Methodology for the accurate determination of number concentration of inorganic nanoparticles (NP) by single particle inductively coupled plasma mass spectrometry (spICP-MS) using the novel dynamic mass flow (DMF) approach is systematically described. Using this method the determination of transport efficiency (TE) is achieved without the need for a reference nanomaterial. The impact of key parameters on the accuracy and uncertainty of the number concentration data obtained with this approach was evaluated. In particular the number of detected NP in the time scan is the major contributing factor to the overall measurement uncertainty. For Au NP of spherical shape with number based concentration in the range of 4.0 × 10¹² to 2.0 × 10¹⁴ kg⁻¹ (depending on the particle size) a relative expanded uncertainty (k = 2) of less than 10% was achieved. This reference methodology was also evaluated for the accurate determination of number concentration of more complex NP namely triethanolamine (TEA)-stabilised TiO₂ NP, for which like-for-like NP reference materials are not available. Using a sample mass flow of 0.3578 g min⁻¹ (with an associated uncertainty of 0.0002 g min⁻¹, k = 1) the average transport efficiencies for Au NP (in trisodium citrate) and TiO₂ NP (in TEA/NaOH) were very similar (7.57 ± 0.13% and 7.77 ± 0.22%, k = 1, respectively). Finally the number concentration values for both NP types agreed well with those obtained using particle tracking analysis (PTA), providing evidence for the good agreement between mass-based TE of the sample and NP-based TE with the newly proposed method.

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.