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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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At least 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

MATEY: multiscale adaptive transformer models for spatiotemporal physical systems

Accurate representation of the multiscale features in spatiotemporal physical systems using vision transformer architectures requires extremely long, computationally prohibitive token sequences. To address this issue, we propose two novel adaptive tokenization schemes that dynamically adjust patch sizes based on local features: one ensures convergent behavior to uniform patch refinement, while the other offers better computational efficiency. Moreover, we present a set of spatiotemporal attention schemes, where the temporal or axial spatial dimensions are decoupled, to evaluate their baseline computational and data efficiencies and to determine whether adaptive tokenization can improve this performance. We assess the performance of the proposed multiscale adaptive model, MATEY, in a sequence of experiments. Compared to a full spatiotemporal attention scheme or a scheme that decouples only the temporal dimension, we find that fully decoupled axial attention is less efficient and expressive, requiring more training time and model parameters to achieve the same accuracy. The experiments on the adaptive tokenization schemes show that, compared to a uniformly refined model, the proposed schemes achieve comparable or improved accuracy at a much lower cost in the tested two-dimensional settings. While the asymptotic analysis suggests the potential for favorable scaling, empirical validation at substantially longer sequence lengths remains to be performed in future work. Finally, we demonstrate in two fine-tuning tasks featuring different physics that models pretrained on PDEBench data outperform the ones trained from scratch, especially in the low data regime with frozen attention.

adaptive tokenization↗
DOE OSTI2022

Representation Learning via Quantum Neural Tangent Kernels

Variational quantum circuits are used in quantum machine learning and variational quantum simulation tasks. Designing good variational circuits or predicting how well they perform for given learning or optimization tasks is still unclear. Here we discuss these problems, analyzing variational quantum circuits using the theory of neural tangent kernels. We define quantum neural tangent kernels, and derive dynamical equations for their associated loss function in optimization and learning tasks. We analytically solve the dynamics in the frozen limit, or lazy training regime, where variational angles change slowly and a linear perturbation is good enough. We extend the analysis to a dynamical setting, including quadratic corrections in the variational angles. We then consider a hybrid quantum classical architecture and define a large-width limit for hybrid kernels, showing that a hybrid quantum classical neural network can be approximately Gaussian. The results presented here show limits for which analytical understandings of the training dynamics for variational quantum circuits, used for quantum machine learning and optimization problems, are possible. These analytical results are supported by numerical simulations of quantum machine-learning experiments.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Deep Transfer Learning Across Cancer Registries for Information Extraction from Pathology Reports

Automated text information extraction from cancer pathology reports is an active area of research to support national cancer surveillance. A well-known challenge is how to develop information extraction tools with robust performance across cancer registries. In this study we investigated whether transfer learning (TL) with a convolutional neural network (CNN) can facilitate cross-registry knowledge sharing. Specifically, we performed a series of experiments to determine whether a CNN trained with single-registry data is capable of transferring knowledge to another registry or whether developing a cross-registry knowledge database produces a more effective and generalizable model. Using data from two cancer registries and primary tumor site and topography as the information extraction task of interest, our study showed that TL results in 6.90% and 17.22% improvement of classification m acro F -score o ver the baseline single-registry models. Detailed analysis illustrated that the observed improvement is evident in the low prevalence classes.

