A quantitative evaluation of the 2nd derivative mode in electron energy loss spectroscopy
Not provided.
Spectroscopy↗
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Results for “electron energy loss spectroscopy”
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Discovering invariant spatial features in electron energy loss spectroscopy images on the mesoscopic and atomic levels
Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional data sets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here, we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core-loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader.
36 MATERIALS SCIENCE↗
Electron energy loss spectroscopy techniques for the study of microbial chromium(VI) reduction
Electron energy loss spectroscopy (EELS) techniques were used to determine oxidation state, at high spatial resolution, of chromium associated with the metal-reducing bacteria, Shewanella oneidensis, in anaerobic cultures containing Cr(VI)O4(2-). These techniques were applied to fixed cells examined in thin section by conventional transmission electron microscopy (TEM) as well as unfixed, hydrated bacteria examined by environmental cell (EC)-TEM. Two distinct populations of bacteria were observed by TEM: bacteria exhibiting low image contrast and bacteria exhibiting high contrast in their cell membrane (or boundary) structure which was often encrusted with high-contrast precipitates. Measurements by EELS demonstrated that cell boundaries became saturated with low concentrations of Cr and the precipitates encrusting bacterial cells contained a reduced form of Cr in oxidation state + 3 or lower.
Evaluation Studies↗
Electron energy loss spectroscopy study of ceria sinters and nanoparticles irradiated with swift heavy ions
The electronic energy loss spectra of ceria (CeO 2 ) irradiated with swift heavy ions (27 MeV Xe and 946 MeV Au) in the electronic slowing down regime were measured for bulk sintered samples and nanoparticles by using a double Cs-corrected transmission electron microscope. The low-loss region as well as the core-loss region, including the oxygen K-edge and cerium M 4, 5 white lines, were recorded. No strong lattice disorder was found in the low-loss peaks for both types of samples showing the same bulk oxygen plasmon loss peak at about 15 eV. However, there is a clear evidence of cerium reduction to the trivalent oxidation state after irradiation for the sintered samples as shown by the K-edge shape of oxygen and decrease of the Ce M 4 /M 5 intensity ratio. A similar change of the M 4 /M 5 intensity ratio was observed for the irradiated nanoparticles with respect to the virgin sample owing to the high energy input inside the nanograin. The effect of radiation damage on electron energy loss spectroscopy data is analyzed for both types of samples and irradiation conditions.
Crystallographic defects↗
Characterization of Fuel Cladding Chemical Interaction on a High Burnup U-10Zr Metallic Fuel via Electron Energy Loss Spectroscopy Enhanced by Machine Learning
Fuel cladding chemical interaction (FCCI) is one of the main performance limiting factors for metallic nuclear fuels. The interaction destabilizes the martensitic microstructure and deteriorates mechanical properties of HT-9 cladding. The detection of low atomic number elements (Z<10) and overlapping of elemental peaks can be problematic in interpreting energy dispersive X-ray spectroscopy (EDS) data. Electron energy loss spectroscopy (EELS) provides precise elemental edge energy values and can detect elements with a low atomic number. This work utilizes EELS to study the distribution of lanthanides and light elements at the interaction region. The sample was prepared from the FCCI region of a U-10Zr (wt.%) solid fuel with HT-9 cladding, irradiated to a burnup of 13.2 at.%. Processing the EELS data included three major steps: 1) enhance the signal to noise ratio by denoising the spectrum with principal component analysis (PCA) method, removing background and performing deconvolution; 2) identify chemical elements with core energy loss edges; 3) confirm different phases using a popular machine learning method, K-means. This work presents qualitative assessment of lanthanides and light elements like carbon (C) and oxygen (O) enhanced by the application of machine learning algorithms. By comparing with EDS elemental maps, EELS provides higher resolution chemical maps, reveals the distribution of carbon at the interaction region supporting the formation of zirconium carbide, a rind-like microstructure feature that was proposed to mitigate the chemical interaction. Furthermore, the plasmon peak map was also found to indicate an energy shift associated with the formation of phases/compounds. K-means clustering method was used on the processed electron energy loss (EEL) spectrum to automatically reveal different phases. The resulting clustered maps from K-means clustering align well with elemental maps confirming certain phases, especially Fe-Ce and Zr-C, in the FCCI region.
