Dispersed Ag nanoparticles were prepared in aqueous solutions in the presence of pure poly[2-(dimethylamino)ethyl methacrylate] (poly-DMAEMA), poly[2-deoxy-2-methacrylamido-D-glucose] (poly-MA G), and their copolymers of poly[MAG-DMAEMA] with different mole fractions. Polymers contributed to the silver reduction, formation of nanoparticles, and stabilization of suspensions. No agglomerations of nanoparticles are formed. For each sample, more than one thousand silver particles were measured by transmission and scanning transmission electron microscopy to determine their number vs diameter and volume versus diameter distributions. The samples with the

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Comparative size and structure characterization of silver and selenium nanoparticles obtained and stabilized in different polymer solutions was performed by transmission electron microscopy (TEM) and small-angle X-ray scattering (SAXS). Effects of instrumental properties, nature of the samples, data collecting and data processing on accuracy of measurements are highlighted and summarized. Numerical differences in the mode diameter values derived from the TEM and SAXS data were found to have different sources. The SAXS results can be misleading in case of small particles (2-4 nm), for instance, Ag nanoparticles formed and stabilized in some aqueous polymer solutions due to instrumental limits, while TEM can provide sufficient statistics on such nanoparticles. SAXS is efficient in characterization of size distributions for soft Se-polymer composite particles of 20 to 100 nm in diameter. TEM is mandatory for investigating the chemical and phase composition of particles in mixtures, and their formation mechanism.

The main challenge to manufacture nanoparticles for applications in catalysis, medicine and pharmaceuticals is a mass production of stable nanoparticles with a narrow size distribution to target and control specific effects. Therefore reliable and fast statistical analysis of (nano)particles is of great interest especially for the particle size less than 10 nm due to strong chemical and biological activity associated with high penetrating capabilities through cell membranes. We would like to see these particles, to know their structure and composition, and to measure sizes. The intelligent, fast and reliable program can be a very useful tool for image analysis of “small” nanoparticles in TEM/STEM images. In our work, we show that fitting of the calculated grayscale distribution to the real distribution in (S)TEM images is able to provide the maximum accuracy in measurements of the particle diameters in opposite to algorithms based on image binarization. We apply such fitting to the truth in the vicinity of a nanoparticle image revealing the mass‐thickness, diffraction, and Z‐contrast. In order to describe the dependence of grayscale from thickness of nanoparticles the polynomial g(t) = g 0 +g 1 t+g 2 t 2 +… with sufficiently high power (≥2) and uncertain coefficients was chosen. The high‐degree polynomial is required to take into account the possible non‐monotonic dependence of the grayscale from particle thickness due to the presence of diffraction contrast (in opposite to pure mass‐thickness contrast). Monotonic dependence of the grayscales from specimen thickness is the characteristic of mass‐thickness contrast of particles (amorphous or crystalline particles positioned out of Bragg conditions) in TEM images and Z‐contrast in STEM images. The transfer function of CCD cameras determined the grayscale in the given point of the micrograph through the intensity of the incident wave has also monotonic character. Only the presence of diffraction contrast in the images breaks the monotonic dependence. Thickness of the spherical nanoparticle in a point having (x,y)‐coordinates can be expressed as t(x,y)=((d/2) 2 ‐(x‐x c ) 2 ‐(y‐y c ) 2 )^(1/2). During fitting, the uncertain coefficients g i , coordinates of the particle center (x c ,y c ), and the particle diameter d are computing. Our algorithm for particle recognition and measuring sizes is proposed and realized in the program ANN (Automatic Nanoparticle Numerator). Our algorithm for particle recognition and measuring sizes out of thresholding approach is proposed and realized in the program ANN (Automatic Nanoparticle Numerator). The comparative study of distributions of silver nanoparticle synthesized in different polymer‐water solutions determined manually (about 1000 particles), using ImageJ and ANN was performed (Fig.1 and Fig.2). It shoved a good agreement between results obtained manually and with ANN.