Exobiology Extant Life Surveyor: EELS: EELS Mission Concept Special Presentation
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Carpenter, Kalind↗
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Survival, growth and tag retention of juvenile European eel ( Anguilla anguilla L.) with implanted 12 mm passive integrated transponder tags and acoustic tags
To evaluate the efficiency of tagging juvenile European eels with implanted 12 mm passive integrated transponder (PIT) tags or Eel/Lamprey acoustic transmitters (ELATs), the authors studied tag retention, survival and growth of eels (7–25 g). Experimental eels were obtained from an eel farm, tagged and then released in a series of shallow dug-out ponds with a surface area of c. 200 m 2 . Tagged and control eels were distributed evenly, with 50 tagged and 50 control eels in each of four ponds, giving a total of 200 tagged and 200 control eels mixed. After 76 days, the ponds were drained, and eels were sampled and measured. A total of 344 eels (86%) were recaptured, indicating high survival. Tag retention was 99% as only one of the recaptured PIT-tagged eels had lost the tag and none of the ELAT tagged. The results demonstrated that tagging juvenile eels >16 cm with these small tags is indeed feasible. The growth of tagged and control fish was differentiated but generally low in length and negative in mass but did not differ between the three groups.
54 ENVIRONMENTAL SCIENCES↗
Safe passage of American Eels through a novel hydropower turbine
Abstract Objective Study the effects of downstream passage through a novel turbine designed for fish safety, the Restoration Hydro Turbine (RHT), on American Eels Anguilla rostrata in a recirculating turbine test facility. Methods A 55‐cm‐diameter RHT was operated under 10 m of hydraulic head and 667 revolutions/min. In total, 131 eels were passed through the turbine and 43 eels were used as experimental controls (length = 33.9–65.5 cm). High‐speed video of passage through the runner region was captured for 89% of turbine‐passed eels, and injury and behavioral effects were recorded immediately before and after passage, as well as after a 48‐h holding period. A subset of 37 eels was additionally examined with X‐ray imaging for internal injuries. Result The 48‐h survival rate for both treatment and control groups was 100%, with no major internal or external injuries present after the holding period. Conclusion This is a substantial improvement over eel survival rates through conventional Kaplan and Francis turbines, which may range from 40% to 95%, and suggests that hydropower turbines designed for safe downstream fish passage could be implemented without major impacts to eels.
59 BASIC BIOLOGICAL SCIENCES↗
Fe and O EELS Studies of Ion Irradiated Murchison CM2 Carbonaceous Chondrite Matrix
Introduction: The physical and chemical response of hydrated carbonaceous chondrite materials to space weathering processes is poorly understood. Improving this understanding is a key part of establishing how regoliths on primitive carbonaceous asteroids respond to space weathering processes, knowledge that supports future sample return missions (Hayabusa 2 and OSIRISREx) that are targeting objects of this type. We previously reported on He+ irradiation of Murchison matrix and showed that the irradiation resulted in amorphization of the matrix phyllosilicates, loss of OH, and surface vesiculation. Here, we report electron energy-loss spectroscopy (EELS) measurements of the irradiated material with emphasis on the Fe and O speciation. Sample and Methods: A polished thin section of the Murchison CM2 carbonaceous chondrite was irradiated with 4 kilovolts He(+) (normal incidence) to a total dose of 1 x 10(exp 18) He(+) per square centimeter. We extracted thin sections from both irradiated and unirradiated regions in matrix using focused ion beam (FIB) techniques with electron beam deposition for the protective carbon strap to minimize surface damage artifacts from the FIB milling. The FIB sections were analyzed using a JEOL 2500SE scanning and transmission electron microscope (STEM) equipped with a Gatan Tridiem imaging filter. EELS spectra were collected from 50 nanometer diameter regions with an energy resolution of 0.7 electronvolts FWHM at the zero loss. EELS spectra were collected at low electron doses to minimize possible artifacts from electron-beam irradiation damage. Results and Discussion: Fe L (sub 2,3) EELS spectra from matrix phyllosilicates in CM chondrites show mixed Fe(2+)/Fe(3+) oxidation states with Fe(3+)/Sigma Fe approximately 0.5. Fe L(sub 2,3) spectra from the irradiated/ amorphized matrix phyllosilicates show higher Fe(2+)/Fe(3+) ratios compared to spectra obtained from pristine material at depths beyond the implantation/amorphization layer. We also obtained O Ka spectra from phyllosilicates in both regions of the sample. The O Ka spectra show a pre-edge feature at approximately 530.5 electronvolts that is related to O 2p states hybridized with Fe 3d states. The intensity ratio of the O Ka pre-edge peak relative to the main part of the O Ka edge (that results from transitions of O 1s to 2p states) is lower in the irradiated layer compared to the pristine material and may reflect the loss of O (as OH) as was observed by IR spectroscopy. Conclusions: In addition to amorphization and OH loss, EELS spectra of He(+) irradiated matrix phyllosilicates in Murchison show that some of the Fe(3+) is reduced to Fe(2+). Spectral deconvolution is underway to extract quantitative ratios from the EELS spectra.
Keller, L. P.↗
TEAMER: Electrically Engaged Undulation (EEL) System
The Electrically Engaged UnduLation (EEL) system is a buoyancy-driven submersible device for powering oceanographic instruments. Physically, EEL is a slender body whose flexible spine is made up of energy units interconnected by uniaxial hinges. Each unit consists of a pair of piezoelectric elements that converts the bending stress into electrical current to a battery charging circuit. An outer plastic skin forms a seal against water and allows for flexibility at hinge locations. At the top is a bluff body with electronics that holds a ballast for buoyancy adjustment. The bluff body is also responsible for creating fluid instabilities in its wake. When gliding through the water (mode 2), the spine will flex in response to the alternating vortices that shed from the head. This "lock-in" phenomenon occurs when the frequency at which vortices shed resonates with the EEL natural frequency, during which the efficient gaits were found in species of sea snake, eels, and fish. For active propulsion, a single motor can be placed at the first segment and provide the oscillatory input for propulsion similar to a dolphin's kick. Such anguilliform swimming is both efficient and nearly silent compared to a spinning propeller. Ultimately, mimicking bio-locomotion provides a viable path to a drag-reduced, self-propelled energy harvesting system for ocean monitoring.
16 TIDAL AND WAVE POWER↗
MnEdgeNet for accurate decomposition of mixed oxidation states for Mn XAS and EELS L2,3 edges without reference and calibration
Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer and redox centers for electronic, and electrocatalytic and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date, although the measurements of the Mn L2,3 edges are straightforward given the sample is prepared properly, an accurate decomposition of the mix valence states of Mn remains non-trivial. For both EELS and XAS, 2+, 3+, and 4+ reference spectra need to be taken on the same instrument/beamline and preferably in the same experimental session because the instrumental resolution and the energy axis offset could vary from one session to another. To circumvent this hurdle, in this study, we adopted a deep learning approach and developed a calibration-free and reference-free method to decompose the oxidation state of Mn L2,3 edges for both EELS and XAS. A deep learning regression model is trained to accurately predict the composition of the mix valence state of Mn. To synthesize physics-informed and ground-truth labeled training datasets, we created a forward model that takes into account plural scattering, instrumentation broadening, noise, and energy axis offset. With that, we created a 1.2 million-spectrum database with 1-by-3 oxidation state composition ground truth vectors. The library includes a sufficient variety of data including both EELS and XAS spectra. By training on this large database, our convolutional neural network achieves 85% accuracy on the validation dataset. We tested the model and found it is robust against noise (down to PSNR of 10) and plural scattering (up to t/λ = 1). We further validated the model against spectral data that were not used in training. In particular, the model shows high accuracy and high sensitivity for the decomposition of Mn 3 O 4 , MnO, Mn 2 O 3 , and MnO 2 . The accurate decomposition of Mn 3 O 4 experimental data shows the model is quantitatively correct and can be deployed for real experimental data. Our model will not only be a valuable tool to researchers and material scientists but also can assist experienced electron microscopists and synchrotron scientists in the automated analysis of Mn L edge data.
25 ENERGY STORAGE↗
Building workflows for an interactive human-in-the-loop automated experiment (hAE) in STEM-EELS
Exploring the structural, chemical, and physical properties of matter on the nano- and atomic scales has become possible with the recent advances in aberration-corrected electron energy-loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). However, the current paradigm of STEM-EELS relies on the classical rectangular grid sampling, in which all surface regions are assumed to be of equal a priori interest. However, this is typically not the case for real-world scenarios, where phenomena of interest are concentrated in a small number of spatial locations, such as interfaces, structural and topological defects, and multi-phase inclusions. One of the foundational problems is the discovery of nanometer- or atomic-scale structures having specific signatures in EELS spectra. Herein, we systematically explore the hyperparameters controlling deep kernel learning (DKL) discovery workflows for STEM-EELS and identify the role of the local structural descriptors and acquisition functions in experiment progression. In agreement with the actual experiment, we observe that for certain parameter combinations the experiment path can be trapped in the local minima. We demonstrate the approaches for monitoring the automated experiment in the real and feature space of the system and knowledge acquisition of the DKL model. Based on these, we construct intervention strategies defining the human-in-the-loop automated experiment (hAE). This approach can be further extended to other techniques including 4D STEM and other forms of spectroscopic imaging. The hAE library is available on Github at https://github.com/utkarshp1161/hAE/tree/main/hAE.
Pratiush, Utkarsh [Univ. of Tennessee, Knoxville, ↗
Electrically Engaged UnduLation (EEL) Marine Energy System
Current technologies at the forefront of instrumentation for ocean observation through acoustic mapping are powered by batteries that cannot achieve the lasting durations required for high-resolution measurements. Pyro-E has designed an Electrically Engaged unduLation (EEL) system capable of in situ energy harvest and deployment in sub-sea configurations. By extracting kinetic energy from the ocean, EEL proposes to provide power output ranging from 1 mW to 1,000 mW. Such a magnitude of power can sustain active instrumentation with onboard processing of data and over-the-horizon telemetry. Pyro-E and Oak Ridge National Laboratory have utilized software tools and powerful computing resources to develop a techno-economic design of the EEL capable of harvesting 5 W. The material response of the EEL under simulated hydromechanical loads is described through time-accurate predictions. All physical tests were conducted by Pyro-E to guide simulations at Oak Ridge National Laboratory.
16 TIDAL AND WAVE POWER↗
The Eel Pulsar Wind Nebula: A PeVatron-candidate Origin for HAWC J1826-128 and HESS J182-130
HAWC J1826–128 is one of the brightest Galactic TeV γ-ray sources detected by the High Altitude Water Cherenkov (HAWC) observatory, with photon energies extending up to nearly ~100 TeV. This HAWC source spatially coincides with the H.E.S.S. TeV source HESS J1826–130 and the "Eel" pulsar wind nebula (PWN), which is associated with the GeV pulsar PSR J1826–1256. In the X-ray band, Chandra and XMM-Newton revealed that the Eel PWN is composed of both a compact nebula (~15'') and diffuse X-ray emission (~6' × 2') extending away from the pulsar. Our NuSTAR observation detected hard X-ray emission from the compact PWN up to ~20 keV and evidence of the synchrotron burn-off effect. In addition to the spatial coincidence between HESS J1826–130 and the diffuse X-ray PWN, our multiwavelength spectral energy distribution (SED) analysis using X-ray and γ-ray data establishes a leptonic origin of the TeV emission associated with the Eel PWN. Furthermore, our evolutionary PWN SED model suggests (1) a low PWN B-field of ~1 μG, (2) a significantly younger pulsar age (t ~ 5.7 kyr) than the characteristic age (τ = 14.4 kyr), and (3) a maximum electron energy of ${E}_{\max }=2$ PeV. The low B-field, as well as the putative supersonic motion of the pulsar, may account for the asymmetric morphology of the diffuse X-ray emission. Our results suggest that the Eel PWN may be a leptonic PeVatron particle accelerator powered by the ~6 kyr old pulsar PSR J1826–1256 with a spin-down power of 3.6 × 10 36 erg s –1 .
79 ASTRONOMY AND ASTROPHYSICS↗
Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders
Abstract A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using FeL‐edge ΔEpeak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems.
