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At least 865 records · Page 48

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

This work presents procedures for implementing machine learning methods into existing algorithms for multi-hole probe calibration and data reduction. It demonstrates that using artificial neural networks (ANNs) can decrease the amount of calibration data needed to achieve a specific calibration uncertainty by over 50%, while also significantly reducing data reduction times. Instead of surface fitting methods, ANNs are employed. Initially, directional calibration coefficients related to flow angles are computed based on pressure measurements, and then these flow angles serve as input parameters for subsequent ANNs to iteratively define Mach number, static pressure, and total pressure. In an alternative approach, new calibration coefficients directly relate pressure measurements from the five-hole probe to the quantities of interest, thereby eliminating the need for iterative algorithms used in conventional surface fitting methods. This method offers several advantages: an average increase of less than 1%in calibration uncertainty for flow angles and a significant reduction in data reduction times to a few seconds on average. Additionally, the methodology is confirmed to avoid both over- and under-fitting.

Machine Learning↗

Application of Machine Learning Techniques in Calibration and Data Reduction of Multi-Hole Probes

This work presents procedures to implement machine learning methods in the existing algorithms for multi-hole probe calibrations and data reduction. It is shown here, that utilizing artificial neural networks (ANNs) can reduce the amount of calibration data that needs to be acquired in order to obtain a specific calibration uncertainty, by more than 50% while simultaneously reducing data reduction times significantly. ANNs were used instead of the surface fitting methods, where first, the directional calibration coefficients related to the flow angles are calculated based on the pressure measurements, and then the flow angles are used as a set of the input parameters for the following ANNs to define Mach number and static and total pressure iteratively. In a second approach, novel calibration coefficients were used to directly relate the pressure measurements from five-hole probe to the quantities of interest thus, eliminating the need for iterative algorithms used in the conventional surface fitting methods. The advantageous features of this method are an average increase of less than 1% in the calibration uncertainty for flow angles and significant reduction of the data reduction times (few seconds). In addition, we confirmed the methodology to avoid over-fitting and under-fitting.

Machine Learning↗

Probing the Nanoscale Excitonic Landscape and Confinement of Excitons in Gated Monolayer Semiconductors

Engineering and probing excitonic properties at the nanoscale remains a central challenge in quantum photonics and optoelectronics. While exciton confinement via electrical control and strain engineering has been demonstrated in 2D semiconductors, substantial nanoscale heterogeneity limits the scalability of 2D quantum photonic device architectures. Here, in this work, we use cathodoluminescence spectroscopy to probe the excitonic landscape of monolayer WS 2 under electrostatic gating. Exploiting the high spatial resolution of the converged electron beam, we resolve a homojunction arising between gated and ungated regions. Moreover, we reveal an exciton confinement channel arising from an unconventional doping mechanism driven by the interplay between the electron beam and the applied gate fields. These findings offer new insights into the optoelectronic behavior of monolayer semiconductors under the combined influence of electron-beam excitation and electrostatic gating. Our approach provides a pathway for exciton manipulation at the nanoscale and opens opportunities for controlling quantum-confined exciton transport in 2D materials.

2D materials↗

Machine Learning for Optical Scanning Probe Nanoscopy

Abstract The ability to perform nanometer‐scale optical imaging and spectroscopy is key to deciphering the low‐energy effects in quantum materials, as well as vibrational fingerprints in planetary and extraterrestrial particles, catalytic substances, and aqueous biological samples. These tasks can be accomplished by the scattering‐type scanning near‐field optical microscopy (s‐SNOM) technique that has recently spread to many research fields and enabled notable discoveries. Herein, it is shown that the s‐SNOM, together with scanning probe research in general, can benefit in many ways from artificial‐intelligence (AI) and machine‐learning (ML) algorithms. Augmented with AI‐ and ML‐enhanced data acquisition and analysis, scanning probe optical nanoscopy is poised to become more efficient, accurate, and intelligent.

Chen, Xinzhong↗

Probing transport energies and defect states in organic semiconductors using energy resolved electrochemical impedance spectroscopy

Abstract Determining the relative energies of transport states in organic semiconductors is critical to understanding the properties of electronic devices and in designing device stacks. Futhermore, defect states are also highly important and can greatly impact material properties and device performance. Recently, energy‐resolved electrochemical impedance spectroscopy (ER‐EIS) is developed to probe both the ionization energy (IE) and electron affinity (EA) as well as sub‐bandgap defect states in organic semiconductors. Herein, ER‐EIS is compared to cyclic voltammetry (CV) and photoemission spectroscopies for extracting IE and EA values, and to photothermal deflection spectroscopy (PDS) for probing defect states in both polymer and molecular organic semiconductors. The results show that ER‐EIS determined IE and EA are in better agreement with photoemission spectroscopy measurements as compared to CV for both polymer and molecular materials. Furthermore, the defect states detected by ER‐EIS agree with sub‐bandgap features detected by PDS. Surprisingly, ER‐EIS measurements of regiorandom and regioregular poly(3‐hexylthiophene) (P3HT) show clear defect bands that occur at significantly different energies. In regioregular P3HT the defect band is near the edge of the occupied states while it is near the edge of the unoccupied states in regiorandom P3HT.

