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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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IELI is an NLP-based system designed to transform text into structured knowledge graphs, integrate domain-specific ontologies, and answer conceptual logic-based queries. This poster talks about how redesigning IELI can help address scalability and modularity challenges, as well as improving responsiveness and health monitoring of the system.
The past few decades have witnessed significant advances in our understanding of how stars form, and there has been an associated increase in our knowledge of conditions and phenomena in the early solar system. These have led to the formulation of a paradigm for the origin of the solar system that is sufficiently complete that its basic elements can be tested directly through observations. A simple, but profound, consequence of the paradigm is that most if not all stars should be accompanied by planetary systems. The accuracy of instruments that can be used in such searches has improved to the point that Jupiter-like companions to a number of nearby stars could be detected. However, the results to date are that no other planetary systems have been detected, and the absence of detection is becoming statistically significant, particularly as it relates to the existence of brown dwarf companions to main-sequence stars.
Complex networks of information processing systems, or information supply chains, present challenges for performance analysis. Here, we establish a mathematical setting, in which a process within an information supply chain can be analyzed in terms of the functionality of the system’s components. Principles of this methodology are rigorously defended and induce a model for determining the reliability for the various products in these networks. Our model does not limit us from having cycles in the network, as long as the cycles do not contain negation. It is shown that our approach to reliability resolves the nonuniqueness caused by cycles in a probabilistic Boolean network. An iterative algorithm is given to find the reliability values of the model, using a process that can be fully automated. This automated method of discerning reliability is beneficial for systems managers. As a systems manager considers systems modification, such as the replacement of owned and maintained hardware systems with cloud computing resources, the need for comparative analysis of system reliability is paramount. The model is extended to handle conditional knowledge about the network, allowing one to make predictions of weaknesses in the system. Finally, to illustrate the model’s flexibility over different forms, it is demonstrated on a system of components and subcomponents.
Following a nationwide open competition, the University Coalition for Fossil Energy Research (UCFER) was established in October 2015 through a cooperative agreement between Penn State and the Department of Energy (DOE) National Energy Technology Laboratory (NETL). Penn State lead UCFER with the objective of advancing basic and applied research for clean and low-carbon energy based on fossil fuels in support of the DOE’s mission. UCFER focused on research that improves the efficiency of production and use of fossil energy resources, while minimizing the environmental impacts and reducing greenhouse gas emissions. Penn State lead a team of nine universities (Massachusetts Institute of Technology, The Pennsylvania State University, Princeton University, Texas A&M University, University of Kentucky, University of Southern California, The University of Tulsa, University of Wyoming, and Virginia Polytechnic and State University) during the competition stage, adding seven more universities in 2017 (Carnegie Mellon University, Louisiana State University, The Ohio State University, University of North Dakota, University of Pittsburgh, University of Utah, and West Virginia University). This Coalition exhibited a wide geographical distribution across the U.S. bringing a wide variety of fossil energy expertise. This national university alliance was a major collaborative effort with NETL to address specific topics of R&D in NETL’s mission area, which involved one or more of NETL’s five core competencies (Geologic and Environmental Systems, Materials Engineering and Manufacturing, Energy Conversion Engineering, Systems Engineering and Analysis, and Computational Science and Engineering). The first five to six months of the project was the definitization stage. During this period, Penn State worked closely with NETL to finalize the Coalition organizational structure and By- Laws, prepare a statement of substantial involvement and a statement of project objectives, and develop operations and membership plans. A major component of this stage included preparing an execution plan to solicit research, evaluate proposals, recommend selected projects to NETL, and award projects. In addition, a plan was prepared to monitor projects, review projects, disseminate knowledge from research projects and develop an online system for Coalition research portfolio management. This included developing a website and several databases. The first of six rounds of solicitations started in mid-2016. Projects from the sixth solicitation started February 1, 2021, and ended January 31, 