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At least 199 records · Page 11

Aqueous Carbon Capture Using Guanidinium-Functionalized Hollow Fiber Sorbent Contactors

As part of the growing suite of technologies aimed at combatting rising temperatures, negative emissions technologies have become a powerful tool in the global effort to minimize the consequences of human-induced climate change. Among these, carbon removal from aqueous sources, which contain much higher carbon concentrations than the atmosphere, remains largely unexplored. Indeed, developing robust and efficient carbon capture materials for usage in complex aqueous environments remains a significant challenge. Here, we explore the potential of functionalizing polyvinylidene fluoride (PVDF) hollow fiber contactors grafted with a guanidinium-derived polymer sorbent for carbon removal from aqueous sources, including saline waters. Computational screening against amine-based analogs is utilized to identify guanidinium as a promising motif for bicarbonate (HCO 3 – ) ions binding. To leverage this finding, synthesis of a guanidinium polymer and subsequent covalent grafting onto PVDF hollow fibers is employed to structured polymer–sorbent–grafted hollow fiber contactors. Our prototype achieves an initial HCO 3 – removal of 34% with an increase to 98% after four cycles. The functionalized fibers demonstrate aqueous stability over 13 adsorption/desorption cycles in model NaHCO 3 solutions where regeneration is facilitated by a mild pH swing. Importantly, the system maintains selective performance in the presence of competitive chloride ions over multiple cycles; carbon removal remained above 10% even at high (10:1) NaCl/NaHCO 3 ratios. These findings demonstrate the feasibility of sorbent-based aqueous carbon removal and highlight its potential as a promising approach for negative emissions.

carbon capture↗

Flexible and Effective Object Tiering for Heterogeneous Memory Systems

Computing platforms that package multiple types of memory, each with their own performance characteristics, are quickly becoming mainstream. To operate efficiently, heterogeneous memory architectures require new data management solutions that are able to match the needs of each application with an appropriate type of memory. As the primary generators of memory usage, applications create a great deal of information that can be useful for guiding memory management, but the community still lacks tools to collect, organize, and leverage this information effectively. To address this gap, this work introduces a novel software framework that collects and analyzes object-level information to guide memory tiering. The framework includes tools to monitor the capacity and usage of individual data objects, routines that aggregate and convert this information into tier recommendations for the host platform, and mechanisms to enforce these recommendations according to user-selected policies. Moreover, the developed tools and techniques are fully automatic, work on standard Linux systems, and do not require modification or recompilation of existing software. Using this framework, this study evaluates and compares the impact of a variety of design choices for memory tiering, including different policies for prioritizing objects for the fast memory tier as well as the frequency and timing of migration events. In conclusion, the results, collected on a modern Intel platform with conventional DDR4 SDRAM as well as Intel Optane NVRAM, show that guiding data tiering with object-level information can enable significant performance and efficiency benefits compared with standard hardware- and software-directed data-tiering strategies for a diverse set of memory-intensive workloads.

97 MATHEMATICS AND COMPUTING↗

Trisodium Phosphate Phases and Solubility in Alkaline Solutions Relevant to Radioactive Waste Processing

The U.S. Department of Energy’s Hanford Site faces significant challenges in managing millions of gallons of legacy radioactive waste, where phosphate precipitation can obstruct pipelines during retrieval and processing. To resolve long-standing inconsistencies in reported solubility and clarify factors governing trisodium phosphate hydrates, we examined the Na3PO4:NaOH:H2O system. Powder and single-crystal X-ray diffraction revealed that commercial precursors undergo transformations that produce multiple hydrates, including three previously unreported phases comprising an ordered polymorph of Na3(PO4)·12H2O·1/6NaOH, Na3PO4·5H2O, and Na3PO4·9H2O. Computational modeling indicated that the ordered dodecahydrate is more stable than its disordered counterpart, suggesting kinetic persistence of structural disorder. Solubility measurements were conducted with solutions prepared either from anhydrous Na3PO4 or from Na3(PO4)·12H2O·1/6NaOH and revealed that release of interstitial NaOH from the hydrate precursor elevated solution alkalinity, thereby significantly reducing phosphate solubility relative to solutions prepared with anhydrous Na3PO4 across 20–44 °C. These results begin to reconcile inconsistencies in prior solubility data and clarify how phase composition dictates phosphate precipitation under alkaline conditions.

