Interface Engineering of Helium Confinement in Argon-Preplated MCM-41 Nanopores

Rahul Soni, Nathan S. Nichols, Sutirtha Paul, Garfield Warren, Paul Sokol and Adrian Del Maestro

arXiv:2608.05603

Abstract

Atomic-scale modification of mesopore interfaces provides a route to tune the confinement experienced by adsorbed fluids, but how a specific interface preparation translates into the resulting microscopic confinement potential remains unclear. Here, we show that preplating MCM-41 with an argon monolayer modifies the effective pore interface by occupying strongly attractive regions of the heterogeneous silica surface and screening its atomic-scale corrugation. Grand-canonical Monte Carlo simulations of argon adsorption, low-temperature molecular dynamics, and helium test-particle insertion are combined with adsorption isotherms and neutron-scattering measurements to characterize the preplated pore at the atomic scale. Helium test-particle insertion calculations show that the modified interface shifts the helium adsorption minimum to an annular region inside the pore and produces a confinement landscape dominated by a smooth radial component. The resulting radial confinement potential can be described by a continuum cylindrical model, providing microscopic support for the effective potential used in earlier quantum Monte Carlo studies. Residual corrugation persists over multiple spatial scales and is accurately captured by a Gaussian process surrogate. These results demonstrate how atomic preplating can tailor nanopore confinement and provide an experimentally constrained microscopic potential for predictive studies of confined quantum fluids.

Description

This repository includes data files, scripts, codes and analysis used to generate the figures in this paper.

Data

The data in this project was generated via LAMMPS. Since several LAMMPS trajectory/dumps and TPI datasets are too large for a convenient distribution through GitHub, the data associated with this project are divided between GitHub and Zenodo.

Data included in GitHub

This repository contains the smaller datasets and processed data files in the data directory, required for the analysis and figures. The included files are under these subdirectories:

data/
data/lammps/Ar_nstats/
data/lammps/Str_factor/

as well as the MCM structure and selected processed configurations for visualization via OVITO:

data/lammps/Data_MCM_2x1x5.data
data/lammps/Data_MCM_Ar_MD_90K.data
data/lammps/Data_MCM_Ar_frozen_4K.data
data/lammps/Nexp_data_MCM-41_Ar.txt

Data stored on Zenodo

The following two large subdirectories are stored on Zenodo:

Ar_dumpfiles/
TPI_data/

Ar_dumpfiles/ contains the GCMC Ar trajectory/dump files for the five independent simulation seeds and chemical-potential scan.

TPI_data/ contains the data generated from the helium test-particle insertion calculations, including the multiple frozen Ar+MCM configurations used for averaging.

The datasets can be downloaded through .

After downloading the datasets from Zenodo place the two subdirectories under data/lammps/, so that the repo has the following structure:

data/lammps/
-> Ar_nstats/
-> Ar_dumpfiles/       # downloaded from Zenodo
-> TPI_data/           # downloaded from Zenodo
-> Str_factor/

Software requirements

The workflow was generated in a Linux/HPC environment and the following softwares were used to obtain the raw & processed data and figures:

• Bash
• LAMMPS (version: 29 Oct 2020)
• NumPy and Python3
• Gnuplot
• pdftoppm (for PDF-to-PNG conversion)

Obtaining raw & processed data

Unless stated otherwise, the bash scripts in src/ should be executed from the main directory and not from inside 'src/'

For multiseed GCMC runs at 90 K do:

bash src/Submit_GCMC_multiseed_runs.sh

To collect raw GCMC data, as well as analyse and generate processed GCMC data, do the following:

bash src/data_collection_GCMC_multiseed.sh
bash src/data_analysis_nstats.sh
bash src/data_analysis_gr_rho.sh
bash src/data_analysis_Sq.sh

For multiseed MD and TPI runs at 4 K do:

bash src/Submit_MD_multiseed_runs.sh
bash src/Submit_TPI_multiseeed_runs.sh

To collect raw TPI data, as well as analyse and generate processed TPI data, do the following:

bash src/data_collection_TPI.sh
bash src/data_analysis_TPI.sh

Once all the raw and processed data has been stored in the data/ directory, generate all the paper figures by running the Create_figures.sh script inside plotting_scripts/ subdirectory. Note that, the script should be executed from src/plotting_scripts/ as:

bash Create_figures.sh

Support

This work was performed with support from the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Award Number DE-SC0024333. Nathan S. Nichols was supported by the Office of Science, U.S. Department of Energy, under contract DE-AC02-06CH11357.

Figures

Figure 1: Sturture of the $2\times 1\times 5$ MCM-41 pore

Figure 2: Adsorption uptake of argon inside MCM-41 pore as a function of relative pressure $P/P_{0}$

Figure 3: Radial density profiles for selected chemical potentials

Figure 4: Ar-Ar pair correlation function of monolayer

Figure 5: Debye powder averaged structure factor of Ar-Ar + Ar-MCM

Figure 6: Top view of Ar inside MCM-41 at $T=90$ K and $T=4$ K

Figure 7: Helium confinement potential heatmap

Figure 8: Cylindrically averaged helium confinement potential

Figure 9: Screeing factor

Figure 10: Z disorder

Figure 11: Disorder Analysis

Figure 12: GP comparisons

Figure 13: First and second Ar layer occupancies

Figure 14: First order derivatives of the first and second Ar layer occupancies