2023. Projects that were selected represented twelve technology lines. Approximately $16.6 million in funding was available for the six solicitations. Most of the funding was provided by DOE, Office of Fossil Energy (DOEFE) with the DOE Fuel Cells Technologies Office (DOE-FCTO) providing funding for a few projects. Coalition universities submitted 259 proposals in response to the solicitations, requesting approximately $67.0 million in funding, and forty-three projects were selected. However, one project withdrew after the principal investigator left the university. The management of the Coalition projects was a major activity by Penn State. Managing the Coalition projects consisted of monitoring the projects, reviewing the projects through annual technical review meetings, disseminating knowledge from the research projects, and developing an online system for Coalition research portfolio management. Penn State’s OMT monitored projects to ensure that all milestones (technical, schedule, budget) were met, expenditures were allowable, cost share (when applicable) were reported, and all technical reports were submitted. The OMT also posted the technical reports electronically on a secure members-only website for access and review by the Coalition members. Disseminating knowledge from the research projects was done through a website, newsletters, various meetings, conferences, journal articles, publicity/press releases, and project summaries that were prepared after each project was completed. Penn State kept NETL apprised of UCFER progress through quarterly reports (thirtyone were submitted by Penn State), verbal and written communications, yearly updates at the annual technical review meetings, and cost accrual reports. The UCFER project had a significant impacty. The UCFER program established the first national university alliance in fossil energy research with a major collaboration effort with DOE NETL that addressed specific topics in NETL’s research and development mission areas. It generated inter-university collaborations, which was another program interest. Twenty-two out of 259 proposals contained collaborations (≈8.5%) and three of forty-two funded projects involved inter-university collaborations (≈7.0%). The forty-two funded projects provided support at fourteen universities involving 269 personnel. Research was conducted by 106 faculty, 115 graduate and undergraduate students, forty-six research staff and post-doctoral scholars, and two visiting scholars. Students and post-doctoral scholars were also on-site at NETL through CRADAs. In addition, non-Coalition participants included six universities and sixteen companies and national laboratories. The non-Coalition participants were involved as subcontractors, providers of cost share, performed unpaid consultation and sample analysis, served as advisory board members, or were providers of samples and materials for testing. UCFER also produced visibility in that fifty-six refereed journal articles were published, fifty-seven conference papers and twenty-three posters were prepared, 190 presentations were given, eight patent applications were filed, two books/book chapters were written, and ten software codes were developed. Collaboration between NETL and the individual projects was a major requirement for all funded projects. This included NETL staff time to support collaboration, consultation, technical guidance, sample preparation and analysis, internships at NETL, on-site testing and equipment usage by Coalition participants at NETL, co-mentoring students, and coauthoring journal articles and conference papers. Collaboration was impacted by COVID-19 in that not all on-site activities could be performed. NETL personnel were coauthors on eight of the conference papers (fourteen percent of the conference papers that were prepared) and seventeen of the journal articles (thirty percent of the journal articles that were prepared). A website was developed for an online proposal solicitation and review process and to provide exposure to UCFER. A website analysis highlighted the large amount of member and general public interest in UCFER by interpreting access statistics from March 2016 through June 2023. Visitors to the site originated from many different organizations, businesses, and countries. The website provided a means to disseminate information to both the general public and the UCFER members and was successfully used for outreach activities. In addition, NETL required that RFP release, proposal submission, and proposal reviews all be performed online. Penn State successfully developed these capabilities in a secure section of the website, which were used throughout the UCFER project. It is recognized that each project had its technical successes. In addition, highlighted successes were compiled and summarized from the research projects. Information was requested from the PIs of completed projects. In addition, Penn State’s Operations Management Team reviewed subcontract reports to identify project successes. Examples of information requested from PIs included (not all-inclusive): new projects that have been funded as a result of UCFER funding; new commercial products; establishment of a new center; new patent; new software; best paper awards; highly-cited work; and graduate student successes. A total of forty-eight highlighted successes were reported.