Graham, Trenton R. (ORCID:0000000189078004)↗

A Solvatochromic Near Infrared Fluorophore Sensitive to the Full Amyloid Beta Aggregation Pathway

Alzheimer's disease has long been associated with the aggregation of amyloid beta peptides (Aβ42) into macroscale plaques, although specific neurodegenerative agents have not been definitively identified. Much evidence has pointed to the soluble nanoscale oligomers that form early in the Aβ42 aggregation pathway, but there is little understanding of these structures, their mechanisms of formation, or how they grow into plaques. Here, we show that a solvatochromic fluorophore with near-infrared (NIR) emission can track synthetic Aβ42 aggregation through environment-sensitive spectral shifts from the earliest time points through plaque formation. This azide-functionalized phosphine oxide azetidine rhodol (Phazr-N3) shows large polarity-dependent changes in fluorescence emission, with maxima shifting from 630 nm in toluene to 703 nm in aqueous buffer, and a maximum quantum yield of 62%. Upon induction of Aβ42 aggregation, we observe immediate solvatochromic changes in Phazr-N3 fluorescence, with multiple apparent phases over 12 h, and which culminate before the onset of any major fluorescence changes of conformation-specific aggregation fluorophore thioflavin T. Solution anisotropy measurements show a low micromolar affinity of Phazr-N3 for disordered, free Aβ42 in solution, and real-time measurements are consistent with rapid liquid-liquid phase separation and slow dehydration of the growing aggregate. Spectral imaging of synthetic plaques stained in the presence of live cells and lipid-binding protein albumin shows over 4000-fold Phazr-N3 fluorescence intensity above background under no-wash conditions, and over 100-fold intensity above coplated microglial cells or a large excess of albumin. This use of a solvatochromic probe with structure-independent binding to free Aβ42 offers real-time, minimally invasive insight into the full Aβ42 aggregation pathway.

Wang, Zeming↗

LossLens: Diagnostics for Machine Learning Through Loss Landscape Visual Analytics

Modern machine learning often relies on optimizing a neural network's parameters using a loss function to learn complex features. Beyond training, examining the loss function with respect to a network's parameters (i.e., as a loss landscape) can reveal insights into the architecture and learning process. While the local structure of the loss landscape surrounding an individual solution can be characterized using a variety of approaches, the global structure of a loss landscape, which includes potentially many local minima corresponding to different solutions, remains far more difficult to conceptualize and visualize. To address this difficulty, we introduce LossLens, a visual analytics framework that explores loss landscapes at multiple scales. LossLens integrates metrics from global and local scales into a comprehensive visual representation, enhancing model diagnostics. Here we demonstrate LossLens through two case studies: visualizing how residual connections influence a ResNet-20, and visualizing how physical parameters influence a physics-informed neural network (PINN) solving a simple convection problem.

97 MATHEMATICS AND COMPUTING↗

Investigating Radiation-Induced Actinide Species in Solution

Our fundamental understanding of actinide radiation-induced redox chemistry is crucial to nuclear fuel cycle development, due to the unavoidable exposure of these elements to ionizing radiation fields, both inherent and from in-process applications. Plutonium (Pu) and americium (Am) both possess multiple oxidation states, the careful manipulation of which are essential in the study and utilization of their rich chemistry, developing new nuclear technologies, and securing the long-term sustainability of nuclear power. However, knowledge in this area is far from complete. Consequently, we have studied the radiation-induced chemistry of both Pu and Am through a variety of techniques. Temperature-controlled electron pulse radiolysis has been used to study Am for the first time, determining the feasibility of Am redox reactions under used nuclear fuel reprocessing conditions, (e.g. nitric acid, non-ambient temperature). Additionally, we have developed an experimentally evaluated multi-scale computer model for the prediction of gamma radiation-induced Pu redox chemistry due to radiolysis and disproportionation reactions in concentrated nitric acid solutions.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

