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Abstract The structures, strain fields, and defect distributions in solid materials underlie the mechanical and physical properties across numerous applications. Many modern microstructural microscopy tools characterize crystal grains, domains and defects required to map lattice distortions or deformation, but are limited to studies of the (near) surface. Generally speaking, such tools cannot probe the structural dynamics in a way that is representative of bulk behavior. Synchrotron X-ray diffraction based imaging has long mapped the deeply embedded structural elements, and with enhanced resolution, dark field X-ray microscopy (DFXM) can now map those features with the requisite nm-resolution. However, these techniques still suffer from the required integration times due to limitations from the source and optics. This work extends DFXM to X-ray free electron lasers, showing how the $$10^{12}$$ 10 12 photons per pulse available at these sources offer structural characterization down to 100 fs resolution (orders of magnitude faster than current synchrotron images). We introduce the XFEL DFXM setup with simultaneous bright field microscopy to probe density changes within the same volume. This work presents a comprehensive guide to the multi-modal ultrafast high-resolution X-ray microscope that we constructed and tested at two XFELs, and shows initial data demonstrating two timing strategies to study associated reversible or irreversible lattice dynamics.
A better understanding of amorphous aluminum oxide’s structure and electronic properties is obtained through combined experimental and computational approaches. Grazing incidence X-ray scattering measurements were carried out on aluminum oxide thin films grown using thermal atomic layer deposition. The corresponding pair distribution functions (PDFs) showed structures similar to previously reported PDFs of solid-state amorphous alumina and molten alumina. Structural models based on crystalline alumina polymorphs (PDFgui) and amorphous alumina (molecular dynamics, MD) were examined for structural comparisons to the experimental PDF data. Smaller MD models were optimized and verified against larger models to allow for quantum chemical electronic structure calculations. The electronic structure of the amorphous alumina models yields additional insight into the band structure and electronic defects present in amorphous alumina that are not present in crystalline samples.
Main advantages of the air-coupled ultrasound testing (ACUT) and electromagnetic testing (EMT) techniques for NDE of CFRP composites were non-contact sensing, scalability for high-speed inspection, cost-effectiveness, and non-hazardous operation. Despite these advantages, no systems that would satisfy the project requirements were commercially available. Hence, one of the major efforts of the Michigan State University (MSU) team at the initial stage of the project was to close this technological gap by developing, optimizing, and validating array sensors that would provide sufficient sensitivity, spatial coverage, and resolution for robust defect detection. Optimization of the ACUT and EMT sensor designs was performed using experimentally validated finite element models. Initial experiments using array probes were conducted on relatively flat CFRP samples. In parallel, the MSU team designed and assembled a portable platform with two robotic arms. The robots were equipped with newly designed sensors that enabled high-speed NDE of curved CFRP parts. Presently, the developed robotic platform can be used as a demo/template NDE system, which is easily adaptable to manufacturing environments and in-line NDE. The ACUT NDE system developed by the MSU team used a high-power 4-channel pulser receiver for parallel data acquisition. The array probes were designed by stacking commercially available ACUT transducers, which operated in the frequency range between 100 kHz and 500 kHz. MSU optimized the excitation procedure and developed wave focusing cones so as to reduce the crosstalk between the transducers and to provide higher pulse repletion frequency (PRF). The through-transmission (TT) and single-side access (SSA) inspection modes were successfully implemented. In the TT-ACUT, structural defects in CFRP were detected by passing ultrasonic waves through the test part. Hence, the ACUT transmitters and receivers needed to be placed on the opposite sides of the test part. In the SSA-ACUT, guided waves (GW) were excited in the test part using the transmitters and were sensed by the receivers from the same side. Multi-channel TT-ACUT and SSA-ACUT provided high-speed NDE, and were successfully validated on CFRP test samples with interlaminar delaminations and other embedded defects The EM techniques developed by the MSU team included: 1) eddy current testing (ECT), 2) capacitive imaging (CI) and hybrid dual-mode imaging. In ECT, structural damage was detected in CFRP using coils sensor arrays. In ECT, the excitation magnetic field is generated by passing an alternating current through a coil, which is placed above the test sample. The excitation field penetrates the conductive sample and induces the eddy currents in its transect. In turn, the eddy currents generate the reaction field, which affects the total field sensed by a coil. Hence, the presence of structural