There are ten nickel enzymes found across biological systems, each with a distinct active site and reactivity that spans reductive, oxidative, and redox–neutral processes. We focus on the reductive enzymes, which catalyze reactions that are highly germane to the modern-day climate crisis: [NiFe] hydrogenase, carbon monoxide dehydrogenase, acetyl coenzyme A synthase, and methyl coenzyme M reductase. The current mechanistic understanding of each enzyme system is reviewed along with existing knowledge gaps, which are addressed through the development of protein-derived models, as described here. Finally, this opinion is intended to highlight the advantages of using robust protein scaffolds for modeling multiscale contributions to reactivity and inspire the development of novel artificial metalloenzymes for other small molecule transformations.
A new concept of refractory high-entropy metal-ceramic composites (HEMCC) has been proposed that combines the outstanding physical properties of both high-entropy alloy (HEA) and high-entropy ceramic (HEC). As the first HEMCC system, to the best of our knowledge, TiTaNbZr-(TiTaNbZr)C, has been developed by a powder metallurgy process. The HEA and HEC phases exhibit body-centered cubic (BCC) and rock-salt B1 crystal structures, respectively, and both phases have non-equimolar chemical compositions. Further, with the increase of the HEC phase in HEMCC, the hardness is enhanced while the density and fracture toughness are decreased. HEA50C sintered from 50vol% HEA and 50vol% HEC precursor powders shows a favorable combination of flexural strength (541±48MPa) and fracture toughness (6.93±0.27 MPa·m 1/2 ) at room temperature and a high compressive strength at 1300ºC (275MPa). The optimized mechanical performance of HEMCC might be attributed to the combination of the ductile HEA and strong HEC phases, smaller grain size, and crack arrest at HEC/HEA interfaces.
In this work, we report on a windowless, high-density, gas flow target at Jefferson Lab that was used to measure $r_p$, the root-mean-square charge radius of the proton. To our knowledge, this is the first such system used in a fixed-target experiment at a (non-storage ring) electron accelerator. The target achieved its design goal of an areal density of 2$\times$10$^{18}$ atoms/cm$^2$, with the gas uniformly distributed over the 4 cm length of the cell and less than 1% residual gas outside the cell. This design eliminated scattering from the end caps of the target cell, a problem endemic to previous measurements of the proton charge radius in electron scattering experiments, and permitted a precise, model-independent extraction of $r_p$ by reaching unprecedentedly low values of $Q^2$, the square of the electron's transfer of four-momentum to the proton.
Developing artificial intelligence (AI) and machine learning (ML) methods that can accelerate scientific discoveries and advance science has become one of the important research directions for the AI/ML research community. It has been gaining increasing attention from researchers in diverse scientific areas, including biomedical science, materials science, climate science, physics, chemistry, and many others. Data-driven AI/ML innovations to enable reliable predictions and optimal decision making for scientific discoveries face several critical challenges, among which are high system complexity, large search space, incomplete knowledge, and small data, all of which demand novel strategies to effectively address them. Meeting these challenges and thereby accelerating scientific discoveries and industrial innovations, calls for research that can take full advantage of the latest advances in AI/ML to integrate data-driven techniques with scientific knowledge and is able to execute them in modern high-performance computing (HPC) environments at scale. This Patterns special collection "Accelerating scientific discoveries through data-driven innovations" features articles that showcase the promising roles of AI/ML and data-driven modeling in accelerating scientific discoveries and may inspire the next wave of data-driven innovations in various scientific domains.
