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At least 973 records · Page 54

Generative AI for design of nanoporous materials: review and future prospects

Generative artificial intelligence (AI) is emerging as a powerful tool for advancing the design of nanoporous materials such as metal–organic frameworks, covalent–organic frameworks, and zeolites. These materials have potential application in important areas such as carbon capture, catalysis, gas storage, chemical separation, and drug delivery due to their modular, tunable structures, and their performance in these areas depends on precise control over their structure, chemical functionalities, and properties. Herein, we provide a review of generative AI algorithms that are emerging as powerful tools for the design of nanoporous materials, namely generative adversarial networks, variational autoencoders, diffusion models, genetic algorithms, reinforcement learning, and large language models. Some models are particularly good at generating diverse and high-quality designs, while others excel at exploring large design spaces or optimizing materials with desired properties. Certain algorithms also allow for efficient transitions between different designs, and some offer versatility in generating materials based on textual input. We discuss the advantages, limitations, and applications of these algorithms in porous material design and emphasize the future potential of integrating AI with experimental workflows to accelerate the development and validation of AI-generated materials.

36 MATERIALS SCIENCE↗

Gene editing and CRISPR in the clinic: current and future perspectives

Genome editing technologies, particularly those based on zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and CRISPR (clustered regularly interspaced short palindromic repeat DNA sequences)/Cas9 are rapidly progressing into clinical trials. Most clinical use of CRISPR to date has focused on ex vivo gene editing of cells followed by their re-introduction back into the patient. The ex vivo editing approach is highly effective for many disease states, including cancers and sickle cell disease, but ideally genome editing would also be applied to diseases which require cell modification in vivo. However, in vivo use of CRISPR technologies can be confounded by problems such as off-target editing, inefficient or off-target delivery, and stimulation of counterproductive immune responses. Current research addressing these issues may provide new opportunities for use of CRISPR in the clinical space. In this review, we examine the current status and scientific basis of clinical trials featuring ZFNs, TALENs, and CRISPR-based genome editing, the known limitations of CRISPR use in humans, and the rapidly developing CRISPR engineering space that should lay the groundwork for further translation to clinical application.

59 BASIC BIOLOGICAL SCIENCES↗

Determining the Minimum Phase‐to‐Phase Gap Distance for Unconventional Transmission Lines Using Numerical Method

With the increasing demand for power transmission, compact, high surge impedance loading (HSIL) high-voltage transmission lines have emerged as a viable solution due to their reduced land acquisition costs and higher power delivery capability. The compactness of a transmission line depends on effective insulation coordination, particularly in determining the phase-to-phase clearance, which is governed by the critical flashover voltage under switching and lightning overvoltage conditions. Traditional methods for phase-to-phase clearance rely on empirical formulas derived from experimental data, which are convenient for conventional high-voltage lines. However, unconventional HSIL lines require a faster and more adaptable evaluation method, as they involve optimized conductor positioning to reduce right-of-way requirements while enhancing natural power loadability. This study presents a simplified numerical approach to determine the minimum phase-to-phase gap, utilizing arc propagation viability curves, and offers an efficient alternative to conventional empirical methods. The proposed method was successfully applied to a 500 kV conventional line as well as 500 and 735 kV unconventional line designs, demonstrating its capability in accurately assessing insulation requirements. Results reveal that the method can support reduced gap clearances while still maintaining reliability, thereby validating its usefulness in optimizing compact transmission line configurations.

