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NASA NTRSDate not supplied

Developmental Flight Instrumentation: Review of Space Shuttle, Ares I-X, and Artemis I

Ascent vehicles in the developmental stages of the program are instrumented with Developmental Flight Instrumentation (DFI) sensors. These sensors establish a link between a vehicle and engineers on the ground to communicate conditions experienced during the ascent. These data are then compared to pre-flight predictions used in the design process. The aerodynamic, acoustic, thermal, and structural data are either telemetered to ground stations during the ascent or stored on the vehicle for post-flight recovery and archived at the Huntsville Operations Support Center (HOSC). Following NASA's Artemis I Space Launch System (SLS) launch on November 16, 2020, data from three separate programs are available at the HOSC: Space Shuttle Program (Space Transport System (STS)), Constellation Program (Ares I-X), and Artemis Program (SLS). Availability of these data presents a unique opportunity to examine DFI data from three distinct vehicles and analyze the broad impact of the DFI data on the understanding of transonic aerodynamics. Classical spectrogram and Empirical Mode Decomposition techniques were used to present data in aerodynamically analogous regions on each vehicle. On the SLS and Ares I-X, a region downstream of the Launch Abort System motors was chosen. Comparing SLS and the STS, a region downstream of booster Froward Attach Hardware was selected as analogous flow region. Some other regions of interest were also identified. Although similarities in flow features on three vehicles were identified, some challenges in the comparison were also encountered, especially due to poor temporal and spatial resolution of Shuttle measurements.

Space Shuttle↗

NASA NTRSDate not supplied

Artemis I Flight Instrumentation Data Quality Assessment and Processing

This paper is in support of the SciTech 2024 Space Launch System Aerosciences Special Sessions being organized by Brent Pomeroy and Jeremy Pinier. On November 16th, 2022, NASA launched an inaugural test flight of the Space Launch System (SLS) carrying the Orion capsule around the moon. The vehicle was instrumented with thousands of Developmental Flight Instrumentation (DFI) sensors, which monitored aerodynamic, acoustic, structural, and thermal environments. These data are used to validate experimental and numerical tools used to predict conditions the vehicle experiences during ascent. Prior to launch, a set of tests were performed to quantify the expected quality of the DFI flight data. These included testing of individual components, such as data acquisition units, at the Systems Integration Laboratory (SIL) and testing of integrated vehicle components at the Vehicle Assembly Building (VAB). This paper summarizes these tests. Some aspects of post processing of data are also discussed.

Developmental Flight Instrumentation↗

NASA NTRSDate not supplied

Initial On-Orbit Spectral Calibration of the PACE Ocean Color Instrument

The NASA Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) mission Project Science Team has used Ocean Color Instrument (OCI) measurements of Fraunhofer lines in spectra of sunlight reflected by the solar diffuser and measurements of atmospheric absorption bands in cloudtop and ocean spectra to characterize the spectral calibration of OCI on orbit. Multiple lines have been analyzed for both the ultraviolet to visible (UVVIS, 340−607 nm) and visible to near-infrared (VISNIR, 597−897 nm) grating spectrographs. The spectrographs yield hyperspectral observations with 5 nm bandwidths and 0.625 nm sampling intervals. The on-orbit observations have been compared with the prelaunch spectral calibration of OCI performed by the Goddard Laser for Absolute Measurement of Radiance (GLAMR) during thermal vacuum testing to track any changes in the calibration since launch. The calibration analyzed the line positions and strengths for the Fraunhofer lines for each spectrograph by comparing the solar spectra measured by OCI with predicted solar spectra derived from the solar reference spectrum and the BRDF of the solar diffuser, convolved with the OCI relative spectral responses. The calibration also compared the line positions of the atmospheric absorption bands with the model transmissions used by the PACE Project. The line position comparisons show that the root mean square (RMS) spectral difference between the measured and predicted spectra is 0.15 nm, the average spectral shift is 0.062 nm, and the residual spectral dispersion over the wavelength range of the Fraunhofer lines is 0.17 nm. All three estimates of the spectral accuracy of OCI meet the instrument functional requirement of a spectral accuracy of 0.5 nm and are well within the 0.625 nm sampling interval of the data. The line strength comparisons between measured and predicted spectra are essentially the same. These results show that the spectral calibration of OCI on orbit has not drifted since the prelaunch calibration of OCI by GLAMR and that the on-orbit spectral calibration of OCI is stable over time. These results also provide a baseline for monitoring the future spectral performance of OCI on orbit.

Radiometric Calibration↗

NASA NTRS2024

Pre-Launch Calibration and Validation of the Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) Instrument

The Airborne Hyper-Angular Rainbow Polarimeter (AirHARP) is a new imaging polarimeter instrument capable of sampling a single Earth target from up to 120 viewing angles, in four spectral channels, and in three linear polarization states across a 114° field of view (FOV). AirHARP is telecentric in the image space and simultaneously images three linear polarization states with no moving parts. These two aspects of the design allow for a simple and efficient quantitative calibration. Using coefficients derived at the center of the lens and the detector flatfields, we can calibrate the entire AirHARP sensor in a variety of laboratory, field, and space environments. We show that this telecentric calibration technique yields a 1σ absolute uncertainty of 0.25 % in degree of linear polarization (DOLP) in the laboratory for all channels and for pixels around the optical axis. To validate across the FOV, we compare our multi-angle reflectance and polarization data with the Research Scanning Polarimeter (RSP) over targets sampled during the NASA Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign. We use the error-normalized difference technique to estimate how well the instruments compare relative to their error models. We find that AirHARP and the RSP reasonably agree for reflectance and DOLP within 2 standard deviations of their mutual uncertainty at 550, 670, and 870 nm and over a limited set of ocean and desert scenes. This calibration technique makes the Hyper-Angular Rainbow Polarimeter (HARP) design attractive for new spaceborne climate missions: HARP CubeSat (2020–2022), HARP2 (2024–) on the NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE), and beyond.

Airborne Hyper-Angular Rainbow Polarimeter↗

DOE OSTI2022

Snowmass Instrumentation Frontier IF08 Topical Group Report: Noble Element Detectors

Particle detectors making use of noble elements in gaseous, liquid, or solid phases are prevalent in neutrino and dark matter experiments and are also used to a lesser extent in collider-based particle physics experiments. These experiments take advantage of both the very large, ultra-pure target volumes achievable and the multiple observable signal pathways possible in noble-element based particle detectors. As these experiments seek to increase their sensitivity, novel and improved technologies will be needed to enhance the precision of their measurements and to broaden the reach of their physics programs. The areas of R&D in noble element instrumentation that have been identified by the HEP community in the Snowmass process are highlighted by five key messages: IF08-1) Enhance and combine existing modalities (scintillation and electron drift) to increase signal-to-noise and reconstruction fidelity; IF08-2) Develop new modalities for signal detection in noble elements, including methods based on ion drift, metastable fluids, solid-phase detectors and dissolved targets. Collaborative and blue-sky R&D should also be supported to enable advances in this area; IF08-3) Improve the understanding of detector microphysics and calibrate detector response in new signal regimes; IF08-4) Address challenges in scaling technologies, including material purification, background mitigation, large-area readout, and magnetization; and IF08-5) Train the next generation of researchers, using fast-turnaround instrumentation projects to provide the design-through-result training no longer possible in very-large-scale experiments. This topical group report identifies and documents recent developments and future needs for noble element detector technologies. In addition, we highlight the opportunity that this area of research provides for continued training of the next generation of scientists.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Common Cause Failure Analysis for Nuclear Power Plant Instrumentation and Control Systems

This presentation is prepared for the IAEA Virtual Consultancy Meeting on Preparation of a Coordinated Research Project on Common Cause Failures in Nuclear Power Plant Instrumentation and Control Systems from February 5 ? 8, 2024. It provides an overview of the INL activities on the common cause failure analysis for instrumentation and control system in nuclear power plants.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2023

Development of Instrumented Advanced Test Reactor Irradiation Capsule Experiment for In-situ Thermal Conductivity Measurements of High-Density Fuels

Idaho National Laboratory (INL) is developing a first-of-a-kind leadout instrumented capsule experiment design to enable in-situ measurement capabilities in the Advanced Test Reactor (ATR) core. The Ceramic Advanced Thermal Evolution Research (CRATER) experiment supports the aLEU program objective to accelerate fuel performance irradiation testing for identifying alternative high-assay, low enriched uranium (HALEU) fuel systems. CRATER is a fueled, instrumented capsule experiment to measure in-situ temperature and thermal conductivity of ceramic fuels. Two ceramic fuel types will be used, uranium mono-nitride (UN) and uranium mono-carbide (UC), with a third metallic fuel used for comparison (UMo). The three fuel specimens will use a stainless-steel cladding. Programmatic objectives include linear heat generation rates (LHGR) of 210 ± 25 Watts per cm. and an inner clad temperature of 300-450 °C. The evolution of fuel thermal conductivity during irradiation has never been successfully measured in-situ for these systems and this experiment is designed to use advances in measurement sciences to characterize how thermal transport properties evolve while in reactor. Neutronic simulations of the experiment and its surrounding reactor environment were conducted using the Monte Carlo N-Particle Transport code (MCNP) and result in optimized fuel enrichment to meet target linear heat generation rates (LHGRs) influencing fuel temperatures, and fuel burnup requirements. Fabrication research and development (R&D) efforts are underway to produce annular right cylinder UC and UN pellets using carbothermic reduction and nitridation (or hydride-dehydride-nitride) synthesis methods, followed double-action die cold isostatic pressing.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Risk-informed Graded Approach for Reliability and Performance Assessment of Sensor and Instrumentation Systems within Advanced Condition Monitoring Technologies

Advanced condition monitoring (ACM) technologies, such as digital twins, are innovative strategies designed to provide real-time health insights, including the remaining useful life of components. The primary goal of ACM is to predict and alert operators to potential functional failures before they occur. ACM systems achieve this by integrating predictive models with various sensor instrumentation, analog-to-digital converters, data warehouses, and data pre-processors. These sensor and instrumentation systems (SIS) are essential for forming a comprehensive understanding of component conditions and ensuring the predictive success of ACM programs. Introducing new technologies like ACM involves varying degrees of risk that can impact plant reliability. Therefore, risk mitigation should be commensurate with the performance and reliability of the developed technology, following a risk-informed graded approach (RIGA). Establishing a RIGA process requires a clear understanding of the hazards and reliability of all subsystems, including their interdependencies and potential impacts on the overall system. Given the critical role of SIS in ACM, this work reviews hazard identification and reliability quantification methods for SIS. It also considers these methods' implications when developing a RIGA process for ACM.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Parallel sorting algorithm classification: is manual instrumentation necessary?

Understanding parallel algorithms is crucial for accelerating scientific simulations on complex, distributed memory, high-performance computers. Modern algorithm classification approaches learn semantics directly from source code to differentiate between algorithms, however, accessing source code is not always possible. We can learn about parallel algorithms from observing their performance, as programs running the same algorithms and using the same hardware should exhibit similar performance characteristics. We present an approach to learn algorithm classes from parallel performance data directly in order to classify algorithms without access to the source code. We extend previous work to enable classifying parallel sorting algorithms using automatic instrumentation instead of requiring manual region annotations in the source code. In this work, we design and demonstrate a study for classification of parallel sorting algorithms using parallel performance data collected from automatic instrumentation, and evaluate the performance of our new methodology on classification. We leverage Caliper to collect the performance data, Thicket for our exploratory data analysis (EDA), and PyTorch and Scikit-learn to evaluate the effectiveness of random forests, support vector machines (SVMs), decision trees, neural networks, and logistic regressions on parallel performance data. Additionally, we study noise in parallel performance data, whether the removal of noise and pre-processing of the data is necessary to accurately classify parallel sorting algorithms, and determine the effectiveness of features created from performance data. In conclusion, we demonstrate classification accuracy for these five different models of up to 97.7% across four different parallel algorithm classes.

Algorithm Classification↗

DOE OSTI2024

Optimization of ray-tracing simulations to confirm performance of the GP-SANS instrument at the High-Flux Isotope Reactor

The CG-2 beamline at the High Flux Isotope Reactor (HFIR) exhibits a notable discrepancy between observed count rates and the count rates we would expect based on a Monte-Carlo neutron ray-trace simulation. These simulations consistently predict count rates approximately five times greater than those observed in four separate experimental runs involving different instrument configurations. This discrepancy suggests that certain factors are causing losses in measurements that are not adequately accounted for in the simulation, in particular guide reflectivity or misalignment. To investigate these discrepancies, a high-dimensional simulation parameter approach is applied in order to understand the losses. Region of Interest (ROI) groups along the instrument are assigned to different surfaces of the guide components within the simulation. This allows the parameters of those guide components to be varied as a group to minimize the complexity of the search space. The result is an optimization of simulation parameters using an iterative scheme that aims to minimize the difference between experimentally measured count rates and simulated count rates across all tested collimator combinations. This proposed methodology holds the potential to reveal previously unrecognized sources of intensity loss in the CG-2 beamline at HFIR and improve the accuracy of simulations, leading to enhanced understanding and performance of the beamline for various scientific applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Evaluating Cloud Properties at Scott Base: Comparing Ceilometer Observations With ERA5, JRA55, and MERRA2 Reanalyses Using an Instrument Simulator

This study compares CL51 ceilometer observations made at Scott Base, Antarctica, with statistics from the ERA5, JRA55, and MERRA2 reanalyses. To enhance the comparison we use a lidar instrument simulator to derive cloud statistics from the reanalyses which account for instrumental factors. The cloud occurrence in the three reanalyses is slightly overestimated above 3 km, but displays a larger underestimation below 3 km relative to observations. Unlike previous studies, we see no relationship between relative humidity and cloud occurrence biases, suggesting that the cloud biases do not result from the representation of moisture. We also show that the seasonal variation of cloud occurrence and cloud fraction, defined as the vertically integrated cloud occurrence, are small in both the observations and the reanalyses. We also examine the quality of the cloud representation for a set of weather states derived from ERA5 surface winds. The variability associated with grouping cloud occurrence based on weather state is much larger than the seasonal variation, highlighting weather state is a strong control of cloud occurrence. All the reanalyses continue to display underestimates below 3 km and overestimates above 3 km for each weather state. But the variability in ERA5 statistics matches the changes in the observations better than the other reanalyses. We also use a machine learning scheme to estimate the quantity of supercooled liquid water cloud from the ceilometer observations. Ceilometer low-level supercooled liquid water cloud occurrences are considerably larger than values derived from the reanalyses, further highlighting the poor representation of low-level clouds in the reanalyses.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Extraction of mechanical properties of materials through deep learning from instrumented indentation

Instrumented indentation has been developed and widely utilized as one of the most versatile and practical means of extracting mechanical properties of materials. This method is particularly desirable for those applications where it is difficult to experimentally determine the mechanical properties using stress–strain data obtained from coupon specimens. Such applications include material processing and manufacturing of small and large engineering components and structures involving the following: three-dimensional (3D) printing, thin-film and multilayered structures, and integrated manufacturing of materials for coupled mechanical and functional properties. Here, we utilize the latest developments in neural networks, including a multifidelity approach whereby deep-learning algorithms are trained to extract elastoplastic properties of metals and alloys from instrumented indentation results using multiple datasets for desired levels of improved accuracy. We have established algorithms for solving inverse problems by recourse to single, dual, and multiple indentation and demonstrate that these algorithms significantly outperform traditional brute force computations and function-fitting methods. Moreover, we present several multifidelity approaches specifically for solving the inverse indentation problem which 1) significantly reduce the number of high-fidelity datasets required to achieve a given level of accuracy, 2) utilize known physical and scaling laws to improve training efficiency and accuracy, and 3) integrate simulation and experimental data for training disparate datasets to learn and minimize systematic errors. The predictive capabilities and advantages of these multifidelity methods have been assessed by direct comparisons with experimental results for indentation for different commercial alloys, including two wrought aluminum alloys and several 3D printed titanium alloys.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Robustness of cosmic birefringence measurement against Galactic foreground emission and instrumental systematics

The polarization of the cosmic microwave background (CMB) can be used to search for parity-violating processes like that predicted by a Chern-Simons coupling to a light pseudoscalar field. Such an interaction rotates E modes into E modes in the observed CMB signal through an effect known as cosmic birefringence. Even though isotropic birefringence can be confused with the rotation produced by a miscalibration of the detectors' polarization angles, the degeneracy between both effects is broken when Galactic foreground emission is used as a calibrator. Here, in this work, we use realistic simulations of the High-Frequency Instrument of the Planck mission to test the impact that Galactic foreground emission and instrumental systematics have on the recent birefringence measurements obtained through this technique. Our results demonstrate the robustness of the methodology against the miscalibration of polarization angles and other systematic effects, like intensity-to-polarization leakage, beam leakage, or cross-polarization effects. However, our estimator is sensitive to the EB correlation of polarized foreground emission. Here we propose to correct the bias induced by dust EB by modeling the foreground signal with templates produced in Bayesian component-separation analyses that fit parametric models to CMB data. Acknowledging the limitations of currently available dust templates like that of the Commander sky model, high-precision CMB data and a characterization of dust beyond the modified blackbody paradigm are needed to obtain a definitive measurement of cosmic birefringence in the future.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

VISION: a modular AI assistant for natural human-instrument interaction at scientific user facilities

Scientific user facilities, such as synchrotron beamlines, are equipped with a wide array of hardware and software tools that require a codebase for human-computer-interaction. This often necessitates developers to be involved to establish connection between users/researchers and the complex instrumentation. The advent of generative AI presents an opportunity to bridge this knowledge gap, enabling seamless communication and efficient experimental workflows. Here we present a modular architecture for the Virtual Scientific Companion by assembling multiple AI-enabled cognitive blocks that each scaffolds large language models (LLMs) for a specialized task. With VISION, we performed LLM-based operation on the beamline workstation with low latency and demonstrated the first voice-controlled experiment at an x-ray scattering beamline. The modular and scalable architecture allows for easy adaptation to new instruments and capabilities. Development on natural language-based scientific experimentation is a building block for an impending future where a science exocortex—a synthetic extension to the cognition of scientists—may radically transform scientific practice and discovery.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Validation of emission-line galaxies target selection algorithms for the Dark Energy Spectroscopic Instrument using the MMT Binospec

ABSTRACT The forthcoming Dark Energy Spectroscopic Instrument (DESI) experiment plans to measure the effects of dark energy on the expansion of the Universe and create a 3D map of the Universe using galaxies up to z ∼ 1.6 and QSOs up to z ∼ 3.5. In order to create this map, DESI will obtain spectroscopic redshifts of over 30 million objects; among them, a majority are [O ii] emitting star-forming galaxies known as emission-line galaxies (ELGs). These ELG targets will be pre-selected by drawing a selection region on the g − r versus r − z colour–colour plot, where high-redshift ELGs form a separate locus from the lower redshift ELGs and interlopers. In this paper, we study the efficiency of three ELG target selection algorithms – the Final Design Report (FDR) cut based on the DEEP2 photometry, Number Density Modelling (NDM) and Random Forest – to determine how the combination of these three algorithms can be best used to yield a simple selection boundary that will be best suited to meet DESI’s science goals. To do this, we selected 17 small patches in the DESI footprint where we run the three target selection algorithms to pre-select ELGs based on their photometry. We observed the pre-selected ELGs using the MMT Binospec, which is similar in functionality to the DESI instrument, to obtain their spectroscopic redshifts and fluxes of 1054 ELGs. By analysing the redshift and fluxing distribution of these galaxies, we find that although NDM performed the best, simple changes in the FDR definition would also yield sufficient performance.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Hooks versus Pedicle Screws at the Upper Instrumented Level: An In Vitro Biomechanical Comparison

Study Design. Controlled laboratory study Objective. To compare motions at the upper instrumented vertebra (UIV) and supra-adjacent level (UIV+1) between two fixation techniques in thoracic posterior spinal fusion (PSF) constructs. We hypothesized there would be greater motion at UIV+1 after cyclic loading across all constructs and bilateral pedicle screws (BPS) with posterior ligamentous compromise would demonstrate the greatest UIV+1 range of motion (ROM). Summary of Background Data. Proximal junctional kyphosis (PJK) is a well-recognized complication following long thoraco-lumbar PSF, however its mechanism is poorly understood. Methods. Twenty-seven thoracic functional spine units (FSU) were randomly divided into three UIV fixation groups (n=9): (1) BPS, (2) bilateral transverse process hooks (TPH), and (3) BPS with compromise of the posterior elements between UIV and UIV+1 (BPS-C). Specimens were tested on a servohydraulic materials testing system in native state, following instrumentation, and after cyclic loading. FSUs were loaded in flexion-extension (FE), lateral bending (LB), and axial rotation (AR). Results. After cyclic testing, the TPH group had a mean 29.4% increase in FE ROM at UIV+1 versus 76.6% in the BPS group ( P <0.05). The BPS-C group showed an increased FE of 49.9% and 62.19% with sectioning of the facet joints and interspinous ligament respectively prior to cyclic testing. Conclusion. Bilateral pedicle screws at the UIV led to greater motion at UIV+1 compared to bilateral TPH after cyclic loading. This is likely due to the increased rigidity of BPS compared to TPH leading to a “softer” transition between the TPH construct and native anatomy at the supra-adjacent level. Facet capsule compromise led to a 49.9% increase in UIV+1 motion, underscoring the importance of preserving the posterior ligamentous complex. Clinical studies that account for fusion rates are warranted to determine if constructs with a “soft transition” result in less PJK in vivo.

Neurosciences & Neurology↗

DOE OSTI2025

An iterative CMB lensing estimator minimizing instrumental noise bias

Noise maps from cosmic microwave background (CMB) experiments are generally statistically anisotropic, due to scanning strategies, atmospheric conditions, or instrumental effects. Any mismodeling of this complex noise can bias the reconstruction of the lensing potential and the measurement of the lensing power spectrum from the observed CMB maps. We introduce a new CMB lensing estimator based on the maximum (MAP) reconstruction that is minimally sensitive to these instrumental noise biases. By modifying the likelihood to rely exclusively on correlations between CMB map splits with independent noise realizations, we minimize autocorrelations that contribute to biases. In the regime of many independent splits, this maximum closely approximates the optimal MAP reconstruction of the lensing potential. In simulations, we demonstrate that this method is able to determine lensing observables that are immune to any noise mismodeling with a negligible cost in signal-to-noise ratio. Our estimator enables unbiased and nearly optimal lensing reconstruction for next-generation CMB surveys.

Legrand, Louis [University of Cambridge (United Ki↗

DOE OSTI2021

Beamline K11 DIAD: a new instrument for dual imaging and diffraction at Diamond Light Source

The Dual Imaging and Diffraction (DIAD) beamline at Diamond Light Source is a new dual-beam instrument for full-field imaging/tomography and powder diffraction. This instrument provides the user community with the capability to dynamically image 2D and 3D complex structures and perform phase identification and/or strain mapping using micro-diffraction. The aim is to enable in situ and in operando experiments that require spatially correlated results from both techniques, by providing measurements from the same specimen location quasi-simultaneously. Using an unusual optical layout, DIAD has two independent beams originating from one source that operate in the medium energy range (7–38 keV) and are combined at one sample position. Here, either radiography or tomography can be performed using monochromatic or pink beam, with a 1.4 mm × 1.2 mm field of view and a feature resolution of 1.2 µm. Micro-diffraction is possible with a variable beam size between 13 µm × 4 µm and 50 µm × 50 µm. One key functionality of the beamline is image-guided diffraction, a setup in which the micro-diffraction beam can be scanned over the complete area of the imaging field-of-view. This moving beam setup enables the collection of location-specific information about the phase composition and/or strains at any given position within the image/tomography field of view. The dual beam design allows fast switching between imaging and diffraction mode without the need of complicated and time-consuming mode switches. Real-time selection of areas of interest for diffraction measurements as well as the simultaneous collection of both imaging and diffraction data of (irreversible) in situ and in operando experiments are possible.

36 MATERIALS SCIENCE↗