Hailstones and extreme precipitation generate substantial economic losses across the United States (US) and the globe. Their strong association with short-lived, intense convective storms poses a great challenge in predicting their future changes. Here, we conducted model simulations at 1.2 km grid spacing for severe convective storms with large hail and heavy precipitation that occurred in two typical types of synoptic-scale environments in spring seasons over the central US under both current and future climate conditions. We find that the responses of large hail (diameters >2.5 cm) to anthropogenic climate change (ACC) are markedly different between the hailstorms developed in the two types of synoptic-scale environments, with over 110% increase in large hail occurrences for the frontal systems, whereas less than 30% increase for the Great Plains low-level jet (GPLLJ) systems. This is explained by the larger increase in convective intensity and updraft width and a smaller increase in warm cloud depth in the frontal storms compared with the GPLLJ storms. Interestingly, the occurrences and intensity of heavy precipitation (rain rate >20 mm hr-1) in both types of systems are similarly sensitive to ACC (e.g., 40% and 33% increases in the occurrences for the frontal and GPLLJ systems, respectively). These results advance our knowledge of hail projection and have important implications for managing risks for future hail.
Electron bifurcation (EB) is a mechanism of biological energy transduction in which multiple oxidation–reduction (redox) reactions are thermodynamically coupled within a single enzyme, enabling the enzyme to harness the excess free energy from an exergonic process to drive an endergonic process. Because of this unprecedented chemistry, there is interest to translate EB principles to artificial and bioengineered systems, but a hurdle is that knowledge pertaining to the fundamental design principles of EB enzymes remains scarce. Here, we investigated the fundamental physical and electronic properties of electron transfer sites in a spectroscopically uncharacterized member of the BfuABC family of EB enzymes, the NADH-dependent reduced-ferredoxin:NADP + oxidoreductase from Thermococcus sibiricus (Tsi NfnABC). Cryo-EM structures of Tsi NfnABC previously demonstrated that it contains twelve redox cofactors: two flavins (one FAD and one FMN), eight [4Fe–4S] clusters, and two [2Fe–2S] clusters. The FMN, one [4Fe–4S] cluster, and one [2Fe–2S] cluster comprise the bifurcating active site termed the electron-bifurcating flavobicluster (BF-FBC), which is found in all BfuABC family members. By using electron paramagnetic resonance spectroscopy, we identified spectral signatures originating from interactions between the FMN radical and [4Fe–4S] + cluster in the BF-FBC and observed temperature dependent behavior of the BF-FBC's [2Fe–2S] + cluster indicative of moderately slow spin–lattice relaxation. Additionally, we uncovered numerous spectral features corresponding to half-integer, S > ½ spin states of [4Fe–4S] + clusters, including one attributable to the consequences of lysine-ligation of a [4Fe–4S] cluster unique to NfnABC. We contextualize these findings to electron transfer theory and NfnABC's structure. Our insights further the understanding of how enzymes are designed to exert control over electron transfer to conduct thermodynamically challenging reactions.
Growing materials data and data-driven informatics drastically promote the discovery and design of materials. While there are significant advancements in data-driven models, the quality of data resources is less studied despite its huge impact on model performance. In this work, we focus on data bias arising from uneven coverage of materials families in existing knowledge. Observing different diversities among crystal systems in common materials databases, we propose an information entropy-based metric for measuring this bias. To mitigate the bias, we develop an entropy-targeted active learning (ET-AL) framework, which guides the acquisition of new data to improve the diversity of underrepresented crystal systems. We demonstrate the capability of ET-AL for bias mitigation and the resulting improvement in downstream machine learning models. This approach is broadly applicable to data-driven materials discovery, including autonomous data acquisition and dataset trimming to reduce bias, as well as data-driven informatics in other scientific domains.