Alawad, Mohammed↗
DOE OSTI2021

Electrochemical Modeling of Iodide Oxidation in Metal-Halide Molten Salts

The oxidation of iodide in NaI-AlBr 3 , NaI-AlCl 3 , and NaI-GaCl 3 molten salts was analyzed using simulation software to extract relevant kinetic parameters. The experimental oxidation potentials were ordered AlCl 3 < AlBr 3 < GaCl 3 , with higher oxidation potentials correlating with softer Lewis acidity of the metal halide. An iodide oxidation and metal halide speciation model was developed and simulated to fit the electrochemical response, enabling determination of electrochemical charge transfer parameters and chemical equilibrium constants. NaI-AlBr 3 displayed the fastest electron transfer rates yet showed the lowest current densities. All salts revealed smaller than expected current densities, explained by equilibrium between various species, where some are not electrochemically active at the studied potentials. These equilibrium reactions are due to the various metal halide species, controlling the reactant concentration of iodide and the resultant current. We hypothesize the electrochemically active iodide species, present as a metal halide monomer (MX 3 I − ), is decreased dramatically from the expected concentration, sequestered as a more stable metal halide dimer species (M 2 X 6 I − ) with a higher oxidation potential. Traditional Tafel analysis of the experimental data supports the validity of the simulations. These results increase understanding of iodide oxidation in low-temperature Lewis acidic molten salts and inform task-specific molten salt design.

Lee, Rose Y. (ORCID:0000000226587220)↗
DOE OSTI2024

SRF Cavity Instability Detection with Machine Learning at CEBAF

During the operation of the Continuous Electron Beam Accelerator Facility (CEBAF), one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities during these trip events. These challenges make the identification of an unstable cavity out of the hundreds installed at CEBAF a difficult and time-consuming task. To tackle these issues, a fast data acquisition system (DAQ) for the legacy SRF cavities has been developed, which records the sample at 5 kHz. A Principal Component Analysis (PCA) approach is being developed to identify anomalous SRF cavity behavior. We will discuss the present status of the DAQ system and PCA model, along with initial performance metrics. Overall, our method offers a practical solution for identifying unstable SRF cavities, contributing to increased beam availability and machine reliability.

Carpenter, A.↗
DOE OSTI2013

Rainfall-driven Flooding Capability Development Report

Globally, floods are among the most frequent and costly natural disasters, both socially and economically. Many of these floods result from excess rainfall collecting in streams and rivers, then overtopping banks and flowing overland into urban environments, damaging critical infrastructure. In Fiscal Year 2012, the National Infrastructure Simulation and Analysis Center’s (NISAC) goal was to enhance its existing flood modeling and simulation capability to be able to estimate flooding from rainfall-runoff events. To accomplish this task, NISAC modified the existing flood modeling code-base using commodity high-performance computing hardware, making it more suitable for regional rainfall events. NISAC also implemented new modules within the existing flood code to account for hydrologic processes, such as spatially variable rainfall and infiltration. Analysts subjected the new hydrologic model to a verification and validation process, wherein analysts evaluated the model using a bench-scale experiment and watershed scale rainfall-runoff events. The bench-scale experiment indicated that the new hydrologic model is mass and momentum conserving and can adequately simulate runoff for depths of inches of water. The watershed scale simulations indicated that the hydrologic model is a good predictor of the discharge hydrograph when compared to observations if the input parameters, such as roughness and hydraulic conductivity, are adequately calibrated. The intended use of the hydrologic model is to estimate flood potential during extreme rainfall events. The accuracy of the predictions is largely dependent on the availability of accurate spatial data for soil and roughness parameters. In addition, each simulation should be accompanied with a calibration exercise, which requires historical rainfall and river discharge observations.

13 HYDRO ENERGY↗
DOE OSTI2019

Modeling Mesoscale Processes of Scalable Synthesis (Final Report)

The main objective of the center is to develop tools that can help for the understanding of equilibrium and non-equilibrium properties of nanoparticles in aqueous electrolytes. To achieve this goal, we need to develop new computational methodologies that can integrate molecular level description such as the electronic structure analysis to mesoscale description such as generalized hydrodynamics. The challenge is to develop systematic coarse graining techniques that can adaptively choose the level of description needed for the particular task. For example, if bonding breaking is involved, then one should use appropriate models from quantum mechanics to accurately model the time scale and molecular mechanism for the breaking process. However, to model transport process or the process of self-assembly, much less detailed models can be used. There are two main components to this task. One is to develop accurate models that bridge different levels of description. The classical example is the QM/MM model that bridges quantum mechanics and molecular mechanics for studying chemical reactions of large molecules. This work was awarded the Nobel prize this year. For our purpose, we also need to develop methods that bridge molecular and continuum levels of description. Another main task is to develop systematic coarse-graining methods that can give rise to accurate mesoscale and macroscale models, starting from more detailed models at the atomic or molecular scale. None of these ideas are new. By now they have been fashionable topics for quite some time. But neither problem is satisfactorily resolved. The main problem is that uncontrolled approximations are used. As a result, we have very little idea about the validity and accuracy of these models. The main purpose of the center is to exploit the Mori-Zwanzig formalism to achieve the goals stated above. The Mori-Zwanzig formalism is a general tool for eliminating degrees of freedom from existing models. What is attractive about this formalism is its generality. The flip side is that it is also somewhat of a tautology. In order to make it a practical tool, one has to make approximations and this is where the trouble comes. Instead of focusing on Mori-Zwanzig formalism, the PI proposes to study the closely related renormalization group formalism (RG). Like Mori-Zwanzig, RG is a general strategy for eliminating small scale degrees of freedom in a model. Unlike Mori-Zwanzig, RG has the advantage that at least for some problems, one can write a PDE (partial differential equation) type of model for the exact RG flow, without any approximation. This gives us a solid starting point for discussing and analyzing approximations. The PI studied approximate RG flows for turbulent diffusion and related problems. However, extending it to more practical problems proved to be hard. The second line that the PI has pursued is to develop special numerical algorithms for studying particularly important events for dynamics processes in meso-scale systems. The PI succeeded in doing so for the problem of understanding phase transition in metallic systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Requirements Description of the REBUS Software for the Versatile Test Reactor

This report presents the modeling and simulation capabilities of Argonne National Laboratory’s fuel cycle analysis code, the REactor BUrnup System (REBUS), that will be relied on for the VTR project. These capabilities will then be used to establish the set of REBUS verification tasks necessary to verify REBUS for usage on the VTR project. A similar path was followed for the DIF3D software where its’ requirements and verification tasks were established. REBUS has been maintained by Argonne since the early 1960s to support its reactor design mission. That software transitioned from the original REBUS to REBUS-2 in the mid 1970s and to REBUS-3 in the mid 1980s. Since then REBUS has gone through many revisions to the current REBUS-11. Note that this version numbering is consistent with the progression of DIF3D, the base flux solver that REBUS is built upon. The name REBUS refers to a pictorial based puzzle as the original developers were inspired by having to track thousands of unique fuel assemblies as they are inserted into the reactor, depleted, shuffled, discharged, and reprocessed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

SRF Cavity Instability Detection with Machine Learning at CEBAF

During the operation of the Continuous Electron Beam Accelerator Facility (CEBAF), one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities during these trip events. These challenges make the identification of an unstable cavity out of the hundreds installed at CEBAF a difficult and time-consuming task. To tackle these issues, a fast data acquisition system (DAQ) for the legacy SRF cavities has been developed, which records the sample at 5 kHz. A Principal Component Analysis (PCA) approach is being developed to identify anomalous SRF cavity behavior. We will discuss the present status of the DAQ system and PCA model, along with initial performance metrics. Overall, our method offers a practical solution for identifying unstable SRF cavities, contributing to increased beam availability and machine reliability.

Carpenter, A.↗
DOE OSTI2024

Results of Long-Term Analyses of the Salt (Macro) Batch 13 Tank 42H Qualification Sample

Savannah River National Laboratory (SRNL) analyzed samples from Tank 42H in support of qualification of Salt Waste Processing Facility (SWPF) Salt Batch 13. This document reports the long-term results of the analyses of the sample of Tank 42H. Analysis of this sample indicates that the material does not display any unusual characteristics or observations, such as visually apparent solids. This report satisfies Deliverable 3 of the Technical Task Request (TTR).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Results of Long-Term Analyses of the Salt (Macro) Batch 14 Tank 21H Qualification Sample

