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DOE OSTI2026

Expanding Access to Science Participation: A FAIR Framework for Petascale Data Visualization and Analytics

The massive data generated by scientists daily serve as both a major catalyst for new discoveries and innovations, as well as a significant roadblock that restricts access to the data. Here, our paper introduces a new approach to removing Big Data barriers and democratizing access to petascale data for the broader scientific community. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hiding the complexities of dealing with file systems or cloud services. We enable FAIR (Findable, Accessible, Interoperable, and Reusable) access to datasets such as NASA’s petascale climate datasets. Our paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction utilizes state-of-the art progressive compression algorithms and machine-learning insights to power scalable visualization dashboards for petascale data. The result provides users with the ability to identify extreme events or trends dynamically, expanding access to scientific data and further enabling discoveries. We validate our approach by improving the ability of climate scientists to visually explore their data via three fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution. These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.

Computer science↗

DOE OSTI2019

Biomimetic, Soft-Material Synapse for Neuromorphic Computing: from Device to Network

Neuromorphic computing refers to a variety of brain-inspired computers, devices, and models inspired by the interconnectivity, performance, and energy efficiency of the human brain. Unlike the ubiquitous von Neumann computer architectures with complex processor cores and sequential computation, biological neurons and synapses operate by storing and processing information simultaneously with the capacity of flexible adaptation resulting in massive computational capability with much less power consumption. The search for a synaptic material which can closely imitate bio-synapse has led to an alamethicin-doped, synthetic biomembrane which can emulate key synaptic functions due to generic memristive property enabling learning and computation. This two-terminal, biomolecular memristor, in contrast to its solid-state counterparts, features similar structure, switching mechanism, and ionic transport modality as biological synapses while consuming considerably lower power. In this paper, we outline a methodology for using this biomolecular synapse to build neural networks capable of solving real-world problems. The physical mechanism underlying its volatile memristance is explored followed by the development of a model of this device for circuit simulation. We outline a circuit design technique to integrate this synapse with solid-state neuron circuit for hardware implementation. Based on these results, we develop a high level simulation framework and use a training scheme called Evolutionary Optimization for Neuromorphic System (EONS) to generate networks for solving two problems, namely iris dataset classification and EEG classification task. The small network size and comparable to state-of-the-art accuracy of these preliminary networks show its potential to enhance synaptic functionality in next generation neuromorphic hardware.

Sakib hasan, Md↗

DOE OSTI2014

A GPU-accelerated and Monte Carlo-based intensity modulated proton therapy optimization system

Purpose: Conventional spot scanning intensity modulated proton therapy (IMPT) treatment planning systems (TPSs) optimize proton spot weights based on analytical dose calculations. These analytical dose calculations have been shown to have severe limitations in heterogeneous materials. Monte Carlo (MC) methods do not have these limitations; however, MC-based systems have been of limited clinical use due to the large number of beam spots in IMPT and the extremely long calculation time of traditional MC techniques. In this work, the authors present a clinically applicable IMPT TPS that utilizes a very fast MC calculation. Methods: An in-house graphics processing unit (GPU)-based MC dose calculation engine was employed to generate the dose influence map for each proton spot. With the MC generated influence map, a modified least-squares optimization method was used to achieve the desired dose volume histograms (DVHs). The intrinsic CT image resolution was adopted for voxelization in simulation and optimization to preserve spatial resolution. The optimizations were computed on a multi-GPU framework to mitigate the memory limitation issues for the large dose influence maps that resulted from maintaining the intrinsic CT resolution. The effects of tail cutoff and starting condition were studied and minimized in this work. Results: For relatively large and complex three-field head and neck cases, i.e., >100 000 spots with a target volume of ∼1000 cm{sup 3} and multiple surrounding critical structures, the optimization together with the initial MC dose influence map calculation was done in a clinically viable time frame (less than 30 min) on a GPU cluster consisting of 24 Nvidia GeForce GTX Titan cards. The in-house MC TPS plans were comparable to a commercial TPS plans based on DVH comparisons. Conclusions: A MC-based treatment planning system was developed. The treatment planning can be performed in a clinically viable time frame on a hardware system costing around 45 000 dollars. The fast calculation and optimization make the system easily expandable to robust and multicriteria optimization.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2012

