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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Fragme∩t: An Open‐Source Framework for Multiscale Quantum Chemistry Based on Fragmentation

Fragment-based quantum chemistry offers a means to circumvent the nonlinear computational scaling of conventional electronic structure calculations, by partitioning a large calculation into smaller subsystems then considering the many-body interactions between them. Variants of this approach have been used to parameterize classical force fields and machine learning potentials, applications that benefit from interoperability between quantum chemistry codes. However, there is a dearth of software that provides interoperability yet is purpose-built to handle the combinatorial complexity of fragment-based calculations. To fill this void we introduce “Fragme∩t”, an open-source software application that provides a tool for community validation of fragment-based methods, a platform for developing new approximations, and a framework for analyzing many-body interactions. Fragme∩t includes algorithms for automatic fragment generation and structure modification, and for distance- and energy-based screening of the requisite subsystems. Checkpointing, database management, and parallelization are handled internally and results are archived in a portable database. Interfaces to various quantum chemistry engines are easy to write and exist already for Q-Chem, PySCF, xTB, Orca, CP2K, MRCC, Psi4, NWChem, GAMESS, and MOPAC. Applications reported here demonstrate parallel efficiencies around 96% on more than 1000 processors but also showcase that the code can handle large-scale protein fragmentation using only workstation hardware, all with a codebase that is designed to be usable by non-experts. Fragme∩t conforms to modern software engineering best practices and is built upon well established technologies including Python, SQLite, and Ray. The source code is available under the Apache 2.0 license.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Development and validation of capabilities to measure thermal properties of layered monolithic U-Mo alloy plate-type fuel

The uranium-molybdenum (U-Mo) alloy in a monolithic form has been proposed as one fuel design capable of converting some of the world’s highest power research reactors from the use of high enriched uranium (HEU) to low enriched uranium (LEU). One aspect of the fuel development and qualification process is to demonstrate appropriate understanding of thermal conductivity behavior of the fuel system as a function of temperature and expected irradiation conditions. The purpose of this paper is to verify and validate the functionality of equipment methods installed in hot cells for eventual measurements on irradiated uranium-molybdenum (U-Mo) monolithic fuel specimens, procedures to operate the equipment, and models to extract the desired thermal properties. The results presented here demonstrate the adequacy of the equipment, procedures and models that have been developed for this purpose based on measurements conducted on surrogate depleted uranium-molybdenum (DU-Mo) alloy samples containing a zirconium diffusion barrier and clad in aluminum alloy 6061 (AA6061). The results are in excellent agreement with thermal property data reported in the literature for similar U-Mo alloys as a function of temperature.

multi-layer↗
DOE OSTI2020

Advancing Fusion with Machine Learning Research Needs Workshop Report

Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2018

Generalized trigonometric Fourier-series method with automatic time step control for non-linear point kinetics equations

The trigonometric Fourier-series method (TFS) is generalized to provide approximate solutions for non-linear point kinetics equations with feedback using varying step sizes. This method can provide a very stable solution against the size of the discrete time step allowing much larger step sizes to be used. Systems of the point kinetics equations are solved using Fourier-series expansion over a partition of the total time interval. The approximate solution requires determining the series coefficients over each time step in that partition. These coefficients are determined using the high-order derivatives of the solution vector at the beginning of the time step introducing a system of linear algebraic equations to be solved at each step. This system is similar to the Vandermonde system. Two successive orders of the partial sums are used to estimate the local truncation error. This error and some other constrains are used to produce the largest step size allowable at each step while keeping the error within a specific tolerance. The process of calculating suitable step sizes should be automatic and inexpensive. Convergence and stability of the proposed method are discussed and a new formula is introduced to maintain stability. The proposed method solves the general linear and non-linear kinetics problems. The method has been applied to five different types of reactivities including step/ramp insertions with temperature feedback. The method is seemed to be valid for larger time intervals than those used in the conventional numerical integration, and is thus useful in reducing computing time. Computational results are found to be consistent with the analysis, they demonstrate that the convergence of the iteration scheme can be accelerated and the resulting computing times can be greatly reduced while maintain computational accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

