The Effect of Pressure on the Sieving Coefficient of a 20nm membrane with 10nm gold

It was speculated that the rate of diffusion through a charged membrane had a direct relationship with pressure – as pressure increases, the kinetic energy of the charged particles is more likely to overcome the electrostatic repulsion of the membrane, until at a high enough pressure the repulsive force is small enough to be ignored.

To investigate this hypothesis, I measured light scattering of the filtrate of 20nm membranes with a 10nm gold particle solution at 6psi, 8psi, and 10psi. I intended to include .5psi and 2 psi in that, but for some reason any pressure less than 6psi failed to move any water through the membrane at all – even when I had wet both sides.

Results:

6psi
time (m) absorbance % of original gold
5 0.03395 8.101385 Average:
10 0.033475 6.1746425 8.6895485
15 0.034625 10.8393875
20 0.0345 10.33235 Sieving Coefficient (S/So)
25 0.033925 7.9999775 0.08
8psi
3 0.042075 41.0588225 Average:
6 0.03865 27.165995 30.1068125
9 0.0375 22.50125
12 0.04155 38.929265 Sieving Coefficient (S/So)
15 0.0371 20.87873 0.3
10psi
3 0.073175 167.2097525 * Some of the filtrate must have evaporated out overnight
6 0.047325 62.3543975
9 0.043175 45.5207525 Average:
12 0.046125 57.4868375 77.8089005
15 0.045875 56.4727625
Average excluding 3min:
55.4586875
Sieving Coefficient (S/So)
0.55

The relationship is clearly direct, as we expected, but to fully characterize what is likely a logistic curve I’ll need to take a few more data points at higher pressures. The Pressure V. Sieving coefficient is plotted below, with the dotted line representing the logistical curve we are looking for:

These values, or at least the sieving coefficient of 10psi, agree with values in Tom’s ACS nano paper – he reported for filters with apparent cutoffs greater than 10nm, 10nm gold particles under 10psi of pressure pass through with a sieving coefficient of ~.55 (High-Performance Separation of Nanoparticles with Ultrathin Porous Nanocrystalline Silicon Membranes Thomas R. Gaborski, Jessica L. Snyder, Christopher C. Striemer, David Z. Fang, Michael Hoffman, Philippe M. Fauchet, and James L. McGrath ACS Nano 2010 4 (11), 6973-6981)

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One Comment

  1. Great. Can you create a plot of your data like the one we discussed in the office?

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