Advancing chemical lability assessments of organic matter using a synthesis of FT-ICR MS data across diverse environments and experiments
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Geochemistry & Geophysics↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Extreme-scale EV charging infrastructure planning for last-mile delivery using high-performance parallel computing
Here, this paper addresses stochastic charger location and allocation problems under queue congestion for last-mile delivery using electric vehicles (EVs). The objective is to decide where to open charging stations and how many chargers of each type to install, subject to budgetary and waiting-time constraints. We formulate the problem as a mixed-integer non-linear program, where each station-charger pair is modeled as a multiserver queue with stochastic arrivals and service times to capture the notion of waiting in fleet operations. The model is extremely large, with billions of variables and constraints for a typical metropolitan area; even loading the model in solver memory is difficult, let alone solving it. To address this challenge, we develop a Lagrangian-based dual decomposition framework that decomposes the problem by station and leverages parallelization on high-performance computing systems, where the subproblems are solved by using a cutting plane method and their solutions are collected at the master level. We also develop a three-step rounding heuristic to transform the fractional subproblem solutions into feasible integral solutions. Computational experiments on data from the Chicago metropolitan area with hundreds of thousands of households and thousands of candidate stations show that our approach produces high-quality solutions in cases where existing exact methods cannot even load the model in memory. We also analyze various policy scenarios, demonstrating that combining existing depots with newly built stations under multiagency collaboration substantially reduces costs and congestion. These findings offer a scalable and efficient framework for developing sustainable large-scale EV charging networks.
Capacity allocation↗
Single Molecule Spectroscopy Studies of Acid–Base Chemical Gradients Using Nile Red as a Probe of Local Surface Acidity
Single molecule spectroscopy studies of local acidity along bifunctional acid-base gradients are reported. Gradients are prepared by directional vapor phase diffusion and subsequent reaction of 3-aminopropyl-trimethoxysilane with a uniform silica film. Gradient formation is confirmed by spectroscopic ellipsometry and by static water contact angle measurements. X-ray photoelectron spectroscopy is used to characterize the nitrogen content and degree of nitrogen protonation along the gradient. Nile Red is employed as the probe dye in single molecule spectroscopy studies of these gradients. While Nile Red is well-known for its solvent sensitivity, it is used here, for the first time, to sense the acid/base properties of the film in two-color wide-field fluorescence imaging experiments. The data reveal broad bimodal distributions of Nile Red emission spectra that vary along the gradient direction. The single-molecule results are consistent with solution-phase ensemble acid/base studies of the dye. The former reveal a gradual transition from a surface dominated by basic aminosilane sites at the high-amine end of the gradient to one dominated by acidic silanol sites at the low-amine end. The sub-diffraction-limited spatial resolution afforded by superlocalization of the single molecules reveals spatial correlations in the acid/base properties of the gradient over ~ 200 nm distances. Furthermore, these studies provide data relevant to the use of aminosilane-modified silica in bifunctional, cooperative chemical catalysis.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Validated Reactive Force Field Quantifies MXene Interfacial Properties, Mechanics, and Thermal Transport
MXenes combine rich surface chemistry, mechanical strength, and high conductivity for a multitude of emerging applications. Predictive modeling supports accelerated materials designs and has been limited by the absence of validated and transferable force fields. Here, we introduce an interpretable, reactive INTERFACE force field (IFF and IFF-R) for Ti 3 C 2 T x MXenes that is trained based on chemical knowledge and achieves quantitative agreement with experiments across lattice parameters (<0.5%), density (<0.2%), liquid contact angles, Raman spectra, and the in-plane elastic modulus (∼320 GPa). The models cover surface terminations from hydroxyl (−OH) to fluorine (−F) groups and are extensible to other chemistries. We introduce pH-resolved surface chemistry and identify dopamine adsorption mechanisms at MXene–aqueous interfaces supported by QCM-D and UV–Vis experiments. The data reveal coplanar and perpendicular binding modes and concentration-dependent multilayer assembly. We predict previously inaccessible properties, including termination-dependent cleavage energies, interlayer shear moduli and dynamic shear failure, nanoindentation and brittle fracture, anisotropic in-plane and out-of-plane thermal conductivities, including the role of defects. Agreement with available experimental data is consistently close and exceeds DFT accuracy across the benchmark properties examined. The IFF/IFF-R model is compatible with CHARMM, AMBER, OPLS, and CVFF force fields for simulations of MXenes with diverse surface terminations, electrolyte interfaces, biointerfaces, and polymer composites without additional parameters. Parameter sets, 3D models, and analysis scripts are provided for community use. The validated, reactive, and transferable IFF framework facilitates predictive design of MXene-based films, membranes, sensing interfaces, and composites.
