Abstract:
Hierarchical (micro, meso & macro) porosity in materials plays a crucial role in influencing the movement of ions which governs the energy and power density during energy storage and conversion. The extant available methods to characterise porosity across scales (nano to meso to macro) lacks rigour and accuracy. Having accurate assessment of the porosity in materials can unlock new designs of electrodes for energy efficient energy storage and conversion devices such as batteries, supercapacitors and fuel cells. Through this work, we report the systematic development of a method to fully characterise the carbon porous networks using a molecular dynamics simulation testbed. Our work entails modelling and simulation of porous carbon structures using quenched molecular dynamics (QMD) simulations using Gaussian Approximation potential (GAP) and benchmarking the results with prior literature. This modelling technique can reliably be used for quantitative characterisation of the interconnectivity in porous structures to study ionic movements and charge transfer mechanisms. A new parameter, namely nearest neighbour search (NNS) coefficient was introduced to quantify homogeneity and networking in the porous structures. NNS coefficient increased from 1.62 to 1.92 with decrease of the annealing temperature from 8000 K to 4000 K in carbon. The procedure outlined was although tested on porous carbon networks, but adaptable to study any other material system at multi-length scales.