TY - JOUR A1 - Hodoroaba, Vasile-Dan A1 - Rades, Steffi A1 - Borghetti, P. A1 - Ortel, Erik A1 - Wirth, Thomas A1 - Garcia, S. A1 - Gómez, E. A1 - Blanco, M. A1 - Alberto, G. A1 - Martra, G. T1 - Organic surface modification and analysis of titania nanoparticles for self‐assembly in multiple layers N2 - The characteristics of TiO2 coatings can greatly influence their final performance in large‐scale applications. In the present study, self‐assembly of TiO2 nanoparticles (NPs) in multiple layers was selected as a deposition procedure on various substrates. For this, the main prerequisite constitutes the surface modification of both NPs and substrate with, for example, silane coupling agents. A set of functionalized TiO2 NPs has been produced by reaction with either (3‐aminopropyl)triethoxysilane (APTES) or (3‐aminopropyl)phosphonic acid (APPA) to functionalize the NP surface with free amino‐groups. Then, the complementary functionalized NP set can be obtained from an aliquot of the first one, through the conversion of free surface amino groups to aldehydes by reaction with glutaraldehyde (GA). Several types of TiO2 NPs differing in size, shape, and specific surface area have been functionalized. Fourier‐transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), SEM/ energy‐dispersive X‐ray spectroscopy (EDS), XPS, Auger electron spectroscopy (AES), and Time‐of‐Flight (ToF)‐SIMS analyses have been carried out to evaluate the degree of functionalization, all the analytical methods employed demonstrating successful functionalization of TiO2 NP surface with APTES or APPA and GA. KW - TiO2 KW - Nanoparticles KW - Surface functionalization KW - Layer-by-layer deposition KW - Surface chemical analysis PY - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-508601 SN - 1096-9918 VL - 52 IS - 12 SP - 829 EP - 834 PB - John Wiley & Sons Ltd AN - OPUS4-50860 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 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 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:b43-515084 VL - 10 IS - 1 SP - 18910 PB - Springer Nature AN - OPUS4-51508 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -