The behavior of iodine in stabilized granular activated carbon and silver mordenite in cementitious
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Yamagata, Alessandra Fujii↗
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A Deep State Space Model for Rainfall‐Runoff Simulations
The classical way of studying the rainfall‐runoff processes in the water cycle relies on conceptual or physically‐based hydrologic models. Deep learning (DL) has recently emerged as an alternative and blossomed in the hydrology community for rainfall‐runoff simulations. However, the decades‐old Long Short‐Term Memory (LSTM) network remains the benchmark for this task, outperforming newer architectures like Transformers. In this work, we propose a State Space Model (SSM), specifically the Frequency Tuned Diagonal State Space Sequence (S4D‐FT) model, for rainfall‐runoff simulations. The proposed S4D‐FT is benchmarked against the established LSTM and a physically‐based Sacramento Soil Moisture Accounting model under in‐sample and out‐of‐sample simulation setups across 531 watersheds in the contiguous United States (CONUS). Results show that S4D‐FT is able to outperform the LSTM model across diverse regions under both simulation setups, especially for regions that feature snowmelt‐driven or intermittent flow regimes. In contrast, S4D‐FT tends to underperform in flashier, high‐magnitude flow regimes, likely due to its global state‐space convolution computation that emphasizes slow, storage‐driven dynamics, which makes it less effective at picking up short bursts and noisy spikes in the data. In summary, our pioneering introduction of the S4D‐FT for rainfall‐runoff simulations challenges the dominance of LSTM in the hydrology community and expands the arsenal of DL tools available for hydrological modeling.
Wang, Yihan [Univ. of Oklahoma, Norman, OK (United↗
Nanometer Scale Imaging to Develop Quantitative Descriptors of Bipolar Membrane Junction Structure
Swings in pH can be achieved by electrically polarizing a bipolar membrane (BPM) to drive water dissociation at the BPM junction for electrochemical conversion and separation processes. BPM junction design is critical to tailor performance for specific applications; however, characterization techniques capable of resolving the nanometer scale physical structure of the junction are limited. We present sample preparation, imaging, and analysis workflows that are adaptable to a variety of BPM junction architectures. Atomic force microscopy produces BPM junction images with nanometer scale lateral resolution for samples with and without a graphene oxide water dissociation catalyst in the junction. Subsequent image segmentation and analysis quantify line edge roughness and catalyst layer thickness as descriptors of junction structure. Comparison of pre- and post-electrodialysis junctions suggests electric field-induced alignment of catalyst particles during electrodialysis. This characterization workflow can inform manufacturing protocols, computational modeling, and failure mode analysis for next-generation BPMs.
97 MATHEMATICS AND COMPUTING↗
3D reconstruction of an inertial-confinement fusion implosion with neural networks using multiple heterogeneous data sources
3D asymmetries are major degradation mechanisms in inertial-confinement fusion implosions at the National Ignition Facility (NIF). These asymmetries can be diagnosed and reconstructed with the neutron imaging system (NIS) on three lines of sight around the NIF target chamber. Conventional tomographic reconstructions are used to reconstruct the 3D morphology of the implosion using NIS [Volegov et al., J. Appl. Phys. 127, 083301 (2020)], but the problem is ill-posed with only three imaging lines of sight. Asymmetries can also be diagnosed with the real-time neutron activation diagnostics (RTNAD) and the neutron time-of-flight (nToF) suite. Since the NIS, RTNAD, and nToF each sample a different part of the implosion using different physical principles, we propose that it is possible to overcome the limitations of too few imaging lines of sight by performing 3D reconstructions that combine information from all three heterogeneous data sources. This work presents a new machine learning-based reconstruction technique to do just this. By using a simple physics model and group of neural networks to map 3D morphologies to data, this technique can easily account for data of multiple different types. A simple proof-of-principle is presented, demonstrating that this technique can accurately reconstruct a hot-spot shape using synthetic primary neutron images and a hot-spot velocity vector. In particular, the hot-spot’s asymmetry, quantified as spherical harmonic coefficients, is reconstructed to within ±4% of the radius in 90% of test cases. In the future, this technique will be applied to actual NIS, RTNAD, and nToF data to better understand 3D asymmetries at the NIF.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Conditional Pseudo-Reversible Normalizing Flow for Surrogate Modeling in Quantifying Uncertainty Propagation