Development of a Griffin model of the advanced test reactor

In the pursuit of a higher fidelity deterministic simulation capability of the Advanced Test Reactor, it is important to have a fast yet accurate deterministic neutronics model. Here, to achieve this, we employed an advanced two-step method. The first step involves generating homogenized cross sections using OpenMC, a cutting-edge Monte Carlo neutron transport code. OpenMC offers excellent modular capabilities, allowing for easy component integration and flexibility in incorporating new designs into the model. The second step involves deterministic transport calculations, which are performed using Griffin, a reactor physics application based on the Multiphysics Object-Oriented Simulation Environment (MOOSE). To ensure the accurate spatial resolution and assignment of material cross sections, a Cubit-generated mesh for the Advanced Test Reactor is utilized as an intermediate step between the OpenMC and Griffin models; Griffin utilizes the mesh for its finite element solution, while OpenMC material identifications are written to the mesh file to be used in Griffin material assignments. Additionally, a Python-based script converts the cross sections generated by OpenMC into the ISOXML format required by Griffin. Initial comparisons using the Griffin diffusion solver indicated good agreement between the neutron multiplication factors obtained from the standalone OpenMC model and the Griffin model, with differences of less than 10 pcm in the 2D geometry configuration; it was later determined that this agreement was likely due to compensating effect and was more likely on the order of –700 pcm relative to the OpenMC solution. However, in three-dimensional calculations, an unacceptably large error (almost 8,000 pcm) was found in the Griffin solution with the diffusion solver. Subsequent calculations using Griffin’s discrete ordinates solver demonstrated substantially improved agreement, within 116 pcm of the OpenMC solution used to generate the cross sections for Griffin. Building on this capability, future work will seek to perform more detailed validation calculations. The ultimate goal is to evaluate both transient and multiphysics simulations of the reactor.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Constructing Highly Porous Low Iridium Anode Catalysts Via Dealloying for Proton Exchange Membrane Water Electrolyzers

Iridium (Ir) is the most active and durable anode catalyst for the oxygen evolution reaction (OER) for proton exchange membrane water electrolyzers (PEMWEs). However, their large-scale applications are hindered by high costs and scarcity of Ir. Lowering Ir loadings below 1.0 mgcm -2 causes significantly reduced PEMWE performance and durability. Therefore, developing efficient low Ir-based catalysts is critical to widely commercializing PEMWEs. Herein, an approach is presented for designing porous Ir metal aerogel (MA) catalysts via chemically dealloying IrCu alloys. In this study, the unique hierarchical pore structures and multiple channels of the Ir MA catalyst significantly increase electrochemical surface area (ECSA) and enhance OER activity compared to conventional Ir black catalysts, providing an effective solution to design low-Ir catalysts with improved Ir utilization and enhanced stability. An optimized membrane electrode assembly (MEA) with an Ir loading of 0.5 mg Ir cm -2 generated 2.0 A cm -2 at 1.79 V, higher than the Ir black at a loading of 2.0 mg Ir cm -2 (1.63 A cm -2 ). The low-Ir MEA demonstrated an acceptable decay rate of ≈40 µV h -1 during durability tests at 0.5 (>1200 h) and 2.0 A cm -2 (400 h), outperforming the commercial Ir-based MEA (175 µV h -1 at 2.0 mg Ir cm -2 ).

