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Combined Optical and Radio-Frequency Perspectives on a Hybrid Cloud-To-Ground Lightning Flash Observed by the FORTE Satellite
In this work, we use the coincident optical and radio-frequency (RF) measurements taken by the Fast On-orbit Recording of Transient Events (FORTE) satellite to shed light on common optical signatures recorded by NASA and NOAA lightning imagers during Cloud-to-Ground (CG) lightning. We build flash cluster data for FORTE using the same techniques as the NASA/NOAA instruments to document the optical/RF evolution of an oceanic hybrid -CG flash. The flash began with strong Very High Frequency (VHF)-band emission from a Narrow Bipolar Event (NBE) that initiated a period of normal bilevel intracloud (IC) activity in two vertical layers (8 and 12 km) that lasted for 490 ms. VHF waveforms show step leader activity ahead of the return stroke. All impulsive VHF sources after the stroke come from the lower (8 km) layer. K-changes are noted following the return stroke, but no subsequent strokes are detected. The optical flash began 136 ms after the NBE RF pulse. Twenty-two of the 33 optical groups were dim and occurred during the in-cloud phase of the flash. This activity included both isolated pulses and sustained periods of illumination over tens of milliseconds. Initial cloud pulses accounted for 23% of the total optical radiance from the flash. Illumination during the return stroke contributed 58% of the total radiance, and the K-changes and cloud pulses after the stroke supplied the remaining 19%. These results highlight the benefit of having RF alongside optical lightning measurements for clarifying signatures in the optical data and providing information on their physical origins.
Femtosecond exciton dynamics in WSe 2 optical waveguides
Van-der Waals (vdW) atomically layered crystals can act as optical waveguides over a broad range of the electromagnetic spectrum ranging from Terahertz to visible. Unlike common Si-based waveguides, vdW semiconductors host strong excitonic resonances that may be controlled using non-thermal stimuli including electrostatic gating and photoexcitation. Here, we utilize waveguide modes to examine photo-induced changes of excitons in the prototypical vdW semiconductor, WSe 2 , prompted by femtosecond light pulses. Using time-resolved scanning near-field optical microscopy we visualize the electric field profiles of waveguide modes in real space and time and extract the temporal evolution of the optical constants following femtosecond photoexcitation. By monitoring the phase velocity of the waveguide modes, we detect incoherent A-exciton bleaching along with a coherent optical Stark shift in WSe 2 .
Integration of full divertor detachment with improved core confinement for tokamak fusion plasmas
Divertor detachment offers a promising solution to the challenge of plasma-wall interactions for steady-state operation of fusion reactors. Here, we demonstrate the excellent compatibility of actively controlled full divertor detachment with a high-performance (β N ~ 3, H 98 ~ 1.5) core plasma, using high-β p (poloidal beta, β p > 2) scenario characterized by a sustained core internal transport barrier (ITB) and a modest edge transport barrier (ETB) in DIII-D tokamak. The high-β p high-confinement scenario facilitates divertor detachment which, in turn, promotes the development of an even stronger ITB at large radius with a weaker ETB. This self-organized synergy between ITB and ETB, leads to a net gain in energy confinement, in contrast to the net confinement loss caused by divertor detachment in standard H-modes. These results show the potential of integrating excellent core plasma performance with an efficient divertor solution, an essential step towards steady-state operation of reactor-grade plasmas.
Deep learning-based segmentation of lithium-ion battery microstructures enhanced by artificially generated electrodes
Accurate 3D representations of lithium-ion battery electrodes, in which the active particles, binder and pore phases are distinguished and labeled, can assist in understanding and ultimately improving battery performance. Here, we demonstrate a methodology for using deep-learning tools to achieve reliable segmentations of volumetric images of electrodes on which standard segmentation approaches fail due to insufficient contrast. We implement the 3D U-Net architecture for segmentation, and, to overcome the limitations of training data obtained experimentally through imaging, we show how synthetic learning data, consisting of realistic artificial electrode structures and their tomographic reconstructions, can be generated and used to enhance network performance. We apply our method to segment x-ray tomographic microscopy images of graphite-silicon composite electrodes and show it is accurate across standard metrics. We then apply it to obtain a statistically meaningful analysis of the microstructural evolution of the carbon-black and binder domain during battery operation.
Crystal dissolution by particle detachment
Crystal dissolution, which is a fundamental process in both natural and technological settings, has been predominately viewed as a process of ion-by-ion detachment into a surrounding solvent. Here we report a mechanism of dissolution by particle detachment (DPD) that dominates in mesocrystals formed via crystallization by particle attachment (CPA). Using liquid phase electron microscopy to directly observe dissolution of hematite crystals — both compact rhombohedra and mesocrystals of coaligned nanoparticles — we find that the mesocrystals evolve into branched structures, which disintegrate as individual sub-particles detach. The resulting dissolution rates far exceed those for equivalent masses of compact single crystals. Applying a numerical generalization of the Gibbs-Thomson effect, we show that the physical drivers of DPD are curvature and strain inherently tied to the original CPA process. Based on the generality of the model, we anticipate that DPD is widespread for both natural minerals and synthetic crystals formed via CPA.
