arXiv subjects

John H. Lienhard

Publications and source records attributed to John H. Lienhard.

4 recordsLinked to original sources

arXiv2025

Membrane Charge Effects on Solute Transport in Polyamide Membranes

Polyamide membranes, such as nanofiltration (NF) and reverse osmosis (RO) membranes, are widely used for water desalination and purification. However, the mechanisms of solute transport and solute rejection due to charge interactions remain unclear at the molecular level. Here we use molecular dynamics (MD) simulations to examine the transport of single-solute feeds through charged nanofiltration membranes with different membrane charge concentrations of COO$^{\text{-}}$ and NH$_2\!^+$ corresponding to different pH levels. Results show that Na$^+$ and Cl$^{\text{-}}$ solute ions are better rejected when the membrane has a higher concentration of negatively charged groups, corresponding to a higher pH, whereas CaCl$_2$ is well-rejected at all pH levels studied. These results are consistent with experimental findings which are performed at the same pH conditions as simulation setup. Moreover, solute transport behavior depends on the membrane functional group distribution. When COO$^{\text{-}}$ functional groups are concentrated at membrane feed surface, ion permeation into the membrane is reduced. Counter-ions tend to associate with charged functional groups while co-ions seem to pass by the charged groups more easily. In addition, steric effects play a role when ions of opposite charge cluster in pores of the membrane. This study reveals solute transport and rejection mechanisms related to membrane charge and provides insights into how membranes might be designed to achieve specific desired solute rejection.

cond-mat.soft↗

arXiv2024

Symmetry criteria for the equality of interior and exterior shape factors

Lienhard (2019) reported that the shape factor of the interior of a simply-connected region ($Ω$) is equal to that of its exterior ($\mathbb{R}^2\backslashΩ$) under the same boundary conditions. In that study, numerical examples supported the claim in particular cases; for example, it was shown that for certain boundary conditions on circles and squares, the conjecture holds. In the present paper, we show that the conjecture is not generally true, unless some additional condition is met. We proceed by elucidating why the conjecture does in fact hold in all of the examples analysed by Lienhard. We thus deduce a simple criterion which, when satisfied, ensures the equality of interior and exterior shape factors in general. Our criterion notably relies on a beautiful and little-known symmetry method due to Hersch (1982) which we introduce in a tutorial manner.

physics.class-ph↗

arXiv2023

Attention-enhanced neural differential equations for physics-informed deep learning of ion transport

Species transport models typically combine partial differential equations (PDEs) with relations from hindered transport theory to quantify electromigrative, convective, and diffusive transport through complex nanoporous systems; however, these formulations are frequently substantial simplifications of the governing dynamics, leading to the poor generalization performance of PDE-based models. Given the growing interest in deep learning methods for the physical sciences, we develop a machine learning-based approach to characterize ion transport across nanoporous membranes. Our proposed framework centers around attention-enhanced neural differential equations that incorporate electroneutrality-based inductive biases to improve generalization performance relative to conventional PDE-based methods. In addition, we study the role of the attention mechanism in illuminating physically-meaningful ion-pairing relationships across diverse mixture compositions. Further, we investigate the importance of pre-training on simulated data from PDE-based models, as well as the performance benefits from hard vs. soft inductive biases. Our results indicate that physics-informed deep learning solutions can outperform their classical PDE-based counterparts and provide promising avenues for modelling complex transport phenomena across diverse applications.

cs.LG↗

arXiv2023

Physics-constrained neural differential equations for learning multi-ionic transport

Continuum models for ion transport through polyamide nanopores require solving partial differential equations (PDEs) through complex pore geometries. Resolving spatiotemporal features at this length and time-scale can make solving these equations computationally intractable. In addition, mechanistic models frequently require functional relationships between ion interaction parameters under nano-confinement, which are often too challenging to measure experimentally or know a priori. In this work, we develop the first physics-informed deep learning model to learn ion transport behaviour across polyamide nanopores. The proposed architecture leverages neural differential equations in conjunction with classical closure models as inductive biases directly encoded into the neural framework. The neural differential equations are pre-trained on simulated data from continuum models and fine-tuned on independent experimental data to learn ion rejection behaviour. Gaussian noise augmentations from experimental uncertainty estimates are also introduced into the measured data to improve model generalization. Our approach is compared to other physics-informed deep learning models and shows strong agreement with experimental measurements across all studied datasets.

cs.LG↗