14 SOLAR ENERGY↗

Exploring Physics of Ferroelectric Domain Walls in Real Time: Deep Learning Enabled Scanning Probe Microscopy

The functionality of ferroelastic domain walls in ferroelectric materials is explored in real-time via the in situ implementation of computer vision algorithms in scanning probe microscopy (SPM) experiment. The robust deep convolutional neural network (DCNN) is implemented based on a deep residual learning framework (Res) and holistically nested edge detection (Hed), and ensembled to minimize the out-of-distribution drift effects. The DCNN is implemented for real-time operations on SPM, converting the data stream into the semantically segmented image of domain walls and the corresponding uncertainty. Further the pre-defined experimental workflows perform piezoresponse spectroscopy measurement on thus discovered domain walls, and alternating high- and low-polarization dynamic (out-of-plane) ferroelastic domain walls in a PbTiO 3 (PTO) thin film and high polarization dynamic (out-of-plane) at short ferroelastic walls (compared with long ferroelastic walls) in a lead zirconate titanate (PZT) thin film is reported. This work establishes the framework for real-time DCNN analysis of data streams in scanning probe and other microscopies and highlights the role of out-of-distribution effects and strategies to ameliorate them in real time analytics.

36 MATERIALS SCIENCE↗

Probing Axion-Like-Particles at the CERN Gamma Factory

We report the aim of the proposed CERN Gamma Factory is to produce ≈10 17 photons per second with energies up to 400 MeV. The photon beam intensity is expected to be a factor of $\mathcal{O}$(10 7 ) larger than that of the presently available photon beams in the MeV energy range. In this work, its potential to probe physics beyond the Standard Model is explored. In particular, searches for axion like particles (ALPs) with dominant couplings to photons are discussed and various production scenarios - fixed target, photon-photon collision, and conversion by a magnetic field - and detection schemes considered - via decay to photons or back-conversion. It is found that the Gamma Factory in a fixed target mode can probe ALPs with mass m a ≲ $\mathcal{O}$(100 MeV) and decay constants larger than 10 7 GeV, improving by an order of magnitude the discovery potential of previous beam dump experiments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Does H 2 Temperature‐Programmed Reduction Always Probe Solid‐State Redox Chemistry? The Case of Pt/CeO 2

Abstract Redox reactions on the surface of transition metal oxides are of broad interest in thermo, photo, and electrocatalysis. H 2 temperature‐programmed reduction (H 2 ‐TPR) is commonly used to probe oxide reducibility by measuring the rate of H 2 consumption during temperature ramps, assuming that this rate is controlled by oxide reduction. However, oxide reduction involves several elementary steps, such as H 2 dissociation and H‐spillover, before surface reduction and H 2 O formation occur. In this study, we evaluated the kinetics of H 2 consumption over CeO 2 and Pt/CeO 2 with varying Pt loadings and structures to identify the elementary steps probed by H 2 ‐TPR. Literature often attributes changes in H 2 ‐TPR characteristics with Pt addition to increased CeO 2 reducibility. However, our analysis revealed that the H 2 consumption rate is measurement of the rate of H‐spillover at Pt‐CeO 2 interfaces and is determined by the concentration of Pt species on Pt nanoclusters that dissociate H 2 . Therefore, lower temperature H 2 consumption observed with Pt addition does not indicate higher CeO 2 reducibility. Measurements on samples with mixtures of Pt single‐atoms and nanoclusters demonstrated that H 2 ‐TPR can effectively quantify dilute Pt nanocluster concentrations, suggesting caution in directly linking H 2 ‐TPR characteristics to oxide reducibility while highlighting alternative material insights that can be gleaned.

