Abstract: Technological progress allows for an ever-faster acquisition of hyperspectral data, challenging the users to keep up with interpreting the recorded data. Matrix factorization, the representation of data sets by bases (or loads) and coefficient (or score) images is long used to support the interpretation of complex data sets. We propose in this publication Simplex Volume Maximization (SiVM) for the analysis of X-ray fluorescence (XRF) imaging data sets. SiVM selects archetypical data points that represents the data set and thus provides easily understandable bases, preserves the non-negative character of XRF data sets and has low demands concerning computing resources. We apply SiVM on an XRF data set of Hans Memling's Portrait of a man from the Lespinette family from the collection of the Mauritshuis (The Hague, NL) and discuss capabilities and shortcomings of SiVM. (C) 2017 Elsevier B.V. All rights reserved.

Abstract: Novel indium oxide (In2O3) nanobricks have been prepared by template-less and surfactant-free hydrothermal synthesis method and were characterized by X-ray diffraction (XRD), Raman spectroscopy, photoluminescence (PL) spectroscopy and field emission scanning electronic microscopy (FESEM). The synthesized In2O3 nanobricks were successfully immobilized on the surface of glassy carbon electrode for the detection of Parabens (butylparaben). Owing to the unique structure and intriguing properties of these In2O3 nanobricks, the nanostructured thin-film electrode has shown an obvious electrocatalytic activity for the detection of butylparaben (BP). The detection limit (LOD) was estimated as 3 s/m and the sensitivity (LOQ) was calculated as 10 s/m and were found to be 0.08 μM and 0.26 μA μM−1 cm−2 respectively. This sensor showed high sensitivity compared with the reported electrochemical sensors for the detection of BP. The fabricated sensor was successfully applied for the detection of butyl paraben in real cosmetic samples with good recovery ranging from 96.0 to 100.3%.

Abstract: Using first-principles calculations we systematically investigate the atomic, electronic and magnetic properties of novel two-dimensional materials (2DM) with a stoichiometry C3N which has recently been synthesized. We investigate how the number of layers affect the electronic properties by considering monolayer, bilayer and trilayer structures, with different stacking of the layers. We find that a transition from semiconducting to metallic character occurs which could offer potential applications in future nanoelectronic devices. We also study the affect of width of C3N nanoribbons, as well as the radius and length of C3N nanotubes, on the atomic, electronic and magnetic properties. Our results show that these properties can be modified depending on these dimensions, and depend markedly on the nature of the edge states. Functionalization of the nanostructures by the adsorption of H adatoms is found induce metallic, half-metallic, semiconducting and ferromagnetic behavior, which offers an approach to tailor the properties, as can the application of strain. Our calculations give insight into this new family of C3N nanostructures, which reveal unusual electronic and magnetic properties, and may have great potential in applications such as sensors, electronics and optoelectronic at the nanoscale.

Abstract: By using first-principles molecular dynamics, we address the issue of structural stability for the C-60 Ge-m(m) family of doped heterofullerenes through a set of calculations targeting C48Ge12 and C40Ge20. Three kinds of theoretical tools are employed: (a) static structural optimization, (b) a bonding analysis based on localized orbitals (Wannier wavefunctions and centers) and (c) first-principles molecular dynamics at finite temperature. This latter tool allows concluding that the segregated form of C40Ge20 is less stable than its Si-based counterpart. However, the non-segregated forms of C40Ge20 and C40Si20 have comparable stabilities at finite temperatures. (C) 2012 Elsevier B. V. All rights reserved.

Abstract: Illuminated manuscript fragments are some of the best preserved objects of Western cultural heritage. Therefore, scholars are limited to non-invasive – often point-based – methods, to answer questions on material usage, technique, origin and previous treatments. These powerful methods yield specific information; however, the information is limited to the number of points analyzed. Imaging spectroscopies such as MA-XRF and MA-rFTIR combine specificity with the power of imaging, resulting in distribution images that are interpretable by non-spectroscopists and the public at large. In this paper the possible added value of using imaging spectroscopy is discussed. Do these methods yield the same results as an extensive point-based spectroscopic campaign and can they bring novel information? As a case study, a 15th century illuminated manuscript fragment is employed in order to explore the differences between these approaches and present an inventory of their advantages and limitations. (C) 2018 Elsevier B.V. All rights reserved.

