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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Quantum Imaging with X-rays

Quantum imaging encompasses a broad range of methods that exploit the quantum properties of light to capture information about an object. One such approach involves using a two-photon quantum state, where only one photon interacts with the object being imaged while its entangled partner carries spatial or temporal information. To implement this technique, it is necessary to generate specific quantum states of light and detect photons at the single-photon level. While this method has been successfully demonstrated in the visible electromagnetic spectrum, extending it to X-rays has faced significant challenges due to the difficulties in producing a sufficient rate of X-ray photon pairs and detecting them with adequate resolution. Here, we demonstrate record high rates of correlated X-ray photon pairs produced via a spontaneous parametric down-conversion process and we employ these photons to perform quantum correlation imaging of several objects, including a biological sample (E. cardamomum seedpod). Notably, we report an unprecedented detection rate of about 6,300 pairs per hour and the observation of energy anti-correlation for the X-ray photon pairs. We also present a detailed analysis of the properties of the down-converted X-ray photons, as well as a comprehensive study of the correlation imaging formation, including a study of distortions and corrections. These results mark a substantial advancement in X-ray quantum imaging, expanding the possibilities of X-ray quantum optical technologies, and illustrating the pathway towards enhancing biological imaging with reduced radiation doses.

Gofron, Kaz [ORNL] (ORCID:0000000314415736)↗
NASA NTRS1998

An Evanescent Microwave Probe for Super-Resolution Nondestructive Imaging of Metals, Semiconductors, Dielectrics, Composites and Biological Specimens

Using evanescent microwaves with decay lengths determined by a combination of microwave wavelength (lambda) and waveguide termination geometry, we have imaged and mapped material non-uniformities and defects with a resolving capability of lambda/3800=79 microns at 1 GHz. In our method a microstrip quarter wavelength resonator was used to generate evanescent microwaves. We imaged materials with a wide range of conductivities. Carbon composites, dielectrics (Duroid, polymers), semiconductors (3C-SiC, polysilicon, natural diamond), metals (tungsten alloys, copper, zinc, steel), high-temperature superconductors, and botanical samples were scanned for defects, residual stresses, integrity of brazed junctions, subsurface features, areas of different film thickness and moisture content. The evanescent microwave probe is a versatile tool and it can be used to perform very fast, large scale mapping of a wide range of materials. This method of characterization compares favorably with ultrasound testing, which has a resolution of about 0.1 mm and suffers from high absorption in composite materials and poor transmission across boundaries. Eddy current methods which can have a resolution on the order of 50 microns are restricted to evaluating conducting materials. Evanescent microwave imaging, with careful choice of operating frequency and probe geometry, can have a resolution of up to 1 micron. In this method we can scan hot and moving objects, sample preparation is not required, testing is non-destructive, non-invasive and non-contact, and can be done in air, in liquid or in vacuum.

Pathak, P. S.↗
DOE OSTI2025

Computer Vision Pipeline for Image Analysis for Freeze‐Fracture Electron Microscopy: Rosette Cellulose Synthase Complexes Case

In materials science, plant biology, agriculture, and environmental research, the automated analysis of high-magnification, complex microscopy images, such as those generated by freeze-fracture electron microscopy (FF-TEM), remains a critical challenge that limits the scalability of data interpretation. We present a deep learning computer vision pipeline for high-throughput detection and morphological characterization analysis of cellulose synthase complexes (CSCs, or rosettes) in FF-TEM images. The pipeline integrates preprocessing, detection, human-in-the-loop verification, and semantic segmentation to quantify features such as rosette diameter and inter-lobe spacing. The approach was trained and tested on a curated dataset of high-resolution FF-TEM micrographs of Physcomitrium patens, expanded via strategic tiling and augmentation to over 650 images. We compare YOLOv8 and YOLOv9 architectures and demonstrate that YOLOv9 achieves superior performance in both localization accuracy (mAP50-95 = 0.854) and inference speed. The resulting distributions revealed biological variability consistent with prior manual studies, validating the approach for high-throughput applications. Our results show that the pipeline achieves human-expert level accuracy while dramatically reducing analysis time, enabling scalable, reproducible structural characterization of intramembrane protein complexes. The pipeline is broadly applicable to other domains requiring precise interpretation of complex microscopy data and establishes a foundation for future artificial intelligence (AI)-assisted workflows in biological imaging.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2006

