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Ir(hkl) Surface Electrochemistry in a Nonadsorbing Acidic Medium
The fundamental properties of electrochemical materials depend on the multiple and often complex interactions between electrode surface sites and electrolyte species at the electrochemical interface. Despite Iridium use in electrolyzer systems, much of its surface electrochemistry remains underexplored. This study investigates the surface electrochemistry of Ir(111), Ir(100), and Ir(110) surfaces in acidic media. Using cyclic voltammetry and CO charge displacement experiments, we establish the charge states and adsorbate coverages as a function of the electrode potential, revealing the presence of hydrogen and hydroxyl co-adsorption at low potentials on (111), and almost no coverage of H ad on (110) facet. In situ Shell Isolated Nanoparticle Enhanced Raman Spectroscopy experiments provide direct evidence of the formation of key adsorbate species, such as hydrogen, hydroxyl, and oxygen, but most importantly, their interactions with interfacial water, confirmed by Density Functional Theory calculations. Our findings highlight the role of co-adsorption and interspecies interactions, with microkinetic adsorption voltammetry simulations corroborating the influence of lateral interactions on adsorption dynamics, particularly for Ir(100) where the OHad formation occurs as a sharp adsorption/desorption current. Our results underscores the importance of interfacial water and hydrogen bonding networks in shaping the electrochemical behavior on Ir surfaces, refining our baseline understanding of the Ir surface electrochemistry necessary for the development of advanced Ir-based electrochemical materials.
Electronic structure prediction of medium and high entropy alloys across composition space
We propose machine learning (ML) models to predict the electron density — the fundamental unknown of a material’s ground state — across the composition space of concentrated alloys. From this, other physical properties can be inferred, enabling accelerated exploration. A significant challenge is that the number of descriptors and sampled compositions required for accurate prediction grows rapidly with species. To address this, we employ Bayesian Active Learning (AL), which minimizes training data requirements by leveraging uncertainty quantification capabilities of Bayesian Neural Networks. Compared to the strategic tessellation of the composition space, Bayesian-AL reduces the number of training data points by a factor of 2.5 for ternary (SiGeSn) and 1.7 for quaternary (CrFeCoNi) systems. We also introduce easy-to-optimize, body-attached-frame descriptors, which respect physical symmetries while keeping descriptor-vector size nearly constant as alloy complexity increases. Our ML models demonstrate high accuracy and generalizability in predicting both electron density and energy across composition space.
Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida
Although synthetic biology can produce valuable chemicals in a renewable manner, its progress is still hindered by a lack of predictive capabilities. Media optimization is a critical, and often overlooked, process which is essential to obtain the titers, rates and yields needed for commercial viability. Here, we present a molecule- and host-agnostic active learning process for media optimization that is enabled by a fast and highly repeatable semi-automated pipeline. Its application yielded 60% and 70% increases in titer, and 350% increase in process yield in three different campaigns for flaviolin production in Pseudomonas putida KT2440. Explainable Artificial Intelligence techniques pinpointed that, surprisingly, common salt (NaCl) is the most important component influencing production. The optimal salt concentration is very high, comparable to seawater and close to the limits that P. putida can tolerate. The availability of fast Design-Build-Test-Learn (DBTL) cycles allowed us to show that performance improvements for active learning are rarely monotonous. This work illustrates how machine learning and automation can change the paradigm of current synthetic biology research to make it more effective and informative, and suggests a cost-effective and underexploited strategy to facilitate the high titers, rates and yields essential for commercial viability.