36 MATERIALS SCIENCE↗

Technical, economic, and load-following capabilities assessment of grid-connected geothermal and geothermal-solar hybrid systems

The technical and economic performance as well as the load-following capabilities of grid-connected geothermal hybrid systems were assessed in this work. The analyzed geothermal hybrid configuration is composed of a binary geothermal plant integrated with a concentrating solar-thermal system and underground thermal energy storage (UTES) through a primary heat exchanger. Physics-based models for the hybrid system for plant generation capacities of 1, 25, and 50 MW were developed from validated models for each subsystem. Also, an economic model was developed that accounts for different hybrid system capabilities, solar field sizes, and thermal storage duration. The advantage of the geothermal hybrid system was assessed by comparing the performance with the baseline benchmark geothermal plant with a similar configuration and generation capacity. It was found that hybridizing geothermal plants with concentrating solar and thermal energy storage not only improves the thermal efficiency by up to 8 percentage points when additional heat from the solar-UTES loop rises the evaporator temperatures from 70 to 125 °C, but also enhances the load-following capability for the geothermal plant, which can meet a typical residential load profile with a power rate of change 0.25 kW/s with an absolute error under 13 kW for a 1 MW plant. Other benefits of hybridization include resource preservation and a potential LCOE reduction of up to 56% for a 50 MW geothermal hybrid plant having a 50% solar share, a 1.4 solar multiple, and 24-h storage capacity. The results presented in this work demonstrate that hybridizing geothermal systems transforms them into a flexible and cost-effective solution for addressing the dynamic requirements of modern electric grids.

15 GEOTHERMAL ENERGY↗

Portable fiber optic sensor for rare earth elements and other critical metals using photoluminescence methods

Rare earth elements and other metals are vital to a range of technologies that are used in the energy and defense sectors. However, monopolistic market conditions have caused significant concern over the stability of the critical metal supply chain, and this has spurred extensive efforts in many nations to produce these metals domestically, both from conventional sources such as mining and well as from unconventional sources such as coal and its utilization byproducts. Slow and expensive characterization methods pose a significant barrier for both metals prospecting and process monitoring. A promising solution to this challenge is the development of highly sensitive luminescent sensors for metals, which can offer low costs, portability, and sensitivity. Anionic zinc adeninate metal-organic frameworks (BioMOFs) are known to distinguishing and detect part-per-billion levels of terbium, europium, samarium, and dysprosium in water by sensitizing the narrow, element-specific emission bands from these lanthanides. Here, a BioMOF material is immobilized onto a large diameter, solarization-resistant fiber optic tip integrated with a portable, low-cost spectrometer for rare earth element sensing. Immobilizing the sensing material on fiber instead of dispersing the sensing material in solution offers several advantages: it facilitates solvent removal, which enhances luminescent signal from the sensitized lanthanides, and it also allows the BioMOF to be recycled for multiple uses. The sensing system was deployed on a simulated process stream and exhibited qualitative agreement with inductively-coupled plasma mass spectrometry for terbium and europium detection, highlighting the potential for the sensing system to be deployed for real-world applications. By using different sensing materials, the same portable sensor may be deployed to detect other energy relevant metals such as cobalt, providing a cost-effective and sensitive platform for critical metal characterization.

Crawford, Scott↗

"Source Term Modeling for Advanced Gas Micro-Reactors"

Maintaining the safety of the public, environment, and operating personnel is the most important factor in designing, operating, maintaining, and decommissioning nuclear reactors. In recent years, there has been a growing interest in the development of micro-reactors employing TRi-structural ISOtropic (TRISO)-coated particle fuel. In gas reactors, TRISO fuel plays an important role in the safety case for high temperature reactors because of the fission product retention properties of the fuel. This ability enables the use of a functional containment strategy for the reactor where multiple barriers are used to prevent fission product release to the environment. Part of the safety analysis of these advanced reactors is the assessment of radionuclide releases under normal and accident conditions through the multiple credited safety barriers. Using conservative assumptions, a mechanistic analysis can be performed to quantify these releases that combines the probabilistic assessment of failure with analytic solutions to radionuclide transport equations. Source term modeling for TRISO fuel has been performed for previous reactor designs; however, these models are outdated, in many cases proprietary, and need updates to be applied to the current state of TRISO fuel technology and alternative gas reactor core configurations [1]. Currently, the only publicly available source term assessment for gas reactors is an expert-based Monte Carlo simulation based on the effectiveness of the fuel kernel, coating layers, and graphite block in a modular high temperature gas reactor [2]. Thus, there is a need to develop a simple, versatile, and mechanistic model of fission product release and transport in gas reactor cores that could be applied to a variety of reactors through user inputs and reactor-specific radionuclide inventories. The release is calculated by the diffusion of the key safety important fission products through the kernel, silicon carbide (SiC), graphite for both intact and defective TRISO particles based on fuel and graphite temperatures in the reactor under normal operation. These releases from the fuel enter the coolant where they can plate-out on cooler surfaces. A clean-up model is included for designs with a coolant purification system to remove fission gases. This initial distribution of fission products in the reactor serves as an initial condition for potential releases under postulated accident conditions. The model then can calculate the fission product release for any transient temperature profile and fission product releases can then be used to assess radiological dose to the workers and the public using conventional dose tools. Data on the diffusion of fission products is based on historic German TRISO experiments and the more current Department of Energy (DOE) Advanced Gas Reactor (AGR) TRISO fuel development program. The model is coded in python with inputs and outputs in excel spreadsheets, as well as python plotting utilities to aid in the interpretation of the results. References: [1] INL, NGNP Mechanistic Source Term White Paper, INL-10-17997, July 2010. [2] David A. Petti, Richard R. Hobbins, Peter Lowry, Hans Gougar, “Representative Source Terms and The Influence of Reactor Attributes on Functional Containment in Modular High Temperature Gas-cooled Reactors,” Nuclear Technology, Vol. 184, p. 181-197, Nov. 2013.

07 ISOTOPE AND RADIATION SOURCES↗

Framework to select robust energy retrofit measures for residential communities

Residential building energy retrofits are essential for enhancing environmental sustainability and reducing energy costs. The selection of retrofit measures is influenced by factors such as building systems, occupant behavior, government policy, weather variability, and climate change, all of which can significantly impact energy performance. Compared to retrofitting individual homes, evaluating and selecting optimal retrofit solutions for an entire community is challenging due to diverse residential compositions and variability present. Therefore, engineering robustness is crucial for ensuring consistent energy performance and resilience across different conditions. In this context, robustness refers to the ability of a retrofit measure to maintain its functionality and remain an optimal choice despite external disturbances or changes in inputs and conditions. This study presents a framework for evaluating the robustness of multiple retrofit measures across various building systems, occupant behaviors, and environmental scenarios at the community level. The framework comprises five key steps: scenario model development, integration of the National Residential Efficiency Measures database, energy performance simulation, cost-benefit aggregation, and retrofit solution selection. Each step enhances the framework’s robustness by incorporating the diversity of building characteristics, occupant behaviors, environmental conditions, retrofit options, and evaluation criteria. The framework’s effectiveness is demonstrated through a case study in southern Michigan in the United States, which includes 63 one-story single-family houses, 121 two-story single-family houses, and 8 townhouses. The study identifies furnace retrofits as the most robust solution for the entire community, consistently achieving source energy reductions of 4.7 %–8.0 % and payback period of 10–20 years across various scenarios. These findings are consistent with previous research, indicating the framework’s potential for broader applications in optimizing community-scale residential energy retrofits.

Shu, Lei↗

Electrodialysis and nitrate reduction (EDNR) to enable distributed ammonia manufacturing from wastewaters

Underutilized wastewaters containing dilute levels of reactive nitrogen (Nr) can help rebalance the nitrogen cycle. This study describes electrodialysis and nitrate reduction (EDNR), a reactive electrochemical separation architecture that combines catalysis and separations to remediate nitrate and ammonium-polluted wastewaters while recovering ammonia. By engineering operating parameters (e.g., background electrolyte, applied potential, electrolyte flow rate), we achieved high recovery and conversion of Nr in both simulated and real wastewaters. The EDNR process demonstrated long-term robustness and up-concentration that recovered >100 mM ammonium fertilizer solution from agricultural runoff that contained 8.2 mM Nr. EDNR is the first reported process to our knowledge that remediates dilute real wastewater and recovers ammonia from multiple Nr pollutants, with an energy consumption (245 MJ per kg NH 3 –N in simulated wastewater, 920 MJ per kg NH 3 –N in agricultural runoff) on par with the state-of-the-art. Demonstrated first at proof-of-concept and engineered to technology readiness level (TRL) 4–5, EDNR shows great promise for distributed wastewater treatment and sustainable ammonia manufacturing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Convergence Criteria for Multiphysics Simulations