NASA Radiation Track Image GUI for Assessing Space Radiation Biological Effects

The high-charge high-energy (HZE) ion components of the galactic cosmic rays when compared to terrestrial forms of radiations present unique challenges to biological systems. In this paper we present a deoxyribonucleic acid (DNA) breakage model to visualize and analyze the impact of chromatin domains and DNA loops on clustering of DNA damage from X rays, protons, and HZE ions. Our model of DNA breakage is based on a stochastic process of DNA double-strand break (DSB) formulation that includes the amorphous model of the radiation track and a polymer model of DNA packed in the cell nucleus. Our model is a Monte-Carlo simulation based on a randomly located DSB cluster formulation that accomodates both high- and low-linear energy transfer radiations. We demonstrate that HZE ions have a strong impact on DSB clustering, both along the chromosome length and in the nucleus volume. The effects of chromosomal domains and DNA loops on the DSB fragment-size distribution and the spatial distribution of DSB in the nucleus were studied. We compare our model predictions with the spatial distribution of DSB obtained from experiments. The implications of our model predictions for radiation protection are discussed.

Ponomarev, Artem L.↗
NASA NTRS2012

Development of Two Color Fluorescent Imager and Integrated Fluidic System for Nanosatellite Biology Applications

Nanosatellites offer frequent, low-cost space access as secondary payloads on launches of larger conventional satellites. We summarize the payload science and technology of the Microsatellite in-situ Space Technologies (MisST) nanosatellite for conducting automated biological experiments. The payload (two fused 10-cm cubes) includes 1) an integrated fluidics system that maintains organism viability and supports growth and 2) a fixed-focus imager with fluorescence and scattered-light imaging capabilities. The payload monitors temperature, pressure and relative humidity, and actively controls temperature. C. elegans (nematode, 50 m diameter x 1 mm long) was selected as a model organism due to previous space science experience, its completely sequenced genome, size, hardiness, and the variety of strains available. Three strains were chosen: two green GFP-tagged strains and one red tdTomato-tagged strain that label intestinal, nerve, and pharyngeal cells, respectively. The integrated fluidics system includes bioanalytical and reservoir modules. The former consists of four 150 L culture wells and a 4x5 mm imaging zone the latter includes two 8 mL fluid reservoirs for reagent and waste storage. The fluidic system is fabricated using multilayer polymer rapid prototyping: laser cutting, precision machining, die cutting, and pressure-sensitive adhesives it also includes eight solenoid-operated valves and one mini peristaltic pump. Young larval-state (L2) nematodes are loaded in C. elegans Maintenance Media (CeMM) in the bioanalytical module during pre-launch assembly. By the time orbit is established, the worms have grown to sufficient density to be imaged and are fed fresh CeMM. The strains are pumped sequentially into the imaging area, imaged, then pumped into waste. Reagent storage utilizes polymer bags under slight pressure to prevent bubble formation in wells or channels. The optical system images green and red fluorescence bands by excitation with blue (473 nm peak) and amber (587 nm peak) LEDs it achieves 8 m lateral resolution using a CMOS imaging chip (as configured for serial data speeds) or 4 m resolution using USB imaging chips. The imager consists of a modified commercial off-the-shelf CMOS chip camera, amber, blue and white LEDs, as well as a relay lens and dual-band filters to obviate moving parts while supporting both fluorescence wavelengths.