Lee, Jaeha↗

Elucidating the Role of Electric Fields in Fe Oxidation via an Environmental Atom Probe

Abstract We quantify the effects of intensely applied electric fields on the Fe oxidation mechanism. The specimen are pristine Fe single crystals exposing a variety of surface structures identified by field ion microscopy. These crystals are simultaneously exposed to low pressures of pure oxygen gas, on the order of 10 −7 mbar, while applying intense electric fields on their surface of several tens of volts per nanometer. The local composition of the different surface structures is probed directly and in real time using an Environmental Atom Probe and successfully compared with first principles‐based models. We found that rough Fe{244} and Fe{112} facets are more reactive toward oxygen than compact Fe{024} and Fe{011} facets. Results demonstrate that the influence of an electric field on the oxidation kinetics depends on the timescales that are involved as the system evolves toward equilibrium. The initial oxidation kinetics show that strong increases in electric fields facilitate the formation of an oxide. However, as one approaches equilibrium, high field values mitigate this formation. Ultimately, this study elucidates how high externally applied electric fields can be used to dynamically exploit reaction dynamics at the nanoscale towards desired products in a catalytic reaction at mild reaction conditions.

Lambeets, Sten V. [Physical and Computational Scie↗

Does H 2 Temperature–Programmed Reduction Always Probe Solid–State Redox Chemistry? The Case of Pt/CeO 2

Redox reactions on the surface of transition metal oxides are of broad interest in thermo, photo, and electrocatalysis. H 2 temperature-programmed reduction (H 2 -TPR) is commonly used to probe oxide reducibility by measuring the rate of H 2 consumption during temperature ramps, assuming that this rate is controlled by oxide reduction. However, oxide reduction involves several elementary steps, such as H 2 dissociation and H-spillover, before surface reduction and H 2 O formation occur. In this study, we evaluated the kinetics of H 2 consumption over CeO 2 and Pt/CeO 2 with varying Pt loadings and structures to identify the elementary steps probed by H 2 -TPR. Literature often attributes changes in H 2 -TPR characteristics with Pt addition to increased CeO 2 reducibility. However, our analysis revealed that the H 2 consumption rate is measurement of the rate of H-spillover at Pt-CeO 2 interfaces and is determined by the concentration of Pt species on Pt nanoclusters that dissociate H 2 . Furthermore, lower temperature H 2 consumption observed with Pt addition does not indicate higher CeO 2 reducibility. Measurements on samples with mixtures of Pt single-atoms and nanoclusters demonstrated that H 2 -TPR can effectively quantify dilute Pt nanocluster concentrations, suggesting caution in directly linking H 2 -TPR characteristics to oxide reducibility while highlighting alternative material insights that can be gleaned.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Surface Local Impurity Scattering as a Probe for Topological Kondo Insulators

Shortly after the discovery of topological band insulators, topological Kondo insulators (TKIs) is also theoretically predicted. The latter has ignited revival interest in the properties of Kondo insulators. Currently, the feasibility of topological nature in SmB 6 is intensively analyzed by several complementary probes. Here by starting with a minimal-orbital Anderson lattice model, the local electronic structure is explored in a Kondo insulator. It is showed that the two strong topological regimes sandwiching the weak topological regime give rise to a single Dirac cone, which is located near the center or corner of the surface Brillouin zone. It is further found that, when a single impurity is placed on the surface, low-energy resonance states are induced in the weak scattering limit for the strong TKI regimes and the resonance level moves monotonically across the hybridization gap with the strength of impurity scattering potential; while low energy states can only be induced in the unitary scattering limit for the weak TKI regime, where the resonance level moves universally toward the center of the hybridization gap. These impurity-induced low-energy quasiparticles will lead to characteristic signatures in scanning tunneling microscopy/spectroscopy, which has recently found success in probing into exotic properties in heavy fermion systems.

36 MATERIALS SCIENCE↗

Inorganic semiconductor quantum dots as a saturated excitation (SAX) probe for subdiffraction imaging

The photoluminescence (PL) saturation of CdSe/ZnS core/shell inorganic semiconductor quantum dots (QDs) and its utility as a probe for saturated excitation (SAX) microscopy are reported. Under saturating excitation power densities, the PL signal was demodulated and recorded at harmonics of the fundamental frequency. For commercially available Qdot® 655 ITK™ QDs, the power density required to achieve saturation was dependent upon the local environment of the QDs. For QDs deposited and dried on a glass substrate, the excitation power density required for PL saturation was less than 1 kW/cm 2 . Compared to this, saturation of PL for QDs dispersed in water required an excitation power density greater than 200 kW/cm 2 . This observation is manifested as a limitation in the imaging of hydrated samples, as demonstrated for HeLa cells labelled with biotinylated-phalloidin followed by labelling with streptavidin-coated QDs. Finally as saturation affects the obtained spatial resolution in several imaging formats, including confocal imaging, the provided data will aid in obtaining the optimal spatial resolution when using QD probes to image biological samples.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