An analysis of the structure of an n-dodecane spray flame using TPDF modelling

With a view to understanding ignition and combustion behaviours in diesel engines, this study investigates several aspects of ignition and combustion of an n-dodecane spray in a high pressure, high temperature chamber, known as Spray A, using data resulting from modelling using the transported probability density function (TPDF) method. The model has been validated comprehensively with good to excellent agreement in our previous work against all available experimental data including for mixture-fraction and velocity fields in non-reacting cases, and flame lift-off length and ignition delay in reacting cases. This good agreement encourages further investigation of the numerical model results to help understand the structure of this flame, which serves to complement the experimental information that is available, which is very limited due to the difficult experimental conditions in which this flame exists. For example, quantitative experimental measurements of local mixture-fraction, temperature, velocity gradients, etc. are not yet possible in reacting cases. Analysis of the model results shows that two-stage ignition is found to occur across the ambient temperature conditions considered: the first stage is rapidly initiated on the lean side where temperatures are high and sequentially moves to richer, cooler conditions. The first stage is extremely resilient to turbulence, occurring in a region of very low Damkohler number. The second stage of ignition occurs first in rich mixtures in a region behind the head of the fuel jet where mixture gradients are low, and appears to be influenced strongly by turbulence. Relative to a homogeneous reactor, it is delayed on the lean side but advanced on the rich side, suggesting entrainment and mixing from the early igniting lean regions into richer mixtures is an important moderator of the ignition process. The second-stage ignition front propagates at very high velocities initially, suggesting it is a sequential ignition moving according to gradients of ignition delay and/or residence time. The flame stabilises however on the lean side in a region of much lower velocity, where turbulent velocity fluctuations are sufficiently high such that turbulent transport influences the propagation. It stabilises in a region of low Damkohler number which implies that a competition of chemistry versus micro-mixing might also be involved in stabilisation. The stabilisation mechanism is investigated by an analysis of the transport budgets, showing the flame is stabilised by autoignition but moderated by turbulent diffusion. Further analysis of the flame index supports this stabilisation mechanism, and demonstrates the simultaneous existence of non-premixed and premixed combustion modes in the same flame. Analysis of the flow fields also reveals that local entrainment and dilatation are important flow features near the flame base. (C) 2015 The Combustion Institute. Published by Elsevier Inc. All rights reserved.

Diesel↗
DOE OSTI2025

Computational and experimental analysis of structural and Thermophysical properties of LiX-KX (X = chloride, iodide, or bromide) molten salts

Molten salts containing lithium (Li) and potassium (K) halides, including chlorides (Cl), bromides (Br), and iodides (I), are pivotal in advanced technological applications including nuclear reactor technologies, thermal batteries, and industrial pyrochemical processes. Despite their significance, the availability of the temperature-dependent structural and thermophysical properties of these molten salts remain scarce. Here, this study addresses this knowledge gap by predicting and measuring the key properties such as coordination number, radial distribution function, density, heat capacity, and volumetric thermal expansion of molten LiI-KI, LiBr-KBr and LiCl-KCl eutectic mixtures. Utilizing ab initio molecular dynamics (AIMD) simulations, we predict these properties of LiX-KX (X=Br,I, Cl) across various temperatures, with chlorides as a benchmark. Experimental measurements using Archimedes methods and Differential Scanning Calorimetry validate the densities and heat capacities of these molten salts. Our comprehensive analysis of the structural and thermophysical properties provides critical insights into the behavior of these molten salts at various temperatures, enhancing the understanding necessary for their application in advanced technologies.

36 - MATERIALS SCIENCE↗
DOE OSTI2014

Contouring and Constraining Bowel on a Full-Bladder Computed Tomography Scan May Not Reflect Treatment Bowel Position and Dose Certainty in Gynecologic External Beam Radiation Therapy