Arafat, Easir [The University of Texas at Dallas, ↗

Planck 2018 results - IV. Diffuse component separation

We present full-sky maps of the cosmic microwave background (CMB) and polarized synchrotron and thermal dust emission, derived from the third set of Planck frequency maps. These products have significantly lower contamination from instrumental systematic effects than previous versions. The methodologies used to derive these maps follow closely those described in earlier papers, adopting four methods (Commander, NILC, SEVEM, and SMICA) to extract the CMB component, as well as three methods (Commander, GNILC, and SMICA) to extract astrophysical components. Our revised CMB temperature maps agree with corresponding products in the Planck 2015 delivery, whereas the polarization maps exhibit significantly lower large-scale power, reflecting the improved data processing described in companion papers; however, the noise properties of the resulting data products are complicated, and the best available end-to-end simulations exhibit relative biases with respect to the data at the few percent level. Using these maps, we are for the first time able to fit the spectral index of thermal dust independently over 3° regions. We derive a conservative estimate of the mean spectral index of polarized thermal dust emission of β d = 1.55 ± 0.05, where the uncertainty marginalizes both over all known systematic uncertainties and different estimation techniques. For polarized synchrotron emission, we find a mean spectral index of β s = -3.1 ± 0.1, consistent with previously reported measurements. We note that the current data processing does not allow for construction of unbiased single-bolometer maps, and this limits our ability to extract CO emission and correlated components. The foreground results for intensity derived in this paper therefore do not supersede corresponding Planck 2015 products. For polarization the new results supersede the corresponding 2015 products in all respects.

79 ASTRONOMY AND ASTROPHYSICS↗

Recent evolutions in the LTDP CDF project

In the latest years, CNAF (the national center of the Italian Institute for Nuclear Physics INFN dedicated to Research and Development on Information and Communication Technologies) has been working on the Long Term Data Preservation (LTDP) project for the CDF experiment, active at Fermilab from 1990 to 2011. The main aims of the project are to protect the most relevant part of the CDF RUN-2 data collected between 2001 and 2011 and already stored on tape at CNAF (4 PB), as well as to ensure the availability and the access to the analysis facility to those data over time. Lately, the CDF database, hosting information about CDF datasets such as their structure, file locations and metadata, has been imported from Fermilab to CNAF. Also, the Sequential Access via Metadata (SAM) station data handling tool for CDF data management, that allows to manage data transfers and to retrieve information from the CDF database, has been properly installed and configured at CNAF. This was a fundamental step in the perspective of a complete decommissioning of CDF services on Fermilab side. An access system has been designed and tested to submit CDF analysis jobs, using CDF software distributed via CERN Virtual Machine File System (CVMFS) and requesting delivery of CDF files stored on CNAF tapes, as well as data present only on Fermilab storage archive. Moreover, the availability and the correctness of all CDF data stored on CNAF tapes has been verified. This paper describes all these recent evolutions in detail, presenting the future plans for the LTDP project at CNAF.

Fornari, Federico↗

Distributed Machine Learning Workflow with PanDA and iDDS in LHC ATLAS

Machine Learning (ML) has become one of the important tools for High Energy Physics analysis. As the size of the dataset increases at the Large Hadron Collider (LHC), and at the same time the search spaces become bigger and bigger in order to exploit the physics potentials, more and more computing resources are required for processing these ML tasks. In addition, complex advanced ML workflows are developed in which one task may depend on the results of previous tasks. How to make use of vast distributed CPUs/GPUs in WLCG for these big complex ML tasks has become a popular research area. In this paper, we present our efforts enabling the execution of distributed ML workflows on the Production and Distributed Analysis (PanDA) system and intelligent Data Delivery Service (iDDS). First, we describe how PanDA and iDDS deal with large-scale ML workflows, including the implementation to process workloads on diverse and geographically distributed computing resources. Next, we report real-world use cases, such as HyperParameter Optimization, Monte Carlo Toy confidence limits calculation, and Active Learning. Finally, we conclude with future plans.

