TY - JOUR A1 - Pellegrino, F. A1 - Sordello, F. A1 - Mino, L. A1 - Prozzi, M. A1 - Mansfeld, Ulrich A1 - Hodoroaba, Vasile-Dan A1 - Minero, C. T1 - Polyethylene Glycol as Shape and Size Controller for the Hydrothermal Synthesis of SrTiO3 Cubes and Polyhedra JF - Nanomaterials N2 - Understanding the correlation between the morphological and functional properties of particulate materials is crucial across all fields of physical and natural sciences. This manuscript reports on the investigation of the effect of polyethylene glycol (PEG) employed as a capping Agent in the synthesis of SrTiO3 crystals. The crucial influence of PEG on both the shape and size of the strontium titanate particles is revealed, highlighting the effect on the photocurrents measured under UV–Vis irradiation. KW - Polyethylene glycol KW - Strontium titanate KW - Controlled morphology KW - Photoelectrochemistry KW - Electron microscopy KW - EDS PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-512892 DO - https://doi.org/10.3390/nano10091892 VL - 10 IS - 9 SP - 1892 PB - MDPI CY - Basel, CH AN - OPUS4-51289 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Pellegrino, F. A1 - Isopescu, R. A1 - Pellutiè, L. A1 - Sordello, F. A1 - Rossi, A. M. A1 - Ortel, Erik A1 - Martra, G. A1 - Hodoroaba, Vasile-Dan A1 - Maurino, V. T1 - Machine learning approach for elucidating and predicting the role of synthesis parameters on the shape and size of TiO2 nanoparticles JF - Scientific Reports N2 - In the present work a series of design rules are developed in order to tune the morphology of TiO2 nanoparticles through hydrothermal process. Through a careful experimental design, the influence of relevant process parameters on the synthesis outcome are studied, reaching to the develop predictive models by using Machine Learning methods. The models, after the validation and training, are able to predict with high accuracy the synthesis outcome in terms of nanoparticle size, polydispersity and aspect ratio. Furthermore, they are implemented by reverse engineering approach to do the inverse process, i.e. obtain the optimal synthesis parameters given a specific product characteristic. For the first time, it is presented a synthesis method that allows continuous and precise control of NPs morphology with the possibility to tune the aspect ratio over a large range from 1.4 (perfect truncated bipyramids) to 6 (elongated nanoparticles) and the length from 20 to 140 nm. KW - Machine learning KW - Nanoparticles KW - Titanium dioxide KW - Size KW - Shape KW - Synthesis PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-515084 DO - https://doi.org/10.1038/s41598-020-75967-w VL - 10 IS - 1 SP - 18910 PB - Springer Nature AN - OPUS4-51508 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -