m8ta
use https for features.
text: sort by
tags: modified
type: chronology
{436} is owned by tlh24.
{1489}
hide / / print
ref: -0 tags: surface plasmon resonance voltage sensing antennas PEDOT imaging spectroscopy date: 12-05-2019 16:47 gmt revision:1 [0] [head]

Electro-plasmonic nanoantenna: A nonfluorescent optical probe for ultrasensitive label-free detection of electrophysiological signals

  • Use spectroscopy to measure extracellular voltage, via plasmon concentrated electrochromic effects in doped PEDOT.

{1214}
hide / / print
ref: -0 tags: brain micromotion magnetic resonance imaging date: 01-28-2013 01:38 gmt revision:0 [head]

PMID-7972766 Brain and cerebrospinal fluid motion: real-time quantification with M-mode MR imaging.

  • Measured brain motion via a clever MR protocol. (beyond my present understanding...)
  • ventricles move at up to 1mm/sec
  • In the Valsava maneuver the brainstem can move 2-3mm.
  • Coughing causes upswing of the CSF.

{1082}
hide / / print
ref: -0 tags: feedback stability resonance butterworth matlab date: 01-22-2012 03:46 gmt revision:4 [3] [2] [1] [0] [head]

Just fer kicks, I tested what happens to low-order butterworth filters when you maladjust one of the feedback coefficients.

[B, A] = butter(2, 0.1);
[h, w] = freqz(B,A);
A(2) = A(2) * 0.9;
[h2, ~] = freqz(B,A);
hold off
subplot(1,2,1)
plot(w,abs(h))
hold on; plot(w,abs(h2), 'r')
title('10% change in one FB filter coef 2nd order butterworth')
xlabel('freq, rads / sec'); 
ylabel('filter response');

% do the same for a higher order filter. 
[B, A] = butter(3, 0.1);
[h, w] = freqz(B,A);
A(2) = A(2) * 0.9;
[h2, ~] = freqz(B,A);
subplot(1,2,2)
hold on
plot(w,abs(h), 'b')
plot(w,abs(h2), 'r')
title('10% change in one FB filter coef 3rd order butterworth')
xlabel('freq, rads / sec'); 
ylabel('filter response');

The filters show a resonant peak, even though feedback was reduced. Not surprising, really; a lot of systems will show reduced phase margin and will begin to oscillate when poles are moved. Does this mean that a given coefficient (anatomical area) is responsible for resonance? By itself, of course not; one can not extrapolate one effect from one manipulation in a feedback system, especially a higher-order feedback system.

This, of course hold in the mapping of digital (or analog) filters to pathology or anatomy. Pathology is likely reflective of how the loop is structured, not how one element functions (well, maybe).

For a paper, see {1083}

{814}
hide / / print
ref: Zhang-2009.02 tags: localized surface plasmon resonance nanoparticle neural recording innovative date: 01-15-2012 23:00 gmt revision:4 [3] [2] [1] [0] [head]

PMID-19199762[0] Optical Detection of Brain Cell Activity Using Plasmonic Gold Nanoparticles

  • Used 140 nm diameter, 40 nm thick gold disc nanoparticles set in a 400nm array, illuminated by 850nm diode laser light.
    • From my reading, it seems that the diameter of these nanoparticles is important, but the grid spacing is not.
  • These nanoparticles strongly scatter light, and the degree of scattering is dependent on the local index of refraction + electric field.
  • The change in scattering due to applied electric field is very small, though - ~ 3e-6 1/V in the air-capacitor setup, ~1e-3 in solution when stimluated by cultured hippocampal neurons.
  • Noteably, nanoparticles are not diffraction limited - their measurement resolution is proportional to their size. Compare with voltage-sensitive dyes, which have a similar measurement signal-to-noise ratio, are diffraction limited, may be toxic, and may photobleach.

____References____

[0] Zhang J, Atay T, Nurmikko AV, Optical detection of brain cell activity using plasmonic gold nanoparticles.Nano Lett 9:2, 519-24 (2009 Feb)