Knowledge about nonequilibrium dynamics in spin systems is of great importance to both fundamental science and technological applications. Inelastic neutron scattering (INS) is an indispensable tool to study spin excitations in complex magnetic materials. However, conventional INS spectrometers currently only perform steady-state measurements and probe averaged properties over many collision events between spin excitations in thermodynamic equilibrium, while the exact picture of re-equilibration of these excitations remains unknown. In this paper, we report on the design and implementation of a time-resolved laser–neutron pump–probe capability at hybrid spectrometer (beamline 14-B) at the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory. This capability allows us to excite out-of-equilibrium magnons with a nanosecond pulsed laser source and probe the resulting dynamics using INS. Here, we discussed technical aspects to implement such a capability in a neutron beamline, including choices of suitable neutron instrumentation and material systems, laser excitation scheme, experimental configurations, and relevant firmware and software development to allow for time-synchronized pump–probe measurements. We demonstrated that the laser-induced nonequilibrium structure factor is able to be resolved by INS in a quantum magnet. In conclusion, the method developed in this work will provide SNS with advanced capabilities for performing out-of-equilibrium measurements, opening up an entirely new research direction to study out-of-equilibrium phenomena using neutrons.
We present results from applying the SNAD anomaly detection pipeline to the third public data release of the Zwicky Transient Facility (ZTF DR3). The pipeline is composed of three stages: feature extraction, search of outliers with machine learning algorithms, and anomaly identification with followup by human experts. Our analysis concentrates in three ZTF fields, comprising more than 2.25 million objects. A set of four automatic learning algorithms was used to identify 277 outliers, which were subsequently scrutinized by an expert. From these, 188 (68 per cent) were found to be bogus light curves – including effects from the image subtraction pipeline as well as overlapping between a star and a known asteroid, 66 (24 per cent) were previously reported sources whereas 23 (8 per cent) correspond to non-catalogued objects, with the two latter cases of potential scientific interest (e.g. one spectroscopically confirmed RS Canum Venaticorum star, four supernovae candidates, one red dwarf flare). Moreover, using results from the expert analysis, we were able to identify a simple bi-dimensional relation that can be used to aid filtering potentially bogus light curves in future studies. We provide a complete list of objects with potential scientific application so they can be further scrutinised by the community. These results confirm the importance of combining automatic machine learning algorithms with domain knowledge in the construction of recommendation systems for astronomy. Our code is publicly available.
Geopolymers, as a potentially environmentally friendly alternative to Portland cement, are increasingly attracting attention in the construction industry. Various methods have been applied for customizing the properties of geopolymers and improving their commercial viability. One of the promising methods for refining the properties of geopolymers such as their toughness is the use of short fibers. The effectiveness of a high-strength short fiber in the geopolymer matrix is largely dependent on the interfacial bonding between the fiber and its surrounding matrix. While the importance of this interfacial chemistry is highlighted in the literature, the characteristics of this bonding structure have not been fully understood. In this paper, we aim to investigate the bonding mechanism between the carbon fiber and metakaolin-based geopolymer matrix. For the first time, the existence and nature of the chemical bonding at the interfacial region (interphase) between carbon fiber and geopolymer matrix has been revealed. X-ray pair distribution function computed tomography (PDF-CT), field emission-scanning electron microscopy imaging, and nanoindentation techniques are employed to discern the chemo-mechanical properties of the interphase. PDF-CT results show the emergence of a new atom-atom correlation at the interfacial region (around 1.82 angstrom). This correlation is a characteristic of interfacial bonding between the fiber and its surrounding matrix, where the existence of chemical linkages (potentially Al-V-O-C) between fibers and the matrix contributes to the adhesion between the two constituents making up the composite. Due to such chemical bonding, the nanomechanical properties of the interfacial region fall between that of the carbon fiber and geopolymer. The combination of advanced techniques is proved useful for enhancing our understanding of the interfacial chemistry between fibers and the binding matrix. This level of knowledge facilitates the engineering of composite systems through the manipulation of their nanostructure.
ABSTRACT Context-specific genome-scale model (CS-GSM) reconstruction is becoming an efficient strategy for integrating and cross-comparing experimental multi-scale data to explore the relationship between cellular genotypes, facilitating fundamental or applied research discoveries. However, the application of CS modeling for non-conventional microbes is still challenging. Here, we present a graphical user interface that integrates COBRApy, EscherPy, and RIPTiDe, Python-based tools within the BioUML platform, and streamlines the reconstruction and interrogation of the CS genome-scale metabolic frameworks via Jupyter Notebook. The approach was tested using -omics data collected for Methylotuvimicrobium alcaliphilum 20Z R , a prominent microbial chassis for methane capturing and valorization. We optimized the previously reconstructed whole genome-scale metabolic network by adjusting the flux distribution using gene expression data. The outputs of the automatically reconstructed CS metabolic network were comparable to manually optimized i IA409 models for Ca-growth conditions. However, the CS model questions the reversibility of the phosphoketolase pathway and suggests higher flux via primary oxidation pathways. The model also highlighted unresolved carbon partitioning between assimilatory and catabolic pathways at the formaldehyde-formate node. Only a very few genes and only one enzyme with a predicted function in C1 metabolism, a homolog of the formaldehyde oxidation enzyme ( fae1-2 ), showed a significant change in expression in La-growth conditions. The CS-GSM predictions agreed with the experimental measurements under the assumption that the Fae1-2 is a part of the tetrahydrofolate-linked pathway. The cellular roles of the tungsten (W)-dependent formate dehydrogenase ( fdhAB ) and fae homologs ( fae1-2 and fae3 ) were investigated via mutagenesis. The phenotype of the f dhAB mutant followed the model prediction. Furthermore, a more significant reduction of the biomass yield was observed during growth in La-supplemented media, confirming a higher flux through formate. M. alcaliphilum 20Z R mutants lacking fae1-2 did not display any significant defects in methane or methanol-dependent growth. However, contrary to fae1, the fae1-2 homolog failed to restore the formaldehyde-activating enzyme function in complementation tests. Overall, the presented data suggest that the developed computational workflow supports the reconstruction and validation of CS-GSM networks of non-model microbes. IMPORTANCE The interrogation of various types of data is a routine strategy to explore the relationship between genotype and phenotype. An efficient approach for integrating and cross-comparing experimental multi-scale data in the context of whole-genome-based metabolic network reconstruction becomes a powerful tool that facilitates fundamental and applied research discoveries. The present study describes the reconstruction of a context-specific (CS) model for the methane-utilizing bacterium, Methylotuvimicrobium alcaliphilum 20Z R . M. alcaliphilum 20Z R is becoming an attractive microbial platform for the production of biofuels, chemicals, pharmaceuticals, and bio-sorbents for capturing atmospheric methane. We demonstrate that this pipeline can help reconstruct metabolic models that are similar to manually curated networks. Furthermore, the model is able to highlight previously overlooked pathways, thus advancing fundamental knowledge of non-model microbial systems or promoting their development toward biotechnological or environmental implementations.