97 MATHEMATICS AND COMPUTING↗

ITER ECH Transmission Line System Design and Status

The electron cyclotron (EC) heating & current drive (H&CD) system on ITER provides plasma heating by generating, transmitting, and launching high-intensity, high-frequency (170 GHz) electromagnetic wave energy steerable across the plasma cross-section. The transmission line (TL) subsystem connects the Matching Optics Unit (MOU) on each of the 24 gyrotrons to the 32 feed points in the four upper launchers and the 24 feed points in the equatorial launcher. Each TL must be able to operate at up to 1.2 MW of input power for up to 1 hour pulse lengths. The TL system contains 50 mm water-cooled corrugated waveguide, 90o miter bends, 140o miter bends, polarizer miter bend pairs, switches, expansion units, pumpouts, DC breaks, MOU-TL adapters, Radio Frequency (RF) loads, and isolation shutter valves. A detailed finite element analysis has been used to verify the thermo-mechanical performance of each component. The microwave performance has been analyzed using a 2-D electromagnetic code combined with a Monte Carlo code. This approach allows the impact of manufacturing and installation tolerances to be assessed and optimized to provide a high probability of achieving the system performance requirements. Prototypes of the waveguide and TL components have been fabricated and tested at high-power. Production contracts are now being issued for fabrication and delivery of the waveguide and components to ITER.

Hanson, Gregory↗

Quantifying Movement Motivations, Demand, and Inflow-Outflow Dynamics in Four Cities (New York, Chicago, Austin, and San Diego) During COVID-19

The COVID-19 pandemic has impacted a wide range of human activities, from food delivery habits to major moving and travel decisions. Results indicate multiple pandemic-related factors have influenced millions of relocation decisions by Americans (e.g., health risk, financial pressures, more space, and employment), and there are various positive economic and social outcomes of this influence (e.g., remote work and education), enabling more affordable living and opportunity. This paper addresses COVID-19 impacts on mobility, especially involving permanent relocations. Survey design and data analysis with U-Haul targeted customers in Austin, New York, San Diego, and Chicago to understand mobility, new moving dynamics, and motivations.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗

Exploring the Effects of Population and Employment Characteristics on Truck Flows: An Analysis of NextGen NHTS Origin-Destination Data

Truck transportation remains the dominant mode of US freight transportation because of its advantages, such as the flexibility of accessing pickup and drop-off points and faster delivery. Because of the massive freight volume transported by trucks, understanding the effects of population and employment characteristics on truck flows is critical for better transportation planning and investment decisions. The US Federal Highway Administration published a truck travel origin-destination data set as part of the Next Generation National Household Travel Survey program. This data set contains the total number of truck trips in 2020 within and between 583 predefined zones encompassing metropolitan and nonmetropolitan statistical areas within each state and Washington, DC. In this study, origin-destination-level truck trip flow data was augmented to include zone-level population and employment characteristics from the US Census Bureau. Census population and County Business Patterns data were included. The final data set was used to train a machine learning algorithm-based model, Extreme Gradient Boosting (XGBoost), where the target variable is the number of total truck trips. Shapley Additive ExPlanation (SHAP) was adopted to explain the model results. Results showed that the distance between the zones was the most important variable and had a nonlinear relationship with truck flows.

Uddin, Majbah↗

Evaluating Supply Prioritization Strategies for Risk-Informed Decision Making in an Arbitrary Gas Network

Supply disruptions and infrastructure failures in natural gas networks present critical challenges to energy reliability and risk-informed planning. This study evaluates two supply prioritization strategies, Maximum Delivery Prioritization (MDP) and Demand-Based Prioritization (DBP), within an arbitrary natural gas network under conditions of supply shortage. Model performance under both strategies is assessed in response to node and edge failure using demand satisfaction metrics, system-wide and localized dependency scores, and geographic information system (GIS)-based spatial analysis. Results show that DBP better preserves supply for high-demand nodes, while MDP offers broader coverage. The underlying network topology plays a critical role in shaping prioritization outcomes. Integrated GIS visualization enhances the interpretability of vulnerability assessments, revealing structurally critical components and localized vulnerabilities. The proposed framework supports scalable, data-driven decision-making for infrastructure planners and engineers, enabling improved disruption recovery and efficiency in constrained natural gas networks. These insights contribute to the development of more robust energy systems capable of withstanding stress and disruptions.