flaws will alter the eddy current flow and the picked-up signal. ECT is mostly sensitive to local changes of the electric conductivity of the test sample, and CFRPs are mostly conductive in the direction of carbon fibers. Hence, ECT was well suited for the detection of fiber damage/fiber irregularities. The MSU team developed printed circuit boards (PCB) with coil sensor arrays optimized for NDE of CFRP. Unlike most commercial probes designed for ECT of metallic structures, the MSU array probes were designed for operation in [1-10] MHz frequency range, which was optimal for low-conductive CFRP. Multiple sensing topologies (coil groups excitation/sensing arrangements) were implemented and successfully validated. Capacitive Imaging (CI) technique developed by MSU was complementary to ECT. In contrast to ECT, which was sensitive to local changes of the electrical conductivity, the CI was sensitive to local changes of the dielectric constant. Therefore, CI could provide information about matrix damage/matrix irregularities in CFRP. The MSU CI sensor arrays were made of multiple circular or rectangular open-plate capacitors printed on PCB. Sensors of this type are not commercially available. In addition to ECT and CI, the MSU team developed a hybrid (dual-mode) inductive/capacitive measurement technique that synergistically combined the benefits of inductive and capacitive sensing for rapid NDE of fiber reinforced polymer (FRP) composite structures. Fiber damage and fiber irregularities in FRPs were detected by configuring hybrid sensors as coil sensors. Similarly, matrix damage, matrix irregularities and interlaminar delaminations were detected by configuring hybrid sensors as capacitive sensors. ECT and CI were performed sequentially by means of electronic switching. Hence, eliminating the need for mounting two separate sensor arrays on the probe. Portable robotic platform was developed by MSU for multi-technique high-speed NDE of CFRP test parts. The platform had two 6-axis robots, which enabled inspection of curved parts in approximately a 6×6×6 ft 3 active scan area. On the software side, the MSU team integrated scripts for NDE hardware control with scripts for robot motion control. MSU also implemented automated path planning for the robots, reconstruction of part’s surfaces via stereovision, 3D rendering of inspection data, and image processing algorithms for enhanced defect detection. Automotive composite parts manufactured by Plasan Composites from Phase I were used to validate the ACUT and EMT techniques on representative testbeds. Among those parts were three X-braces for a Dodge Viper, one composite calibration plaque with known defects at known locations, and four other test sections, including sections from a front splitter, a corner section from a composite hood, and a high-pressure RTM panel made using non crimp fabric. Other test samples included CFRP and GFRP calibration plates with fiber/matrix defects fabricated at MSU/CVRC.
In this work, ultrafast infrared and electronic spectroscopy are used to examine the dynamics of triplet pair separation following singlet fission in amorphous and crystalline films of the model singlet fission chromophore, 6,13-bis(triisopropylsilylethynyl)pentacene (TIPS-Pn). Direct probing of correlated triplet pair intermediates through their unique vibrational frequencies, infrared electronic transitions, and indirectly through their visible triplet absorptions reveals that triplet pair separation occurs on similar picosecond timescales in both amorphous and crystalline films despite their markedly different average intermolecular coupling strengths. Although triplet pair separation occurs on similar timescales in both environments, measurements of diffusion-controlled triplet-triplet annihilation reveal that the diffusivity of triplet excited states is an order of magnitude lower in amorphous films. The data reveal the presence of sparse triplet traps in the amorphous environment that inhibit the transport of triplet excitons in comparison to crystalline films. These observations inform recent efforts to develop disordered and polymeric singlet fission sensitizers that contain amorphous regions. In particular, the data suggest that it may be possible to nanostructure amorphous or polymeric singlet fission sensitizers to allow ultrafast triplet pair separation and harvesting in photovoltaic and light emitting applications despite their low triplet exciton diffusivity.
We report transient highly strained structural states in individual palladium (Pd) nanocrystals, electronically heated using an optical laser, which precede their uniform thermal expansion. Using an X-ray free-electron laser probe, the evolution of individual 111 Bragg peaks is measured as a function of delay time at various laser fluences. Above a laser fluence threshold at a sufficient pump-probe delay, the Bragg peak splits into multiple peaks, indicating heterogeneous strain, before returning to a single peak, corresponding to even heat distribution throughout the lattice expanded crystal. Our findings are supported by a lattice displacement and strain model of a single nanocrystal at different delay times, which agrees with the experimental data. Our observations have implications for understanding femtosecond laser interactions with metals and the potential photo-catalytic performance of Pd.