We introduce a conditional pseudo-reversible normalizing flow (PR-NF) that directly learns conditional probability distributions from noisy physical models to efficiently quantify both forward and inverse uncertainty propagation. Traditional surrogate modeling approaches approximate only the deterministic component of physical models, requiring separate noise characterization and computationally expensive sampling methods for inverse problems. Here, in this work, we develop the conditional PR-NF model to directly learn and efficiently generate samples from the conditional probability density functions (PDFs). The training process utilizes dataset consisting of input-output pairs without requiring prior knowledge about the noise and the function. Once trained, our model efficiently generates samples from conditional PDFs for any input within the training domain. Moreover, the pseudo-reversibility feature allows for the use of fully connected neural network architectures, which simplifies the implementation and enables theoretical analysis. We provide a rigorous convergence analysis of the conditional PR-NF model, showing its ability to converge to the target conditional PDF using the Kullback−Leibler divergence. To demonstrate the effectiveness of our method, we apply it to several benchmark tests and a real-world geologic carbon storage problem.
97 MATHEMATICS AND COMPUTING↗
Electronic Probing and Manipulation of Low-Dimensional Magnets (Final Report)
We investigate the effects of reduced dimensionality of nanomagnets on the dynamical properties at low temperatures where quantum effects such as confinement, coherence, decoherence, and environmental coupling may be important. The experimental focus is on low-temperature transport measurements of such nanomagnets and cryogenic preamplification, followed by theoretical modeling of magnetic dynamics. In addition, we explore quantum effects in magnetic dynamics for their potential as quantum sensors of spin. We are particularly interested in the regime of strong environmental coupling, where novel phases of magnetic matter such as spin-boson may develop. The key new results are improved signal-to-noise ratio of magnetic tunneling junctions through low temperature preamplification that enables us to find the spin-dependent component of the current noise; a new powerful technique to simulate magnetic dynamics in the quantum coherent regime using the master equation formalism; and, finally, experimental hint of a novel state of matter in rare-earth doped permalloy ferromagnetic samples.
75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
ELG×LRG Distribution through Dark Matter Halo Dynamics
We investigate the clustering and halo occupation distribution (HOD) of DESI Y1 emission-line (ELGs) and luminous red (LRGs) galaxies at 0.8 < z < 1.1, including their cross-correlation (ELG×LRG), using the A BACUS S UMMIT suite and a new Halo Occupation Model (H OME ) for galaxy multitracers. This integrates intrahalo dynamics, halo exclusion, and quenching, bridging insights from hydrodynamical, HOD, abundance-matching, and semianalytic studies. Leveraging full phase-space information from the Uchuu N-body simulation, and sampling satellites from dark-matter particle positions via physically motivated prescriptions, Home reproduces the anisotropic clustering down to s = 200 h −1 kpc with unprecedented accuracy. Model parameters are inferred solely from two-point statistics using a two-level Bayesian framework, yielding high-fidelity ELG, LRG, and cross-reference catalogs. We find that satellite ELGs behave as incoherent flows within their parent halos, dominating the clustering below 4 h −1 Mpc. The HOD from the best-fit Home has the following properties: (i) 90.50% (85.91%) of ELGs (LRGs) are central galaxies without satellites, residing in halos of M vir ∼ 6.6 × 10 11 (1.2 × 10 13 ) h −1 M ⊙ ; (ii) the ELG×LRG cross-correlation is governed by central-central pairs and shaped by halo exclusion on 2–5 h −1 Mpc scales; (iii) 9.50% (14.09%) of ELGs (LRGs) are satellites, of which 1.09% (3.52%) inhabit halos with a central galaxy of the same species in a maximally conformal configuration, 7.02% (0.005%) orbit complementary hosts in a minimally conformal state, and 0.58% (10.57%) are orphans. The high sensitivity of Home precisely captures the dynamics of satellites in different host environments, opening a promising avenue for understanding systematics and the dynamical nature of dark matter, potentially distinguishing gravity models.
