2011
DOI: 10.1039/c1cp20184k
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Quantum fluctuations increase the self-diffusive motion of para-hydrogen in narrow carbon nanotubes

Abstract: Quantum fluctuations significantly increase the self-diffusive motion of para-hydrogen adsorbed in narrow carbon nanotubes at 30 K comparing to its classical counterpart. Rigorous Feynman's path integral calculations reveal that self-diffusive motion of para-hydrogen in a narrow (6,6) carbon nanotube at 30 K and pore densities below ∼29 mmol cm(-3) is one order of magnitude faster than the classical counterpart. We find that the zero-point energy and tunneling significantly smoothed out the free energy landsca… Show more

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Cited by 4 publications
(8 citation statements)
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“…Therefore, in this kinetic isotope QS, the aperture size plays an important role for determining the diffusion kinetics and thereby overall separation. This has initiated an ongoing effort to elucidate this phenomenon and to find more effective ultramicroporous materials …”
Section: Introductionmentioning
confidence: 99%
“…Therefore, in this kinetic isotope QS, the aperture size plays an important role for determining the diffusion kinetics and thereby overall separation. This has initiated an ongoing effort to elucidate this phenomenon and to find more effective ultramicroporous materials …”
Section: Introductionmentioning
confidence: 99%
“…10,11 As in all adsorption separation processes, the essential requirement is a porous material (called adsorbent) that preferentially adsorbs one component (or one family of related components) from a mixed feed. [12][13][14][15][16][17][18][19][20] This selectivity may depend on a difference in adsorption equilibrium (i.e., equilibrium selectivity) or on a difference in sorption rates (i.e., kinetic selectivity). 14,19 As pointed out by Ruthven et al, 21 the majority of pressure swing adsorption (PSA) processes are ''equilibrium driven'' in the sense that the selectivity depends on differences in the equilibrium affinities.…”
Section: Introductionmentioning
confidence: 99%
“…We computed imaginary-time correlation function from PIMC simulations in the canonical ensemble, , G v ( normalτ j ) = normalδ j 1 1 3 m normalε 1 N normalε 2 normalα = 1 N d boldr 1 · · · d boldr P P false( r 1 , ... , r P false) × false( r α j r α j - 1 false) · ( boldr normalα 2 boldr normalα 1 ) where δ is the Kronecker delta function, ε = β/ P , N is the total number of particles, P denotes the number of beads, r j is a shorthand notation for the position vectors of all particles assisted with bead j , r α j is the position vector of liquid particle α of bead j and P ( r 1 ,..., r P ) is the regular sampling function used in standard cyclic PIMC method (with r 0 = r P ). , In our PIMC, imaginary-time correlation function was collected every 10 configurations. According to our previous works, , we inverted ill-posed integral equation given by eq…”
Section: Simulation Detailsmentioning
confidence: 99%
“…To the best of our knowledge, only Feynman–Hibbs (FH) effective potentials have been used to study the dynamic properties of quantum fluids confined in nanopores at cryogenic temperatures. These semiclassical methods are very easy to implement in conventional Newtonian molecular dynamics codes because they use the concept of the molecular trajectory in a phase-space . However, there are no definite positions of quantum particles according to fundamental Heisenberg’s uncertainty principle .…”
Section: Introductionmentioning
confidence: 99%