Peterson, Steven [ORNL] (ORCID:0000000287672998)↗

Advancing Asset Management in Water Infrastructure Systems

Aging water system infrastructure, including drinking water, wastewater, and stormwater, poses a growing challenge for utilities and municipalities. These water systems have well documented challenges with respect to their age, condition, and level of service. ASCE annual report cards consistently rate these infrastructure systems in the United States as underfunded, overcapacity, or past service life (ASCE 2025 Report Card). For example, Chini and Stillwell (2017) estimated that the mean water loss in drinking water systems, i.e., non-revenue water, is approximately 16% across the United States. These concerns are not just relegated to the United States, with Courtenay, British Columbia, identifying 17% of their water main pipes as in a ‘poor’ condition state, defined as a category condition 5 out of 5 (City of Courtenay, 2024). These cases illustrate the challenges utilities are facing to manage extensive networks of infrastructure to deliver a consistent and high level of service. For buried infrastructure such as water systems, studies suggest that preventative interventions can lead to lower maintenance costs and fewer service disruptions (Mazumder et al, 2018; Li et al, 2014). The demonstrated need and benefit of appropriately applied asset management is juxtaposed against the relatively sparse literature that evaluates water systems within an asset management construct. Since 2020, just 37 papers specifically reference asset management in the Journal of Water Resources Planning and Management. Of those, only a few specifically look to develop strategies for improved asset management. Therefore, we highlight four key research areas that represent opportunities for advancement of asset management research for water systems. First, advances in condition assessment and forecasting are needed to better estimate asset deterioration using diverse datasets. Second, machine learning (ML) and artificial intelligence (AI) hold promise for predictive maintenance and investment prioritization, though questions of generalizability and model transparency remain. Third, applying a value of information framework can guide utilities in making cost-effective sensor deployment and data collection decisions, to direct monitoring strategies towards data-informed asset management decisions. Finally, integrated infrastructure management is critical, requiring coordinated planning with other infrastructure systems and stakeholder engagement to reduce costs and enhance service delivery.

Chini, Christopher M.↗

Laser power density dependence on charge state distribution of Ta ion laser plasma

Laser power density per pulse, which is commonly expressed with the unit of “W/cm2,” is an important parameter to characterize ablation plasma. To match a design charge state of heavy ion beam induced by a laser ion source, a laser power density must be carefully chosen. Above around 108 W/cm2 of laser power density, laser ablation plasma is emitted from the surface of solid material. Then, up to 109 W/cm2, the most abundant charge state is 1+. Because the ionization energy increases with higher charge states, increasing the laser intensity leads to the charge state distribution shifting higher. Increasing the density to increase charge states also results in lower time of flight due to higher velocities. The maximum laser power density is obtained by the smallest available laser spot size on the target material which is determined by the quality of the laser beam. For many accelerator applications, higher charge state beams are preferred. In particular cases, singly charge ion beams are demanded. Therefore, production of intermediate charge state beams has not been investigated well. In this study, we selected Ta4+ as an example demanded beam and tried to clarify how the transition of charge state distribution depends on laser power density. Conclusively, the possible specification of a laser ion source for Ta4+ delivery was elucidated.

Okamura, Masahiro (ORCID:0000000255521706)↗

Development and modeling for a small-scale, rapidly heated high explosives initiation time (HEIT) experiment

Most small-scale assessments of explosive sensitivity, including the popular drop-weight impact test, convolute thermal and mechanical phenomena to the extent that it has been extremely challenging to decipher how an explosive ignites and propagates reactions. For instance, an impact generates heat through a range of dissipation mechanisms, which can in turn, depending on the reaction rates of the explosive, lead to chemical decomposition. To deconvolute the various contributions to the sub-shock initiation and propagation of explosive reactions, we describe the development and modeling of the High Explosives Initiation Time (HEIT) test - a new, small-scale, high-throughput experiment designed to rapidly heat milligram quantities of energetic materials confined within small diameter steel needles. Specifically, we have modeled and designed a 250 joule pulsed power system capable of rapidly delivering electrical current to the needles, resulting in rapid heat delivery to the sample. In conclusion, the energy deposition rate into the sample is controlled by different transmission line topologies. Modeling in COMSOL is performed to understand the energy required to heat up the explosive sample.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Advanced manufacturing provides tailor-made solutions for crystallography with x-ray free-electron lasers