Purpose: To evaluate, in a gynecologic cancer setting, changes in bowel position, dose-volume parameters, and biological indices that arise between full-bladder (FB) and empty-bladder (EB) treatment situations; and to evaluate, using cone beam computed tomography (CT), the validity of FB treatment presumption. Methods and Materials: Seventeen gynecologic cancer patients were retrospectively analyzed. Empty-bladder and FB CTs were obtained. Full-bladder CTs were used for planning and dose optimization. Patients were given FB instructions for treatment. For the study purpose, bowel was contoured on the EB CTs for all patients. Bowel position and volume changes between FB and EB states were determined. Full-bladder plans were applied on EB CTs for determining bowel dose-volume changes in EB state. Biological indices (generalized equivalent uniform dose and normal tissue complication probability) were calculated and compared between FB and EB. Weekly cone beam CT data were available in 6 patients to assess bladder volume at treatment. Results: Average (±SD) planned bladder volume was 299.7 ± 68.5 cm{sup 3}. Median bowel shift in the craniocaudal direction between FB and EB was 12.5 mm (range, 3-30 mm), and corresponding increase in exposed bowel volume was 151.3 cm{sup 3} (range, 74.3-251.4 cm{sup 3}). Absolute bowel volumes receiving 45 Gy were higher for EB compared with FB (mean 328.0 ± 174.8 vs 176.0 ± 87.5 cm{sup 3}; P=.0038). Bowel normal tissue complication probability increased 1.5× to 23.5× when FB planned treatments were applied in the EB state. For the study, the mean percentage value of relative bladder volume at treatment was 32%. Conclusions: Full-bladder planning does not necessarily translate into FB treatments, with a patient tendency toward EB. Given the uncertainty in daily control over bladder volume for treatment, we strongly recommend a “planning-at-risk volume bowel” (PRV{sub B}owel) concept to account for bowel motion between FB and EB that can be tailored for the individual patient.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

A parallel domain decomposition-based implicit method for the Cahn–Hilliard–Cook phase-field equation in 3D

We present a numerical algorithm for simulating the spinodal decomposition described by the three dimensional Cahn–Hilliard–Cook (CHC) equation, which is a fourth-order stochastic partial differential equation with a noise term. The equation is discretized in space and time based on a fully implicit, cell-centered finite difference scheme, with an adaptive time-stepping strategy designed to accelerate the progress to equilibrium. At each time step, a parallel Newton–Krylov–Schwarz algorithm is used to solve the nonlinear system. We discuss various numerical and computational challenges associated with the method. The numerical scheme is validated by a comparison with an explicit scheme of high accuracy (and unreasonably high cost). We present steady state solutions of the CHC equation in two and three dimensions. The effect of the thermal fluctuation on the spinodal decomposition process is studied. We show that the existence of the thermal fluctuation accelerates the spinodal decomposition process and that the final steady morphology is sensitive to the stochastic noise. We also show the evolution of the energies and statistical moments. In terms of the parallel performance, it is found that the implicit domain decomposition approach scales well on supercomputers with a large number of processors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Characterization of urania vaporization with transpiration coupled thermogravimetry

Determining equilibrium vapor pressures of materials is made easier by transpiration measurements. However, the traditional technique involves condensing the volatiles entrained in a carrier gas outside of the hot measurement zone. One potential problem is deposition en route to a cooled collector. Thermogravimetric analysis (TGA) can be used to measure in situ mass loss due to vaporization and therefore obviate the need to analyze the entire gas train due to premature plating of vapor species. Therefore, a transpiration coupled TGA technique was used to determine equilibrium pressures of UO3 gas over fluorite structure UO2+x and U3O8 at T = (1573 and 1773) K. Moreover, we compared to calculations from models and databases in the open literature. Our study gives clarity to the thermochemical data for UO3 gas and validates the mass loss transpiration method using thermogravimetry for determining equilibrium vapor pressures of non-stoichiometric oxides.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Quantifying deformation during Zry-4 burst testing: a comparison of BISON and a combined in-situ digital image correlation and infrared thermography method

The development and verification of nuclear fuel-cladding performance codes require extensive testing to establish empirical correlations, necessitating more accelerated testing methods that can replace large validation studies. Here, in the present work, a technique was developed and implemented to experimentally quantify the relationship between temperature, cladding internal pressure, and strain in-situ during a burst test, with the specific aim of generating data for code validation and model refinement. Digital image correlation was used to measure hoop, axial, and radial strain, and infrared thermography to quantify axial temperature gradients. Several key experimental modifications to traditional LOCA burst testing were necessary, but are shown to effectively have little impact on burst temperatures. The time/temperature dependent pressure and time/spatially dependent thermal gradient data were used with BISON to simulate cladding burst and strain rates. Although the measured DIC strains reach appreciable amounts at slightly lower temperatures than BISON simulated strains, there is good agreement between the measured and simulated strains in terms of magnitude and rates leading up to burst. However, in the few seconds prior to and during burst, the BISON simulated strains are significantly lower than the measured strains, indicating the potential for model improvement. The combined experiment and simulation technique may be applied to any new cladding concept to accelerate fuel qualification.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Three-dimensional flow model development for thermal mixing and stratification modeling in reactor system transients analyses

