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The photocatalytic (PC) performance of titanium dioxide (TiO2) nanoparticles strongly depends on their specific surface, the presence of crystal defects, their crystal phase, and the exposed crystal facets. In order to understand which of these factors contributes most significantly to the PC activity of TiO2 colloids, all of them have to be individually analyzed. This study entails the synthesis of five anatase nanocrystal samples. By maintaining the same reactant ratios as well as hydrothermal sol–gel synthesis route and only varying the autoclaving time or temperature, different crystallite sizes are obtained under comparable experimental conditions. A decrease in PC performance with increase in specific surface area is found. Such an unexpected counterintuitive result establishes the basis for a better understanding of the crucial factors that ultimately determine the PC activity. These are investigated by studying nanocrystals bulk and surface structure and morphology using a selection of complementary analysis methods (X-ray photoelectron spectroscopy (XPS), X-ray absorption fine structure (XAFS), X-ray diffraction (XRD)…). It is found that a change in the nanocrystal morphology from an equilibrium state truncated tetragonal bipyramid to a more elongated rod-like structure accompanied by an increase in oxygen vacancies is responsible for an augmented PC activity of the TiO2 nanocrystals.
The study described in this paper was conducted in the framework of the European nPSize project (EMPIR program) with the main objective of proposing new reference certified nanomaterials for the market in order to improve the reliability and traceability of nanoparticle size measurements. For this purpose, bimodal populations as well as complexly shaped nanoparticles (bipyramids, cubes, and rods) were synthesized. An inter-laboratory comparison was organized for comparing the size measurements of the selected nanoparticle samples performed with electron microscopy (TEM, SEM, and TSEM), scanning probe microscopy (AFM), or small-angle X-ray scattering (SAXS). The results demonstrate good consistency of the measured size by the different techniques in cases where special care was taken for sample preparation, instrument calibration, and the clear definition of the measurand. For each characterization method, the calibration process is described and a semi-quantitative table grouping the main error sources is proposed for estimating the uncertainties associated with the measurements. Regarding microscopy-based techniques applied to complexly shaped nanoparticles, data dispersion can be observed when the size measurements are affected by the orientation of the nanoparticles on the substrate. For the most complex materials, hybrid approaches combining several complementary techniques were tested, with the outcome being that the reliability of the size results was improved.
he main objective of the nPSize project is to improve the measurement capabilities for nanoparticle size based on both measurement methods traceable to SI units and new reference materials. Two basic approaches have been used in order to develop measurement procedures resulting in traceable results of the nanoparticle size distribution: physical modelling for the methods used in the project (TSEM, SEM, AFM and SAXS) and machine learning.
Physical modelling: In this part, the physical models associated with different shape measurements for the techniques TSEM, SEM, AFM and SAXS have been collected and further developed with the aim to simulate the resulting signal as measured by the individual methods. Uncertainties and traceability associated with each model were investigated and evaluated. In the following, the progress on these physical models is reported for each individual method.
Machine Learning modelling: The aim of this part is to use machine learning to enable automatic measurement of nanoparticle shape from expert a-priori information only. No physical model will be used as a-priori information in this task.
The accuracy and traceability of the size results obtained by each technique will be analyzed and compared with the physical modelling. A machine learning database will then be used to create automatic detection algorithms.