Reduced strontium barium niobate, SrxBa1 − xNb2O6 (SBN), is a potential candidate for oxide thermoelectrics. In order to understand the effects of oxygen reduction on the structure and properties of SBN, room temperature Raman spectra of reduced and unreduced crystals with composition x  =  0.61 (SBN61) have been measured, fitted, and compared, for incident light polarized successively along the crystallographic a‐ and c‐axes. Unexpectedly, the low wavenumber spectra (<200 cm−1) of reduced SBN are found to display much better resolved and intense peaks than those of unreduced SBN, suggestive of a more ordered and compact lattice arrangement in reduced SBN. Shape changes of certain peaks and the disappearance or appearance of other peaks (30 cm−1/1000 cm−1) are also observed as a result of reduction. Comparison of the experimental spectra and fits of the unreduced and reduced crystals are suggestive of a redistribution of the remaining oxygen anions, structural rearrangements, and the development of supergrowth or intergrowth structures in reduced SBN. The reduced spectra are found to be very similar to the published spectra of other complex oxides such as the H‐form of niobium oxide, H‐Nb2O5, or TiNb2O7, which are made up of a regular layered arrangement of blocks of corner‐sharing and edge‐sharing oxygen octahedra. These structural changes may play an important role in the enhanced electrical conductivity of reduced SBN perpendicular to the layers.

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A machine learning based approach has been developed to classify Raman spectroscopic data. The algorithm is based on a one dimensional neural network (1D-CNN) architecture which is trained with synthetic data that can incorporate sensor specific characteristics such as spectral range, spectral resolution and noise. The synthetic spectra are based on high SNR measurements which are then augmented by mixing target and background signatures. The CNN is trained to consider target representations in the presence of certain background materials including glass and HDPE. These additional target representations allow the CNN to make detections for materials taken through a container.Within this paper the performance of CNNs trained for Raman sensor systems has been evaluated using real data collected using the ThermoFisher FirstDefender. The evaluation data consists of various target chemicals (including explosives) and interferents (including household materials) collected through glass and plastic vials. The data was acquired with a controlled range of collection settings, including integration time and laser power, available on the unit. The performance of the 1D-CNN approach has demonstrated high classification accuracies, high probability of detection and low false alarm rates. Specifically, these metrics have been calculated as a function of signal to noise ratio. Additionally, a sensitivity analysis was conducted using an acetonitrile standard diluted in water which demonstrates the CNN's capability of detecting all dilutions of acetonitrile down to weight concentrations of <1%. This sensitivity analysis was mirrored using a mixture of potassium chlorate and Vaseline. The CNN demonstrated detections down to 10% by weight of potassium chlorate.