Molecular-resolution imaging of ice crystallized from liquid water by cryogenic liquid-cell TEM
Despite the ubiquity of ice, a molecular-resolution image of nanoscopic defects or microstructures in ice crystallized from liquid water has never been obtained. This is mainly due to the difficulties in preparing and preserving crystalline ice samples that can survive under high-resolution imaging conditions. Here, we report the stabilization and Å-resolution electron imaging of ice Ih crystallized from liquid water by developing cryogenic liquid-cell transmission electron microscopy (CRYOLIC-TEM). We combine lattice mapping with molecular dynamics simulations to reveal that ice formation is highly tolerant to nanoscale defects such as misoriented subdomains and trapped gas bubbles, which are stabilized by molecular-scale structural motifs. Importantly, bubble surfaces adopt low-energy nanofacets and create negligible strain fields in the surrounding crystal. These bubbles can dynamically nucleate, grow, migrate, dissolve, and coalesce under electron irradiation and be monitored in situ near a steady state. This work improves our understanding of water crystallization behaviors at a molecular spatial resolution.
Signatures of nonthermal carriers in nonlinear photoluminescence
Ultrafast light emission from plasmonic nanostructures provides a sensitive probe of the energetic electrons generated by intense optical excitation. The way this emission scales with excitation intensity has long been read through the lens of heated electron populations at an elevated temperature, yet this picture cannot reconcile the variety of behaviors seen across different materials and experiments. We argue that transient nonthermal electrons play a far larger role than has been appreciated. We present a unified description that reproduces emission behavior across diverse systems and outline experiments needed to resolve their signatures.
Deep learning for exploring ultra-thin ferroelectrics with highly improved sensitivity of piezoresponse force microscopy
Hafnium oxide-based ferroelectrics have been extensively studied because of their existing ferroelectricity, even in ultra-thin film form. However, studying the weak response from ultra-thin film requires improved measurement sensitivity. In general, resonance-enhanced piezoresponse force microscopy (PFM) has been used to characterize ferroelectricity by fitting a simple harmonic oscillation model with the resonance spectrum. However, an iterative approach, such as traditional least squares (LS) fitting, is sensitive to noise and can result in the misunderstanding of weak responses. In this study, we developed the deep neural network (DNN) hybrid with deep denoising autoencoder (DDA) and principal component analysis (PCA) to extract resonance information. The DDA/PCA-DNN improves the PFM sensitivity down to 0.3 pm, allowing measurement of weak piezoresponse with low excitation voltage in 10-nm-thick Hf 0.5 Zr 0.5 O 2 thin films. Our hybrid approaches could provide more chances to explore the low piezoresponse of the ultra-thin ferroelectrics and could be applied to other microscopic techniques.
Active oversight and quality control in standard Bayesian optimization for autonomous experiments
The fusion of experimental automation and machine learning has catalyzed a new era in materials research, prominently featuring Gaussian Process (GP) Bayesian Optimization (BO) driven autonomous experiments. Here we introduce a Dual-GP approach that enhances traditional GPBO by adding a secondary surrogate model to dynamically constrain the experimental space based on real-time assessments of the raw experimental data. This Dual-GP approach enhances the optimization efficiency of traditional GPBO by isolating more promising space for BO sampling and more valuable experimental data for primary GP training. We also incorporate a flexible, human-in-the-loop intervention method in the Dual-GP workflow to adjust for unanticipated results. We demonstrate the effectiveness of the Dual-GP model with synthetic model data and implement this approach in autonomous pulsed laser deposition experimental data. This Dual-GP approach has broad applicability in diverse GPBO-driven experimental settings, providing a more adaptable and precise framework for refining autonomous experimentation for more efficient optimization.
Inverse design of photonic surfaces via multi fidelity ensemble framework and femtosecond laser processing
We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization. The combined MF ensemble can generate multiple disparate sets of laser-processing parameters that can each produce the same target input spectral emissivity with high accuracy (root mean squared errors < 2%). SHapley Additive exPlanations analysis shows transparent model interpretability of the complex relationship between laser parameters and spectral emissivity. Finally, the MF ensemble is experimentally validated by fabricating and evaluating photonic surface designs that it generates for improved efficiency energy harvesting devices. Our approach provides a powerful tool for advancing the inverse design of photonic surfaces in energy harvesting applications.
