Synthesis and characterization of metal-chalcogenide MQ 2-Nanoparticles (M = Mo, W, Zr; Q = S, O)

Here, we present the adaptation and optimization of (i) the solvothermal and (ii) the metal-organic chemical vapor deposition (MOCVD) approach as simple methods for the high-yield synthesis of MQ2 (M=Mo, W, Zr; Q = O, S) nanoparticles. Extensive characterization was carried out using X-ray diffraction (XRD), scanning and transmission electron micros¬copy (SEM/TEM) combined with energy dispersive X-ray analysis (EDXA), Raman spectroscopy, thermal analyses (DTA/TG), small angle X-ray scattering (SAXS) and BET measurements. After a general introduction to the state of the art, a simple route to nanostructured MoS2 based on the decomposition of the cluster-based precursor (NH4)2Mo3S13∙xH2O under solvothermal conditions (toluene, 653 K) is presented. Solvothermal decomposition results in nanostructured material that is distinct from the material obtained by decomposition of the same precursor in sealed quartz tubes at the same temperature. When carried out in the presence of the surfactant cetyltrimethyl¬ammonium bromide (CTAB), the decomposition product exhibits highly disordered MoS2 lamellae with high surface areas. The synthesis of WS2 onion-like nanoparticles by means of a single-step MOCVD process is discussed. Furthermore, the results of the successful transfer of the two-step MO¬CVD based synthesis of MoQ2 nanoparticles (Q = S, Se), comprising the formation of amorphous precursor particles and followed by the formation of fullerene-like particles in a subsequent annealing step to the W-S system, are presented. Based on a study of the temperature dependence of the reactions a set of conditions for the formation of onion-like structures in a one-step reaction could be derived. The MOCVD approach allows a selective synthesis of open and filled fullerene-like chalcogenide nanoparticles. An in situ heating stage transmission electron microscopy (TEM) study was employed to comparatively investigate the growth mechanism of MoS2 and WS2 nanoparticles obtained from MOCVD upon annealing. Round, mainly amorphous particles in the pristine sample trans¬form to hollow onion-like particles upon annealing. A significant difference between both compounds could be demonstrated in their crystallization conduct. Finally, the results of the in situ hea¬ting experiments are compared to those obtained from an ex situ annealing process under Ar. Eventually, a low temperature synthesis of monodisperse ZrO2 nanoparticles with diameters of ~ 8 nm is introduced. Whereas the solvent could be omitted, the synthesis in an autoclave is crucial for gaining nano-sized (n) ZrO2 by thermal decomposition of Zr(C2O4)2. The n-ZrO2 particles exhibits high specific surface areas (up to 385 m2/g) which make them promising candidates as catalysts and catalyst supports. Co-existence of m- and t-ZrO2 nano-particles of 6-9 nm in diameter, i.e. above the critical particle size of 6 nm, demonstrates that the particle size is not the only factor for stabilization of the t-ZrO2 modification at room temperature. In conclusion, synthesis within an autoclave (with and without solvent) and the MOCVD process could be successfully adapted to the synthesis of MoS2, WS2 and ZrO2 nanoparticles. A comparative in situ heating stage TEM study elucidated the growth mechanism of MoS2 and WS2 fullerene-like particles. As the general processes are similar, a transfer of this synthesis approach to other layered transition metal chalcogenide systems is to be expected. Application of the obtained nanomaterials as lubricants (MoS2, WS2) or as dental filling materials (ZrO2) is currently under investigation.

Visualization and validation of (Q)SAR models

Analyzing and modeling relationships between the structure of chemical compounds, their physico-chemical properties, and biological or toxic effects in chemical datasets is a challenging task for scientific researchers in the field of cheminformatics. Therefore, (Q)SAR model validation is essential to ensure future model predictivity on unseen compounds. Proper validation is also one of the requirements of regulatory authorities in order to approve its use in real-world scenarios as an alternative testing method. However, at the same time, the question of how to validate a (Q)SAR model is still under discussion. In this work, we empirically compare a k-fold cross-validation with external test set validation. The introduced workflow allows to apply the built and validated models to large amounts of unseen data, and to compare the performance of the different validation approaches. Our experimental results indicate that cross-validation produces (Q)SAR models with higher predictivity than external test set validation and reduces the variance of the results. Statistical validation is important to evaluate the performance of (Q)SAR models, but does not support the user in better understanding the properties of the model or the underlying correlations. We present the 3D molecular viewer CheS-Mapper (Chemical Space Mapper) that arranges compounds in 3D space, such that their spatial proximity reflects their similarity. The user can indirectly determine similarity, by selecting which features to employ in the process. The tool can use and calculate different kinds of features, like structural fragments as well as quantitative chemical descriptors. Comprehensive functionalities including clustering, alignment of compounds according to their 3D structure, and feature highlighting aid the chemist to better understand patterns and regularities and relate the observations to established scientific knowledge. Even though visualization tools for analyzing (Q)SAR information in small molecule datasets exist, integrated visualization methods that allows for the investigation of model validation results are still lacking. We propose visual validation, as an approach for the graphical inspection of (Q)SAR model validation results. New functionalities in CheS-Mapper 2.0 facilitate the analysis of (Q)SAR information and allow the visual validation of (Q)SAR models. The tool enables the comparison of model predictions to the actual activity in feature space. Our approach reveals if the endpoint is modeled too specific or too generic and highlights common properties of misclassified compounds. Moreover, the researcher can use CheS-Mapper to inspect how the (Q)SAR model predicts activity cliffs. The CheS-Mapper software is freely available at http://ches-mapper.org.