The behavior of engineered systems is often influenced by multiple physical phenomena, such as mechanical deformation, heat transfer, and chemical species transport and reactions. There are often strong interactions between these phenomena, and there is increasing interest in applying coupled-physics models to improve understanding of physical behavior under complex environmental conditions. Multiple simulation frameworks that facilitate coupled-physics simulations are in widespread use, and these employ a variety of techniques to account for interactions between those physics. Many frameworks solve the physics models independently and transfer results between them. Alternatively, a single monolithic system of equations for every physics model can be formed and solved. Each of these approaches has its benefits and drawbacks, and the optimal approach varies depending on the nature of the problem. The open-source MOOSE framework was developed targeting solution of large-scale multiphysics problems. Although it provides options for all these coupling approaches, its standard approach for multiphysics solutions is to form and solve a single monolithic system of equations containing the unknowns for all physics models. MOOSE provides a streamlined approach for users to define the solution variables, the terms in the partial differential equations pertaining to each variable, and interactions between solution variables. One aspect of the monolithic solution approach that can be problematic, however, is defining appropriate convergence criteria for the nonlinear system. A standard approach is to determine convergence is to simply take a norm of the residual vector corresponding to the full vector of unknowns. However, if the residual vector contains variables for multiple physics models, the magnitudes of those variables can differ significantly, and the variables can converge at significantly different rates from each other. It is important to ensure that the variables for each of the physics are converged, and also ensure that the convergence criteria are not excessively stringent in cases when there is little change in the solution. This talk presents representative multiphysics problems to highlight these issues, and shows strategies for convergence criteria in MOOSE that are robust for multiphysics models under a variety of conditions.

97 - MATHEMATICS AND COMPUTING↗

Basic Research Needs for Inverse Methods for Complex Systems under Uncertainty

Inverse problems, which aim to infer unknown properties of a system using experimental and observational data, are central to addressing many of the U.S. Department of Energy’s (DOE) most critical scientific and engineering challenges. Accurate, computationally efficient, and data-efficient solutions to inverse problems are essential for advancing DOE mission-critical science drivers, including analyzing data from large-scale experimental facilities, optimizing fusion reactor performance, accelerating materials discovery, enhancing geophysical imaging, improving wildfire predictions, and enabling autonomous systems and digital twins. However, these problems are becoming increasingly complex, often involving nonlinear, highdimensional, and interconnected systems and models that span multiple physics and scales, while relying on data with varying quantity, quality, and information content. Compounding these challenges is the uncertainty inherent in DOE-relevant systems, where errors in inputs, noise in data, incompleteness of data, and discrepancies between models and reality constrain the accuracy and precision of solutions. At the same time, the convergence of recent scientific computing trends—scientific machine learning, artificial intelligence, and computing advances such as exascale computing—is creating unprecedented opportunities for tackling these challenges. The cross-cutting nature of inverse problems, combined with their growing complexity and rapidly evolving data and algorithmic demands, strongly motivates the formulation of a prioritized research agenda to maximize their capabilities and impact. In response to this need, DOE’s Advanced Scientific Computing Research (ASCR) program in the Office of Science convened the Workshop on Basic Research Needs for Inverse Problems for Complex Systems Under Uncertainty in June 2025. This workshop brought together experts across disciplines to identify grand challenges and major opportunities in the field. Through collaborative discussions, the workshop defined transformative research directions aimed at addressing the mathematical, statistical, and computational challenges posed by inverse problems under uncertainty. As a result of these efforts, four priority research directions (PRDs) were identified to guide future research and development in this area. These PRDs, summarized below, represent a roadmap for advancing the foundational science and mathematics of inverse problems, enabling robust, scalable, and uncertainty-aware solutions that are critical for DOE applications.