Scientific Opportunities for Monitoring of Environmental Remediation Sites (SOMERS) - 12224

The US Department of Energy (DOE) is responsible for risk reduction and cleanup of its nuclear weapons complex. DOE maintains the largest cleanup program in the world, currently spanning over a million acres in 13 states. The inventory of contaminated materials includes 90 million gallons of radioactive waste, 6.4 trillion liters of groundwater, and 40 million cubic meters of soil and debris. It is not feasible to completely restore many sites to pre-disposal conditions. Any contamination left in place will require monitoring, engineering controls and/or land use restrictions to protect human health and environment. Research and development efforts to date have focused on improving characterization and remediation. Yet, monitoring will result in the largest life-cycle costs and will be critical to improving performance and protection. Through an inter-disciplinary effort, DOE is addressing a need to advance monitoring approaches from sole reliance on cost- and labor-intensive point-source monitoring to integrated systems-based approaches such as flux-based approaches and the use of early indicator parameters. Key objectives include identifying current scientific, technical and implementation opportunities and challenges, prioritizing science and technology strategies to meet current needs within the DOE complex for the most challenging environments, and developing an integrated and risk-informed monitoring framework. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

Hydrogeologic Setting and Groundwater Contaminant Conceptual Model, Los Alamos National Laboratory - 18681

Our understanding of the complex hydrogeologic setting at Los Alamos National Laboratory (LANL) has evolved significantly over the previous decade. The semi-arid, high-desert setting consists of generally dry drainages in canyons that head either in uplands to the west of the Laboratory or on the high mountain-front plateau where the Laboratory is spread across approximately 40 square miles. Some canyon drainages have spatially limited reaches with perennial surface water from natural runoff and spring sources and/or from permitted effluent sources. The Rio Grande rift basin-fill stratigraphy includes primary volcanic deposits from pyroclastic eruptions and intermediate composition lava flows, and fluvial and alluvial fan deposits. Highly complex and spatially variable stratigraphy governs the occurrences of three groundwater zones: shallow 'alluvial groundwater', deep perched groundwater, and a thick 'regional' aquifer that is the water supply for the community of Los Alamos and the Laboratory. The current understanding of the hydrogeologic framework was greatly expanded as part of a site hydrogeologic characterization program completed in 2005. Higher resolution information exists in area where more detailed groundwater characterization has been conducted to support specific project objectives. A conceptual model for occurrences of contaminants in each of the three groundwater zones is governed by four factors: contaminant solubility/mobility, the mass of historical release, presence of a continuous hydraulic driver, and 'breakthrough' geology, defined as geology conducive to infiltration of shallow groundwater to deeper groundwater zones. The Laboratory's operations were historically somewhat spatially partitioned. Most of the radionuclide work (and historical releases) was conducted in the northern portion of the Laboratory, high explosives work has taken place exclusively in the southwestern portion, and treated sanitary effluent was released at a limited number of outfalls. Historical material disposal areas (MDAs) are dispersed throughout the Laboratory, and active MDAs are all located on a single dry mesa at the eastern part of the Laboratory. Evidence exists of transport of anthropogenic constituents to the regional aquifer. However, years of groundwater characterization have identified only two cases for which all the key factors were present and a complete contaminant pathway to the regional aquifer has resulted in contamination at levels above an applicable standard. Hexavalent chromium contamination associated with historical use as a corrosion inhibitor in a power plant is present beneath Mortandad Canyon, and RDX contamination associated with high-explosives machining is present beneath Canon de Valle. Each of those project areas have refined conceptual and numerical models that address contaminant migration pathways, spatial extent of perched-intermediate groundwater, mass balance, and fate and transport within the regional aquifer. No known contamination is present in the regional aquifer associated with MDAs, and interestingly, much of the Laboratory's groundwater background dataset is from wells located beneath active MDAs. A comprehensive groundwater monitoring program is in place to track the evolution of chromium and RDX contamination, and to identify any other constituents that may be attributable to historical LANL operations. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2025

Machine Learning‐Guided Discovery of High‐Entropy Perovskite Oxide Electrocatalysts via Oxygen Vacancy Engineering

Abstract High‐entropy perovskite oxides (HEPOs) have recently emerged as multifunctional catalysts. However, the HEPOs’ structural and compositional complexity hinders the easy and accurate extrapolation of activity indicators, which are essential for establishing structure‐property correlations. Here, OxiGraphX, is introduced as a novel graph neural network (GNN) model designed to capture the complex relationships among structure, composition, and atomic chemical environments for accurate prediction of oxygen vacancy formation energies (OVFEs) in HEPOs. By integrating machine learning (ML), density functional theory (DFT), and experimental validation, this work demonstrates an efficient framework for rapidly and accurately screening HEPO electrocatalysts for oxygen evolution reaction (OER). The OxiGraphX predicts OVFEs with a precision exceeding existing data, enabling the identification of compositions of higher oxygen vacancy content (OVC) and, thus, higher catalytic activity. Furthermore, the model explores latent spaces that translate effectively into experimental domains, bridging computational predictions with real‐world applications. This approach accelerates the discovery of high‐performance HEPO catalysts while providing deeper insights into their catalytic mechanisms.

Chemistry↗

NASA NTRSDate not supplied

Ares: A Coupling Methodology for Ablation Modeling

To enable modeling of complex and coupled ablation problems, a multi-physics framework is developed. A methodology for modeling shape change in coupled systems is presented. The approach taken to model gas-surface interactions and translate coupled surface phenomena to physically meaningful boundary conditions in the distinct solvers is discussed. Particular emphasis is placed on the nature of coupled boundary conditions pertaining to surface energy and mass balances as well as surface chemistry modeling. The developed methodology is used to simulate a shear test in arc-jet conditions in order to assess the validity of the coupled approach as well as the implementation of the relevant physical processes.

EDL↗

DOE OSTI2018

Thin-film X-ray filters on microstructured substrates and their thermophysical properties

It is shown that structured substrates having micron- or submicron-sized through holes and coated with an ultrathin organic film can be used for the fabrication of thin-film X-ray filters via direct growth of functional layers on a substrate by sputter deposition, without additional complex processing steps. An optimised process is considered for the fabrication of X-ray filters on support structures in the form of electroplated fine nickel grids and on track-etched polymer membranes with micron- and submicron-diameter through pores. ‘Optimisation’ is here taken to mean matching the sputter deposition conditions with the properties of substrates so as to avoid overheating. The filters in question are intended for both imaging and single-channel detectors operating in the soft X-ray and vacuum UV spectral regions, at wavelengths from 10 to 60 nm. Thermal calculations are presented for the heating of ultrathin layers of organic films and thin-film support substrates during the sputter deposition of aluminium or other functional materials. The paper discusses approaches for cooling thin-film composites during the sputter deposition process and the service of the filters in experiments and gives a brief overview of the works that utilised filters produced by the described technique on microstructured substrates, including orbital solar X-ray research in the framework of the CORONAS programme and laboratory laser plasma experiments. (invited paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Development of MOSCATO: A CFD-Level Electrochemistry and Corrosion Simulator for Molten Salt Systems

For both coolant and fueled variants of molten salt reactors (MSRs), the corrosion of structural materials is a significant challenge. The corrosion stems from chemical and electrochemical reactions initiated by fissile material, fission products, and impurities in the salt. Lower-fidelity models rely on empirical correlations for mass transfer, simplified lumped temperature profiles, and similar assumptions. They do not capture detailed spatial variations in complex geometries, creating the need for high-fidelity modeling to bridge this gap.As we approach the demonstration and possible deployment of MSRs in this decade, the development of a high-fidelity, high-performance simulator becomes imperative. To simulate the complex electrochemical environment and corrosion within molten salt systems, we have developed the Molten Salt Chemistry And TranspOrt (MOSCATO) code. This endeavor is comprised of three essential components. First, mass transfer equations are coupled with the Navier-Stokes equations in order to account for the transport of species in the salt. Second, the diffusion of alloy constituents, such as Cr, Fe, Ni, etc. is simulated within the structural metals. Third, the alloy and salt domains are coupled to account for the heterogeneous chemical and electrochemical reactions that occur at the salt-alloy interface.MOSCATO manages all three components within the framework of the highly scalable, open-source spectral element method computational fluid dynamics code Nek5000/NekRS. This integration enables MOSCATO to harness the immense computational power of modern high-performance computing resources, ensuring both high fidelity and computational speed.In addition to code development, we have initiated a comprehensive verification and validation campaign, utilizing data from diverse sources. First, MOSCATO's electrochemical solver was verified with reference numerical data. Then validation occurred against experiments: one of a thermal galvanic cell and the other for corrosion in flowing molten salt of FLiNaK (LiF-NaF-KF). This campaign verified and validated MOSCATO as a reliable tool for simulating electrochemical environments and corrosion in molten salt systems.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2004

Transportation Network Topologies

A discomforting reality has materialized on the transportation scene: our existing air and ground infrastructures will not scale to meet our nation's 21st century demands and expectations for mobility, commerce, safety, and security. The consequence of inaction is diminished quality of life and economic opportunity in the 21st century. Clearly, new thinking is required for transportation that can scale to meet to the realities of a networked, knowledge-based economy in which the value of time is a new coin of the realm. This paper proposes a framework, or topology, for thinking about the problem of scalability of the system of networks that comprise the aviation system. This framework highlights the role of integrated communication-navigation-surveillance systems in enabling scalability of future air transportation networks. Scalability, in this vein, is a goal of the recently formed Joint Planning and Development Office for the Next Generation Air Transportation System. New foundations for 21PstP thinking about air transportation are underpinned by several technological developments in the traditional aircraft disciplines as well as in communication, navigation, surveillance and information systems. Complexity science and modern network theory give rise to one of the technological developments of importance. Scale-free (i.e., scalable) networks represent a promising concept space for modeling airspace system architectures, and for assessing network performance in terms of scalability, efficiency, robustness, resilience, and other metrics. The paper offers an air transportation system topology as framework for transportation system innovation. Successful outcomes of innovation in air transportation could lay the foundations for new paradigms for aircraft and their operating capabilities, air transportation system architectures, and airspace architectures and procedural concepts. The topology proposed considers air transportation as a system of networks, within which strategies for scalability of the topology may be enabled by technologies and policies. In particular, the effects of scalable ICNS concepts are evaluated within this proposed topology. Alternative business models are appearing on the scene as the old centralized hub-and-spoke model reaches the limits of its scalability. These models include growth of point-to-point scheduled air transportation service (e.g., the RJ phenomenon and the 'Southwest Effect'). Another is a new business model for on-demand, widely distributed, air mobility in jet taxi services. The new businesses forming around this vision are targeting personal air mobility to virtually any of the thousands of origins and destinations throughout suburban, rural, and remote communities and regions. Such advancement in air mobility has many implications for requirements for airports, airspace, and consumers. These new paradigms could support scalable alternatives for the expansion of future air mobility to more consumers in more places.

Holmes, Bruce J.↗

NASA NTRS2004

Transportation Network Topologies

A discomforting reality has materialized on the transportation scene: our existing air and ground infrastructures will not scale to meet our nation's 21st century demands and expectations for mobility, commerce, safety, and security. The consequence of inaction is diminished quality of life and economic opportunity in the 21st century. Clearly, new thinking is required for transportation that can scale to meet to the realities of a networked, knowledge-based economy in which the value of time is a new coin of the realm. This paper proposes a framework, or topology, for thinking about the problem of scalability of the system of networks that comprise the aviation system. This framework highlights the role of integrated communication-navigation-surveillance systems in enabling scalability of future air transportation networks. Scalability, in this vein, is a goal of the recently formed Joint Planning and Development Office for the Next Generation Air Transportation System. New foundations for 21st thinking about air transportation are underpinned by several technological developments in the traditional aircraft disciplines as well as in communication, navigation, surveillance and information systems. Complexity science and modern network theory give rise to one of the technological developments of importance. Scale-free (i.e., scalable) networks represent a promising concept space for modeling airspace system architectures, and for assessing network performance in terms of scalability, efficiency, robustness, resilience, and other metrics. The paper offers an air transportation system topology as framework for transportation system innovation. Successful outcomes of innovation in air transportation could lay the foundations for new paradigms for aircraft and their operating capabilities, air transportation system architectures, and airspace architectures and procedural concepts. The topology proposed considers air transportation as a system of networks, within which strategies for scalability of the topology may be enabled by technologies and policies. In particular, the effects of scalable ICNS concepts are evaluated within this proposed topology. Alternative business models are appearing on the scene as the old centralized hub-and-spoke model reaches the limits of its scalability. These models include growth of point-to-point scheduled air transportation service (e.g., the RJ phenomenon and the Southwest Effect). Another is a new business model for on-demand, widely distributed, air mobility in jet taxi services. The new businesses forming around this vision are targeting personal air mobility to virtually any of the thousands of origins and destinations throughout suburban, rural, and remote communities and regions. Such advancement in air mobility has many implications for requirements for airports, airspace, and consumers. These new paradigms could support scalable alternatives for the expansion of future air mobility to more consumers in more places.

Holmes, Bruce J.↗

DOE OSTI2014

SU-F-BRF-01: A GPU Framework for Developing Interactive High-Resolution Patient-Specific Biomechanical Models

Purpose: To develop a GPU-based framework that can generate highresolution and patient-specific biomechanical models from a given simulation CT and contoured structures, optimized to run at interactive speeds, for addressing adaptive radiotherapy objectives. Method: A Massspring-damping (MSD) model was generated from a given simulation CT. The model's mass elements were generated for every voxel of anatomy, and positioned in a deformation space in the GPU memory. MSD connections were established between neighboring mass elements in a dense distribution. Contoured internal structures allowed control over elastic material properties of different tissues. Once the model was initialized in GPU memory, skeletal anatomy was actuated using rigid-body transformations, while soft tissues were governed by elastic corrective forces and constraints, which included tensile forces, shear forces, and spring damping forces. The model was validated by applying a known load to a soft tissue block and comparing the observed deformation to ground truth calculations from established elastic mechanics. Results: Our analyses showed that both local and global load experiments yielded results with a correlation coefficient R{sup 2} > 0.98 compared to ground truth. Models were generated for several anatomical regions. Head and neck models accurately simulated posture changes by rotating the skeletal anatomy in three dimensions. Pelvic models were developed for realistic deformations for changes in bladder volume. Thoracic models demonstrated breast deformation due to gravity when changing treatment position from supine to prone. The GPU framework performed at greater than 30 iterations per second for over 1 million mass elements with up to 26 MSD connections each. Conclusions: Realistic simulations of site-specific, complex posture and physiological changes were simulated at interactive speeds using patient data. Incorporating such a model with live patient tracking would facilitate real time assessment of variations of the actual anatomy and delivered dose for adaptive intervention and re-planning.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

RLMolLM: Reinforcement Learning-Enhanced Language Model Framework for Inverse Molecular Design

Inverse molecular design faces significant challenges due to vast chemical space and complex property requirements. While language models show promise for molecular generation, they struggle with validity, multi-property optimization, and structural constraints. This work presents RLMolLM, a reinforcement learning framework combining Proximal Policy Optimization (PPO) with genetic algorithms to address these limitations. Our approach optimizes multiple user-specified properties including quantitative estimates of drug-likeness (QED), synthetic accessibility (SA), and ADMET (absorption, distribution, metabolism, excretion, and toxicity) endpoints without requiring complete model retraining, while maintaining capability for scaffold-constrained generation where specific substructures must be preserved. We outperform state-of-the-art methods for molecular optimization, achieving best QED scores across GDB13, Moses, and Zinc datasets with up to 31% improvement over previous methods while maintaining excellent validity, uniqueness, and novelty metrics. For simultaneous multi-property optimization, our framework achieves substantial improvements in ADMET properties including 4.5-fold reduction in hERG toxicity and enhanced Caco-2 permeability compared to Moses dataset. Under structural constraints, the framework significantly improves molecular validity while preserving scaffolds and effectively optimizing properties. In conclusion, this versatile solution advances pharmaceutical and materials molecular design through effective integration of reinforcement learning and genetic algorithms with multi-property optimization and scaffold preservation.

Genetic algorithms↗

DOE OSTI2025

Predicting nonequilibrium Green’s function dynamics and photoemission spectra via nonlinear integral operator learning

Understanding the dynamics of nonequilibrium quantum many-body systems is an important research topic in a wide range of fields across condensed matter physics, quantum optics, and high-energy physics. However, numerical studies of large-scale nonequilibrium phenomena in realistic materials face serious challenges due to intrinsic high-dimensionality of quantum many-body problems and the absence of time-invariance. The nonequilibrium properties of many-body systems can be described by the dynamics of the correlator, or the Green's function of the system, whose time evolution is given by a high-dimensional system of integro-differential equations, known as the Kadanoff–Baym equations (KBEs). The time-convolution term in KBEs, which needs to be recalculated at each time step, makes it difficult to perform long-time numerical simulation. In this paper, we develop an operator-learning framework based on recurrent neural networks (RNNs) to address this challenge. We utilize RNNs to learn the nonlinear mapping between Green's functions and convolution integrals in KBEs. By using the learned operators as a surrogate model in the KBE solver, we obtain a general machine-learning scheme for predicting the dynamics of nonequilibrium Green's functions. Besides significant savings per each time step, the new methodology reduces the temporal computational complexity from $O(N_t^3)$ to $O(N_t)$ where N t is the number of steps taken in a simulation, thereby making it possible to study large many-body problems which are currently infeasible with conventional KBE solvers. Through various numerical examples, we demonstrate the effectiveness of the operator-learning based approach in providing accurate predictions of physical observables such as the reduced density matrix and time-resolved photoemission spectra. Moreover, our framework exhibits clear numerical convergence and can be easily parallelized, thereby facilitating many possible further developments and applications.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Initial Investigation of Coupled Electron-Photon Transport with SCEPTRE and RAPTURE (Rev.1.1)

The SCEPTRE1 code models coupled photon and electron transport in material media using discrete ordinates methods. The code can use different solvers in 1D and 3D in parallel, promising users the ability to run problems that are significant both in physics and computational scale. One capability that is perhaps unique at this time in the NNSA complex is SCEPTRE’s coupled photon– electron transport physics in a discrete ordinates approach. For example, incident x–rays can generate a significant electron population, whose transport can be simulated within the same code. In this context, fully–coupled means that photons can produce electrons and electrons can produce photons; uncoupled or no–coupling means photons do not produce electrons. Partial–coupling, an intermediate option in which photons can produce electrons but electrons do not produce photons, is not considered in the present work. The MCNP code can treat this physics with a Monte Carlo approach. MCNP should be viewed as complementary to SCEPTRE in both computational method (Monte Carlo versus discrete ordinates) as well as data libraries. SCEPTRE and MCNP should provide important checks on each other, especially from complementary modeling frameworks of a S n (discrete ordinates) mesh based method versus Monte Carlo particle based method. In order to learn more about SCEPTRE, and how it might inform engineering work at the Laboratory, we used it to carry out a set of initial studies. This was a series of 1D simulations of photon beams hitting Aluminum slabs. Our studies mainly used SCEPTRE directly, and made little use of the closely associated RAPTURE code (more on this below). The goals of these studies were first, to familiarize ourselves with operating the code, and second, to begin to assess the importance of coupled electron–photon transport. To support these studies, we began to develop a set of scripts to initialize inputs, run tools, process outputs, and plot results. An initial version of these scripts has been committed to the Lightning workflow repository. This memo relates the results of those studies.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2000

A Robust Biomarker

Polymers of bacterial origin, either through cell secretion or the degraded product of cell lysis, form isolated mucoidal strands as well as well-developed biofilms on interfaces. Biofilms are structurally and compositionally complex and are readily distinguishable from abiogenic films. These structures range in size from micrometers to decimeters, the latter occurring as the well-known, mineralised biofilms called stromatolites. Compositionally bacterial polymers are greater than 90 % water, with while the majority of the macromolecules forming the framework of the polymers consisting of polysaccharides (with and some nucteic acids and proteins). These macromolecules contain a vaste amount of functional groups, such as carboxyls, hydroxyls, and phosphoryls which are implicated in cation-binding. It is the elevated metal- binding capacity which provides the bacterial polymer with structural support and also helps to preserves it for up to 3.5 b.y. in the terrestrial rock record. The macromolecules, thus, can become rapidly mineralised and trapped in a mineral matrix. Through early and late diagenesis (bacterial degradation, burial, heat, pressure and time) they break down, losing the functional groups and, gradually, their hydrogen atoms. The degraded product is known as "kerogen". With further diagenesis and metamorphism, all the hydrogen atoms are lost and the carbonaceous matter becomes graphite. until the remnant carbonaceous material become graphitised. This last sentence reads a bit as if ALL these macromolecules break down and end up as graphite., but since we find 441 this is not true for all of the macromolecules. We have traced fossilised polymer and biofilms in rocks from throughout Earth's history, to rocks as old as the oldest being 3.5 b.y.-old. Furthermore, Time of Flight Secondary Ion Mass Spectrometry has been able to identify individual macromolecules of bacterial origin, the identities of which are still being investigated, in all the samples containing fossil biofilm, including the 3.5 b.y..-old carbonaceous cherts from South Africa and Australia. As a result of the unique compositional, structural and "mineralisable" properties of bacterial polymer and biofilms, we conclude that bacterial polymers and biofilms constitute a robust and reliable biomarker for life on Earth and could be a potential biomarker for extraterrestrial life.

Westall, F.↗

DOE OSTI2025

Field-Driven Simulations to Probe the Impact of Ionic Correlations on Solution Transport Coefficients in Binary, Ternary, and Reciprocal Quaternary Aqueous Electrolytes

Ionic correlations play a critical role in governing transport properties of mixed-salt aqueous electrolyte solutions, yet their quantitative characterization remains challenging, particularly for multicomponent aqueous systems. Here, we develop a general nonequilibrium molecular dynamics framework to efficiently compute Onsager transport coefficients and ionic correlations in mixed-salt aqueous solutions. Using field-driven simulations, we obtain accurate Onsager matrices for LiCl/ KCl, KCl/KBr, and LiBr/KCl electrolyte solutions with significantly reduced computational cost relative to equilibrium Green−Kubo methods. The framework enables direct assessment of how attractive cation−anion and repulsive like-ion interactions contribute to conductivity and salt diffusivity across compositions. While static ion pairing exhibits strong composition dependence, dynamic ion correlations remain nearly invariant, leading to constant deviations from Nernst−Einstein predictions. These results highlight the disconnect between static ion association and dynamic transport correlations, and they establish a transferable approach for analyzing ion transport in complex electrolyte environments relevant to separation processes and electrochemical systems.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Strategies for the Photoreduction of Tc-99 Pertechnetate to Low-Valent Tc by Keggin Polyoxometalates

Technetium-99 (half-life of 2.1 × 10 5 yrs, β max = 0.29 MeV) is a hazardous radiological contaminant, which, in its predominant form of pertechnetate (TcO 4 – ), is highly mobile in the environment. Most strategies for the removal of pertechnetate from the environment involve uptake and/or absorption of pertechnetate using resins, clays, cationic metal-organic frameworks and even thorium borate ceramic like materials. Alternative approaches have involved the reduction and subsequent sequestration of lower valent technetium species using iron, sulfides, or iron sulfides. Here, our lab has explored this strategy using the lacunary alpha-2 Wells–Dawson polyoxometalate (α 2 -[P 2 W 17 O 61 ] 10– ) to both reduce and sequester lower valent technetium, and we have reported on the ligand features that stabilize the reduced species. In this work we investigate the potential of “plenary” Keggin POMs (XW 12 O 40 n – ) (X = P, Si, Al, n = 3, 4, 5, respectively) to both reduce TcO 4 – and stabilize the reduced Tc species. Specifically, we report on the mechanism by which the reduction of technetium occurs and find that PW 12 , SiW 12 , and AlW 12 promote the reduction of TcO 4 – to lower valent states. X-ray absorption spectroscopy was used to confirm a combination of Tc IV {in the form of TcO 2 · 2H 2 O and Tc 2 (µ-O) 2 4+ } and Tc V , which is subsequently complexed into a POM defect as a Tc V =O species.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