Self-diffusion of Aliphatic Alcohols

Partington, J. R.; Hudson, R. F.; Bagnall, Kenneth W.

doi:10.1038/169583a0

Cited by 56 publications

(31 citation statements)

References 3 publications

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“…The latter has small mass effects and therefore, the tracer method should show a small deviation [57]. Although there are many measurements of the diffusion coefficient of benzene from [22][23][24][25][26][27][28][29][30], we measured the self-diffusion coefficient of benzene using water as a reference. Our result showed that the Mills et al data [29] is the most reliable since the value the authors report is within the standard uncertainty.…”

Section: Solventmentioning

confidence: 99%

“…In the past a significant number of the published diffusion coefficients for solvent molecules were obtained using the diaphragm and capillary-cell protocols [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39], and PFG-NMR [40][41][42][43][44][45][46][47]. However, the published values exhibit scatter and are not comparable because the first method requires tracer isotope molecules.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Assessment of diffusion coefficients of general solvents by PFG-NMR: Investigation of the sources error

Kato

Saito

Nabeshima

et al. 2006

Journal of Magnetic Resonance

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Section: Solventmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Assessment of diffusion coefficients of general solvents by PFG-NMR: Investigation of the sources error

Kato

Saito

Nabeshima

et al. 2006

Journal of Magnetic Resonance

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“…As mentioned before, in this work we have mapped three water molecules to a DPD bead. As a water molecule has a volume [57] of approximately 30Å 3 , our assumption that a bead represents three water molecules results in a DPD bead volume of ∼90Å 3 . In our case, we have chosen a fluid bead density of r 3 c = 3, therefore the volume corresponding to a cutoff radius is r 3 c = 3 × 90 Å 3 = 270 Å 3 or r c ∼ = 6.5 Å.…”

Section: Mapping Of Length-and Time Scalesmentioning

confidence: 99%

“…Following Groot and Rabone [53], the diffusion constant of a DPD bead is D bead = 0.1707(r 2 c / ), where is the timescale. The experimental diffusion constant of ambient water is D w = 2.43 × 10 −5 cm 2 /s [57]. Therefore, for three water molecules in each bead, …”

Section: Mapping Of Length-and Time Scalesmentioning

confidence: 99%

Adsorption behavior of model proteins on surfaces

Patterson

Lı́sal

Colina

2011

Fluid Phase Equilibria

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“…Equating this to the experimental diffusion coefficient of water [14] D water = (2.43 ± 0.01)×10 −5 cm 2 /s, leads to the time scale…”

Section: Parameterisationmentioning

confidence: 99%

Applications of Dissipative Particle Dynamics

Groot

2004

Novel Methods in Soft Matter Simulations

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Abstract. Dissipative Particle Dynamics (DPD) is one of the most promising simulation techniques for studies of mesoscopic properties of soft matter systems. Here, we discuss DPD, its parameterisation in simple systems, as well as in polymeric systems using the Flory-Huggins theory, and generalisations of DPD. Block copolymer mesophase separation, polymers and membranes in surfactant solutions, and biomembrane morphology and rupture will shown as specific examples. Why Mesoscopic Simulation?Over the last two decades most simulation studies have concentrated on the motion of individual atoms in systems of a few nanometers and a few nanoseconds. Other simulation methods concentrate exclusively on the macroscopic world of planes, trains and automobiles. However, between the nano-and macroscopic scale ranges some forty decades in volume and time. The holy grail of theoretical physics is to bridge this gap. This is due to the fact that in many cases simulation of this intermediate regime is essential for understanding macroscopic phenomena, e.g. molecules ordering spontaneously on mesoscopic length and time scales. This category of problems includes life and biological phenomena such as membrane structuring, perforation and trafficking. As a matter of fact, this list contains all soft condensed matter including surfactants, polymers and (multi)block copolymers that show microphase separation, or form gels or glassy systems, see Fig. 1.What could we expect if we would be able to extend the time scale over which we can simulate a physical system? If we take the example of lipid bilayers, we find that new phenomena occur every time we increase the time scale at which we look at our system [1]. On the shortest time scale of a few picoseconds the lipids show bond and angle fluctuations of dihedral angles within the same molecule. On larger time scales of a few tens of picoseconds, trans-gauche isomerizations of dihedrals occur [2]. On a time scale of a few nanoseconds the phospholipids rotate around their axis, and on the timescale of tens of nanoseconds two lipids switch place within a bilayer, giving rise to lateral diffusion. Within this time scale the individual lipids orient, and lipid membranes show protrusions [3]. Finally, on a time-scale of 100 ns peristaltic motions and undulations occur [4]. By virtue of parallelization over several processors or PC clusters, hardware developments have now pushed the limit of molecular simulations to 100 ns [4]. Nevertheless, there is a limit beyond which hardware developments cannot help us. For instance, phenomena such as co-operative motion in phase transitions, insertion of large molecules

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Self-diffusion of Aliphatic Alcohols

Cited by 56 publications

References 3 publications

Assessment of diffusion coefficients of general solvents by PFG-NMR: Investigation of the sources error

Assessment of diffusion coefficients of general solvents by PFG-NMR: Investigation of the sources error

Adsorption behavior of model proteins on surfaces

Applications of Dissipative Particle Dynamics

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