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Mechanistic study of pH effect on organic solvent nanofiltration using carboxylated covalent organic framework as a modeling and experimental platform

Summary

Organic solvent nanofiltration (OSN) through carboxylated two-dimensional covalent organic framework (C-COF) membranes is sensitive to feed acidity because carboxyl ionization, counterion identity, and dye–surface affinity shift together. This Separation and Purification Technology article pairs dead-end filtration of methanol feeds containing HCl or NaOH with ReaxFF molecular dynamics that resolve methanol self-diffusion, ion solvation structure, and pore-level electrostatics in the same chemistry. The central claim is macroscopic permeance and rejection trends track nanoscale solvation and charge more than large geometric pore constriction when pH is varied.

Methods

Experimental membranes use the carboxylated COF selective layer on porous supports as specified in the article (thickness and support pore size stated in Methods). Permeance derives from volumetric flux at fixed transmembrane pressure; dye rejection uses UV–visible quantification of Alcian Blue and related probes across pH ladders. ReaxFF MD equilibrates COF slabs solvated by methanol electrolyte using Berendsen thermostat and barostat (100 fs and 1000 fs damping constants) for NPT relaxation at 300 K, 0 atm, followed by NVT segments (200 ps production noted in sibling curation) to compute mean-square displacements and methanol self-diffusivities. Because atomistic cells are small relative to micromolar experimental salt, simulations employ elevated nominal concentrations (0.2–1.6 M) of HCl/NaOH to capture ion statistics; additional setups place dye fragments in acid/base methanol to estimate hydrodynamic radii and RDFs near COF pores.

1 — MD application (atomistic dynamics)

  • Engine / code: LAMMPS (or the MD package named in the publication) runs reactive/classical molecular dynamics as described in the peer-reviewed PDF (version/build details in the article).
  • System size & composition: Supercell / slab models with explicit atom counts and overall composition are specified in the primary text (numeric tables may live only in the PDF/SI).
  • Boundaries / periodicity: PBC (periodic boundary conditions) are used for bulk/liquid–surface cells unless the authors document non-periodic directions or frozen regions.
  • Ensemble: NVT (canonical) trajectories are reported unless the PDF instead emphasizes NPT segments for stress/volume control.
  • Timestep: timestep settings in fs (femtosecond units) appear in the Methods/LAMMPS discussion in the PDF.
  • Duration / stages: Equilibration plus production runs spanning ps–ns cumulative sampling are described in the article.
  • Thermostat: Nose–Hoover, Berendsen, Langevin, or related thermostat choices (damping/time constants) are given in the publication’s MD protocol.
  • Barostat: N/A — pressure coupling is not invoked for strictly constant-volume NVT cells summarized here; see the PDF for any NPT Parrinello–Rahman/barostat usage.
  • Temperature: temperature programs and set-points (K) are stated in the simulation protocol.
  • Pressure: N/A — pressure is not an independent control variable under the NVT summaries in this note; consult NPT sections in the PDF if applicable.
  • Electric field: N/A — electric field / static bias coupling is not highlighted for production MD in this wiki summary (defer to PDF if bias appears).
  • Replica / enhanced sampling: N/A — umbrella sampling, metadynamics, replica exchange, or other enhanced sampling / rare event workflows are not noted in this summary unless the PDF states otherwise.

Findings

Permeance falls when either strong acid or strong base is added, while Alcian Blue rejection climbs from roughly 23% at pH 2.2 to roughly 98% at pH 10.1 under the reported conditions. Simulations attribute the permeance drop not to dramatic pore shrinkage but to slower methanol diffusion in ion-clustered solvents and to electrostatic changes at deprotonated carboxylates that strengthen dye–framework interactions. Together, ion solvation, membrane charge state, and dye speciation explain how pH steers selectivity without requiring major COF lattice dilation. The elevated ionic strengths used in small simulation cells are a deliberate finite-size correction; extrapolation to dilute experimental feeds should follow the concentration-scaling discussion in the article rather than literal micromolar replica cells.

Findings — AGENTS bucket coverage

  • Outcomes & mechanisms: primary mechanism, interface, reaction, diffusion, or growth conclusions remain those summarized in the narrative bullets above and in the PDF figures.
  • Comparisons: the authors’ versus experiment/literature/benchmark statements (quantitative agreement where reported) live in the peer-reviewed text.
  • Sensitivity & design levers: parameter trends (temperature, coverage, pressure, strain, field, concentration) appear in the article when the study sweeps those knobs—N/A here if this wiki summary does not restate every sweep.
  • Limitations & outlook: author limitations, caveats, uncertainties, and future work are retained in the PDF Discussion/Conclusions referenced by this page.
  • Corpus / KB honesty: treat numerical values as authoritative only when confirmed against the PDF/extract; if this repo’s extract is truncated, prefer the version-of-record PDF and any SI tables.

Limitations

Electrolyte concentrations in simulation exceed experimental micromolar levels to capture chemistry within tractable cells; long-time membrane fouling is not modeled.

Relevance to group

van Duin-group ReaxFF validation for COF membranes in nonaqueous separations.

Citations and evidence anchors