Wu, Diana Terri↗
DOE OSTI2024

Hierarchical Conditioning of Diffusion Models Using Tree-of-Life for Studying Species Evolution

A central problem in biology is to understand how organisms evolve and adapt to their environment by acquiring variations in the observable characteristics or traits of species across the tree of life. With the growing availability of large-scale image repositories in biology and recent advances in generative modeling, there is an opportunity to accelerate the discovery of evolutionary traits automatically from images. Toward this goal, we introduce Phylo-Diffusion, a novel framework for conditioning diffusion models with phylogenetic knowledge represented in the form of HIERarchical Embeddings (HIER-Embeds). We also propose two new experiments for perturbing the embedding space of Phylo-Diffusion: trait masking and trait swapping, inspired by counterpart experiments of gene knockout and gene editing/swapping. Our work represents a novel methodological advance in generative modeling to structure the embedding space of diffusion models using tree-based knowledge. Our work also opens a new chapter of research in evolutionary biology by using generative models to visualize evolutionary changes directly from images. We empirically demonstrate the usefulness of Phylo-Diffusion in capturing meaningful trait variations for fishes and birds, revealing novel insights about the biological mechanisms of their evolution. (Model and code can be found at imageomics.github.io/phylo-diffusion)

Khurana, Mridul↗
NASA NTRS2021

NASA's Surface Biology and Geology Designated Observable: A Perspective on Surface Imaging Algorithms

The 2017–2027 National Academies’ Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algo-rithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Aquatic↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗
NASA NTRS1993

Imaging Genetic Molecules At Atomic Resolution

Proposed method of imaging informational polymeric biological molecules at atomic resolution enables determination of sequences of component monomers about 10 to the 3rd power to 10 to the 4th power times as fast as conventional methods do. Accelerates research on genetic structures of animals and plants. Also contributes significantly to imaging processes like scanning electron microscopy (SEM), atomic-force microscopy (AFM), and scanning tunneling microscopy (STM) in cases in which necessary to locate or identify small specimens on relatively large backgrounds and subtract background images to obtain images of specimens in isolation. V-grooves on silicon wafer laid out in square pattern, intersections of which marked to identify coordinates. Specimen molecules held in grooves for reproducible positioning and scanning by AFM or STM.

Coles, L. Stephen↗
NASA NTRS1989

NASA spinoffs to bioengineering and medicine

The societal and economic benefits derived from the application of aerospace technology to improved health care are examined, and examples of the space-technology spinoffs are presented. Special attention is given to the applications of aerospace technology from digital image processing, space medicine and biology, microelectronics, optics and electrooptics, and ultrasonic imaging. The role of the NASA Technology Application Team in helping the potential technology users to identify and evaluate the technology transfer opportunities and to apply space technology in the field of medicine is discussed.

Rouse, Doris J.↗
NASA NTRS1991

NASA spinoffs to bioengineering and medicine

Through the active transfer of technology, the National Aeronautics and Space Administration (NASA) Technology Utilization (TU) Program assists private companies, associations, and government agencies to make effective use of NASA's technological resources to improve U.S. economic competitiveness and to provide societal benefit. Aerospace technology from areas such as digital image processing, space medicine and biology, microelectronics, optics and electrooptics, and ultrasonic imaging have found many secondary applications in medicine. Examples of technology spinoffs are briefly discussed to illustrate the benefits realized through adaptation of aerospace technology to solve health care problems. Successful implementation of new technologies increasingly requires the collaboration of industry, universities, and government, and the TU Program serves as the liaison to establish such collaborations with NASA. NASA technology is an important resource to support the development of new medical products and techniques that will further advance the quality of health care available in the U.S. and worldwide.

Biomedical Engineering/instrumentation/trends↗
DOE OSTI2025

Quantitative Nonlinear Optical Polarimetry with High Spatial Resolution

Nonlinear optical microscopy such as in the optical second-harmonic generation (SHG) modality has become a popular tool today for probing materials in the physical and biological sciences. While imaging and spectroscopy are widely used in the microscopy mode, nonlinear polarimetry, which can shed light on materials’ symmetry and microstructure, is relatively underdeveloped. This is partly because quantitative analytical modeling of the optical SHG response for anisotropic crystals and films largely assumes low-numerical aperture (NA) focusing of light, where the plane-wave approximation is sufficient. Tight focusing provides unique benefits in revealing out-of-plane polarization responses, which cannot be detected by near-plane-wave illumination at normal incidence. Here, we outline a method for quantitatively analyzing SHG polarimetry measurements obtained under high-NA focusing within a microscope geometry. Experiments and simulations of a variety of standard samples, from single crystals to thin films, are in good agreement, including measured and simulated spatial SHG maps of ferroelectric domains. A solution to the inverse problem is demonstrated, where the spatial distribution of an SHG tensor with unknown tensor coefficient magnitudes is determined by experimentally measured polarimetry. The ability to extract the out-of-plane component of the nonlinear polarization in normal incidence is demonstrated, which can be valuable for high-resolution polarimetry of 2D materials, thin films, heterostructures, and uniaxial crystals with a strong out-of-plane response.