Savannah River National Laboratory (SRNL) analyzed samples from Tank 21H in support of qualification of Salt Waste Processing Facility (SWPF) Salt Batch 14. This document reports the long-term results of the analyses of the sample of Tank 21H. Analysis of this sample indicates that the material does not display any unusual characteristics or observations, such as visually apparent solids. This report satisfies Deliverable 3 of the Technical Task Request (TTR).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Updates and Validation for the n+ 63,65 Cu Cross Sections [Abstract]

The neutron induced total, elastic, and capture cross sections of 63,65 Cu isotopes were selected for evaluation in the resolved and unresolved resonance energy ranges by the National Criticality Safety Program to resolve discrepancies related to benchmark performance. This is especially evident for the series of ZEUS benchmarks in which copper is used as a reflector. Because copper is also used as structural material in both fission and fusion reactors, the need to address benchmark discrepancies linked to nuclear data deficiencies is a task of primary importance. The aim of this work is to describe the steps of evaluation work towards a consistent improvement of the benchmark performance. The R-matrix analysis with the SAMMY code focused on the 63 Cu(n,γ) reaction channel between 100-300 keV coupled to unresolved resonance region parameters up to 650 keV to fit average cross section data from a recent experiment. Due to the high sensitivity of many benchmarks to elastic scattering angular distribution data, especially for the 65 Cu isotope, the impact of these data was tested by generating Legendre coefficients from both resonance parameters and the Hauser-Feshbach model. Guided by the findings of Shaw et al., the performance of the current evaluation for 65 Cu was compared to that of ENDF/B-VII.1 and ENDF/B-VIII.0 by testing the reactivity coefficients corresponding to the validation suite of experimental criticality benchmarks for thermal, intermediate, and fast systems taken from the International Criticality Safety Benchmark Experiments Project Handbook. The benchmark performance is especially sensitive to 63 Cu(n,γ) and 65 Cu elastic scattering for neutron energies in the 100–500 keV region, whereas 100 keV is the upper limit of the resolved resonance region in the ENDF/B-VIII.0 evaluations for 63,65 Cu. The results highlight the need to handle the transition from the resolved resonance region to the high energy region carefully.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2012

Quantitative Phenomena Identification and Ranking Table (QPIRT) for Bayesian uncertainty quantification

Propagating parameter uncertainty for a nuclear reactor system code is a challenging problem due to often non-linear system response to the numerous parameters involved and lengthy computational times; issues that compound when a statistical sampling procedure is adopted, since the code must be run many times. The number of parameters sampled must therefore be limited to as few as possible that still accurately characterize the uncertainty in the system response. A Quantitative Phenomena Identification and Ranking Table (QPIRT) was developed to accomplish this goal. The QPIRT consists of two steps: a 'Top-Down' step focusing on identifying the dominant physical phenomena controlling the system response, and a 'Bottom-Up' step which focuses on determining the correlations from those key physical phenomena that significantly contribute to the response uncertainty. The Top-Down step evaluates phenomena using the governing equations of the system code at nominal parameter values, providing a 'fast' screening step. The Bottom-Up step then analyzes the correlations and models for the phenomena identified from the Top-Down step to find which parameters to sample. The QPIRT, through the Top-Down and Bottom-Up steps thus provides a systematic approach to determining the limited set of physically relevant parameters that influence the uncertainty of the system response. This strategy was demonstrated through an application to the RELAP5-based analysis of a PWR Total Loss of main Feedwater Flow (TLOFW) accident, also known as feed and bleed' scenario, . Ultimately, this work is the first component in a larger task of building a calibrated uncertainty propagation framework. The QPIRT is an essential piece because the uncertainty of those selected parameters will be calibrated to data from both Separate and Integral Effect Tests (SETs and IETs). Therefore the system response uncertainty will incorporate the knowledge gained from the database of past large IETs. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Sampling and Analysis Plan for Verification Sampling of LANL-Derived Residual Radionuclides in Soils within Tract A-18-2 for Land Conveyance

