Sabine Plitzko

Poisson shot noise parameter estimation from a single scanning electron microscopy image

Scanning electron microscopy (SEM) has an extremely low signal-to-noise ratio leading to a high level of shot noise which makes further processing difficult. Unlike often assumed, the noise stems from a Poisson process and is not Gaussian but depends on the signal level. A method to estimate the noise parameters of individual images should be found. Using statistical modeling of SEM noise, a robust optimal noise estimation algorithm is derived. A non-local means noise reduction filter tuned with the estimated noise parameters on average achieves an 18% lower root-mean-square error than the untuned filter on simulated images. The algorithm is stable and can adapt to varying noise levels.

1601e A new risk grouping concept for high aspect ratio materials – adding fibre rigidity to the picture

Introduction Identification and management of risks related to both established and innovative materials are central aims of occupational safety and health. Therefore, we propose a new grouping scheme to evaluate the risk of High Aspect Ratio Materials (HARM) according to hazard and exposure aspects. Methods Our approach to measure the fibre rigidity (discussion) is based on frequency measurements at resonance conditions of single vibrating fibres by means of radio-frequency engineering and scanning electron microscopy; using Euler-Bernoulli’s beam theory to determine the young modulus. Results The new scheme considers both intrinsic material and handling process-related properties such as bio-durability, toxicity, respirability, HARM morphology/dimensions as well as grade of agglomeration and dust release propensity during/after processing. It is based on the results of extensive research regarding those properties and their scalability for risk assessment, most notably the dustiness. Its utilisation requires data for the mentioned intrinsic and process related properties. Especially with respect to aspects of dustiness, this requires data on HARM release propensities for different handling conditions. However this talk will focus in the fibre rigidity as new parameter. Discussion We propose to include the aspect of rigidity, more precisely the fibre flexural rigidity, as an extension to the fibre-toxicological paradigm as a new parameter for HARM toxicity assessment. Critical fibre rigidity is most probably the key to frustrated phagocytosis or HARM translocation and distinguishes HARM toxicology from that of granular bio-durable particle materials. The potential toxicity of HARMs is widely known and was also shown for CNTs/CNF in many recent studies. We believe that the toxicity should not be evaluated solely by the fibre dimensions in context with systematic animal testing, but propose to combine a fibre’s composition and diameter into the property flexural rigidity. For bio-durable HARM, rigidity has the potential to become an overarching, material independent assessment parameter.

Improved Automatic Recognition of Engineered Nanoparticles in Scanning Electron Microscopy Images

The amount of engineered nanoparticles produced each year has grown for some time and will grow in the coming years. However, if such particles are inhaled, they can be toxic. Therefore, to ensure the safety of workers, the nanoparticle concentrations at workplaces have to be measured. This is usually done by gathering the particles in the ambient air and then taking images using scanning electron microscopy. The particles in the images are then manually identified and counted. However, this task takes much time. Therefore, we have developed a system to automatically find and classify particles in these images (Kockentiedt et al., 2012). In this paper, we present an improved version of the system with two new classification feature types. The first are Haralick features. The second is a newly developed feature which estimates the counts of electrons detected by the scanning electron microscopy for each particle. In addition, we have added an algorithm to automatically choose the classifier type and parameters. This way, no expert is needed when the user wants to train the system to recognize a previously unknown particle type. The improved system yields much better results for two types of engineered particles and shows comparable results for a third type.