MXene↗
Interlaboratory Study for Characterizing Monoclonal Antibodies by Top-Down and Middle-Down Mass Spectrometry
The Consortium for Top-Down Proteomics (www.topdownproteomics.org) launched the present study to assess the state of top-down mass spectrometry (TD MS) and middle-down mass spectrometry (MD MS) for characterizing monoclonal antibody (mAb) primary structures, including their modifications. To meet the needs of the rapidly growing therapeutic antibody market, it is important to develop analytical strategies to characterize the heterogeneity of a therapeutic product’s primary structure reliably and reproducibly. The major objective of the present study was to determine whether current TD/MD MS technologies and protocols can add value to the more commonly employed bottom-up approaches, such as confirming protein integrity, sequencing variable domains, ruling out artefacts, and revealing modifications and their locations. A panel of three mAbs was selected and centrally provided to twenty laboratories worldwide for the analysis: Sigma mAb standard (SiLuLite), NIST mAb standard, and the therapeutic mAb Herceptin (trastuzumab). A variety of different MS instrument platforms and ion dissociation techniques were employed. Overall, the present study confirms that MD/TD MS strategies are valuable and readily available tools in laboratories worldwide. They provide complementary information to the bottom-up approach and can add unique information, which may be crucial for comprehensive mAb characterization. The current limitations, as well as possible solutions to overcome them, are also outlined. One of the limitations revealed by the results of the present study is that the expert knowledge in both experiment and data analysis is indispensable to practice TD/MD MS, but the number of TD/MD MS experts is still very low.
59 BASIC BIOLOGICAL SCIENCES↗
Mixed‐Phase Clouds Over the Southern Ocean as Observed From Satellite and Surface Based Lidar and Radar
Abstract This study investigates the occurrence of mixed‐phase clouds (MPC, i.e., cloud layers containing both liquid and ice water at sub‐freezing temperatures) over the Southern Ocean (SO) using space‐ and surface‐based lidar and radar observations. The occurrence of supercooled clouds is dominated by geometrically thin (<1 km) layers that rarely contain ice. We diagnose layers that are geometrically thicker than 1 km to contain ice ~65% and ~4% of the time from below by surface remote sensors and from above by orbiting remote sensors, respectively. We examine the discrepancy in MPC occurrence statistics as diagnosed from below and above the cloud layer. From above, we find that MPC occurrence has a gradient associated with the Antarctic Polar Front near 55°S with a rare occurrence of satellite‐derived MPC south of that latitude. In contrast, surface sensors find ice in 33% of supercooled liquid water layers. We infer using observing system simulation experiments and data analysis that space‐based lidar cannot identify the occurrence of MPC except when secondary ice‐forming processes operate in convection that is, sufficiently strong to loft ice crystals to cloud tops. We conclude that the CALIPSO phase statistics of MPC have a severe low bias in MPC occurrence. Based on surface‐based statistics in the SO, we present a parameterization of the frequency of MPC as a function of cloud top temperature that differs substantially from that used in recent climate model simulations.
54 ENVIRONMENTAL SCIENCES↗
Structural coordination between active sites of a CRISPR reverse transcriptase-integrase complex
CRISPR-Cas systems provide adaptive immunity in bacteria and archaea, beginning with integration of foreign sequences into the host CRISPR genomic locus and followed by transcription and maturation of CRISPR RNAs (crRNAs). In some CRISPR systems, a reverse transcriptase (RT) fusion to the Cas1 integrase and Cas6 maturase creates a single protein that enables concerted sequence integration and crRNA production. To elucidate how the RT-integrase organizes distinct enzymatic activities, we present the cryo-EM structure of a Cas6-RT-Cas1—Cas2 CRISPR integrase complex. The structure reveals a heterohexamer in which the RT directly contacts the integrase and maturase domains, suggesting functional coordination between all three active sites. Together with biochemical experiments, our data support a model of sequential enzymatic activities that enable CRISPR sequence acquisition from RNA and DNA substrates. These findings highlight an expanded capacity of some CRISPR systems to acquire diverse sequences that direct CRISPR-mediated interference.