BEACON—automated aberration correction for scanning transmission electron microscopy using Bayesian optimization
Aberration correction is an important aspect of modern high-resolution scanning transmission electron microscopy. Most methods of aligning aberration correctors require specialized sample regions and are unsuitable for fine-tuning aberrations without interrupting on-going experiments. Here, we present an automated method of correcting first- and second-order aberrations called BEACON, which uses Bayesian optimization of the normalized image variance to efficiently determine the optimal corrector settings. We demonstrate its use on gold nanoparticles and a hafnium dioxide thin film showing its versatility in nano- and atomic-scale experiments. BEACON can correct all first- and second-order aberrations simultaneously to achieve an initial alignment and first- and second-order aberrations independently for fine alignment. Ptychographic reconstructions are used to demonstrate an improvement in probe shape and a reduction in the target aberration.
Comparison of short-range order in irradiated dysprosium titanates
Abstract The structural response of Dy 2 TiO 5 oxide under swift heavy ion irradiation (2.2 GeV Au ions) was studied over a range of structural length scales utilizing neutron total scattering experiments. Refinement of diffraction data confirms that the long-range orthorhombic structure is susceptible to ion beam-induced amorphization with limited crystalline fraction remaining after irradiation to 8 × 10 12 ions/cm 2 . In contrast, the local atomic arrangement, examined through pair distribution function analysis, shows only subtle changes after irradiation and is still described best by the original orthorhombic structural model. A comparison to Dy 2 Ti 2 O 7 pyrochlore oxide under the same irradiation conditions reveals a different behavior: while the dysprosium titanate pyrochlore is more radiation resistant over the long-range with smaller degree of amorphization as compared to Dy 2 TiO 5 , the former involves more local atomic rearrangements, best described by a pyrochlore-to-weberite-type transformation. These results highlight the importance of short-range and medium-range order analysis for a comprehensive description of radiation behavior.
In situ electron microscopy: atomic-scale dynamics of metal oxidation and corrosion
Corrosion degrades metals through complex microscopic processes that traditional methods often fail to capture. In-situ transmission electron microscopy (TEM) bridges this gap by providing real-time atomic-scale imaging of oxidation and corrosion dynamics. Gas-phase TEM uncovers critical oxidation mechanisms like oxide nucleation and alloying effects, while liquid-phase TEM tracks corrosion initiation. Coupled with emerging techniques like ultrafast direct electron detection cameras, these advancements promise unprecedented resolution in understanding material degradation.
Particle physics at accelerators in the United States and Asia
Particle physics experiments in the United States and Asia have greatly contributed to the understanding of elementary particles and their interactions. With the recent discovery of the Higgs boson at CERN, interest in the development of next-generation colliders has been rekindled. A linear electron–positron collider in Japan and a circular collider in China have been proposed for precision studies of the Higgs boson. In addition to the Higgs programme, new accelerator-based long-baseline neutrino mega-facilities are being built in the United States and Japan. Here, we outline the present status of key particle physics programmes at accelerators, and future plans in the United States and Asia that largely complement approaches being explored in the European Strategy for Particle Physics Update. Furthermore, we encourage the pursuit of this global approach, reaching beyond regional boundaries for optimized development and operations of major accelerator facilities worldwide, to ensure an active and productive future of the field.
Real-time observation of dynamic structure of liquid-vapor interface at nanometer resolution in electron irradiated sodium chloride crystals
The dynamics and structure of the liquid and vapor interface has remained elusive for decades due to the lack of an effective tool for directly visualization beyond micrometer resolution. Here, we designed a simple liquid-cell for encapsulating the liquid state of sodium for transmission electron microscopic (TEM) observation. The real-time dynamic structure of the liquid-vapor interface was imaged and videoed by TEM on the sample of electron irradiated sodium chloride (NaCl) crystals, a well-studied sample with low melting temperature and quantum super-shells of clusters. The nanometer resolution images exhibit the fine structures of the capillary waves, composed of first-time observed three zones of structures and features, i.e. flexible nanoscale fibers, nanoparticles/clusters, and a low-pressure area that sucks the nanoparticles from the liquid to the interface. Although the phenomenons were observed based on irradiated NaCl crystals, the similarities of the phenomenons to predictions suggest our real-time ovserved dynamic structure might be useful in validating long-debated theoretical models of the liquid-vapor interface, and enhancing our knowledge in understanding the non-equilibrium thermodynamics of the liquid-vapor interface to benefit future engineering designs in microfluidics.
Investigating off-Hugoniot states using multi-layer ring-up targets
Abstract Laser compression has long been used as a method to study solids at high pressure. This is commonly achieved by sandwiching a sample between two diamond anvils and using a ramped laser pulse to slowly compress the sample, while keeping it cool enough to stay below the melt curve. We demonstrate a different approach, using a multilayer ‘ring-up’ target whereby laser-ablation pressure compresses Pb up to 150 GPa while keeping it solid, over two times as high in pressure than where it would shock melt on the Hugoniot. We find that the efficiency of this approach compares favourably with the commonly used diamond sandwich technique and could be important for new facilities located at XFELs and synchrotrons which often have higher repetition rate, lower energy lasers which limits the achievable pressures that can be reached.
3D particle transport in multichannel microfluidic networks with rough surfaces
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