Abstract: While it is well-known that the plasma-enhanced catalytic chemical vapor deposition (PECVD) of carbon nanotubes (CNTs) offers a number of advantages over thermal CVD, the influence of the various individual contributing factors is not well understood. Especially the role of ions is unclear, since ions in plasmas are generally associated with sputtering rather than with growing a material. Even so, various studies have demonstrated the beneficial effects of ion bombardment during the growth of CNTs. This review looks at the role of the ions in plasma-enhanced CNT growth as deduced from both experimental and simulation studies. Specific attention is paid to the beneficial effects of ion bombardment. Based on the available literature, it can be concluded that ions can be either beneficial or detrimental for carbon nanotube growth, depending on the exact conditions and the control over the growth process.

Abstract: This study investigates the dry reformation of methane (DRM) over Ni/Al2O3 catalysts in a dielectric barrier discharge (DBD) non-thermal plasma reactor. A novel hybrid machine learning (ML) model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data. To address the non-linear and complex nature of the plasma-catalytic DRM process, the hybrid ML model integrates three well-established algorithms: regression trees, support vector regression, and artificial neural networks. A genetic algorithm (GA) is then used to optimize the hyperparameters of each algorithm within the hybrid ML model. The ML model achieved excellent agreement with the experimental data, demonstrating its efficacy in accurately predicting and optimizing the DRM process. The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance. We found that the optimal discharge power (20 W), CO2/CH4 molar ratio (1.5), and Ni loading (7.8 wt%) resulted in the maximum energy yield at a total flow rate of
51 mL/min. Furthermore, we investigated the relative significance of each operating parameter on the performance of the plasmacatalytic DRM process. The results show that the total flow rate had the greatest influence on the conversion, with a significance exceeding 35% for each output, while the Ni loading had the least impact on the overall reaction performance. This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets, enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

Abstract: There is currently a growing interest in the realisation and optimization of hybrid plasma/catalyst systems for a multitude of applications, ranging from nanotechnology to environmental chemistry. In spite of this interest, there is, however, a lack in fundamental understanding of the underlying processes in such systems. While a lot of experimental research is already being carried out to gain this understanding, only recently the first simulations have appeared in the literature. In this contribution, an overview is presented on atomic scale simulations of plasma catalytic processes as carried out in our group. In particular, this contribution focusses on plasma-assisted catalyzed carbon nanostructure growth, and plasma catalysis for greenhouse gas conversion. Attention is paid to what can routinely be done, and where challenges persist.

Abstract: Pure and Nb-doped TiO2 thick-films were prepared by screen-printing, starting from nanosized powders. Grain growth and crystalline phase modification occurred as consequence of firing at high temperature. It has been shown that niobium addition inhibits grain coarsening and hinders anatase-to-rutile phase transition. These semiconducting films exhibited n-type behavior, while Nb acted as donor-dopant. Gas measurements demonstrated that the films are suitable for CO or NO2 sensing. Microstructural characterization by electron microscopy and differential thermal analysis (DTA) highlights the dependence of gas-sensing behavior on film's properties. (C) 2000 Elsevier Science S.A. All rights reserved.

Abstract: Despite significant progress in single walled carbon nanotube (SWCNT) production by plasma enhanced chemical vapor deposition (PECVD), the growth mechanism in this method is not clearly understood. We employ reactive molecular dynamics simulations to investigate how plasma-based deposition allows growth at low temperature. We first investigate the SWCNT growth mechanism at low and high temperatures under conditions similar to thermal CVD and PECVD. We then show how ion bombardment during the nucleation stage increases the carbon solubility in the catalyst at low temperature. Finally, we demonstrate how moderate energy ions sputter amorphous carbon allowing for SWCNT growth at 500 K. (C) 2013 Elsevier B. V. All rights reserved.