Aromatic acids, such as p-coumaric acid, are valuable chemical intermediates that are used in the specialty chemical industries because they are precursors to phenylpropanoid compounds. The separation of p-coumaric acid from fermentation broths is a critical step in the biochemical production process and more broadly the circular carbon economy. Electrodeionization (EDI) has been applied toward separations of low-carbon chain acids, but purifying p-coumaric acid has been challenging due to fouling and irreversible binding with ion-exchange membranes and resins. Here, we report a new membrane wafer assembly (MWA) consisting of laminated ion exchange membranes to porous ionomer-binder resin wafers for EDI. The MWAs in an EDI stack showed a 7-fold increase in p-coumaric acid capture while also using 70% less specific energy consumption when benchmarked against state-of-the-art resin wafer EDI modules. The more efficient p-coumaric acid recovery was ascribed to (i) the 38% reduction in interfacial transport resistance between the membrane and resin wafer and (ii) using imidazolium anion exchange membranes and ionomer binders in the MWA. MD simulations revealed enhanced transport rates for p-coumarate in imidazolium ionomers through π–π interactions. Adopting the new MWA significantly reduced the amount of ion-exchange membranes in EDI and may lead to drastic capital cost savings.
Electrostatic interactions play a significant role in regulating biological systems and have received increasing attention due to their usefulness in designing advanced stimulus-responsive materials. Polypeptoids are highly tunable N-substituted peptidomimetic polymers that lack backbone hydrogen bonding and chirality. Therefore, polypeptoids are suitable systems to study the effect of noncovalent interactions of substituents without complications of backbone intramolecular and intermolecular hydrogen bonding. In this study, all-atom molecular dynamics (MD) simulations were performed on micelles formed by a series of sequence-defined ionic polypeptoid block copolymers consisting of a hydrophobic segment and a hydrophilic segment in an aqueous solution. By combining the results from MD simulations and experimental small-angle neutron scattering data, further insights were gained into the internal structure of the formed polypeptoid micelles, which is not always directly accessible from experiments. In addition, information was gained into the physics of the noncovalent interactions responsible for the self-assembly of weakly charged polypeptoids in an aqueous solution. While the aggregation number is governed by electrostatic repulsion of the negatively charged carboxylate (COO – ) substituents on the polypeptoid chain within the micelle, MD simulations indicate that the position of the charge on singly charged chains mediates the shape of the micelle through the charge–dipole interactions between the COO – substituent and the surrounding water. Therefore, the polypeptoid micelles formed from the single-charged series offer the possibility for tailorable micelle shapes. In contrast, the polypeptoid micelles formed from the triple-charged series are characterized by more pronounced electrostatic repulsion that competes with more significant charge–sodium interactions, making it difficult to predict the shape of the micelles. This work has helped further develop design principles for the shape and structure of self-assembled micelles by controlling the position of charged moieties on the backbone of polypeptoid block copolymers.
The activity coefficients of ions in polymeric ion-exchange membranes (IEMs) dictate the equilibrium partitioning coefficient of the ions between the membrane and the liquid. It also affects ion transport processes, such as conductivity, in ionexchange membranes. Accurately predicting the ion activity coefficient without experimental data has been elusive as most models are empirical or semi-empirical. This work employs an embedding process that maps microscopic and macroscopic properties for modeling of ion activity coefficients in IEMs with molecular dynamics (MD) and machine learning (ML). This strategy is effective for accurately predicting activity coefficients in various IEM materials, including random copolymer and block copolymer systems. ML algorithms are increasingly being used for the analysis of complex systems when limited knowledge is available. The framework uses small experimental activity coefficient datasets in conjunction with polymer structure information and molecular attributes describing the solvation of ions and polymers to predict the ion activity coefficient in IEMs. Two different ML models were developed to estimate the molecular attributes and the ion activity coefficient. The best ML model accurately predicts the solvation descriptors and ion activity coefficient with an average mean absolute error of <7 and 10%, respectively. Adopting the said approach allows for the estimation of ion activity coefficients in IEMs without the need for new time-consuming MD simulation runs and experiments.
The activity coefficients of ions in polymeric ion-exchange membranes (IEMs) dictates the equilibrium partitioning coefficient of the ions between the membrane and the liquid. It also affects ion transport processes, such as conductivity, in ion-exchange membranes. Accurately predicting the ion activity coefficient without experimental data has been elusive as most models are empirical or semi-empirical. This work employs an embedding process that maps microscopic and macroscopic properties for modeling of ion activity coefficients in IEMs with molecular dynamics and machine learning (ML). This strategy is effective for accurately predicting activity coefficients in various IEMs materials including random copolymer and block copolymer systems. ML algorithms are increasingly being used for the analysis of complex systems when limited knowledge is available. The framework uses small experimental activity coefficient datasets in conjunction with polymer structure information and molecular attributes describing the solvation of ions and polymers to predict the ion activity coefficient in IEMs. Two different ML models were developed to estimate the molecular attributes and the ion activity coefficient. The best ML model accurately predicts the solvation descriptors and ion activity coefficient with an average mean absolute error of <7% and 10%, respectively. Adopting the said approach allow for the estimation of ion activity coefficients in IEMs without the need for new time-consuming MD simulation runs and experiments.
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