Favole, Ginevra [Universidad de La Laguna (Spain);↗
A novel method for measuring ultra-trace levels of U and Th in Au, Pt, Ir, and W matrices using ICP-QQQ-MS employing an O 2 reaction gas
A mass shift method using ICP-MS/MS with O 2 reaction gas is used for ultra-trace determinations of U and Th in samples containing W, Ir, Pt, and Au derived polyatomic interferants without the need for extensive chemical sample preprocessing.
Harouaka, Khadouja↗
Physics-assisted generative adversarial network for X-ray tomography
X-ray tomography is capable of imaging the interior of objects in three dimensions non-invasively, with applications in biomedical imaging, materials science, electronic inspection, and other fields. The reconstruction process can be an ill-conditioned inverse problem, requiring regularization to obtain satisfactory results. Recently, deep learning has been adopted for tomographic reconstruction. Unlike iterative algorithms which require a distribution that is known a priori , deep reconstruction networks can learn a prior distribution through sampling the training distributions. In this work, we develop a Physics-assisted Generative Adversarial Network (PGAN), a two-step algorithm for tomographic reconstruction. In contrast to previous efforts, our PGAN utilizes maximum-likelihood estimates derived from the measurements to regularize the reconstruction with both known physics and the learned prior. Compared with methods with less physics assisting in training, PGAN can reduce the photon requirement with limited projection angles to achieve a given error rate. The advantages of using a physics-assisted learned prior in X-ray tomography may further enable low-photon nanoscale imaging.
47 OTHER INSTRUMENTATION↗
The effect of hardness on polymer-bonded pentaerythritol tetranitrate (PETN) explosive impact sensitivity
Handling sensitivity is an important property to assess when working with explosive samples and can be measured using a variety of tests, including drop-weight impact sensitivity. There exists a longstanding interest in the explosives community on the importance of measurable chemical, physical, and mechanical properties of explosives in impact sensitivity. However, most recent work in this area has explored chemical attributes rather than physical and mechanical properties of explosives. In this study, we measure hardness of explosive samples of pentaerythritol tetranitrate and Sylgard binder (XTX) during the curing process. The samples have been characterized for particle morphology through scanning electron microscopy and handling sensitivity through drop-weight impact testing. The relative importance of states of cure, methods of curing, morphology, and age of material are discussed. The data indicate that although there is a notable difference in morphology and mechanical properties for the samples as the polymer-bonded mixtures cure, the resulting changes to mechanical properties have a minimal effect on the sensitivity of the XTX.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Variance reduction via simultaneous importance sampling and control variates techniques using vegas
Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To improve the accuracy of MC integration, a number of useful variance reduction algorithms have been developed, including importance sampling and control variates. In this work, we demonstrate how these two methods can be applied simultaneously, thus combining their benefits. We provide a python wrapper, named CoVVVR, which implements our approach in the VEGAS program. The improvements are quantified with several benchmark examples from the literature.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) Gas Adsorption Analyzer
Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.
3Flex,BET specific surface area and pore volume, A↗
ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) X-Ray Diffraction
Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.
54 ENVIRONMENTAL SCIENCES↗
ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) Cryogenic Refrigerator Applied to Freezing Test
Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.