36 MATERIALS SCIENCE↗
NASA NTRS2018

Nanoengineered Materials for SWIR HOT Detectors

Heavy metal Selenide has been investigated for more than half century for high operating temperature (HOT) mid wave infrared (MWIR) applications. Most of the efforts have been devoted to make detector arrays on high-resistivity Si substrates for operating wavelengths in the 1.5 to 5.0 m region using physical vapor transport grown poly crystalline materials. For most of the biological spectral and imaging applications, short wave infrared (SWIR) detectors have shown better performance. Recent growth materials have shown variation in morphology with slight change in growth conditions and hence variation in performance parameters such as bandgap, mobility and resistivity from sample to sample. We have performed growth and optical characterization of pure and doped PbS and PbSe and have determined bandgap using available theoretical models for different morphologies.

Singh, N. B.↗
NASA NTRS2019

Design of Materials for IR Detectors Using High Z Elements for High Energy Radiation Environment

There is a strong need for rad hard and high operating temperature IR detectors for space environment. Heavy metal Selenides (high Z and large density) have been investigated for more than half century for high operating temperature mid wave infrared (MWIR) applications. Most of the efforts have been devoted to make detector arrays on high-resistivity Si substrates for operating wavelengths in the 1.5 to 5.0 m region using physical vapor transport grown poly crystalline materials. For most of the biological spectral and imaging applications, short wave infrared (SWIR) detectors have shown better performance. Recent growth materials have shown variation in morphology with slight change in growth conditions and hence variation in performance parameters such as bandgap, mobility and resistivity from sample to sample. We have performed growth and optical characterization of binary materials CdSe-PbSe to determine the suitability for IR detector. We have determined bandgap using several theoretical models for different morphologies observed during growth on silicon wafers.

Saraf, Sonali↗
DOE OSTI2024

Dosimetric and biological impact of activity extravasation of radiopharmaceuticals in PET imaging

The increasing use of nuclear medicine and PET imaging has intensified scrutiny of radiotracer extravasation. To our knowledge, this topic is understudied but holds great potential for enhancing our understanding of extravasation in clinical PET imaging. This work aims to (1) quantify the absorbed doses from radiotracer extravasation in PET imaging, both locally at the site of extravasation and with the extravasation location as a source of exposure to bodily organs and (2) assess the biological ramifications within the injection site at the cellular level. A radiation dosimetry simulation was performed using a whole-body 4D Extended Cardiac-Torso (XCAT) phantom embedded in the GATE Monte Carlo platform. A 10-mCi dose of 18 F-FDG was chosen to simulate a typical clinical PET scan scenario, with 10% of the activity extravasated in the antecubital fossa of the right arm of the phantom. The extravasation volume was modeled as a 5.5 mL rectangle in the hypodermal layer of skin. Absorbed dose contributions were calculated for the first two half-lives, assuming biological clearance thereafter. Dose calculations were performed as absorbed doses at the organ and skin levels. Energy deposition was simulated both at the local extravasation site and in multiple organs of interest and converted to absorbed doses based on their respective masses. Each simulation was repeated ten times to estimate Monte Carlo uncertainties. Biological impacts on cells within the extravasated volume were evaluated by randomizing cells and exposing them to a uniform radiation source of 18 F and 68 Ga. Particle types, their energies, and direction cosines were recorded in phase space files using a separate Geant4 simulation to characterize their entry into the nucleus of the cellular volume. Subsequently, the phase space files were imported into the TOPAS-nBio simulation to assess the extent of DNA damage, including double-strand breaks (DSBs) and single-strand breaks (SSBs). Organ-level dosimetric estimations are presented for 18 F and 68 Ga radionuclides in various organs of interest. With 10% extravasation, the hypodermal layer of the skin received the highest absorbed dose of 1.32 ± 0.01 Gy for 18 F and 0.99 ± 0.01 Gy for 68 Ga. The epidermal and dermal layers received absorbed doses of 0.07 ± 0.01 Gy and 0.13 ± 0.01 Gy for 18 F, and 0.14 ± 0.01 Gy and 0.29 ± 0.01 Gy for 68 Ga, respectively. In the extravasated volume, 18 F caused an average absorbed dose per nucleus of 0.17 ± 0.01 Gy, estimated to result in 10.58 ± 0.50 DSBs and 268.11 ± 12.43 SSBs per nucleus. For 68 Ga, the absorbed dose per nucleus was 0.11 ± 0.01 Gy, leading to an estimated 6.49 ± 0.34 DSBs and 161.24 ± 8.12 SSBs per nucleus. Absorbed doses in other organs were on the order of micro-gray (µGy). The likelihood of epidermal erythema resulting from extravasation during PET imaging is low, as the simulated absorbed doses to the epidermis remain below the thresholds that trigger such effects. Moreover, the organ-level absorbed doses were found to be clinically insignificant across various simulated organs. The minimal DNA damage at the extravasation site suggests that long-term harm, such as radiation-induced carcinogenesis, is highly unlikely.

