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ref: -0 tags: rogers thermal oxide barrier neural implants ECoG coating accelerated lifetime test date: 12-28-2017 02:29 gmt revision:0 [head]

PMID-27791052 Ultrathin, transferred layers of thermally grown silicon dioxide as biofluid barriers for biointegrated flexible electronic systems

  • Thermal oxide proved the superior -- by far -- water barrier for encapsulation.
    • What about the edges?
  • Many of the polymer barrier layers look like inward-rectifiers:
  • Extensive simulations showing that the failure mode is from gradual dissolution of the SiO2 -> Si(OH)4.
    • Even then a 100nm layer is expected to last years.
    • Perhaps the same principle could be applied with barrier metals. Anodization or thermal oxidation to create a thick, nonporous passivation layer.
    • Should be possible with Al, Ta...

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ref: -1977 tags: polyethylene surface treatment plasma electron irradiation mechanical testing saline seawater accelerated lifetime date: 04-15-2017 06:06 gmt revision:0 [head]

Enhancement of resistance of polyethylene to seawater-promoted degradation by surface modification

  • Polyethylene, when repeatedly stressed and exposed to seawater (e.g. ships' ropes), undergoes mechanical and chemical degradation.
  • Surface treatments of the polyethlyene can improve resistance to this degradation.
  • The author studied two methods of surface treatment:
    • Plasma (glow discharge, air) followed by diacid (adipic acid) or triisocyanate (DM100, = ?) co-polymerization
    • Electron irradiation with 500 kEV electrons.
  • Also mention CASING (crosslinking by activated species of inert gasses) as a popular method of surface treatment.
    • Diffuse-in crosslinkers is a third, popular these days ...
    • Others diffuse in at temperature e.g. a fatty acid - derived molecule, which is then bonded to e.g. heparin to reduce the thrombogenicity of a plastic.
  • Measured surface modifications via ATR IR (attenuated total reflectance, IR) and ESCA (aka XPS)
    • Expected results, carbonyl following the air glow discharge ...
  • Results:
    • Triisocyanate, ~ 6x improvement
    • diacid, ~ 50 x improvement.
    • electron irradiation, no apparent degradation!
      • Author's opinion that this is due to carbon-carbon crosslink leading to mechanical toughening (hmm, evidence?)
  • Quote: since the PE formulation studied here was low-weight, it was expected to lose crystallinity upon cyclic flexing; high density PE's have in fact been observed to become more crystalline with working.
    • Very interesting, kinda like copper. This could definitely be put to good use.
  • Low density polyethylene has greater chain branching and entanglement than high-density resins; when stressed the crystallites are diminished in total bulk, degrading tensile properties ... for high-density resins, mechanical working loosens up the structure enough to allow new crystallization to exceed stress-induced shrinkage of crystallites; hence, the crystallinity increases.

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ref: -0 tags: parylene plasma ALD insulation long-term saline PBS testing date: 04-02-2014 21:32 gmt revision:0 [head]

PMID-23024377 Plasma-assisted atomic layer deposition of Al(2)O(3) and parylene C bi-layer encapsulation for chronic implantable electronics.

  • This report presents an encapsulation scheme that combines Al(2)O(3) by atomic layer deposition with parylene C.
  • Al2O3 layer deposited using PAALD process-500 cycles of TMA + O2 gas.
  • Alumina and parylene coating lasted at least 3 times longer than parylene coated samples tested at 80 °C
    • That's it?
  • The consistency of leakage current suggests that no obvious corrosion was occurring to the Al2O3 film. The extremely low leakage current (≤20 pA) was excellent for IDEs after roughly three years of equivalent soaking time at 37 °C.
    • Still, they warn that it may not work as well for in-vivo devices, which are subject to tethering forces and micromotion.

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ref: work-2999 tags: autocorrelation poisson process test neural data ISI synchrony DBS date: 02-16-2012 17:53 gmt revision:5 [4] [3] [2] [1] [0] [head]

I recently wrote a matlab script to measure & plot the autocorrelation of a spike train; to test it, I generated a series of timestamps from a homogeneous Poisson process:

function [x, isi]= homopoisson(length, rate)
% function [x, isi]= homopoisson(length, rate)
% generate an instance of a poisson point process, unbinned.
% length in seconds, rate in spikes/sec. 
% x is the timestamps, isi is the intervals between them.

num = length * rate * 3; 
isi = -(1/rate).*log(1-rand(num, 1)); 
x = cumsum(isi); 
%%find the x that is greater than length. 
index = find(x > length); 
x = x(1:index(1,1)-1, 1); 
isi = isi(1:index(1,1)-1, 1); 

The autocorrelation of a Poisson process is, as it should be, flat:

Above:

  • Red lines are the autocorrelations estimated from shuffled timestamps (e.g. measure the ISIs - interspike intervals - shuffle these, and take the cumsum to generate a new series of timestamps). Hence, red lines are a type of control.
  • Blue lines are the autocorrelations estimated from segments of the full timestamp series. They are used to how stable the autocorrelation is over the recording
  • Black line is the actual autocorrelation estimated from the full timestamp series.

The problem with my recordings is that there is generally high long-range correlation, correlation which is destroyed by shuffling.

Above is a plot of 1/isi for a noise channel with very high mean 'firing rate' (> 100Hz) in blue. Behind it, in red, is 1/shuffled isi. Noise and changes in the experimental setup (bad!) make the channel very non-stationary.

Above is the autocorrelation plotted in the same way as figure 1. Normally, the firing rate is binned at 100Hz and high-pass filtered at 0.005hz so that long-range correlation is removed, but I turned this off for the plot. Note that the suffled data has a number of different offsets, primarily due to differing long-range correlations / nonstationarities.

Same plot as figure 3, with highpass filtering turned on. Shuffled data still has far more local correlation - why?

The answer seems to be in the relation between individual isis. Shuffling isi order obviuosly does not destroy the distribution of isi, but it does destroy the ordering or pair-wise correlation between isi(n) and isi(n+1). To check this, I plotted these two distributions:

-- Original log(isi(n)) vs. log(isi(n+1)

-- Shuffled log(isi_shuf(n)) vs. log(isi_shuf(n+1)

-- Close-up of log(isi(n)) vs. log(isi(n+1) using alpha-blending for a channel that seems heavily corrupted with electro-cauterizer noise.

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ref: test-0 tags: test date: 03-26-2007 04:24 gmt revision:1 [0] [head]

{figure 1}

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{figure 2}
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