Mixing, thermal-stratification, and mass transport phenomena in large pools or enclosures play major roles for the safety of reactor systems. Depending on the fidelity requirement and computational resources, various modeling methods, from the 0-D perfect mixing model to 3-D Computational Fluid Dynamics (CFD) models, are available. Each is associated with its own advantages and shortcomings. It is very desirable to develop an advanced and efficient thermal mixing and stratification modeling capability embedded in a modern system analysis code to improve the accuracy of reactor safety analyses and to reduce modeling uncertainties. An advanced system analysis tool, SAM, is being developed at Argonne National Laboratory for advanced non-LWR reactor safety analysis. While SAM is being developed as a system-level modeling and simulation tool, a reduced-order three-dimensional module is under development to model the mull-dimensional flow and thermal mixing and stratification in large enclosures of reactor systems. This paper provides an overview of the three-dimensional finite element flow model in SAM, including the governing equations, stabilization scheme, and solution methods. Additionally, several verification and validation tests are presented, including lid-driven cavity flow, natural convection inside a cavity, laminar flow in a channel of parallel plates. Here, based on the comparisons with the analytical solutions and experimental results, it is demonstrated that the developed 3-D fluid model can perform very well for a wide range of flow problems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Propagating neutronic uncertainties for FFTF LOFWOS Test #13

The safety evaluation conducted for licensing a Sodium-cooled Fast Reactor (SFR) may require modeling transients with best-estimate calculation tools that must first be validated against real-world measurements. To provide the community with a valuable benchmarking opportunity for validating SFR analysis tools and methods, the International Atomic Energy Agency (IAEA) initiated a coordinated research project (CRP) in 2018 for the analysis of the Fast Flux Test Facility (FFTF) Loss of Flow Without Scram (LOFWOS) Test #13. The impact of nuclear data uncertainties on neutronics parameters was previously investigated based on the COMMARA-2.0 covariance matrix. Since the transient simulation results are very sensitive to certain reactivity coefficients, it was decided to employ rigorous uncertainty propagation methods to quantify the impact of nuclear data uncertainties on the best-estimate predication of FFTF LOFWOS Test #13. The DAKOTA code is used to propagate neutronic uncertainties through SAS4A/SASSYS-1 transient simulations, while taking into account spatial and reaction-wise correlations within these uncertainties. This study shows that the remaining discrepancies observed between the Argonne National Laboratory (ANL) best-estimate results and the experimental measurements can be partly explained by the uncertainty associated with Gas Expansion Module (GEM) worth, which contributes the majority of the overall nuclear data uncertainty on the output from the FFTF LOFWOS Test #13 transient simulation. This study also confirmed the importance of including spatial and reaction-wise correlations of nuclear data uncertainties on feedback coefficients in the uncertainty propagation to avoid under-estimating their impact during the transient simulations.

42 ENGINEERING↗
DOE OSTI2023

Drug discovery efforts at George Mason University

With over 39,000 students, and research expenditures in excess of $200 million, George Mason University (GMU) is the largest R1 (Carnegie Classification of very high research activity) university in Virginia. Mason scientists have been involved in the discovery and development of novel diagnostics and therapeutics in areas as diverse as infectious diseases and cancer. Below are highlights of the efforts being led by Mason researchers in the drug discovery arena. To enable targeted cellular delivery, and non-biomedical applications, Veneziano and colleagues have developed a synthesis strategy that enables the design of self-assembling DNA nanoparticles (DNA origami) with prescribed shape and size in the 10 to 100 nm range. The nanoparticles can be loaded with molecules of interest such as drugs, proteins and peptides, and are a promising new addition to the drug delivery platforms currently in use. The investigators also recently used the DNA origami nanoparticles to fine tune the spatial presentation of immunogens to study the impact on B cell activation. These studies are an important step towards the rational design of vaccines for a variety of infectious agents. To elucidate the parameters for optimizing the delivery efficiency of lipid nanoparticles (LNPs), Buschmann, Paige and colleagues have devised methods for predicting and experimentally validating the pKa of LNPs based on the structure of the ionizable lipids used to formulate the LNPs. These studies may pave the way for the development of new LNP delivery vehicles that have reduced systemic distribution and improved endosomal release of their cargo post administration. To better understand protein-protein interactions and identify potential drug targets that disrupt such interactions, Luchini and colleagues have developed a methodology that identifies contact points between proteins using small molecule dyes. The dye molecules noncovalently bind to the accessible surfaces of a protein complex with very high affinity, but are excluded from contact regions. When the complex is denatured and digested with trypsin, the exposed regions covered by the dye do not get cleaved by the enzyme, whereas the contact points are digested. The resulting fragments can then be identified using mass spectrometry. The data generated can serve as the basis for designing small molecules and peptides that can disrupt the formation of protein complexes involved in disease processes. For example, using peptides based on the interleukin 1 receptor accessory protein (IL-1RAcP), Luchini, Liotta, Paige and colleagues disrupted the formation of IL-1/IL-R/IL-1RAcP complex and demonstrated that the inhibition of complex formation reduced the inflammatory response to IL-1B. Working on the discovery of novel antimicrobial agents, Bishop, van Hoek and colleagues have discovered a number of antimicrobial peptides from reptiles and other species. DRGN-1, is a synthetic peptide based on a histone H1-derived peptide that they had identified from Komodo Dragon plasma. DRGN-1 was shown to disrupt bacterial biofilms and promote wound healing in an animal model. The peptide, along with others, is being developed and tested in preclinical studies. Other research by van Hoek and colleagues focuses on in silico antimicrobial peptide discovery, screening of small molecules for antibacterial properties, as well as assessment of diffusible signal factors (DFS) as future therapeutics. The above examples provide insight into the cutting-edge studies undertaken by GMU scientists to develop novel methodologies and platform technologies important to drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Development of complex-shaped liver multicellular spheroids as a human-based model for nanoparticle toxicity assessment in vitro

The emergence of human-based models is incontestably required for the study of complex physiological pathways and validation of reliable in vitro methods as alternative for in vivo studies in experimental animals for toxicity assessment. With this objective, we have developed and tested three dimensional environments for cells using different types of hydrogels including transglutaminase-cross-linked gelatin, collagen type I, and growth-factor depleted Matrigel. Cells grown in Matrigel exhibited the greatest cell proliferation and spheroid diameter. Moreover, analysis of urea and albumin biosynthesis revealed that the created system allowed the immortalized liver cell line HepG2 to re-establish normal hepatocyte-like properties which were not observed under the conditions of conventional cell cultures. This study presents a scalable technology for production of complex-shaped liver multicellular spheroids as a system which improves the predictive value of cell-based assays for safety and risk assessment. The time- and dose-dependent toxicity of nanoparticles demonstrates a higher cytotoxic effect when HepG2 cells grown as monolayer than embedded in hydrogels. The experimental setup provided evidence that the cell environment has significant influence on cell sensitivity and that liver spheroid is a useful and novel tool to examine nanoparticle dosing effect even at the level of in vitro studies. Therefore, this system can be applied to a wide variety of potentially hostile compounds in basic screening to provide initial warning of adverse effects and trigger subsequent analysis and remedial actions. - Highlights: • Comparison of HepG2 cells growth in Matrigel, Collagen I gel and gelatin gel. • Examination of nanoparticles (NP) dosing effect at the level of in vitro studies. • Influence of the cell culture media composition on the cytotoxic effect of NP.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Chemical Attribution of Fentanyl Using Multivariate Statistical Analysis of Orthogonal Mass Spectral Data