Chemistry↗
Substrate matters: Coupled phonon modes of a spherical particle on a substrate probed with EELS
Using vibrational electron energy loss spectroscopy (vib-EELS) combined with numerical modeling, we investigate the physical mechanisms governing the phonon coupling between a spherical particle sustaining multipolar surface phonon modes and an underlying thin film. Depending upon their dielectric composition, a variety of hybrid phonon modes arise in the EEL spectrum due to the interaction between polarization charges in the particle and film. Mirror charge effects and phonon mode hybridization are the active mechanisms acting on dielectric and metallic-type films, respectively. Furthermore, processes beyond dipole-dipole interactions are required to describe the sphere-film coupling.
Localized surface phonon response↗
Fabrication and characterization of boron-terminated tetravacancies in monolayer hBN using STEM, EELS and electron ptychography
Tetravacancies in monolayer hexagonal boron nitride (hBN) with consistent edge termination (boron or nitrogen) form triangular nanopores with electrostatic potentials that can be leveraged for applications such as selective ion transport and neuromorphic computing. In order to quantitatively predict the properties of these structures, an atomic-level understanding of their local electronic and chemical environments is required. Moreover, robust methods for their precision manufacture are needed. Here we use electron irradiation in a scanning transmission electron microscope (STEM) at a high dose rate to drive the formation of boron-terminated tetravacancies in monolayer hBN. Characterization of the defects is achieved using aberration-corrected STEM, monochromated electron energy-loss spectroscopy (EELS), and electron ptychography. Z-contrast in STEM and chemical fingerprinting by core-loss EELS enable identification of the edge terminations, while electron ptychography gives insight into structural relaxation of the tetravacancies and provides evidence of enhanced electron density around the defect perimeters indicative of bonding effects.
2D hBN↗
Calibrating cryogenic temperature of TEM specimens using EELS
Cryogenic Scanning/Transmission Electron Microscopy has been established as a leading method to image sensitive biological samples and is now becoming a powerful tool to understand materials' behavior at low temperatures. However, achieving precise local temperature calibration at low temperatures remains a challenge, which is especially crucial for studying phase transitions and emergent physical properties in quantum materials. In this study, we employ electron energy loss spectroscopy (EELS) to measure local cryogenic specimen temperatures. Here we use the temperature-dependent characteristics of aluminum's bulk plasmon peak in EEL spectra, which shifts due to changes in electron density caused by thermal expansion and contraction. We successfully demonstrate the versatility of this method by calibrating different liquid nitrogen cooling holders in various microscopes, regardless of whether a monochromated or non-monochromated electron beam is used. Temperature discrepancies between the actual temperature and the setpoint temperatures are identified across a range from room temperature to 100 K. This work demonstrates the importance of temperature calibrations at intermediate temperatures and presents a straightforward, robust method for calibrating local temperatures of cryogenically-cooled specimens in electron microscopes.
36 MATERIALS SCIENCE↗
Detecting Magnon-phonon coupling in yttrium iron garnet with variable temperature STEM-EELS
Magnons, quanta of spin wave excitations in magnetically ordered materials, have been identified as candidates for several potentially transformative technologies in recent years. Macroscopic techniques, such as neutron scattering or Raman spectroscopy, can be used to identify and analyze magnons, but provide relatively delocalized information about the sample. Understanding how the bonding and local structure of a material interacts with, and influences, the magnon population in a material is a crucial step toward the ability to produce any real-world application utilizing magnons. By leveraging the combined spatial resolution of scanning transmission electron microscopy (STEM) and the energy resolution of monochromated electron energy-loss spectroscopy (EELS) nanoscale analysis of magnons can be performed. While the weak interaction of magnons with the electron beam makes magnon EELS challenging on reasonable timescales, magnon-phonon coupling can be leveraged to understand magnons through their effect on the more easily measured phonons. Here, we examine yttrium iron garnet (YIG) flakes, and demonstrate non-linear, temperature-dependent shifts in the phonon frequencies, consistent with previously described magnon-phonon coupling effects. The ability to measure the temperature-dependence of vibrational frequencies with high precision in individual nanoscale flakes, demonstrates the ability to study magnon-phonon coupling in the STEM with unprecedented spatial resolution.