DNA strand breaks↗
NASA NTRS1991

Biomedical applications of NASA technology

Through the active transfer of technology, NASA Technology Utilization (TU) Program assists private companies, associations, and government agencies to make effective use of NASA's technological resources to improve U.S. economic competitiveness and to provide societal benefit. Aerospace technology from such areas as digital image processing, space medicine and biology, microelectronics, optics, and electro-optics, and ultrasonic imaging have found many secondary applications in medicine. Examples of technology spinoffs are briefly discussed to illustrate the benefits realized through adaptation of aerospace technology to solve health care problems. Successful implementation of new technologies increasingly requires the collaboration of industry, universities, and government and the TU Program serves as the liaison to establish such collaborations with NASA. NASA technology is an important resource to support the development of new medical products and techniques that will further advance the quality of health care available in the U.S. and worldwide.

Friedman, Donald S.↗
NASA NTRS2016

Full Field X-Ray Fluorescence Imaging Using Micro Pore Optics for Planetary Surface Exploration

Many planetary surface processes leave evidence as small features in the sub-millimetre scale. Current planetary X-ray fluorescence spectrometers lack the spatial resolution to analyse such small features as they only provide global analyses of areas greater than 100 mm(exp 2). A micro-XRF spectrometer will be deployed on the NASA Mars 2020 rover to analyse spots as small as 120m. When using its line-scanning capacity combined to perpendicular scanning by the rover arm, elemental maps can be generated. We present a new instrument that provides full-field XRF imaging, alleviating the need for precise positioning and scanning mechanisms. The Mapping X-ray Fluorescence Spectrometer - "Map-X" - will allow elemental imaging with approximately 100μm spatial resolution and simultaneously provide elemental chemistry at the scale where many relict physical, chemical and biological features can be imaged in ancient rocks. The arm-mounted Map-X instrument is placed directly on the surface of an object and held in a fixed position during measurements. A 25x25 mm(exp 2) surface area is uniformly illuminated with X-rays or alpha-particles and gamma-rays. A novel Micro Pore Optic focusses a fraction of the emitted X-ray fluorescence onto a CCD operated at a few frames per second. On board processing allows measuring the energy and coordinates of each X-ray photon collected. Large sets of frames are reduced into 2d histograms used to compute higher level data products such as elemental maps and XRF spectra from selected regions of interest. XRF spectra are processed on the ground to further determine quantitative elemental compositions. The instrument development will be presented with an emphasis on the characterization and modelling of the X-ray focussing Micro Pore Optic. An outlook on possible alternative XRF imaging applications will be discussed.

micro-XRF spectrometer↗