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The analytical chemistry of phosphorus-containing materials is often impeded by the long measurement times and relatively large sample masses needed for 31 P NMR spectroscopy, by the scarcity and access limitations of synchrotron beamlines operating in the energy range of the P K-edge, by the challenges posed by species interconversion during liquid extraction, and by the considerable air-sensitivity typical of many phosphorus-containing materials and nanophases. To this end, we report the design and operation of a new laboratory-based spectrometer to simultaneously perform P Kα and Kβ X-ray emission spectroscopy (XES) while being housed in a research-grade controlled-atmosphere glovebox. Demonstration studies on nickel phosphide nanophases illustrate the importance of air-free XES and the value of simultaneous Kα and Kβ spectroscopy for identifying the P oxidation state and for investigating nanoscale influences on valence level electronic structures. 

We report a comprehensive computational study of unsupervised machine learning for extraction of chemically relevant information in X-ray absorption near edge structure (XANES) and in valence-to-core X-ray emission spectra (VtC-XES) for classification of a broad ensemble of sulphorganic molecules. By progressively decreasing the constraining assumptions of the unsupervised machine learning algorithm, moving from principal component analysis (PCA) to a variational autoencoder (VAE) to t-distributed stochastic neighbour embedding (t-SNE), we find improved sensitivity to steadily more refined chemical information. Surprisingly, when embedding the ensemble of spectra in merely two dimensions, t-SNE distinguishes not just oxidation state and general sulphur bonding environment but also the aromaticity of the bonding radical group with 87% accuracy as well as identifying even finer details in electronic structure within aromatic or aliphatic sub-classes. We find that the chemical information in XANES and VtC-XES is very similar in character and content, although they unexpectedly have different sensitivity within a given molecular class. We also discuss likely benefits from further effort with unsupervised machine learning and from the interplay between supervised and unsupervised machine learning for X-ray spectroscopies. Our overall results, i.e. , the ability to reliably classify without user bias and to discover unexpected chemical signatures for XANES and VtC-XES, likely generalize to other systems as well as to other one-dimensional chemical spectroscopies.