We developed intrinsically safe, robust, low-cost, high-energy-density solid-state Li-ion batteries, by integrating high conductivity garnet-type solid Li ion electrolytes and several different cathodes in tailored microstructures, fabricated by low-cost supported thin-film ceramic techniques, to overcome the primary technological barriers to next generation battery technology. In this ARPA-E project we employed Li-garnet solid-state electrolytes pioneered by one of the co-investigators (Thangadurai) which has room temperature (RT) conductivity of ~10 -3 S cm -1 . The highly stable garnet SSE allowed the use of S, NMC, and other cathodes with Li metal anodes without stability or flammability concerns. We studied the garnet’s stability when exposed to CO 2 and humidity (with or without an ALD coating) and showed that the material can be processed in ambient air for most steps prior to battery assembly, significantly reducing production costs. We extensively studied and scaled up the synthesis of the powder and explored external suppliers of garnet to determine the best pathway for commercial success. We used low-cost and scalable tapecasting fabrication methods to create porous-dense-porous “trilayer” solid electrolyte microstructures with a ~20 μm separating dense layer, and >70% porosity porous layers to host the electrodes. These scalable multilayer ceramic fabrication techniques, without need for dry rooms or vacuum equipment, also provide for dramatically reduced manufacturing costs. The tailored microstructured electrode supports (scaffold) increased interfacial area ~50x, overcoming the high impedance typical of planar geometry SSLiBs, resulting in record-setting 10 mA/cm 2 lithium cycling in a symmetric cell. We expanded our tapecasting ability and showed tapes >25m in length by the end of the project. We demonstrated the ability to sinter garnet without the use of a garnet powderbed, which could have been a major obstacle and waste of material at scale. At the same time, we expanded the size of our trilayers from coin cells (0.7 cm 2 ) to the full format design (30 cm 2 ). To develop full cells, we demonstrated 250 Wh/kg at room temperature with commercial NMC electrode films in a bilayer configuration. Similarly, we demonstrated 280 Wh/kg trilayer Li-S cells at room temperature with >90% capacity retention over 300 cycles. At 90°C we demonstrated 350 Wh/kg with a Li-S trilayer. The high thermal stability of the garnet electrolyte also enabled extended cycling (400 cycles) of a Li-TiS 2 cell at 150 °C, showcasing the exceptional safety and nonflammability of the solid-state trilayer batteries. Ion Storage Systems has taken this technology and knowledge to produce trilayer garnet electrolytes and is building out facilities to commercialize high performance solid state batteries. ISS has hired a strong staff, identified and communicated with a large number of potential partners, and successfully secured follow-on funding.
The use of robots is important, to help prevent people from being exposed to hazardous materials within a glovebox. The research proposal for the summer was to make a pick and place demo for a seven degrees-of-freedom Motoman Sia5d robot. The Robot Operating System (ROS) was used and the knowledge of C++ and Linux had to be applied. The robot was used to pick up and sort four objects (cylinder, sphere, rectangular prism, and cone) within a virtual environment.
The DOE EPSCoR Implementation project “Radioactive waste management: Development of multi-scale experimental and modeling capabilities” helped to develop a team of scientists and engineers from Clemson University, South Carolina State University, and the University of South Carolina to address the disposition of nuclear wastes and study the transport of radioisotopes from a waste repository in the near and far field. The project involved 20 faculty from the three institutions as well as 13 postdoctoral fellows, 32 graduate students, and 32 undergraduate students and was active from 2014-2019. Additionally, we forged new collaborations with nine researchers from DOE laboratories SRNL, LLNL, and ANL as well as the University of Manchester and the China Academy of Engineering Physics. The overarching goal of the project was to understand the conditions under which important classes of co-reactants, ranging from counter ions in crystal lattices to dissolved oxygen in pores, control the chemistry and transport characteristics of radionuclides in engineered waste forms and natural soils. Our approach was to characterize the time and length scales over which non-equilibrium states are maintained by rate-limiting, or rate-enhancing, reactions between radionuclides and co-reactants due to interactions between physical mass-transfer processes (i.e., advection, diffusion) and (biogeo) chemical reactions. We have focused our project on three specific classes of reactions relevant to radionuclide transport at DOE legacy sites: ion exchange/substitution, ligand complexation, and redox-mediated reactions. Understanding radionuclide migration requires detailed knowledge of how changes to a system – whether engineered or natural – drive the behavior of co-reactants, which in turn provide the geochemical context controlling radionuclide transport. Student engagement and training were a primary focus of the project in order to create a pipeline of researchers who could work in the area of nuclear waste disposition to support the state and the nation. Over the duration of the project we worked with 32 undergraduate, graduated 18 M.S. students and 14 Ph.D. students, and advised 13 postdoctoral fellows. The Ph.D students and postdocs have primarily taken positions at DOE laboratories, academia, and industry. Through our collaborative team, numerous follow on projects have been started with over $5M in sponsored research. Additionally, thus far the team has published 53 peer reviewed papers and given over 75 technical presentations.
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