EELS↗
Hollow Ptychography: Toward Simultaneous 4D Scanning Transmission Electron Microscopy and Electron Energy Loss Spectroscopy
With the recent development of high-acquisition-speed pixelated detectors, 4D scanning transmission electron microscopy (4D-STEM) is becoming routinely available in high-resolution electron microscopy. 4D-STEM acts as a “universal” method that provides local information on materials that is challenging to extract from bulk techniques. It extends conventional STEM imaging to include super-resolution techniques and to provide quantitative phase-based information, such as differential phase contrast, ptychography, or Bloch wave phase retrieval. However, an important missing factor is the chemical and bonding information provided by electron energy loss spectroscopy (EELS). 4D-STEM and EELS cannot currently be acquired simultaneously due to the overlapping geometry of the detectors. Here, the feasibility of modifying the detector geometry to overcome this challenge for bulk specimens is demonstrated, and the use of a partial or defective detector for ptycholgaphic structural imaging is explored. Here, results show that structural information beyond the diffraction-limit and chemical information from the material can be extracted together, resulting in simultaneous multi-modal measurements, adding the additional dimensions of spectral information to 4D datasets.
4D-scanning transmitting electron microscopy (STEM↗
Fingerprinting Uranium Oxides with Electron Energy Loss Spectroscopy Supported by Theoretical Computations
Uranium oxides occur in a variety of phases that differ in crystal structure and uranium oxidation states. Electron energy loss spectroscopy (EELS) is one of the few techniques that has sufficient spatial resolution and sensitivity to electronic structure to distinguish amongst phases at the nanoscale. However, beam-sensitive materials such as uranium oxides are subject to spectral modification due to interactions with the electron beam. Therefore, theory support is essential to reliably exclude the impact of beam damage and generate true reference datasets. Here we use a comparison of theoretical and experimental spectra to probe the impact of beam damage on O K-edge and U N-edge (N6,7 and N4,5) EELS spectra of various single-valent and mixed-valence uranium oxide bulk phases. Using a low-dose experimental set-up, we show that the O K-edge theoretical spectra are in excellent agreement with experiment for both peak positions and relative intensities of respective peaks. In contrast, U N-edge features are less distinguishing due to the partially localized nature of the U 5f orbitals and overlapping multiplet and spin–orbit coupling effects. This work demonstrates that O K-edge EELS is sufficiently diagnostic to distinguish a wide range of uranium oxides and that the experimental approach used here minimizes beam damage and allows valence state discrimination across the U(IV), U(V) and U(VI) series. When combined with imaging modes available in electron mi-croscopy, the work enables detailed investigation and characterization of uranium redox transformations at the nanoscale.
Carbone, Jacopo↗
Diamond /111/ studied by electron energy loss spectroscopy in the characteristic loss region
Unoccupied surface states on diamond (111) annealed at greater than 900 C are studied by electron energy loss spectroscopy with valence band excitation. A feature found at 2.1 eV loss energy is attributed to an excitation from occupied surface states into unoccupied surface states of energy within the bulk band gap. A surface band gap of approximately 1 eV is estimated. This result supports a previous suggestion for unoccupied band gap states based on core level energy loss spectroscopy. Using the valence band excitation energy loss spectrosocpy, it is also suggested that hydrogen is removed from the as-polished diamond surface by a Menzel-Gomer-Redhead mechanism.
Pepper, S. V.↗
Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks
Abstract The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal–noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-Gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9%, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy.
