Understanding the role played by moisture in CO2 sorption is key for designing the next generation of solid sorbents such as metal–organic frameworks, which can be used for carbon capture and conversion as well as for molecular sieving, energy storage, etc. The abundance of water in nature and industrial processes, including in anthropogenic sources of CO2 has been shown to significantly affect commercial adsorbent performances, including their uptake capacity and selectivity. However, less is known about the role of humidity on CO2 diffusion, even though it is crucial for economically viable rapid capture processes. In this work, we have used atomistic simulations and experiments to gain insight into the effect of humidity on CO2 adsorption, diffusion and transport properties in UiO-66(Zr), here described as a flexible structure. We show that depending on the water concentration adsorbed in the host nanoporosity, the CO2 adsorption can be enhanced or reduced depending on thermodynamic conditions. At low water loading, isolated molecules interact with low-energy sites of the sorbent. At higher loading, nucleation drives water cluster formation, followed by cluster percolation resulting in a sub-nanoporous adsorbing media decreasing the overall CO2 diffusion compared to the dry structures. We finally show that equilibrium parameters such as self-diffusion coefficients and isotherms can be used to describe the CO2 transport in dry and humid structures through the nano-Darcy equation.
The in situ paradigm proposes to co-locate simulation and analytics on the same compute node to analyze data while still resident in the compute node memory, hence reducing the need for postprocessing methods. A standard approach that proved efficient for sharing resources on each node consists in running the analytics processes on a set of dedicated cores, called helper cores, to isolate them from the simulation processes. Simulation and analytics thus run concurrently with limited interference. In this paper we show that the performance can be improved through a dynamic helper core strategy. We rely on a work stealing scheduler to implement TINS, a task-based in situ framework with an on-demand analytics isolation. The helper cores are dedicated to analytics only when analytics tasks are available. Otherwise the helper cores join the other cores for processing simulation tasks. TINS relies on the Intel R TBB library. Experiments on up to 14,336 cores run a set of representative analytics parallelized with TBB coupled with the hybrid MPI+TBB ExaStamp molecular dynamics code. TINS shows up to 40% performance improvement over various other approaches including the standard helper core.
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