain coverages ( f ⁺ ) of 33%, 50%, and 66% as optimal for the selective transport of +2:−1 salts, +1:−1 salts, and +1:−2 salts, respectively, under conditions where the pattern size ( L ) is significantly larger than the Debye length. Numerical simulations and experiments using CMMs with alternating charged-stripes inkjet-printed onto nanostructure copolymer substrates confirmed these findings. By varying stripe widths to control f ⁺ , pressure-driven filtration experiments demonstrated selective enrichment of MgCl ₂ and K ₂ SO ₄ at the predicted f ⁺ values, with deviations from these values leading to salt rejection. These results highlight the pivotal role of a net-neutral interface in enabling asymmetric salt enrichment. This study positions CMMs as a versatile platform for tuning ion selectivity, addressing challenges in resource recovery, water treatment, and precision separations.

Directional solvent extraction (DSE) is an emerging technology capable of utilizing low-temperature heat sources (e.g., waste heat) for desalination, but the identification of high-performing directional solvents remains a significant unmet need hindering its broader implementation. Herein, we report the design of thermoresponsive ionic liquids with multicomponent ions as a powerful class of directional solvents. Our studies revealed that freshwater yield and ion rejection rates for [choline][NTf₂] were greatly enhanced by the addition of [choline][OH]. Molecular simulations highlight the importance of balancing hydrophilicity and hydrophobicity through the addition of polar, protic functional groups in the development of a reliable structure-function relationship toward identifying an optimal ionic liquid-based directional solvent. The newly developed ionic liquid directional solvents may significantly advance the DSE technology to solve the global water shortage challenge.

This study presents an integrated framework for characterizing the transport properties of surface-charged nanofiltration (NF) membranes through dynamic diafiltration experiments and model-based data analytics. By incorporating startup dynamics, time corrections, and the influence of water flux on solute permeation, the framework effectively captures key transport properties governing membrane performance. A comparison of lag and overflow experimental modes highlights the superior precision of the lag startup mode, improving parameter estimates by 8%, 138%, and 83% as quantified by A-, D-, and E-optimality, respectively. Diafiltration experiments in the diluting regime further enhance model discrimination, enabling the identification of dominant transport mechanisms. This work marks a step toward developing self-driving laboratories (SDLs) that leverage model-based design of experiments (MBDoE) to expedite the characterization and development of high-performance NF membranes. The framework provides critical insights into transport phenomena, enabling the inverse design of membranes tailored to the demands of modern separation systems.

The selective removal of solutes is crucial for ensuring a sustainable water supply, recovering resources, and cost-effective biomanufacturing. Adsorptive membranes are promising in this regard due to their rapid mass transfer and low energy demands. However, state-of-the-art adsorptive membranes offer limited pore sizes and surface chemistries. This study reports the development of adsorptive membranes from reactive phenolphthalein-based (PPH-based) polymers. These polymers, which are molecularly engineered to possess a high density of reactive pendant groups, are transformed into porous membranes through a surface-segregation vapor-induced phase separation (SVIPS) method. Examining the thermodynamic characteristics of the polymer-solvent-nonsolvent system informs the SVIPS manufacturing process and facilitates the formation of diverse membrane morphologies with hydraulic permeabilities ranging from 3400 to 13,500 L m^-2 h^-1 bar^-1. Copper ion binding experiments demonstrate a saturation capacity of 0.9 mmol Cu²⁺ g^-1, indicating high accessibility of the pendant groups for postsynthetic modification. Functionalization with alkyne groups enables one-step click reactions, such as the thiol-yne and Cu(I)-catalyzed azide-alkyne cycloaddition, expanding the membrane functionality. The incorporation of cucurbit[7]uril-azide macrocycles demonstrates the affinity-mediated capture of methyl viologen from solution. The combination of PPH-based polymers and the SVIPS method provides a versatile adsorptive membrane platform with a dense presentation of reactive sites, facilitating customization through diverse and high-yielding reactions.

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.