Reifsnyder, Alexander [ORNL]↗
EELS Studies of Cerium Electrolyte Reveal Substantial Solute Concentration Effects in Graphene Liquid Cells
Graphene liquid cell transmission electron microscopy is a powerful technique to visualize nanoscale dynamics and transformations at atomic resolution. However, the solution in liquid cells is known to be affected by radiolysis, and the stochastic formation of graphene liquid cells raises questions about the solution chemistry in individual pockets. In this study, electron energy loss spectroscopy (EELS) was used to evaluate a model encapsulated solution, aqueous CeCl 3 . First, the ratio between the O K-edge and Ce M-edge was used to approximate the concentration of cerium salt in the graphene liquid cell. It was determined that the ratio between oxygen and cerium was orders of magnitude lower than what is expected for a dilute solution, indicating that the encapsulated solution is highly concentrated. To probe how this affects the chemistry within graphene liquid cells, the oxidation of Ce 3+ was measured using time-resolved parallel EELS. Here, it was determined that Ce 3+ oxidizes faster under high electron fluxes, but reaches the same steady-state Ce 4+ concentration regardless of flux. The time-resolved concentration profiles enabled direct comparison to radiolysis models, which indicate rate constants and g-values of certain molecular species are substantially different in the highly concentrated environment. Finally, electron flux-dependent gold nanocrystal etching trajectories showed that gold nanocrystals etch faster at higher electron fluxes, correlating well with the Ce 3+ oxidation kinetics. Understanding the effects of the highly concentrated solution in graphene liquid cells will provide new insight on previous studies and may open up opportunities to systematically study systems in highly concentrated solutions at high resolution.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Prediction of the Cu oxidation state from EELS and XAS spectra using supervised machine learning
Abstract Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states. However, analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standard samples. This limits analysis throughput and the ability to extract quantitative information from a sample. In this work, we have trained a random forest model capable of predicting the oxidation state of copper based on its L-edge spectrum. Our model attains an R 2 score of 0.85 and a root mean square error of 0.24 on simulated data. It has also successfully predicted experimental L-edge EELS spectra taken in this work and XAS spectra extracted from the literature. We further demonstrate the utility of this model by predicting simulated and experimental spectra of mixed valence samples generated by this work. This model can be integrated into a real-time EELS/XAS analysis pipeline on mixtures of copper-containing materials of unknown composition and oxidation state. By expanding the training data, this methodology can be extended to data-driven spectral analysis of a broad range of materials.
36 MATERIALS SCIENCE↗
Dynamic STEM-EELS for single-atom and defect measurement during electron beam transformations
This study introduces the integration of dynamic computer vision–enabled imaging with electron energy loss spectroscopy (EELS) in scanning transmission electron microscopy (STEM). This approach involves real-time discovery and analysis of atomic structures as they form, allowing us to observe the evolution of material properties at the atomic level, capturing transient states traditional techniques often miss. Rapid object detection and action system enhances the efficiency and accuracy of STEM-EELS by autonomously identifying and targeting only areas of interest. This machine learning (ML)–based approach differs from classical ML in that it must be executed on the fly, not using static data. We apply this technology to V-doped MoS 2 , uncovering insights into defect formation and evolution under electron beam exposure. This approach opens uncharted avenues for exploring and characterizing materials in dynamic states, offering a pathway to increase our understanding of dynamic phenomena in materials under thermal, chemical, and beam stimuli.
47 OTHER INSTRUMENTATION↗
In-situ TEM EELS analysis of memristive thin films for neuromorphic computing
Neuromorphic computing stands as a promising frontier for advancing AI algorithms and applications like ChatGBT, offering significant energy efficiency gains. This paper delves into the hardware design intricacies of memristive thin films and their elementary switching mechanisms, including anion migration, electron migration, and phase transitions. Through comprehensive analysis of electron energy loss spectroscopy (EELS) data via in-situ transmission electron microscopy (TEM), we will deduce the primary memristive switching mechanisms vital for optimizing thin film fabrication parameters and achieving desired film thickness, conductivity, and memory retention. A single crystal ptype Si substrate was used with TiN as the bottom metal electrode, TiO x as the insulating dielectric layer, and Pt as the top metal electrode. In-situ TEM was able to tell us the thin film didn’t behave like a filamentary or phase transition material. EELS data deduced that electron trapping/detrapping was one of the primary switching mechanisms. By shedding light on these elementary mechanisms, our study aims to catalyze the development of more 2 efficient and effective neuromorphic computing systems to be deployed into mainstream technologies.
97 MATHEMATICS AND COMPUTING↗