59 BASIC BIOLOGICAL SCIENCES↗
High entropy powering green energy: hydrogen, batteries, electronics, and catalysis
A reformation in energy is underway to replace fossil fuels with renewable sources, driven by the development of new, robust, and multi-functional materials. High-entropy materials (HEMs) have emerged as promising candidates for various green energy applications, having unusual chemistries that give rise to remarkable functionalities. This review examines recent innovations in HEMs, focusing on hydrogen generation/storage, fuel cells, batteries, semiconductors/electronics, and catalysis—where HEMs have demonstrated the ability to outperform state-of-the-art materials. We present new master plots that illustrate the superior performance of HEMs compared to conventional systems for hydrogen generation/storage and heat-to-electricity conversion. We highlight the role of computational methods, such as density functional theory and machine learning, in accelerating the discovery and optimization of HEMs. The review also presents current challenges and proposes future directions for the field. We emphasize the need for continued integration of modeling, data, and experiments to investigate and leverage the underlying mechanisms of the HEMs that are powering progress in sustainable energy.
batteries↗
A cyclic electrochemical strategy to produce acetylene from CO 2 , CH 4 , or alternative carbon sources
This paper presents the first combined measurement of the double-differential muon neutrino and antineutrino charged-current cross sections with no pions in the final state on hydrocarbon at the off-axis near detector of the T2K experiment. The data analyzed in this work comprise 5.8 × 10$^{20}$ and 6.3 × 10$^{20}$ protons on target in neutrino and antineutrino mode respectively, at a beam energy peak of 0.6 GeV. Using the two measured cross sections, the sum, difference, and asymmetry were calculated with the aim of better understanding the nuclear effects involved in such interactions. The extracted measurements have been compared with the prediction from different Monte Carlo generators and theoretical models showing that the difference between the two cross sections have interesting sensitivity to nuclear effects.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
SLAB: simultaneous labeling and binding affinity prediction for protein–ligand structures
Machine learning models are often used as scoring functions to predict the binding affinity of a protein–ligand complex. These models are trained with limited amounts of data with experimentally measured binding affinity values. A large number of compounds are labeled inactive through single-concentration screens without measuring binding affinities. These inactive compounds, along with the active ones, can be used to train binary classification models, while regression models are trained using compounds with binding affinities only. However, the classification and regression tasks are often handled separately, without sharing the learned feature representations. In this paper, we propose a novel model architecture that jointly performs regression and classification objectives, aiming to maximize data utilization and improve predictive performance by leveraging two complementary tasks. In our setup, the regression yields the binding affinity, whereas the classification task yields the label as active or inactive. We demonstrate our method using PDBbind, the standard 3D structure database, as well as a dataset of flavivirus protease compounds with binding affinity data. Our experiments show that the new joint training strategy improves the accuracy of the model, increasing applicability in various practical drug screening scenarios.
Biological and medical sciences↗
Response of CR-39 nuclear track detectors to protons with non-normal incidence
This paper presents data from experiments with protons at non-normal incidence to CR-39 nuclear track detectors, analyzing the properties of detection efficiency, proton track diameter, track contrast, and track eccentricity. Understanding the CR-39 response to protons incident at an angle is important for designing charged particle detectors for inertial confinement fusion (ICF) applications. This study considers protons with incident energies less than 3 MeV. In this regime, an incident angle of 10° has no effect on CR-39 detection efficiency, and >85% detection efficiency is preserved up through 25° in the range of 1.0 MeV–2.1 MeV. For ICF applications, incident angles above 30° are deemed impractical for detector design due to significant drops in proton detection at all energies. We observe significant reductions in detection efficiency compared to theoretical predictions, particularly at low energies where proton tracks are etched away. The proton track diameter measured by the scan system is observed to decrease with higher incident angles. The track diameters are analyzed with two fitting models, and it is shown that the diameter–energy relation can be fit with the existing models at angles up to 30° . The optical contrast of the tracks tends to increase with the angle, meaning that the tracks are fainter, and a larger increase is observed for higher energies. Eccentricity, a measure of how elongated proton tracks are, increases with the incident angle and drops after the critical angle. The lowest energy tracks remain nearly circular even at higher angles.
