Molybdenum dithiocarbamates (MoDTCs) are a class of lubricant additives widely employed in automotives. Most of the studies concerning MoDTC take into account the dimeric structures because of their industrial relevance, with the mononuclear compounds usually neglected, because isolating and characterizing subgroups of MoDTC molecules are generally difficult. However, the byproducts of the synthesis of MoDTC can impact the friction reduction performance at metallic interfaces, and the effect of mononuclear MoDTC (mMoDTC) compounds in the lubrication has not been considered yet in the literature. In this study, we consider for the first time the impurities of MoDTC consisting of mononuclear compounds and combine experimental and computational techniques to elucidate the interaction of these impurities with binuclear MoDTC in commercial formulations. We present a preliminary strategy to separate a commercial MoDTC product in chemically different fractions. These fractions present different tribological behaviors depending on the relative amount of mononuclear and binuclear complexes. The calculations indicate that the dissociation mechanism of mMoDTC is similar to the one observed for the dimeric structures. However, the different chemical properties of mMoDTC impact the kinetics for the formation of the beneficial molybdenum disulfide (MoS 2 ) layers, as shown by the tribological experiments. These results help to understand the functionality of MoDTC lubricant additives, providing new insights into the complex synergy between the different chemical structures.

This paper addresses the problem of source detection in unknown chemical mixtures in the context of multimodal measurements of spectral data. The proposed approaches are based on supervised linear spectral unmixing under nonnegativity and sparsity constraints with adapted variants of the orthogonal matching pursuit algorithm. Two detection strategies are introduced: fusion of independent unimodal detection results and fusion by a joint multimodal decomposition and detection. Results are evaluated using a real database of ion mobility mass spectrometry (IMMS) data. A significant increase of the detection accuracy is obtained using the joint decomposition based decision as compared to the single modality detection results.

The aim of this paper is to present a method for source detection within unknown chemical mixtures using several measurement modalities. Contrary to the well studied case of single source detection, this approach enables simultaneous detection of multiple chemical components by exploiting the mixing coefficients resulting from supervised linear unmixing and thresholded non-negative least squares. The first contribution of this work is to propose an automated procedure to compute an optimized binary classifier rule for each component independently using a database of known mixtures. The second contribution is to propose a global decision rule based on the fusion of the multimodal decisions using weighting schemes such as those used in multiple classifier systems (MCS). A real database of Ion Mobiliy Mass Spectrometry (IMMS) data is used to evaluate the detection performance. An increase of the detection accuracy is reached using the multiple thresholds within the independent classifiers approach as compared to single modality detection.