In situ and operando force-based atomic force microscopy for probing local functionality in energy storage materials

Electrochemical energy storage is the key enabling component of electric vehicles and solar-/wind-based energy technologies. The enhancement of energy stored requires the detailed understanding of charge storage mechanisms and local electrochemical and electromechanical phenomena over a variety of length scales from atoms to full cells. Classical electrochemical techniques, such as voltammetry, represent the macroscopic electrochemical properties, and consequently do not allow to extract important information about local electrochemical reactions, ions adsorption, intercalations, and transport. Understanding, controlling, and tuning the local electrochemical functionalities in functional energy materials require in situ/operando techniques which limit the use of structural and functional characterization techniques that provide local information. Here, force-based atomic force microscopies (AFMs) have provided novel insights into locally probing electrochemical mechanisms on tens of nanometer and even molecular length scales and provide a viable pathway to probe electrochemical processes in situ/operando. In this review, we highlight the contributions in the development and application of force-based AFM methods to elucidate the local charge storage mechanism in a variety of energy-related materials. We will focus in particular on methods or AFM modalities in a liquid electrolyte environment.

25 ENERGY STORAGE↗

Fast Scanning Probe Microscopy via Machine Learning: Non-Rectangular Scans with Compressed Sensing and Gaussian Process Optimization

Fast scanning probe microscopy enabled via machine learning allows for a broad range of nanoscale, temporally resolved physics to be uncovered. However, such examples for functional imaging are few in number. Here, using piezoresponse force microscopy (PFM) as a model application, a factor of 5.8 reduction in data collection using a combination of sparse spiral scanning with compressive sensing and Gaussian process regression reconstruction is demonstrated. It is found that even extremely sparse spiral scans offer strong reconstructions with less than 6% error for Gaussian process regression reconstructions. Further, the error associated with each reconstructive technique per reconstruction iteration is analyzed, finding the error is similar past ≈15 iterations, while at initial iterations Gaussian process regression outperforms compressive sensing. Finally, this study highlights the capabilities of reconstruction techniques when applied to sparse data, particularly sparse spiral PFM scans, with broad applications in scanning probe and electron microscopies.

36 MATERIALS SCIENCE↗

Probing CP and flavor violation in neutral kaon decays with ALPs

We analyze the three-body decays of the long-lived neutral kaon K L → ππa, where a is an axion-like particle (ALP), and compare them to the two-body decay K L → π 0 a. While the latter requires both flavor violation (FV) and CP violation (CPV), the former can proceed via FV alone, allowing the ratio of decay rates to serve as a probe of CPV of the underlying UV theory. We emphasize the importance of weak-interaction-induced contributions, often neglected in recent calculations. We explore both minimal and non-minimal flavor-violating scenarios, and identify classes of models where ALP production from neutral three-body decays is comparable to — or even dominates over — the two-body decay, despite its reduced phase space. Finally, we discuss the phenomenological implications of our results and show how these decays can provide complementary probes of ALP couplings beyond those accessible via charged kaon channels.

Axions and ALPs↗

The radial action from probe amplitudes to all orders

We extract the relativistic classical radial action from scattering amplitudes, to all orders in perturbation theory, in the probe limit. Our sources include point charges and monopoles, as well as the Schwarzschild and pure-NUT gravitational backgrounds. A characteristic relativistic effect, that scattering trajectories may wind around these sources any number of times, can be recovered when all-order amplitudes are available. We show that the amplitude for scattering a probe off a pure NUT is given by the solution of a transcendental equation involving continued fractions, and explain how to solve this equation to any desired loop order.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Probing light quark Yukawa couplings through angularity distributions in Higgs boson decay

We propose to utilize angularity distributions in Higgs boson decay to probe light quark Yukawa couplings at e + e - colliders. Angularities $τ$ a are a class of 2-jet event shapes with variable and tunable sensitivity to the distribution of radiation in hadronic jets in the final state. Using soft-collinear effective theory (SCET), we present a prediction of angularity distributions from Higgs decaying to quark and gluon states at e + e - colliders to NNLL + $\mathcal{O}$(α s ) accuracy. Due to the different color structures in quark and gluon jets, the angularity distributions from H → $q\overline{q}$ and H → gg show different behaviors and can be used to constrain the light quark Yukawa couplings. We show that the upper limit of light quark Yukawa couplings could be probed to the level of ~ 15% of the bottom quark Yukawa coupling in the Standard Model in a conservative analysis window far away from nonperturbative effects and other uncertainties; the limit can be pushed to ≲ 7 – 9% with better control of the nonperturbative effects especially on gluon angularity distributions and/or with multiple angularities.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