47 OTHER INSTRUMENTATION↗
Learning-based approach to plasticity in athermal sheared amorphous packings: Improving softness
The plasticity of amorphous solids undergoing shear is characterized by quasi-localized rearrangements of particles. While many models of plasticity exist, the precise relationship between the plastic dynamics and the structure of a particle’s local environment remains an open question. Previously, machine learning was used to identify a structural predictor of rearrangements called “softness.” Although softness has been shown to predict which particles will rearrange with high accuracy, the method can be difficult to implement in experiments where data are limited and the combinations of descriptors it identifies are often difficult to interpret physically. Here, we address both of these weaknesses, presenting two major improvements to the standard softness method. First, we present a natural representation of each particle’s observed mobility, allowing for the use of statistical models that are both simpler and provide greater accuracy in limited datasets. Second, we employ persistent homology as a systematic means of identifying simple, topologically informed, structural quantities that are easy to interpret and measure experimentally. We test our methods on two-dimensional athermal packings of soft spheres under quasi-static shear. We find that the same structural information that predicts small variations in the response is also predictive of where plastic events will localize. We also find that an excellent accuracy is achieved in athermal sheared packings using simply a particle’s species and the number of nearest neighbor contacts.
36 MATERIALS SCIENCE↗
Peculiarities of adaptive phase correction of optical wave distortions under conditions of ‘strong’ intensity fluctuations
The reason for the loss of efficiency of adaptive phase correction in the propagation of optical waves in a turbulent atmosphere under conditions of ‘strong’ intensity fluctuations is experimentally explained for the first time. Based on the data from experiments conducted on both horizontal and vertical atmospheric paths, we have found that intensity fluctuations begin to significantly affect phase measurements when the coherence radius of the optical wave becomes less than the radius of the first Fresnel zone. Under these conditions, the main meter of adaptive optics systems, i. e. the Hartmann sensor, no longer provides correct measurements of the phase distribution in the presence of deep amplitude modulation. Based on the study of the behaviour of the mode components of the phase fluctuations reconstructed from the results of measurements in various operating regimes, we have found that, first of all, the amplitudes of the lowest modes of phase fluctuation expansion (tilts, defocusing, and astigmatism) are distorted, which, as the analysis shows, is very different from the regime of weak fluctuations. (atmospheric optics)
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Measurement of the Effective Capture Cross Section of 238 Np in the High Flux Isotope Reactor
The cross sections of 237 Np and 238 Np are important for accurate modeling and simulation of 238 Pu in the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL). Uncertainties in these cross sections can impact the ability to predict and optimize the target design and loading for 238 Pu production targets. The effective capture cross section of 237 Np in the location of pneumatic tube 1 in HFIR was measured as a first step in the measurement of the 238 Np capture and fission cross sections. Here, we describe the flux measurements, 237 Np experiments, and data analysis of the 237 Np capture cross section in HFIR.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
First neutral beam experiments on Wendelstein 7-X
In the previous divertor campaign, the Wendelstein 7-X (W7-X) device injected 3.6 MW of neutral beam heating power allowing for the achievement of densities approaching 2 × 10 20 m -3 , and providing the first initial assessment of fast ion confinement in a drift optimized stellarator. The neutral beam injection (NBI) system on W7-X is comprised of two beam boxes with space for four radio frequency sources each. The 3.6 MW of heating reported in this work was achieved with two sources in the NI21 beam box. The effect of combined electron-cyclotron resonance heating (ECRH) and NBI was explored through a series of discharges varying both NBI and ECRH power. Discharges without ECRH saw a linear increase in the line-integrated plasma density, and strong peaking of the core density, over the discharge duration. The presence of 1 MW of ECRH power was found to be sufficient to control a continuous density rise during NBI operation. Simulations of fast ion wall loads were found to be consistent with experimental infrared camera images during operation. In general, NBI discharges were free from the presence of fast ion induced Alfvénic activity, consistent with low beam betas. These experiments provide data for future scenario development and initial assessment of fast-ion confinement in W7-X, a key topic of the project.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
The Milky Way’s bulge star formation history as constrained from its bimodal chemical abundance distribution
ABSTRACT We conduct a quantitative analysis of the star formation history (SFH) of the Milky Way’s (MW) bulge by exploiting the constraining power of its stellar [Fe/H] and [Mg/Fe] distribution functions. Using Apache Point Observatory Galactic Evolution Experiment survey data, we confirm the previously established bimodal [Mg/Fe]–[Fe/H] distribution within 3 kpc of the inner Galaxy. To fit the chemical bimodal distribution, we use a simple but flexible star formation framework, which assumes two distinct stages of gas accretion and star formation, and systematically evaluate a wide multidimensional parameter space. We find that the data favour a three-phase SFH that consists of an initial starburst, followed by a rapid star formation quenching episode, and a lengthy, quiescent secular evolution phase. The metal-poor, high-α bulge stars ([Fe/H] < 0.0 and [Mg/Fe] > 0.15) are formed rapidly (<2 Gyr) during the early starburst. The density gap between the high- and low-α sequences is due to the quenching process. The metal-rich, low-α population ([Fe/H] > 0.0 and [Mg/Fe] < 0.15) then accumulates gradually through inefficient star formation during the secular phase. This is qualitatively consistent with the early SFH of the inner disc. Given this scenario, a notable fraction of young stars (age <5 Gyr) is expected to persist in the bulge. Combined with extragalactic observations, these results suggest that a rapid star formation quenching process is responsible for bimodal distributions in both the MW’s stellar populations and in the general galaxy population and thus plays a critical role in galaxy evolution.