36 MATERIALS SCIENCE↗
Consistency between reflection momentum-resolved electron energy loss spectroscopy and optical spectroscopy measurements of the long-wavelength density response of Bi 2 Sr 2 CaCu 2 O 8 + x
The density fluctuation spectrum captures many fundamental properties of strange metals. Using momentum-resolved electron energy-loss spectroscopy (M-EELS), we recently showed that the density response of the strange metal Bi 2 Sr 2 CaCu 2 O 8+x (Bi-2212) at large momentum, q, exhibits a constant-in-frequency continuum reminiscent of the marginal Fermi liquid (MFL) hypothesis of the late 1980s. However, reconciling this observation with infrared (IR) optics experiments, which show a well-defined plasmon excitation at q ~ 0, has been challenging. Here we report M-EELS measurements of Bi-2212 using 4× improved momentum resolution, allowing us to reach the optical limit. For momenta q < 0.04 r.l.u., the M-EELS data show a plasmon feature that is quantitatively consistent with IR optics. For q > 0.04 r.l.u., the spectra become incoherent with an MFL-like, constant-in-frequency form. Here we speculate that, at finite frequency, ω, and nonzero q, some attribute of this Planckian metal randomizes the probe electron, causing it to lose information about its own momentum.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
Differentiating the bonding states in calcium carbonate polymorphs by low-loss electron-energy-loss spectroscopy
Calcium carbonate is one of the important building components in organisms, especially the two most common polymorphs, calcite and aragonite. Here, to understand the difference in bonding state, the two polymorphs are characterized by valence (low-loss) electron energy loss spectroscopy. It is found that the difference in Ca M 23 edge originating from 3p to 3d states is consistent with the change of Ca-O bonds in the two studied polymorphs. Surprisingly, the measured Ca M 23 edge is in qualitative agreement with the calculated partial density of states (PDOS) of Ca-d states in contrast to their L edges (from 2p to 3d states) which are strongly influenced by atomic multiplet effect (spin-orbit coupling). This is because the atomic multiplet effect is much reduced for the Ca 3p orbital, which permits the corresponding Ca M 23 edge to be compared with the PDOS results. Our findings show insights that PDOS can potentially be used to interpret the M 23 edge of lighter 3d transition metals such as scandium, titanium, vanadium and chromium when such interpretation may not be achieved for their L edges.
36 MATERIALS SCIENCE↗
Forecasting of in situ electron energy loss spectroscopy
Abstract Forecasting models are a central part of many control systems, where high-consequence decisions must be made on long latency control variables. These models are particularly relevant for emerging artificial intelligence (AI)-guided instrumentation, in which prescriptive knowledge is needed to guide autonomous decision-making. Here we describe the implementation of a long short-term memory model (LSTM) for forecasting in situ electron energy loss spectroscopy (EELS) data, one of the richest analytical probes of materials and chemical systems. We describe key considerations for data collection, preprocessing, training, validation, and benchmarking, showing how this approach can yield powerful predictive insight into order-disorder phase transitions. Finally, we comment on how such a model may integrate with emerging AI-guided instrumentation for powerful high-speed experimentation.
36 MATERIALS SCIENCE↗
Revealing Phase Heterogeneity in Vertically Aligned Nanocomposites via Plan-View Electron Energy Loss Spectroscopy
Hydrogen utilization in clean energy technologies is challenged by limited storage and transport within materials, owing to the complex hydrogen kinetics at interfaces [1]. Understanding these interfacial mechanisms at the nanoscale is crucial for developing improved materials for hydrogen applications, particularly proton-conducting fuel cells (PCFCs). Vertically aligned nanocomposites (VANs) grown by pulsed laser deposition (PLD) offer a unique platform for investigating the interfacial effects on hydrogen transport due to their well-defined interfaces parallel to the direction of charge transport [2-4]. To investigate hydrogen transport, the two phases within the VANs were chosen as BaZr 0.9 Y 0.1 O 3-x (BZY), a known proton conductor, and Pr 0.1 Ce 0.9 O 2-x (PCO), a mixed ionic-electronic conductor [5]. This PCO-BZY VANs architecture allows the investigation of how the interface between a proton conductor and a mixed conductor influences hydrogen transport. However, because of the small size of hydrogen, it is difficult to discern the nature of its interactions with interfaces from bulk measurements at the macroscale, thus necessitating nanoscale measurements [6]. Electron energy loss spectroscopy (EELS) allows for nanometer-resolution probing of the local atomic structure and chemistry at the BZY/PCO interface. In this study, plan-view analysis of PCO-BZY VANs films was employed to characterize the structure and phase distribution of the VANs and investigate the interface between the nanostructures. The films were imaged using scanning electron microscopy (SEM) in the Hitachi S-4800 SEM, collecting secondary electron images using mixed upper and lower detectors. Then, plan-view transmission electron microscopy (TEM) and scanning transmission electron microscopy (STEM) EELS were employed using a JEOL ARM300 microscope operated at 300kV with a Gatan K3 GIF Continuum detector to study the distribution of the BZY and PCO phases through the film. As a result, spectrum images were acquired at a dispersion of 0.18eV per channel and denoised afterward by principal component analysis (PCA) method.