97 MATHEMATICS AND COMPUTING↗

Ameloblastin binding to biomimetic models of cell membranes – A continuum of intrinsic disorder

A 37-residue amino acid sequence corresponding to the segment encoded by exon-5 of murine ameloblastin (Ambn), AB2 (Y67-Q103), has been implicated with membrane association, ameloblastin self-assembly, and amelogenin-binding. Here, our aim was to characterize, at the residue level, the structural behavior of AB2 bound to chemical mimics of biological membranes using NMR spectroscopy. To better define the structure of AB2 using NMR-based methods, recombinant 13 C- and 15 N-labelled AB2 (*AB2) was prepared and data collected free in solution and with deuterated dodecylphosphocholine (dPC) micelles, deuterated bicelles, and both small and large unilamellar vesicles. Amide chemical shift and intensity perturbations observed in 1 H- 15 N HSQC spectra of *AB2 in the presence of bicelles and dPC micelles suggest that a region of *AB2, S6-E36 (murine Ambn S68 – E98), associates with the membrane biomimetics. A CSI-3 analysis of the NMR chemical shift assignments for *AB2 free in solution and bound to dPC micelles indicated the peptide remains disordered except for the adoption of a short, 12-residue α-helix, F10-G21 (murine Ambn F72-G83). In dPC micelles, the NOE NMR data was void of patterns characteristic of long-lived helical structure indicating this helix was transient in nature. A continuum of intrinsic disorder in the membrane-bound state may be responsible for ameloblastin’s ability to dynamically interact with multiple partners at the same site during amelogenesis.

59 BASIC BIOLOGICAL SCIENCES↗

Semi-empirical model for Henry’s law constant of noble gases in molten salts

Henry’s law constant, which describes the proportionality of dissolved gas to partial pressure of free gas in liquid–gas equilibrium systems, can also be applied to mass transport applications. In this work, we investigated an approach for determining the solubility of noble gases in a molten salt liquid utilizing the equilibrium concept of Henry’s gas constant. Henry’s gas constant is described as a mathematical function dependent on the van der Waals radius of the noble gas and the temperature of the molten salt. The alteration in Gibbs free energy encompasses contributions from both surface and volume energies. Enthalpy and entropy are deduced from these surface and volume energies in the Gibbs free energy formulation. A comparative analysis was conducted between the conventional method and our proposed model. Moreover, useful chemical properties can be determined from examination of surface and volume energies. Our findings provide an accurate and general theory of Gibbs free energy that can be validated experimentally based on the model proposed herein. This work unifies the prediction of Henry gas constant and subsequently the entropy and enthalpy calculation for noble gases in a molten salt solution to a single functional form using van der Waals radius of the gas and temperature of the system. This functional form is then used to perform a multiple regression method to find two parameters corresponding to the surface energy and volume energy. These two parameters are consistent between all combinations of noble gas and molten salt.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Drilling Down I/O Bottlenecks with Cross-layer I/O Profile Exploration

I/O performance monitoring tools such as Darshan and Recorder collect I/O-related metrics on production systems and help understand the applications' behavior. However, some gaps prevent end-users from seeing the whole picture when it comes to detecting and drilling down to the root causes of I/O performance slowdowns and where those problems originate. These gaps arise from limitations in the available metrics, their collection strategy, and the lack of translation to actionable items that could advise on optimizations. This paper highlights such gaps and proposes solutions to drill down to the source code level to pinpoint the root causes of I/O bottlenecks scientific applications face by relying on cross-layer analysis combining multiple performance metrics related to I/O software layers. We demonstrate with two real applications how metrics collected in high-level libraries (which are closer to the data models used by an application), enhanced by source-code insights and natural language translations, can help streamline the understanding of I/O behavior and provide guidance to end-users, developers, and supercomputing facilities on how to improve I/O performance. Using this cross-layer analysis and the heuristic recommendations, we attained up to 6.9× speedup from run-as-is executions.

Ather, Hammad↗