Author Correction: Machine learning-led semi-automated medium optimization reveals salt as key for flaviolin production in Pseudomonas putida
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High-throughput approach for investigating interdiffusion in medium- and high-entropy alloys
Interdiffusion experiments are usually time-consuming and tedious since diffusion couples must be annealed at several temperatures for a long time. The efforts required to study interdiffusion in multicomponent alloys increase dramatically as multiple diffusion couples are required to cover broad composition ranges and determine the diffusivities of individual elements in different chemical environments. To circumvent this challenge, we present a high-throughput approach applicable to single-phase and compositionally complex alloys, which are assumed to approximate ideal solid solutions. Here, a simple diffusion-multiple experiment combined with a physically based kinetic model is proposed to efficiently determine the diffusion coefficients of the constituent elements in quaternary CrFeCoNi alloys. Compared with tracer diffusivities reported in the literature, the results, thus, obtained do not differ by more than a factor of 2 and were obtained from a single interdiffusion experiment. In contrast, the diffusivities simulated with commercial mobility and thermodynamic databases are strongly overestimated by a factor ranging from 1 to 16. Therefore, our approach enables high-throughput determination of diffusivities and can help in the design of alloys for high-temperature applications where diffusion plays a key role.
Analytic formula for the interface temperature when a Marshak Wave propagates from one medium to the next
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Fluorescence time profile measurement of LAB based liquid scintillator in response to medium relativistic ion particles
Liquid scintillator is widely used in particle physics experiments due to its high light yield, good timing resolution, scalability and low cost. Certain liquid scintillators exhibit pulse shape discrimination capabilities because of difference in fluorescence timing properties induced by different particles. Its fluoresence timing properties have been measured mostly for radioactive decay sources at MeV energies. Here, we present a novel measurement of fluorescence time properties of Linear Alkyl Benzene (LAB) based liquid scintillator in response to high-energy ions of hydrogen (Z = 1), helium (Z = 2) and krypton at around 200–300 MeV/u for the first time. We compared the results to those from radioactive sources and observed a distinct dE / dX dependence, regardless of the particle type. These findings are essential for physics searches such as the diffuse supernova neutrino background in large liquid scintillator detectors like JUNO, and are also critical towards understanding the underlying scintillation timing mechanism.
Engineering Escherichia coli to produce medium chain oleochemicals from C 2 substrates
Many strategies to create a circular bioeconomy have been proposed. To be successful, CO 2 must be reduced with renewable energy into chemical building blocks, from which the chemical industry can be supported. Circular strategies include leveraging photosynthesis to produce sugar and lipid intermediates or renewable electricity to produce hydrogen or other electron carriers to support CO 2 reduction. Acetogens can anaerobically reduce CO 2 with H 2 to produce mixtures of small organic molecules in gas fermentations. We previously demonstrated that acetate, a common product of gas fermentation, can be converted to the model oleochemical dodecanol in engineered Escherichia coli. Here, we explored the conversion of ethanol and mixtures of ethanol and acetate to the same model oleochemicals. Co-feeding ethanol can supply both carbon and additional reducing power relative to acetate alone. In this work, we engineered E. coli to catabolize ethanol and expressed two distinct ethanol metabolism pathways in different operons and combined them with improved engineered acetate activation. We evaluated the performance of these operons in dodecanol-producing strains when fed ethanol or acetate and found ethanol to be a better carbon source when judged by product titers. The engineered strains fed ethanol produced about two-fold more dodecanol than the strains fed acetate. This increase was in part, due to change in product distribution. Cells fed ethanol produced predominantly dodecanol, whereas cells fed acetate generated a mixture of dodecanol and dodecanoic acid. Dodecanol titers were further improved by employing feeding strategies in controlled bioreactors.
