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Correlative microscopy is a powerful technique that combines the advantages of multiple imaging modalities to achieve a comprehensive understanding of investigated samples. For example, fluorescence microscopy provides unique functional contrast by imaging only specifically labeled components, especially in biological samples. However, the achievable structural information on the sample in its full complexity is limited. Here, the intrinsic label-free carbon contrast of water window soft X-ray microscopy can complement fluorescence images in a correlative approach ultimately combining nanoscale structural resolution with functional contrast. However, soft X-ray microscopes are complex and elaborate, and typically require a large-scale synchrotron radiation source due to the demanding photon flux requirements. Yet, with modern high-power lasers it has become possible to generate sufficient photon flux from laser-produced plasmas, thus enabling laboratory-based setups. Here, we present a compact table-top soft X-ray microscope with an integrated epifluorescence modality for 'in-situ' correlative imaging. Samples remain in place when switching between modalities, ensuring identical measurement conditions and avoiding sample alteration or destruction. We demonstrate our new method by multimodal images of several exemplary samples ranging from nanoparticles to various multicolor labeled cell types. A structural resolution of down to 50 nm was reached., Comment: 15 pages, 8 figures, preprint

Supplemental document

Optics express 30(20), 35671 (2022). doi:10.1364/OE.463216, Published by Soc., Washington, DC

Supplemental Document

This paper incorporates a probabilistic fire loss assessment method for reinforced concrete structures into a cost-benefit analysis to optimize a structural fire design. Economic losses in case of failure and survival of the structure are both quantified with, in the latter case, an estimate of the damage and repairs costs. As a case study, a cost-benefit optimization of the position of rebars in a concrete column is investigated. The column response in fire is evaluated using finite element simulations in SAFIR. Variations in cover thickness result in variations in failure probabilities and, for cases where no failure occurs, variations in repair costs due to heat penetration and residual out-of-plane deformation of the column. The optimum cover thickness is the one that offers the best trade-off between the various repair costs across the range of likely fire intensity levels. This optimum is sensitive to repair decisions such as the tolerance on the acceptable residual out-of-plane deformation after a fire. For the studied cases, the optimum cover thickness is smaller in slender columns than in stocky columns due to greater out-of-plane deformations in the former.

Common structural fire design relies on recommendations from design codes, or (a single or small set of) more advanced numerical analyses. When applying such procedures to the design of structures under normal loading conditions, adequate safety is ensured through calibrated safety factors and ample experience with structural failures. This is however not the case when considering accidental fire loading, where the stochasticity in the structural fire behaviour is rarely fully acknowledged. Therefore, a significant interest in the use of probabilistic approaches to evaluate structural fire performance, which take into account the uncertainty associated with model parameters, can be observed among researchers, with a special focus on the development of fragility curves. The calculation of fragility curves is, however, a laborious task, demanding huge computational expense, mainly attributed to the adoption of advanced calculation procedures and the need for a large number of model evaluations. The present study contributes to addressing the limitations imposed by these computational requirements through the development of surrogate models for fire exposed structural members. To achieve this, a framework for carrying out probabilistic studies of structures under fire through the use of surrogate modelling is presented. The framework is applied to a concrete column subjected to a standard fire and proves efficiency and accurateness for the selected simple example. Future studies will investigate the applicability of the framework to structural assemblies under physically-based fires.