Low-temperature X-ray photoemission electron microscopy (X-PEEM) is used to measure the electric potential at domain walls in improper ferroelectric Er 0.99 Ca 0.01 MnO 3 . Here, by combining X-PEEM with scanning probe microscopy and theory, we develop a model that relates the detected X-PEEM contrast to the emergence of uncompensated bound charges, explaining the image formation based on intrinsic electronic domain-wall properties. In contrast to previously applied low-temperature electrostatic force microscopy (EFM), X-PEEM readily distinguishes between positive and negative bound charges at domain walls. Finally, our study introduces an X-PEEM-based approach for low-temperature electrostatic potential mapping, facilitating nanoscale spatial resolution and data acquisition times on the order of 0.1-1 s.
Infrared-pump, electronic-probe (IPEP) spectroscopy is used to measure heat flow into and out of CdSe nanocrystals suspended in an organic solvent, where the surface ligands are initially excited with an infrared pump pulse. Subsequently, the heat is transferred from the excited ligands to the nanocrystals and in parallel to the solvent. Parallel heat transfer in opposite directions uniquely enables us to differentiate the thermal conductances at the nanocrystal/ligand and ligand/solvent interfaces. Using a novel solution to the heat diffusion equation, we fit the IPEP data to find that the nanocrystal/ligand conductances range from 88 to 135 MW m –2 K –1 and are approximately 1 order of magnitude higher than the ligand/solvent conductances, which range from 7 to 26 MW m –2 K –1 . Transient nonequilibrium molecular dynamics (MD) simulations of nanocrystal suspensions agree with IPEP data and show that ligands bound to the nanocrystal by bidentate bonds have more than twice the per-ligand conductance as those bound by monodentate bonds.
Many of the unexplained phenomena in particle physics and cosmology today, such as the microphysical nature of dark matter, the strong CP problem, and the origin of the neutrino masses, can be resolved by the existence of a light (~GeV), weakly-coupled hidden sector of new physics. Such hidden sectors often predict the existence of “long-lived” particles (LLPs) that travel a far distance from production before decaying into Standard Model particles. Neutrino oscillation experiments, which combine intense particle beams with precise imaging detectors, are well equipped to probe LLP models with new sensitivity. This thesis details a search for a long-lived particle decaying to two muons with the ICARUS liquid argon time projection chamber (LArTPC) neutrino detector in the Short-Baseline Neutrino program at Fermilab. The calibration of the ICARUS time projection chamber (TPC) which enables the search is also presented. Notably, the calibration measures an angular dependence in electron-ion recombination in argon, a novel effect in the detector physics of LArTPCs. The search is performed using data taken with the Neutrinos at the Main Injector (NuMI) beam, with an exposure of 2.41e20 protons on target. No significant excess over background is observed, and we set world-leading limits on two new physics models that predict this process: the Higgs portal scalar and a heavy axion model. We also present the sensitivity in a model-independent way applicable to any new physics model predicting the process K → π + S(→μμ), for a long-lived particle S.
