The chemical or elemental analysis of samples with complex surface topography is challenging for secondary ion mass spectrometry (SIMS), if the three-dimensional structure of the sample is not taken into account. Conventional 3D reconstruction of SIMS data assumes a flat surface and uniform sputtering conditions, which is not the case for many analytical applications involving micro- and nanosized particles, composites, or patterned materials. Reliable analysis of such samples requires knowledge of the actual 3D surface structure to correctly reconstruct the SIMS 3D maps. To this end, we introduce the use of photogrammetric 3D topography reconstruction from scanning helium ion microscopy (HIM) correlated with in situ SIMS data for the reconstruction of 3D SIMS data. The HIM and SIMS data are acquired under in situ conditions in a Zeiss ORION NanoFab HIM using a novel SIMS analyzer. We successfully tested the applicability of the approach to generate 3D models of different samples and show that the combination of SIMS and 3D topography is able to provide insights into the influence of the sample topography in a single instrument and with a single ion column and hence without the need for ex-situ sample analysis or additional instrumentation. These findings offer a path toward ion-based correlative 3D spectromicroscopy (3D-HIM-SIMS) and suggest that many combinations of charged particle based P3D (SEM, HIM) and analytical microscopy techniques, such as SIMS, energy-dispersive X-ray spectroscopy (EDX), or ionoluminescence/cathodoluminescence (IL/CL), can be used for correlative microscopy in 3D.
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http://dx.doi.org/10.1021/acs.analchem.8b02530 | DOI Listing |
Anal Bioanal Chem
December 2024
Faculty of Science and Technology, Seikei University, 3-3-1 Kichijoji-Kitamachi, Musashino, Tokyo, 180-8633, Japan.
Methods that facilitate molecular identification and imaging are required to evaluate drug penetration into tissues. Time-of-flight secondary ion mass spectrometry (ToF-SIMS), which has high spatial resolution and allows 3D distribution imaging of organic materials, is suitable for this purpose. However, the complexity of ToF-SIMS data, which includes nonlinear factors, makes interpretation challenging.
View Article and Find Full Text PDFMedEdPORTAL
December 2024
Associate Professor, Department of Academic Medical Education and Medicine, University of Kentucky College of Medicine and Lexington Veterans Affairs Health Care.
Introduction: A physician's first patient harm event oftentimes occurs during the intern year. Residents encounter and are responsible for medical errors, yet little training is offered in how to properly cope with these events. Earlier and more in-depth education about how to process patient harm events is needed.
View Article and Find Full Text PDFInt J Sports Physiol Perform
December 2024
Division of Health, Engineering, Computing and Science, Te Huataki Waiora School of Health, University of Waikato, Tauranga, New Zealand.
Purpose: Continuous-glucose-monitoring (CGM) sensors provide near-real-time glucose data and have been introduced commercially as a tool to inform nutrition decisions. The aim of this pilot study was to explore how factors such as the menstrual phase, extended running duration, and carbohydrates affect CGM outcomes among trained eumenorrheic females in an outdoor simulated ultraendurance running event.
Methods: Twelve experienced female ultrarunners (age 39 [6] y) participated in this crossover study.
J Mater Chem B
December 2024
School of Physics, University of Sydney, Sydney, NSW 2006, Australia.
Polymeric nanoparticles surface functionalised with fluorescent molecules hold significant potential for advancing diagnostics and therapeutic delivery. Despite their promise, challenges persist in achieving robust attachment of fluorescent molecules for real-time tracking. Weak physical adsorption, pH-dependent electrostatic capture, and hydrophobic interactions often fail to achieve stable attachment of fluorescent markers.
View Article and Find Full Text PDFMultiplexed tissue imaging (MTI) technologies enable high-dimensional spatial analysis of tumor microenvironments but face challenges with technical variability in staining intensities. Existing normalization methods, including z-score, ComBat, and MxNorm, often fail to account for the heterogeneous, right-skewed expression patterns of MTI data, compromising signal alignment and downstream analyses. We present UniFORM, a non-parametric, Python-based pipeline for normalizing both feature- and pixel-level MTI data.
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