Vapor sensing via light reflected from photonic crystals has been increasingly studied as a means to rapidly identify analytes, though few studies have characterized vapor mixtures or chemical warfare agent simulants via this technique. In this work, light reflected from the natural photonic crystals found within the wing scales of the Morpho didius butterfly was analyzed after exposure to binary and tertiary mixtures containing dimethyl methylphosphonate, a nerve agent simulant, and dichloropentane, a mustard gas simulant. Distinguishable spectra were generated with concentrations tested as low as 30 ppm and 60 ppm for dimethyl methylphosphonate and dichloropentane, respectively. Individual vapors, as well as mixtures, yielded unique responses over a range of concentrations, though the response of binary and tertiary mixtures was not always found to be additive. Thus, while selective and sensitive to vapor mixtures containing chemical warfare agent simulants, this technique presents challenges to identifying these simulants at a sensitivity level appropriate for their toxicity.
Uncovering links between processing conditions, microstructure, and properties is a central tenet of materials analysis. It is well known that microstructure determines properties, but expressing these structural features in a universal quantitative fashion has proved to be extremely difficult.Recent efforts have focused on training supervised learning algorithms to place microstructure images into predefined classes, but this approach assumes a level of a priori knowledge that may not always be available. This work expands this idea to the semi-supervised context in which class labels are known with confidence for only a fraction of the microstructures that represent the material system. It is shown that classifiers which perform well on both the high-confidence labeled data and the unlabeled, ambiguous data can be constructed by relying on the labeling consensus of a collection of semi-supervised learning methods. We also demonstrate the use of novel error estimation approaches for unlabeled data to establish robust confidence bounds on the classification performance over the entire microstructure space.
Uncovering links between processing conditions, microstructure, and properties is a central tenet of materials analysis. It is well known that microstructure determines properties, but expressing these structural features in a universal quantitative fashion has proved to be extremely difficult. Recent efforts have focused on training supervised learning algorithms to place microstructure images into predefined classes, but this approach assumes a level of a priori knowledge that may not always be available. This work expands this idea to the semi-supervised context in which class labels are known with confidence for only a fraction of the microstructures that represent the material system. It is shown that classifiers which perform well on both the high-confidence labeled data and the unlabeled, ambiguous data can be constructed by relying on the labeling consensus of a collection of semi-supervised learning methods. We also demonstrate the use of novel error estimation approaches for unlabeled data to establish robust confidence bounds on the classification performance over the entire microstructure space.
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