Parylene-C has been widely used as a biocompatible material for microfluidics and micro total analysis system (mTAS) applications in recent decades. However, its autofluorescence can be a major obstacle for parylene-C based devices used in applications requiring sensitive fluorescence detection. In this paper, Parylene-C was compared with other commonly used polymer and plastic materials in mTAS devices for their autofluorescence. We also report here an in-depth study of the behaviors and mechanisms of the autofluorescence of parylene-C, as well as several other commercialized members in the parylene family, including parylene-D, parylene-N and parylene-HT, using epifluorescence microscopy, fluorimeter and infrared spectroscopy. Strong autofluorescence was induced in parylene-C during short-wavelength excitation (i.e. UV excitation). Variation of autofluorescence intensity of parylene-C film was found to be related to both dehydrogenation and photo-oxidation. Moreover, the influence of microfabrication process on parylene-C autofluorescence was also evaluated. Parylene-HT, which exhibits low initial autofluorescence, decreasing autofluorescence behavior under UV excitation and higher UV stability, can be a promising alternative for mTAS applications with fluorescence detection.
We used a convolutional neural network to infer stellar rotation periods from a set of synthetic light curves simulated with realistic spot-evolution patterns. We convolved these simulated light curves with real TESS light curves containing minimal intrinsic astrophysical variability to allow the network to learn TESS systematics and estimate rotation periods despite them. In addition to periods, we predict uncertainties via heteroskedastic regression to estimate the credibility of the period predictions. In the most credible half of the test data, we recover 10% accurate periods for 46% of the targets, and 20% accurate periods for 69% of the targets. Using our trained network, we successfully recover periods of real stars with literature rotation measurements, even past the 13.7 day limit generally encountered by TESS rotation searches using conventional period-finding techniques. Our method also demonstrates resistance to half-period aliases. We present the neural network and simulated training data, and introduce the software butterpy used to synthesize the light curves using realistic starspot evolution.
The Sentinel-1 satellites equipped with synthetic aperture radars (SARs) provide near-global coverage of the world's oceans every six days. We curate a data set of collocations between SAR and altimeter satellites and investigate the use of deep learning to predict significant wave height from SAR. While previous models for predicting geophysical quantities from SAR rely heavily on feature-engineering, our approach learns directly from low-level image cross-spectra. Training on collocations from 2015 to 2017, we demonstrate on test data from 2018 that deep learning reduces the state-of-the-art root mean squared error by 50%, from 0.6 to 0.3 m when compared to altimeter data. Furthermore, we isolate the contributions of different features to the model performance.
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