A search for long-lived heavy neutral leptons (HNLs) is presented, which considers the hadronic final state and coupling scenarios involving all three lepton generations in the 2–20 GeV HNL mass range for the first time. Events comprising two leptons (electrons or muons) and jets are analyzed in a data sample of proton-proton collisions, recorded with the CMS experiment at the CERN LHC at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 138 fb$^{−1}$. A novel jet tagger, based on a deep neural network, has been developed to identify jets from an HNL decay using various features of the jet and its constituent particles. The network output can be used as a powerful discriminating tool to probe a broad range of HNL lifetimes and masses. Contributions from background processes are determined from data. No excess of events in data over the expected background is observed. Upper limits on the HNL production cross section are derived as functions of the HNL mass and the three coupling strengths V$_{ℓN}$ to each lepton generation ℓ and presented as exclusion limits in the coupling-mass plane, as lower limits on the HNL lifetime, and on the HNL mass. In this search, the most stringent limit on the coupling strength is obtained for pure muon coupling scenarios; values of |$ {V}_{\mu \textrm{N}}^2 $| > 5 (4) × 10$^{−7}$ are excluded for Dirac (Majorana) HNLs with a mass of 10 GeV at a confidence level of 95% that correspond to proper decay lengths of 17 (10) mm.[graphic not available: see fulltext]
Modern scanning probe techniques, such as scanning tunneling microscopy, provide access to a large amount of data encoding the underlying physics of quantum matter. In this work, we show how convolutional neural networks can be used to learn effective theoretical models from scanning tunneling microscopy data on correlated moiré superlattices. Moiré systems are particularly well suited for this task as their increased lattice constant provides access to intra-unit-cell physics, while their tunability allows for the collection of high-dimensional data sets from a single sample. Using electronic nematic order in twisted double-bilayer graphene as an example, we show that incorporating correlations between the local density of states at different energies allows convolutional neural networks not only to learn the microscopic nematic order parameter, but also to distinguish it from heterostrain. These results demonstrate that neural networks are a powerful method for investigating the microscopic details of correlated phenomena in moiré systems and beyond.
Multipactor is a surface-driven electron avalanche phenomenon that degrades the performance and reliability of radio-frequency (RF) systems in particle accelerator and vacuum electronics applications. Multipactor behavior in a given device structure is conventionally assessed through susceptibility charts, which provide a parameter-space characterization of the instability. In this work, we assess the capabilities of machine-learning (ML) models to learn and predict such susceptibility charts and analyze the constraints governing their generalization across materials. Using a simulation-derived dataset spanning six distinct secondary-electron-yield material profiles in a canonical two-surface planar geometry, we train supervised regression models and artificial neural networks to predict the time-averaged electron growth rate, δavg, across the relevant parameter space. Model performance is evaluated using metrics that explicitly probe the structure of susceptibility charts, including Intersection over Union, Structural Similarity Index, and correlation analysis. Tree-based ensemble models outperform neural-network models in reconstructing susceptibility regions and in generalizing across material domains. Principal-component analysis reveals disjoint material feature distributions, indicating that the piecewise mode structure of multipactor susceptibility is difficult to represent with a single global model and that generalization is constrained by data coverage rather than by model complexity. An exhaustive reduced-coverage study further shows that sparse material-space coverage can yield mean performance in the same general range but producing large variability in the susceptibility-region overlap. These results clarify the capabilities of ML-based surrogate models for parameter-space characterization of multipactor discharge. They also provide guidance for their appropriate use in RF system design.
Density-functional theory models of δ-Pu accurately predict crystal structure, phonon density of states, and unit cell volumes but require magnetic degrees of freedom which have never been experimentally verified. Some models invoke an on-site cancellation of spin and orbital moments, engendering a near-zero bulk magnetization, undetectable by most probes. Here, we employ x-ray magnetic circular dichroism at the Pu M 4,5 edges to directly probe spin and orbital moments using the magneto-optical sum rules. The data show no dichroism within experimental error, constraining polarized moments at 6 T and 3 K to μ L,S <0.1μ B . Finally, these experiments point to the absence of even unconventional spin-orbit compensated order in δ-Pu.
The idea of using ultrashort X-ray pulses to obtain images of single proteins frozen in time has fascinated and inspired many. It was one of the arguments for building X-ray free-electron lasers. According to theory, the extremely intense pulses provide sufficient signal to dispense with using crystals as an amplifier, and the ultrashort pulse duration permits capturing the diffraction data before the sample inevitably explodes. This was first demonstrated on biological samples a decade ago on the giant mimivirus. Since then, a large collaboration has been pushing the limit of the smallest sample that can be imaged. The ability to capture snapshots on the timescale of atomic vibrations, while keeping the sample at room temperature, may allow probing the entire conformational phase space of macromolecules. Here we show the first observation of an X-ray diffraction pattern from a single protein, that of Escherichia coli GroEL which at 14 nm in diameter is the smallest biological sample ever imaged by X-rays, and demonstrate that the concept of diffraction before destruction extends to single proteins. From the pattern, it is possible to determine the approximate orientation of the protein. Our experiment demonstrates the feasibility of ultrafast imaging of single proteins, opening the way to single-molecule time-resolved studies on the femtosecond timescale.
