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ref: -0 tags: phosphorescence fluorescence magnetic imaging slicing adam cohen date: 05-29-2019 19:41 gmt revision:8 [7] [6] [5] [4] [3] [2] [head]

A friend postulated using the triplet state phosphorescence as a magnetically-modulatable dye. E.g. magnetically slice a scattering biological sample, rather than slicing optically (light sheet, 2p) or mechanically. After a little digging:

I'd imagine that it should be possible to design a molecule -- a protein cage, perhaps a (fully unsaturated) terpine -- which isolates the excited state from oxygen quenching.

Adam Cohen at Harvard has been working a bit on this very idea, albeit with fluorescence not phosphorescence --

  • Optical imaging through scattering media via magnetically modulated fluorescence (2010)
    • The two species, pyrene and dimethylaniline are in solution.
    • Dimethylaniline absorbs photons and transfers an electron to pyrene to produce a singlet radical pair.
    • The magnetic field represses conversion of this singlet into a triplet; when two singlet electrons combine, they produce exciplex fluorescence.
  • Addition of an aliphatic-ether 12-O-2 linker improves things significantly --
  • Mapping Nanomagnetic Fields Using a Radical Pair Reaction (2011)
  • Which can be used with a 2p microscope:
  • Two-photon imaging of a magneto-fluorescent indicator for 3D optical magnetometry (2015)
    • Notably, use decay kinetics of the excited state to yield measurements that are insensitive to photobleaching, indicator concentration, or local variations in optical excitation or collection efficiency. (As opposed to ΔF/F\Delta F / F )
    • Used phenanthrene (3 aromatic rings, not 4 in pyrene) as the excited electron acceptor, dimethylaniline again as the photo-electron generator.
    • Clear description:
      • A molecule with a singlet ground state absorbs a photon.
      • The photon drives electron transfer from a donor moiety to an acceptor moiety (either inter or intra molecular).
      • The electrons [ground state and excited state, donor] become sufficiently separated so that their spins do not interact, yet initially they preserve the spin coherence arising from their starting singlet state.
      • Each electron experiences a distinct set of hyperfine couplings to it's surrounding protons (?) leading to a gradual loss of coherence and intersystem crossing (ISC) into a triplet state.
      • An external magnetic field can lock the precession of both electrons to the field axis, partially preserving coherence and supressing ISC.
      • In some chemical systems, the triplet state is non-fluorescence, whereas the singlet pair can recombine and emit a photon.
      • Magnetochemical effects are remarkable because they arise at a magnetic field strengths comparable to hyperfine energy (typically 1-10mT).
        • Compare this to the Zeeman effect, where overt splitting is at 0.1T.
    • phenylanthrene-dimethylaniline was dissolved in dimethylformamide (DMF). The solution was carefully degassed in nitrogen to prevent molecular oxygen quenching.

Yet! Magnetic field effects do exist in solution:

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ref: work-0 tags: Cohen Singer SLIPPER machine learning hypothesis generation date: 10-25-2009 18:42 gmt revision:2 [1] [0] [head]

http://www.cs.cmu.edu/~wcohen/slipper/

  • "One disadvantage of boosting is that improvements in accuracy are often obtained at the expense of comprehensibility.
  • SLIPPER = simple learner with iterative pruning to produce error reduction.
  • Inner loop: the weak lerner splits the training data, grows a single rule using one subset of the data, and then prunes the rule using the other subset.
  • They use a confidence-rated prediction based boosting algorithm, which allows the algorithm to abstain from examples not covered by the rule.
    • the sign of h(x) - the weak learner's hyposthesis - is interpreted as the predited label and the magnitude |h(x)| is the confidence in the prediction.
  • SLIPPER only handles two-class problems now, but can be extended..
  • Is better than, though not dramatically so, than c5rules (a commercial version of Quinlan's decision tree algorithms).
  • see also the excellent overview at http://www.cs.princeton.edu/~schapire/uncompress-papers.cgi/msri.ps