RESUMEN
X-ray photoelectron spectroscopy is an indispensable technique for the quantitative determination of sample composition and electronic structure in diverse research fields. Quantitative analysis of the phases present in XP spectra is usually conducted manually by means of empirical peak fitting performed by trained spectroscopists. However, with recent advancements in the usability and reliability of XPS instruments, ever more (inexperienced) users are creating increasingly large data sets that are harder to analyze by hand. In order to aid users with the analysis of large XPS data sets, more automated, easy-to-use analysis techniques are needed. Here, we propose a supervised machine learning framework based on artificial convolutional neural networks. By training such networks on large numbers of artificially created XP spectra with known quantifications (i.e., for each spectrum, the concentration of each chemical species is known), we created universally applicable models for auto-quantification of transition-metal XPS data that are able to predict the sample composition from spectra within seconds. Upon evaluation against more traditional peak fitting methods, we showed that these neural networks achieve competitive quantification accuracy. The proposed framework is shown to be flexible enough to accommodate spectra containing multiple chemical elements and measured with different experimental parameters. The use of dropout variational inference for the determination of quantification uncertainty is illustrated.
Asunto(s)
Redes Neurales de la Computación , Rayos X , Reproducibilidad de los ResultadosRESUMEN
The title compound [Na3(C11H6NO3)2(C2H3O2)] n , crystallized through diffusion of diethyl ether into methanol as needles. There are three crystallographically independent Na(+) cations present, each exhib-it-ing a distorted octa-hedral coordination geometry, two through coordination by five O atoms and one N atom, and one through coordination by six O atoms. A series of inter-molecular Oâ¯Na and Nâ¯Na contacts leads to the formation of chains along the a-axis direction.