Attribution of the origin of an illicit drug relies on identification of compounds indicative of its clandestine production and is a key component of many modern forensic investigations. Here, the results of these studies can yield detailed information on method of manufacture, starting material source, and final product, all critical forensic evidence. In the present work, chemical attribution signatures (CAS) associated with the synthesis of the analgesic fentanyl, N-(1-phenylethylpiperidin-4-yl)-N-phenylpropanamide, were investigated. Six synthesis methods, all previously published fentanyl synthetic routes or hybrid versions thereof, were studied in an effort to identify and classify route-specific signatures. A total of 160 distinct compounds and inorganic species were identified using gas and liquid chromatographies combined with mass spectrometric methods (gas chromatography/mass spectrometry (GC/MS) and liquid chromatography–tandem mass spectrometry-time of-flight (LC–MS/MS-TOF)) in conjunction with inductively coupled plasma mass spectrometry (ICPMS). The complexity of the resultant data matrix urged the use of multivariate statistical analysis. Using partial least-squares-discriminant analysis (PLS-DA), 87 route-specific CAS were classified and a statistical model capable of predicting the method of fentanyl synthesis was validated and tested against CAS profiles from crude fentanyl products deposited and later extracted from two operationally relevant surfaces: stainless steel and vinyl tile. Finally, this work provides the most detailed fentanyl CAS investigation to date by using orthogonal mass spectral data to identify CAS of forensic significance for illicit drug detection, profiling, and attribution.

36 MATERIALS SCIENCE↗
DOE OSTI2019

String Method for Protein–Protein Binding Free-Energy Calculations

A powerful computational strategy to determine the equilibrium association constant of two macromolecules with explicit-solvent molecular dynamics (MD) simulations is the "geometric route", which considers the reversible physical separation of the bound complex in solution. Nonetheless, multiple challenges remain to render this type of methodology reliable and computationally efficient in practice. In particular, in one, formulation of the geometric route relies on the potential of mean force (PMF) for physically separating the two binding partners restrained along a straight axis, which must be selected prior to the calculation. However, practical applications indicate that the calculation of the separation PMF along the predefined rectilinear pathway may be suboptimal and slowly convergent. Recognizing that a rectilinear straight separation pathway is generally not representative of how the protein complex physically separates in solution, we put forth a novel theoretical framework for binding free-energy calculations, leaning on the optimal curvilinear minimum free-energy path (MFEP) determined from the string method. The proposed formalism is validated by comparing the results obtained using both rectilinear and curvilinear pathways for a prototypical host-guest complex formed by cucurbit[7]uril (CB[7]) binding benzene, and for the barnase-barstar protein complex. On the basis of multi-microsecond MD calculations, we find that the calculations following the traditional rectilinear pathway and the string-based curvilinear pathway agree quantitatively, but convergence is faster with the latter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

On-line Inductively Coupled Plasma Mass Spectrometry Reveals Material Degradation Dynamics of Au and Cu Catalysts during Electrochemical CO 2 Reduction

A significant challenge in commercializing electrochemical CO 2 reduction (CO 2 R) is achieving catalyst durability. Here, in this study, online inductively coupled mass spectrometry (ICP–MS) was used to investigate catalyst degradation via nanoparticle detachment and/or dissolution into metal ions under CO 2 R operating conditions in 0.1 M KHCO 3 . We developed an experimental framework with ex situ characterization to validate the online ICP–MS method for in situ evaluation of degradation from metal foils. By varying the applied potential and microenvironment (CO 2 vs N 2 -saturated electrolyte), we gained insights into the degradation of Au and Cu foils under CO 2 R and hydrogen evolution reaction (HER) conditions. While both Au and Cu foils were observed to be stable to dissolution in these regimes, degradation via nanoparticle detachment from the foil surface at the femtogram scale was observed as a function of reaction conditions, providing new insights into material degradation mechanisms. When applying potential steps at −0.1 and −1.0 V vs the reversible hydrogen electrode (RHE), Au was found to degrade via nanoparticle detachment under CO 2 R operating conditions more than under HER conditions, while Cu was found to degrade via nanoparticle detachment in similar amounts during both reactions. Au lost ∼1.8× more mass and ∼7.5× more nanoparticles than Cu under CO 2 R operating conditions. This study demonstrates the use of online ICP–MS to gain insight into the degradation of Au and Cu, the importance of studying unconventional degradation mechanisms such as nanoparticle detachment, and that online ICP–MS can be further utilized to gain fundamental understanding of catalyst durability for a variety of reaction systems.

Yan, Katherine [Stanford Univ., CA (United States)↗