Lian, Jianhui↗
The distribution of [α/Fe] in the Milky Way disc
ABSTRACT Using a sample of red giant stars from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) Data Release 16, we infer the conditional distribution $p([\alpha /{\rm Fe}]\, |\, [{\rm Fe}/{\rm H}])$ in the Milky Way disk for the α-elements Mg, O, Si, S, and Ca. In each bin of [Fe/H] and Galactocentric radius R, we model p([α/Fe]) as a sum of two Gaussians, representing ‘low-α’ and ‘high-α’ populations with scale heights $z_1=0.45\, {\rm kpc}$ and $z_2=0.95\, {\rm kpc}$, respectively. By accounting for age-dependent and z-dependent selection effects in APOGEE, we infer the [α/Fe] distributions that would be found for a fair sample of long-lived stars covering all z. Near the Solar circle, this distribution is bimodal at sub-solar [Fe/H], with the low-α and high-α peaks clearly separated by a minimum at intermediate [α/Fe]. In agreement with previous results, we find that the high-α population is more prominent at smaller R, lower [Fe/H], and larger |z|, and that the sequence separation is smaller for Si and Ca than for Mg, O, and S. We find significant intrinsic scatter in [α/Fe] at fixed [Fe/H] for both the low-α and high-α populations, typically ∼0.04-dex. The means, dispersions, and relative amplitudes of this two-Gaussian description, and the dependence of these parameters on R, [Fe/H], and α-element, provide a quantitative target for chemical evolution models and a test for hydrodynamic simulations of disk galaxy formation. We argue that explaining the observed bimodality will probably require one or more sharp transitions in the disk’s gas accretion, star formation, or outflow history in addition to radial mixing of stellar populations.
79 ASTRONOMY AND ASTROPHYSICS↗
The detailed chemical abundance patterns of accreted halo stars from the optical to infrared
ABSTRACT Understanding the assembly of our Galaxy requires us to also characterize the systems that helped build it. In this work, we accomplish this by exploring the chemistry of accreted halo stars from Gaia-Enceladus/Gaia-Sausage (GES) selected in the infrared from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) Data Release 16. We use high resolution optical spectra for 62 GES stars to measure abundances in 20 elements spanning the α, Fe-peak, light, odd-Z, and notably, the neutron-capture groups of elements to understand their trends in the context of and in contrast to the Milky Way and other stellar populations. Using these derived abundances we find that the optical and the infrared abundances agree to within 0.15 dex except for O, Co, Na, Cu, and Ce. These stars have enhanced neutron-capture abundance trends compared to the Milky Way, and their [Eu/Mg] and neutron-capture abundance ratios (e.g. [Y/Eu], [Ba/Eu], [Zr/Ba], [La/Ba], and [Nd/Ba]) point to r-process enhancement and a delay in s-process enrichment. Their [α/Fe] trend is lower than the Milky Way trend for [Fe/H] > −1.5 dex, similar to previous studies of GES stars and consistent with the picture that these stars formed in a system with a lower rate of star formation. This is further supported by their depleted abundances in Ni, Na, and Cu abundances, again, similar to previous studies of low-α stars with accreted origins.
79 ASTRONOMY AND ASTROPHYSICS↗