Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.18791

Embossing of Tapered, Isometric Micro-Interdigitated Flow Fields for Enhanced Desalination by Faradaic Deionization

Md Habibur Rahman·Sadia Afrin·Kyle C. Smith
View original source ↗Read source document ↗Export metadata ↓

Abstract

Engineered transport architectures can improve electrolyte distribution and active-material utilization in porous electrodes, yet their implementation remains limited by fabrication methods that compromise precision, throughput, active-material preservation, or scalability. Here, an integrated embossing-calendering method is introduced to pattern Prussian blue analog (PBA) porous electrodes with embedded, micro-interdigitated flow fields (e$μ$-IDFFs) for electrochemical desalination. This non-subtractive, roll-to-roll compatible method forms channels by controlling plastic deformation while simultaneously calendering the electrode, preserving active material and reducing patterning time by 100-fold relative to mechanical milling. Indentation experiments and a closed-form model further show that embossing requires 15-fold less force than stamping, while electrode microstructures show local densification beneath embossed channels. PBA electrodes are embossed with novel e$μ$-IDFFs that use continuous width-tapering with isometric flow paths that promote uniform electrolyte distribution, increase rate capability, and reduce polarization. Compared with e$μ$-IDFFs patterned using less scalable means and non-isometric flow paths, these e$μ$-IDFFs desalinate seawater-salinity feeds near to potable levels with 1.5-fold of its thermodynamic energy efficiency and water recovery. Brackish desalination benefits similarly. Tapered, isometric e$μ$-IDFFs and the embossing method introduced to pattern them are readily translated other electrochemical energy storage and separations systems.

Explore related subjects

physics.chem-phphysics.flu-dyn

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Md Habibur Rahman, Sadia Afrin, Kyle C. Smith. 2026-10-13. Embossing of Tapered, Isometric Micro-Interdigitated Flow Fields for Enhanced Desalination by Faradaic Deionization. https://arxiv.org/abs/2610.18791

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Chiral Temporal Structures in Molecular Rotational Dynamics

We introduce chiral rotational wavepackets: coherent superpositions of a few low-lying rotational states of a molecule for which the expectation value of a molecular axis traces a three-dimensional chiral trajectory in the laboratory frame. The handedness is a dynamical property of the quantum state rather than of the equilibrium structure, and we show that it can be imprinted on linear rotors as well as on spherical and symmetric tops. We derive the action of space inversion, time reversal, and their product on such wavepackets in the Wigner $D^J_{MK}$ basis, and define the rotational enantiomers of a given wavepacket. To quantify their handedness we introduce two derivative-based pseudoscalar measures: a geometrical measure built from the curvature and torsion of the trajectory, which is even under time reversal, and a dynamical measure, which is odd under time reversal. The latter is shown to be proportional to the geodesic curvature of the trajectory projected onto the unit sphere, which links it, via the Gauss--Bonnet theorem, to the solid angle enclosed by the projected trajectory and to the parallel-transport holonomy of a molecule-fixed vector. Finally we show that a nonzero geometrical measure is a sufficient condition for the wavepacket to exhibit true chirality through its orientation trajectory in the sense of Barron.

physics.chem-ph↗
arXiv2026

A strategic roadmap for an atomistic machine-learning ecosystem

Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespread and impactful. This success is due largely to the existence of a well-developed and established physics-based modeling framework, ranging from first-principles electronic-structure calculations to molecular dynamics and statistical sampling, into which ML was integrated naturally to reshape long-standing trade-offs between accuracy, efficiency, and scale. Nevertheless, this integration raises both conceptual and practical challenges, from choosing between data-centric and physics-based modeling approaches to adapting established software stacks to modern hardware accelerators and ML libraries. As the field evolves rapidly, fueled in part by widespread enthusiasm but also by tangible impact, it seems appropriate to take a moment to consider the current state of the art and open challenges, and reflect on what can be done to better coordinate efforts across the community. With this goal in mind, several members of this community met in Lausanne in January 2026 at CECAM to discuss algorithms, models, software and hardware infrastructure, and the most promising scientific applications that have become possible thanks to the use of artificial intelligence in atomic-scale simulations. This strategic roadmap paper summarizes the outcomes of these discussions, suggesting some long-term goals, and some concrete actions, to establish a healthy, sustainable and impactful atomistic ML ecosystem.

physics.chem-ph↗
arXiv2026

All-electron machine-learned orbital-free density functional for periodic CHNO systems

We introduce STRUCTURES-P, a machine-learned all-electron orbital-free density functional for periodic CHNO systems. Electrostatics are evaluated analytically in an atom-centered Gaussian basis, while an equivariant network learns the kinetic and exchange-correlation energy from energies and gradients of non-ground-state densities. Minimizing the functional from a superposition of atomic densities recovers the Kohn-Sham ground state: on held-out crystals, the density error falls from 6.7% to 0.4% of the electron count and the energy error reaches 0.25 mHa per atom, half that of a same-architecture interatomic potential. A single functional can be trained for both isolated molecules and periodic systems, and the model generalizes to larger unit cells. It largely preserves relative-energy rankings and reproduces convex-hull vertices on the tested H-N composition slice. Together, these results demonstrate the viability of machine-learned all-electron orbital-free DFT for variational density optimization and energy evaluation in fixed-geometry solids.

physics.chem-ph↗