Serial crystallography at large facilities, such as x-ray free-electron lasers and synchrotrons, evolved as a powerful method for the high-resolution structural investigation of proteins that are critical for human health, thus advancing drug discovery and novel therapies. However, a critical barrier to successful serial crystallography experiments lies in the efficient handling of the protein microcrystals and solutions at microscales. Microfluidics are the obvious approach for any high-throughput, nano-to-microliter sample handling, that also requires design flexibility and rapid prototyping to deal with the variable shapes, sizes, and density of crystals. Here, we discuss recent advances in polymer 3D printing for microfluidics-based serial crystallography research and present a demonstration of emerging, large-scale, nano-3D printing approaches leading into the future of 3D sample environment and delivery device fabrication from liquid jet gas-dynamic virtual nozzles devices to fixed-target sample environment technology.

36 MATERIALS SCIENCE↗

Precision-controlled ultrafast electron microscope platforms. A case study: Multiple-order coherent phonon dynamics in 1T-TaSe2 probed at 50 fs–10 fm scales

We report on the first detailed beam tests attesting the fundamental principle behind the development of high-current-efficiency ultrafast electron microscope systems where a radio frequency (RF) cavity is incorporated as a condenser lens in the beam delivery system. To allow for the experiment to be carried out with a sufficient resolution to probe the performance at the emittance floor, a new cascade loop RF controller system is developed to reduce the RF noise floor. Temporal resolution at 50 fs in full-width-at-half-maximum and detection sensitivity better than 1% are demonstrated on exfoliated 1T-TaSe2 system under a moderate repetition rate. To benchmark the performance, multi-terahertz edge-mode coherent phonon excitation is employed as the standard candle. The high temporal resolution and the significant visibility to very low dynamical contrast in diffraction signals via high-precision phase-space manipulation give strong support to the working principle for the new high-brightness femtosecond electron microscope systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Power matching to pulsed inductively coupled plasmas

Matching of power delivery to nonlinear loads in plasma processing is a continuing challenge. Plasma reactors used in microelectronics fabrication are increasingly multi-frequency and/or pulsed, producing a non-linear and, in many cases, non-steady state electrical termination that can complicate efficient power coupling to the plasma. This is particularly the case for pulsed inductively coupled plasmas where the impedance of the plasma can significantly change during the start-up-transient and undergo an E–H (capacitive-to-inductive) transition. In this paper, we discuss the results from a computational investigation of the dynamics of power matching to pulsed inductively coupled plasmas (Ar/Cl 2 mixtures of tens of mTorr pressure) using fixed component impedance matching networks and their consequences on plasma properties. In this investigation, we used set-point matching where the components of the matching network provide a best-case impedance match (relative to the characteristic impedance of the power supply) at a chosen time during the pulsed cycle. Matching impedance early during the pulse enables power to feed the E-mode, thereby emphasizing capacitive coupling and large excursions in the plasma potential. This early power coupling enables a more rapid ramp-up in plasma density while being mismatched during the H-mode later in the pulse. The early match also produces more energetic ion bombardment of surfaces. Matching late in the pulse diminishes power dissipated in the E-mode at the cost of also reducing the rate of increase in plasma density.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Tuning the dynamics in Fe 3 O 4 nanoparticles for hyperthermia optimization

The AC magnetic susceptibility (ACS) of Fe 3 O 4 nanoparticles was measured between 10 kHz and 4 MHz at different temperatures and in applied DC fields. In this frequency range, magnetostatic interactions impact magnetization dynamics even for dilute assemblies. The ACS spectrum of relaxation frequencies changes both with temperature and the addition of a small DC field. Because both the relaxation peak frequency and amplitude can be tuned with the DC field, these results could be applied to magnetic hyperthermia applications to optimize heat delivery.

Physics↗