Abstract: To understand the structure–property relationship of nanostructures, reliably quantifying parameters, such as the number of atoms along the projection direction, is important. Advanced statistical methodologies have made it possible to count the number of atoms for monotype crystalline nanoparticles from a single ADF STEM image. Recent developments enable one to simultaneously acquire multiple ADF STEM images. Here, we present an extended statistics-based method for atom counting from a combination of multiple statistically independent ADF STEM images reconstructed from non-overlapping annular detector collection regions which improves the accuracy and allows one to retrieve precise atom-counts, especially for images acquired with low electron doses and multiple element structures.

Abstract: In this paper, a methodology is presented to count the number of atoms in heterogeneous nanoparticles based on the combination of multiple annular dark field scanning transmission electron microscopy (ADF STEM) images. The different non-overlapping annular detector collection regions are selected based on the principles of optimal statistical experiment design for the atom-counting problem. To count the number of atoms, the total intensities of scattered electrons for each atomic column, the so-called scattering cross-sections, are simultaneously compared with simulated library values for the different detector regions by minimising the squared differences. The performance of the method is evaluated for simulated Ni@Pt and Au@Ag core-shell nanoparticles. Our approach turns out to be a dose efficient alternative for the investigation of beam-sensitive heterogeneous materials as compared to the combination of ADF STEM and energy dispersive X-ray spectroscopy.

Abstract: The agri-food value chain is a major cause of nitrogen (N) and phosphorus (P) emissions and associated environmental and health impacts. The EU's farm-to-fork strategy (F2F) demands an agri-food value chain approach to reduce nutrient emissions by 50% and fertilizer use by 20%. Substance flow analysis (SFA) is a method that can be applied to study complex systems such as the agri-food chain. A review of 60 SFA studies shows that they often lack detail by not sufficiently distinguishing between nodes, products and types of emissions. The present study aims to assess the added value of detail in SFAs and to illustrate that valuable indicators can be derived from detailed assessments. This aim will be attained by presenting a highly-detailed SFA for the livestock-intensive region of Flanders, Belgium. The SFA distinguishes 40 nodes and 1827 flows that are classified into eight different categories (e.g. by-products, point source emissions) following life cycle methods. Eight novel indicators were calculated, including indicators that assess the N and P recovery potential. Flanders has a low overall nutrient use efficiency (11% N, 18% P). About 55% of the N and 56% of the P embedded in recoverable streams are reused providing 35% and 37% of the total N and P input. Optimized nutrient recycling could replace 45% of N and 48% of P of the external nutrient input, exceeding the target set by the F2F strategy. Detailed accounting for N and P flows and nodes leads to the identification of more recoverable streams and larger N and P flows. More detailed flow accounting is a prerequisite for the quantification of technological intervention options. Future research should focus on including concentration and quality as a parameter in SFAs.

Abstract: Quantitative atomic resolution annular dark field scanning transmission electron microscopy (ADF STEM) has become a powerful technique for nanoparticle atom-counting. However, a lot of nanoparticles provide a severe characterisation challenge because of their limited size and beam sensitivity. Therefore, quantitative ADF STEM may greatly benefit from statistical detection theory in order to optimise the instrumental microscope settings such that the incoming electron dose can be kept as low as possible whilst still retaining single-atom precision. The principles of detection theory are used to quantify the probability of error for atom-counting. This enables us to decide between different image performance measures and to optimise the experimental detector settings for atom-counting in ADF STEM in an objective manner. To demonstrate this, ADF STEM imaging of an industrial catalyst has been conducted using the near-optimal detector settings. For this experiment, we discussed the limits for atomcounting diagnosed by combining a thorough statistical method and detailed image simulations.

Abstract: Computational fluid dynamics (CFD) is a technique that is used increasingly in the biomedical field. Solving the flow equations numerically provides a convenient way to assess the efficiency of therapies and devices, ranging from cardiovascular stents and heart valves to hemodialysis workflows. Also in the respiratory field CFD has gained increasing interest, especially through the combination of three dimensional image reconstruction which results in highend patient-specific models. This paper provides an overview of clinical applications of CFD through image based modeling, resulting from recent studies performed in our center. We focused on two applications: assessment of the efficiency of inhalation medication and analysis of endobronchial valve placement. In the first application we assessed the mode of action of a novel bronchodilator in 10 treated patients and 4 controls. We assessed the local volume increase and resistance change based on the combination of imaging and CFD. We found a good correlation between the changes in volume and resistance coming from the CFD results and the clinical tests. In the second application we assessed the placement and effect of one way endobronchial valves on respiratory function in 6 patients. We found a strong patientspecific result of the therapy where in some patients the therapy resulted in complete atelectasis of the target lobe while in others the lobe remained inflated. We concluded from these applications that CFD can provide a better insight into clinically relevant therapies.