Because
of the vital role of temperature in many biological processes
studied in microfluidic devices, there is a need to develop improved
temperature sensors and data analysis algorithms. The photoluminescence
(PL) of nanocrystals (quantum dots) has been successfully used in
microfluidic temperature devices, but the accuracy of the reconstructed
temperature has been limited to about 1 K over a temperature range
of tens of degrees. A machine learning algorithm consisting of a fully
connected network of seven layers with decreasing numbers of nodes
was developed and applied to a combination of normalized spectral
and time-resolved PL data of CdTe quantum dot emission in a microfluidic
device. The data used by the algorithm were collected over two temperature
ranges: 10–300 K and 298–319 K. The accuracy of each
neural network was assessed via a mean absolute error of a holdout
set of data. For the low-temperature regime, the accuracy was 7.7
K, or 0.4 K when the holdout set is restricted to temperatures above
100 K. For the high-temperature regime, the accuracy was 0.1 K. This
method provides demonstrates a potential machine learning approach
to accurately sense temperature in microfluidic (and potentially nanofluidic)
devices when the data analysis is based on normalized PL data when
it is stable over time.
Flare frequency distributions represent a key approach to addressing one of the largest problems in solar and stellar physics: determining the mechanism that counterintuitively heats coronae to temperatures that are orders of magnitude hotter than the corresponding photospheres. It is widely accepted that the magnetic field is responsible for the heating, but there are two competing mechanisms that could explain it: nanoflares or Alfvén waves. To date, neither can be directly observed. Nanoflares are, by definition, extremely small, but their aggregate energy release could represent a substantial heating mechanism, presuming they are sufficiently abundant. One way to test this presumption is via the flare frequency distribution, which describes how often flares of various energies occur. If the slope of the power law fitting the flare frequency distribution is above a critical threshold, α = 2 as established in prior literature, then there should be a sufficient abundance of nanoflares to explain coronal heating. We performed >600 case studies of solar flares, made possible by an unprecedented number of data analysts via three semesters of an undergraduate physics laboratory course. This allowed us to include two crucial, but nontrivial, analysis methods: preflare baseline subtraction and computation of the flare energy, which requires determining flare start and stop times. We aggregated the results of these analyses into a statistical study to determine that α = 1.63 ± 0.03. This is below the critical threshold, suggesting that Alfvén waves are an important driver of coronal heating.
We demonstrate beyond-octave-span laser wavelength conversion from a 1060 nm pump via optical parametric oscillation in tantala microresonators. Robust control of phase-matching at near-zero-GVD by use of a nanophotonic bandgap enables realization of target wavelengths.
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