Improved high-gradient performance for medium-velocity superconducting half-wave resonators: Surface preparation and trapped flux mitigation
A development effort to improve the performance of superconducting radio-frequency half-wave resonators (SRF HWRs) is underway at the Facility for Rare Isotope Beams (FRIB), where 220 such resonators are in operation. Our goal was to achieve an intrinsic quality factor (𝑄 0 ) of ≥ 2 × 10 10 at an accelerating gradient (𝐸 acc ) of 12 MV/m. FRIB production resonators were prepared with buffered chemical polishing. First trials of electropolishing (EP) and post-EP low-temperature baking of FRIB HWRs allowed us to reach higher gradient (15 MV/m, limited by quench) with a higher quality factor at high gradient, but 𝑄 0 was still below our goal. Trapped magnetic flux during the Dewar test was found to be a source of 𝑄 0 reduction. Three strategies were used to reduce the trapped flux: (i) adding a local magnetic shield (LMGS) to supplement the “global” magnetic shield around the Dewar for reduction of the ambient magnetic field; (ii) performing a “uniform cooldown” (UC) to reduce the thermoelectric currents; and (iii) using a compensation coil to further reduce the ambient field with active field cancellation (AFC). The LMGS improved the 𝑄 0 , but not enough to reach our goal. With UC and AFC, we exceeded our goal, reaching 𝑄 0 = 2.8 ×10 10 at 𝐸 acc = 12 MV/m.
Coherent optical imaging of moving objects hidden in a heavily scattering random medium
Experimental results are presented that provide insight into the physics of statistical imaging in heavily scattering random media based on measured speckle correlations as a function of the change in position of a moving object. In this way, definitive interpretation of a rather complex and earlier theory is achieved, making this work an experimental complement to that theory paper []. Motion could be natural, where the set of positions is estimated or separately obtained, or directed, where a mechanical stage can be used to adjust the object's position. In the experiment, a coherent laser illuminates two scattering diffusers, while an object is translated between them in the resulting speckled field and images are collected in a transmission configuration. Results are shown for various objects of differing size and geometry, allowing the theory to be validated and interpreted with new understanding. This work demonstrates imaging opportunities, and applications include material characterization, environmental imaging and sensing, and deep-tissue imaging. Published by the American Physical Society 2025
Investigating strangeness enhancement in jet and medium via 𝜙(1020) production in 𝑝-Pb collisions at √ s NN = 5.02TeV
This work aims to differentiate strangeness produced from hard processes (jetlike) and softer processes (underlying event) by measuring the angular correlation between a high-momentum trigger hadron (ℎ) acting as a jet proxy and a produced strange hadron [𝜙(1020) meson]. Measuring ℎ−𝜙 correlations at midrapidity in 𝑝-Pb collisions at √ s NN = 5.02TeV as a function of event multiplicity provides insight into the microscopic origin of strangeness enhancement in small collision systems. The jetlike and the underlying-event-like strangeness production are investigated as a function of event multiplicity. They are also compared between a lower and higher momentum region. The evolutions of the per-trigger yields within the near-side (aligned with the trigger hadron) and away-side (in the opposite direction of the trigger hadron) jets are studied separately, allowing for the characterization of two distinct jetlike production regimes. Furthermore, the ℎ−𝜙 correlations within the underlying event give access to a production regime dominated by soft production processes, which can be compared directly to the in-jet production. Comparisons between ℎ−𝜙 and dihadron correlations show that the observed strangeness enhancement is largely driven by the underlying event, where the 𝜙/ℎ ratio is significantly larger than within the jet regions. As multiplicity increases, the fraction of the total 𝜙(1020) yield coming from jets decreases compared to the underlying event production, leading to high-multiplicity events being dominated by the increased strangeness production from the underlying event.
In-medium similarity renormalization group at finite temperature
Not Available
Evolution of radiative thermal instability in a confined medium
Thermally bistable fluid tends to self-organize into clouds of hot and cold material, which are internally uniform and separated by thin conduction fronts. The evolution of these clouds has been studied for isobaric systems, but when pressure is instead treated as a dynamical quantity and allowed to evolve self-consistently, fundamentally different dynamics appear. Such a treatment is necessary in some laboratory plasmas, whose volume is constrained but whose pressure can vary. Here, solutions are derived for the evolution of clouds, accounting for pressure variation and interactions between conduction fronts. Additional stable configurations and secondary instabilities are derived, which may be relevant to fusion plasmas and to the study of photoionized plasma in the laboratory.