Measurements of both the inclusive and differential production cross sections of a top-quark-top-antiquark pair in association with a Z boson ($t\overline{t}Z$) are presented. Final states with two, three or four isolated leptons (electrons or muons) are targeted. The measurements use the data recorded by the ATLAS detector in pp collisions at $\sqrt{s}$ = 13 TeV at the Large Hadron Collider during the years 2015–2018, corresponding to an integrated luminosity of 140 fb -1 . The inclusive cross section is measured to be ${\sigma}_{t\overline{t}Z}$ = 0.86 ± 0.04 (stat.) ± 0.04 (syst.) pb and found to be in agreement with the most advanced Standard Model predictions. The differential measurements are presented as a function of a number of observables that probe the kinematics of the $t\overline{t}Z$ system. Both the absolute and normalised differential cross-section measurements are performed at particle level and parton level for specific fiducial volumes, and are compared with NLO+NNLL theoretical predictions. The results are interpreted in the framework of Standard Model effective field theory and used to set limits on a large number of dimension-6 operators involving the top quark. The first measurement of spin correlations in $t\overline{t}Z$ events is presented: the results are in agreement with the Standard Model expectations, and the null hypothesis of no spin correlations is disfavoured with a significance of 1.8 standard deviations.
We have developed an experimental platform at the National Ignition Facility that employs colliding planar shocks to produce warm dense matter with uniform conditions and enable high-precision equation of state measurements. The platform uses simultaneous x-ray Thomson scattering and x-ray radiography to measure the density, electron temperature, and ionization state in warm dense matter. The experimental platform is designed to create a large volume of uniform plasma (approximately 700×700×150μm 3 ) at pressures approaching 100 Mbar and minimize the distribution of plasma conditions in the x-ray scattering volume, significantly improving the precision of the measurements. Here, in this study, we present the experimental design of the platform and compare hydrodynamic simulations to x-ray radiography data from initial experiments studying hydrocarbons, producing uniform densities within ±25% of the average probed condition. We show that the platform creates a homogeneous plasma that can be characterized using x-ray Thomson scattering. Thus, the new platform enables accurate measurements of plasma conditions necessary to test models for the equation of state and ionization potential depression in the warm dense matter regime.
Results of the Model Unspecific Search in CMS (MUSiC), using proton–proton collision data recorded at the LHC at a centre-of-mass energy of 13TeV, corresponding to an integrated luminosity of 35.9 fb -1 , are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.
Iron is a micronutrient for nearly all life on Earth. It can be used as an electron donor and electron acceptor by iron-oxidizing and iron-reducing microorganisms and is used in a variety of biological processes, including photosynthesis and respiration. While it is the fourth most abundant metal in the Earth’s crust, iron is often limiting for growth in oxic environments because it is readily oxidized and precipitated. Much of our understanding of how microorganisms compete for and utilize iron is based on laboratory experiments. However, the advent of next-generation sequencing and surge in publicly available sequence data has made it possible to probe the structure and function of microbial communities in the environment. To bridge the gap between our understanding of iron acquisition, iron redox cycling, iron storage, and magnetosome formation in model microorganisms and the plethora of sequence data available from environmental studies, we have created a comprehensive database of hidden Markov models (HMMs) based on genes related to iron acquisition, storage, and reduction/oxidation in Bacteria and Archaea. Along with this database, we present FeGenie, a bioinformatics tool that accepts genome and metagenome assemblies as input and uses our comprehensive HMM database to annotate provided datasets with respect to iron-related genes and gene neighborhood. An important contribution of this tool is the efficient identification of genes involved in iron oxidation and dissimilatory iron reduction, which have been largely overlooked by standard annotation pipelines. We validated FeGenie against a selected set of 28 isolate genomes and showcase its utility in exploring iron genes present in 27 metagenomes, 4 isolate genomes from human oral biofilms, and 17 genomes from candidate organisms, including members of the candidate phyla radiation. We show that FeGenie accurately identifies iron genes in isolates. Furthermore, analysis of metagenomes using FeGenie demonstrates that the iron gene repertoire and abundance of each environment is correlated with iron richness. While this tool will not replace the reliability of culture-dependent analyses of microbial physiology, it provides reliable predictions derived from the most up-to-date genetic markers. FeGenie’s database will be maintained and continually updated as new genes are discovered.