Structure and Pore Size Distribution in Nanoporous Carbon

Yanzhou Wang,Tapio Ala-Nissila,Miguel A Caro,Zheyong Fan,Ping Qian

doi:10.1021/acs.chemmater.1c03279

Abstract

We study the structural and mechanical properties of nanoporous (NP) carbon materials by extensive atomistic machine-learning (ML) driven molecular dynamics (MD) simulations. To this end, we retrain a ML Gaussian approximation potential (GAP) for carbon by recalculating the a-C structural database of Deringer and Csányi adding van der Waals interactions. Our GAP enables a notable speedup and improves the accuracy of energy and force predictions. We use the GAP to thoroughly study the atomistic structure and pore-size distribution in computational NP carbon samples. These samples are generated by a melt-graphitization-quench MD procedure over a wide range of densities (from 0.5 to 1.7 g/cm3) with structures containing 131 072 atoms. Our results are in good agreement with experimental data for the available observables and provide a comprehensive account of structural (radial and angular distribution functions, motif and ring counts, X-ray diffraction patterns, pore characterization) and mechanical (elastic moduli and their evolution with density) properties. Our results show relatively narrow pore-size distributions, where the peak position and width of the distributions are dictated by the mass density of the materials. Our data allow further work on computational characterization of NP carbon materials, in particular for energy-storage applications, as well as suggest future experimental characterization of NP carbon-based materials.

Highlights

Nanoporous materials, where the pore-size distribution can be tuned according to growth conditions, can be engineered for specific applications, such as ionic and molecular transport,[1] biosensors,[2] air and water purification,[3] or energy storage.[4]
In this work we computationally examine the structure and pore-size distribution in NP carbons over a wide range of mass densities using state-of-the-art machine-learning-driven molecular dynamics (MLMD) simulations.[12]
The radial distribution function (RDF) converge to their respective average density of particles n (0.025, 0.045, 0.065, and 0.085 Å−3 for 0.5, 0.9, 1.3, and 1.7 g/cm[3], respectively) as the interatomic distance r tends to infinity

Summary

Introduction

Nanoporous materials, where the pore-size distribution can be tuned according to growth conditions, can be engineered for specific applications, such as ionic and molecular transport,[1] biosensors,[2] air and water purification,[3] or energy storage.[4] In particular, nanopores in disordered graphitic carbons arise due to the misalignment and local curvature of the graphene-like sheets that make up these materials. These pores are defined as any interstitial space between graphene planes larger than the typical interlayer spacing in graphite (≈ 3.5 Å).[4] When the pore sizes are of the order of a few nanometers, the graphitic carbons are referred to as “nanoporous” (NP) carbons. The nanopores can accommodate ions which can migrate through the material or intercalate/deintercalate upon application of an external electric field, a mechanism exploited, for instance, in Li-ion batteries.[4]

Methods

Results

Conclusion