Ultimate brightness of a medium-energy synchrotron light source at operational beam intensity
Synchrotron light sources are key instruments of modern science, providing unique opportunities for groundbreaking studies in diverse scientific disciplines and driving innovation in numerous scientific and technological fields. Fourth-generation light sources provide unprecedented capabilities in imaging, spectroscopy and diffraction techniques. Ultimate brightness is the key to advancing to a smaller scale, faster response, and higher data measurement and processing rate. The brightness is primarily determined by the electron beam emittance and energy spread at operational intensity. A common feature of fourth-generation synchrotrons is the short length of the electron bunches combined with a very small transverse beam size. Consequently, the high particle density leads to strong collective effects that significantly increase the emittance and limit the achievable brightness at operational beam intensity. In this article, we summarize our studies of the emittance and brightness scaled with the beam energy and intensity, taking into account the effects of intrabeam scattering, beam-impedance interaction and bunch lengthening provided by higher-harmonic RF systems to identify optimal combinations of machine and beam parameters.
Improving Resiliency in Planning MW-Scale Medium and Heavy Duty EV Charging Stations Considering TSCOTS Optimization
Electrification of heavy-duty (HD) vehicles marks an important milestone and technical challenge in the electric vehicle (EV) industry and the public grid. However, implementing EV charging at this scale will necessitate that traditional truck stops be updated with EV charging infrastructure that could represent 10's of MW in electricity consumption. Furthermore, as the transportation sector is represented as critical infrastructure, supporting resiliency considerations in EV charging infrastructure will be critical. This paper proposes an optimization-based approach for optimally designing a MW-scale microgrid charging network. This approach transforms conventional designed truck stops into a reliable HDEV charging stations capable of overnight slow charging and 30-minute to 1 hour fast charging. Using a mixed-integer linear program formulation blending capacity planning and reliability constraints, an optimal network configuration can be solved for a proposed EV charging station that includes photovoltaic and battery energy storage capabilities.
Thermal Stability of the Dot-in-Well Gain Medium for Photonic Crystal Surface Emitting Lasers
Self-assembled quantum dots (QDs) embedded in InGaAs quantum wells (QWs) are used as active regions for photonic-crystal surface-emitting lasers (PCSELs). An epitaxial regrowth method is developed to fabricate the dot-in-well (DWELL) PCSELs. Here, the epitaxial regrowth starts with the growth of a partial laser structure containing bottom cladding, waveguide, active region, and the photonic crystal (PC) layer. The PC layer is patterned to realize the cavity. Subsequently a top cladding layer is regrown to complete the laser structure. During the regrowth of the top cladding layer, the partial laser structure is subjected to high growth temperatures in excess of 600 °C resulting in an unintentional annealing of the active region. This annealing of the active region can alter the QDs by changing their size resulting in a blue shift in photoluminescence (PL) and narrowing PL emission. This effect results in the misaligning of the gain peak and the cavity resonance, resulting in sub-optimal lasing performance. DWELL active regions are known to have better thermal stability compared to both QDs and QWs and could be an ideal candidate for regrown PCSELs. We successfully demonstrate an optically-pumped epitaxially-regrown DWELL PCSEL with an emission wavelength of 1230 nm operating at room temperature. Furthermore, the DWELL active region shows excellent emission wavelength stability and intensity despite the high temperature regrowth process.
PV Modules Temperature Variation and Patterns in Medium and Utility-Scale Floating PV Systems
This paper presents the preliminary results and findings of the four operational Floating PV systems across the USA. At each site, temperature of five PV modules located at North-West, North-East, Middle, South-West, and, South-East have been monitored through the Resistant Temperature Detector (RTD) sensors. Three RTDs were attached to each PV module on the rear-side along the diagonal at top, middle and bottom cells. The preliminary results reveal wide temperature differences among the inter and intra PV modules. Besides this, wave pattern temperatures were observed in a few PV modules. The final results, findings, and, factors responsible will be investigated during the next few months.