Griffin, Elizabeth [Northwestern University, Evans↗
Identifying Spectral Descriptors for Protonation in BaZr 0.8 Y 0.2 O 3-x with Electron Energy Loss Spectroscopy
Hydrogen economy is of paramount importance in the global transition to a sustainable, clean energy source that contributes to decarbonization efforts. In particular, the proton conducting proton ceramic fuel cells (PCFCs) play a crucial role in promoting hydrogen energy technology [1-5]. In a PCFC, the electrolyte is typically a solid oxide material that enables proton transport and can operate at temperatures lower than those of traditional oxygen ion conducting fuel cells [6]. The reduced operating temperature of PCFCs contributes to their durability, scalability, and efficiency [7,8]. BaZr 0.8 Y 0.2 O 3-x (BZY) is a promising proton conducting solid oxide electrolyte [9,10]. The emphasis on proton conductivity entails the importance of understanding proton content in the system. However, previous studies have largely relied on bulk techniques such as electrochemical impedance spectroscopy [7,11], Karl-Fischer titration [12,13], and thermogravimetric analysis [14] to obtain proton concentration. This is due to the small atomic size and light mass of hydrogen species making direct detection challenging. Bulk methods may be useful in estimating the proton content; however, it overlooks possible proton concentration gradient or segregation that may occur across or within a nanoparticle, especially when proton incorporation occurs nonuniformly through steam exposure on powder samples. In this paper, BZY is protonated at an estimated 0.15 mol of protons via steam exposure. Electron energy loss spectroscopy (EELS) with nanoscale spatial resolution is explored to identify proxy signals for proton detection using a JEOL ARM300 microscope operated at 300 kV with a Gatan GIF Continuum detector.
08 HYDROGEN↗
Electron energy loss spectroscopy of nanoscale local structures in calcium silicate hydrate
Not Available
Liang, Chengyao↗
Using electron energy-loss spectroscopy to measure nanoscale electronic and vibrational dynamics in a TEM
Electron energy-loss spectroscopy (EELS) can measure similar information to x-ray, UV–Vis, and IR spectroscopies but with atomic resolution and increased scattering cross-sections. Recent advances in electron monochromators have expanded EELS capabilities from chemical identification to the realms of synchrotron-level core-loss measurements and to low-loss, 10–100 meV excitations, such as phonons, excitons, and valence structures. EELS measurements are easily correlated with electron diffraction and atomic-scale real-space imaging in a transmission electron microscope (TEM) to provide detailed local pictures of quasiparticle and bonding states. This perspective provides an overview of existing high-resolution EELS (HR-EELS) capabilities while also motivating the powerful next step in the field—ultrafast EELS in a TEM. Ultrafast EELS aims to combine atomic-level, element-specific, and correlated temporal measurements to better understand spatially specific excited-state phenomena. Ultrafast EELS measurements also add to the abilities of steady-state HR-EELS by being able to image the electromagnetic field and use electrons to excite photon-forbidden and momentum-specific transitions. We discuss the technical challenges ultrafast HR-EELS currently faces, as well as how integration with in situ and cryo measurements could expand the technique to new systems of interest, especially molecular and biological samples.
Chemistry↗
Direct Mapping of Fluorine in Cation Disordered Rocksalt Cathodes
Cation-disordered rocksalt (DRX) oxides are promising candidates as next-generation cathodes for lithium-ion batteries. Partial fluorination of the DRX oxides enhances their cyclability. However, the lattice position, concentration, and spatial distribution of fluorine within DRX lattices remain elusive. Here, in this work, we use atom location by channeling-enhanced microanalysis, energy-dispersive X-ray spectroscopy, electron energy loss spectroscopy, and integrated differential phase contrast imaging in a scanning transmission electron microscope to gain atomic-level insights into DRX with nominal composition of Li 1.2 Mn 0.7 Ti 0.1 O 1.7 F 0.3 and Li 1.15 Ni 0.45 Ti 0.3 Mo 0.1 O 1.85 F 0.15 . We reveal that fluorine substitutes oxygen in the DRX lattices. The O/F ratio in terms of O+F = 2 is in the range from 1.92:0.08 to 1.82:0.18. Spatially, fluorine is distributed in the proximity of the Li-rich regions but distinct from lithium fluoride. Additionally, we observe that incorporation of fluorine in the DRX lattice induces a larger variation in cation–anion separation. These observations provide insight into the guided design of oxyfluoride DRX cathodes for high-performance batteries.
(S)TEM↗