54 ENVIRONMENTAL SCIENCES↗
ARM Data for Examining the Ice-Nucleating Particles from SGP Part II (ExINP-SGP II) Aerodynamic Particle Sizer, Condensation Particle Counter, and Meteorological Instrument Data Analysis
Knowledge of airborne particulate matter (PM), especially the particles that have supermicron diameters, is key for understanding ice-nucleating particles (INPs). Supported by the Atmospheric Radiation Measurement (ARM) user facility, we sampled PM and surface soil materials at the Southern Great Plains observatory (SGP; 36° 36′ 18″ N, 97° 29′ 6″ W) to systematically compare the INP abundance and ice nucleation efficiency of different SGP samples (i.e., airborne versus surface materials). The field campaign, named Examing INP from SGP II (ExINP-SGP II), was conducted from 20 January to 20 April, 2021. Our data analysis products include (1) physical surface sorption characterization data (i.e., BET, pore volume) of two sets of samples -- SGP Soil and USDA, (2) X-ray diffraction spectra for the same two samples, (3) immersion freezing assay-based ice nucleation active mass density data as a function of freezing temperature for both ambient and surface samples, and (4) time-series data of ambient meteorological conditions, concentration particle counter-derived aerosol particle concentration, and aerosol particle sizer measurement during ExINP-SGP II.
54 ENVIRONMENTAL SCIENCES↗
Driving and characterizing nucleation of urea and glycine polymorphs in water
Crystal nucleation is relevant across the domains of fundamental and applied sciences. However, in many cases, its mechanism remains unclear due to a lack of temporal or spatial resolution. To gain insights into the molecular details of nucleation, some form of molecular dynamics simulations is typically performed; these simulations, in turn, are limited by their ability to run long enough to sample the nucleation event thoroughly. To overcome the timescale limits in typical molecular dynamics simulations in a manner free of prior human bias, here, we employ the machine learning-augmented molecular dynamics framework “reweighted autoencoded variational Bayes for enhanced sampling (RAVE).” We study two molecular systems—urea and glycine—in explicit all-atom water, due to their enrichment in polymorphic structures and common utility in commercial applications. From our simulations, we observe multiple back-and-forth nucleation events of different polymorphs from homogeneous solution; from these trajectories, we calculate the relative ranking of finite-sized polymorph crystals embedded in solution, in terms of the free-energy difference between the finite-sized crystal polymorph and the original solution state. We further observe that the obtained reaction coordinates and transitions are highly nonclassical.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Parity-odd four-point correlation function from the DESI data release 1 luminous red galaxy sample
The parity-odd four-point function provides a unique probe of fundamental symmetries and potential new physics in the large-scale structure of the Universe. We present measurements of the parity-odd four-point function using the Dark Energy Spectroscopic Instrument (DESI) DR1 luminous red galaxy (LRG) sample and assess its detection significance. Our analysis considers both auto- and cross-correlations, using two complementary approaches to the covariance: (i) the full analytic covariance matrix applied to the uncompressed data vector, and (ii) a compressed data vector combined with a hybrid covariance matrix constructed from simulations and analytic estimates. When using the full analytic covariance matrix without corrections, we observe apparent auto-correlation signals with significance up to 4𝜎. However, this excess is also consistent with a mismatch between the statistical fluctuations estimated from the simulations and those present in the real data. Our findings therefore suggest that the parity-odd signal in the current DESI DR1 LRG sample is consistent with zero. We note, however, that the low completeness of this sample may have a non-negligible impact on the detection sensitivity. Future data releases with improved completeness will be crucial for further investigation.
Hou, Jiamin [Ludwig-Maximilians-Universität; Unive↗
DCTRGAN: improving the precision of generative models with reweighting
Significant advances in deep learning have led to more widely used and precise neural network-based generative models such as Generative Adversarial Networks (Gans). We introduce a post-hoc correction to deep generative models to further improve their fidelity, based on the Deep neural networks using the Classification for Tuning and Reweighting (Dctr) protocol. The correction takes the form of a reweighting function that can be applied to generated examples when making predictions from the simulation. We illustrate this approach using Gans trained on standard multimodal probability densities as well as calorimeter simulations from high energy physics. We show that the weighted Gan examples significantly improve the accuracy of the generated samples without a large loss in statistical power. This approach could be applied to any generative model and is a promising refinement method for high energy physics applications and beyond.
47 OTHER INSTRUMENTATION↗