Public Law 105-119 directs the U.S. Department of Energy (DOE) to convey or transfer parcels of land to the Incorporated County of Los Alamos or their designees and to the Department of Interior, Bureau of Indian Affairs, in trust for the Pueblo de San Ildefonso. Los Alamos National Security is tasked to support DOE in conveyance and/or transfer of identified land parcels no later than September 2022. Under DOE Order 458.1, Radiation Protection of the Public and the Environment (O458.1, 2013) and Los Alamos National Laboratory (LANL or the Laboratory) implementing Policy 412 (P412, 2014), real property with the potential to contain residual radioactive material must meet the criteria for clearance and release to the public. This Sampling and Analysis Plan (SAP) is a second investigation of Tract A-18-2 for the purpose of verifying the previous sampling results (LANL 2017). This sample plan requires 18 projectspecific soil samples for use in radiological clearance decisions consistent with LANL Procedure ENV-ES-TP-238 (2015a) and guidance in the Multi-Agency Radiation Survey and Site Investigation Manual (MARSSIM, 2000). The sampling work will be conducted by LANL, and samples will be evaluated by a LANL-contracted independent lab. However, there will be federal review (verification) of all steps of the sampling process.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Immunophenotype Discovery, Hierarchical Organization, and Template-Based Classification of Flow Cytometry Samples

We describe algorithms for discovering immunophenotypes from large collections of flow cytometry samples and using them to organize the samples into a hierarchy based on phenotypic similarity. The hierarchical organization is helpful for effective and robust cytometry data mining, including the creation of collections of cell populations’ characteristic of different classes of samples, robust classification, and anomaly detection. We summarize a set of samples belonging to a biological class or category with a statistically derived template for the class. Whereas individual samples are represented in terms of their cell populations (clusters), a template consists of generic meta-populations (a group of homogeneous cell populations obtained from the samples in a class) that describe key phenotypes shared among all those samples. We organize an FC data collection in a hierarchical data structure that supports the identification of immunophenotypes relevant to clinical diagnosis. A robust template-based classification scheme is also developed, but our primary focus is in the discovery of phenotypic signatures and inter-sample relationships in an FC data collection. This collective analysis approach is more efficient and robust since templates describe phenotypic signatures common to cell populations in several samples while ignoring noise and small sample-specific variations. We have applied the template-based scheme to analyze several datasets, including one representing a healthy immune system and one of acute myeloid leukemia (AML) samples. The last task is challenging due to the phenotypic heterogeneity of the several subtypes of AML. However, we identified thirteen immunophenotypes corresponding to subtypes of AML and were able to distinguish acute promyelocytic leukemia (APL) samples with the markers provided. Clinically, this is helpful since APL has a different treatment regimen from other subtypes of AML. Core algorithms used in our data analysis are available in the flowMatch package at www.bioconductor.org. It has been downloaded nearly 6,000 times since 2014.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Machine Learning in the Context of Laser-Induced Breakdown Spectroscopy

The integration of machine learning (ML) with Laser-Induced Breakdown Spectroscopy (LIBS) has revolutionized the analytical capabilities of LIBS. The combi-nation of both methods enables more accurate and efficient data analysis. While LIBS itself is a powerful technique for elemental analysis, the vast amount of spectral data it generates can be hard to interpret. Machine learning addresses these challenges by leveraging algorithms that can learn from data, identify patterns, and make predictions without explicit programming for the interpretation of each specific task. In LIBS application, ML techniques are used to enhance various analytical processes. For example, ML algorithms can classify materials based on their spectral fingerprints, predict the concentration of elements in a sample, and identify underlying patterns within complex datasets. Here, this application improves the precision of LIBS analyses while significantly reducing the time required for data processing and interpretation. In this chapter, the fundamental concepts of ML will be discussed first. Following this, the process of data splitting and the importance of feature selection will be examined. Several machine learning methods will then be closely examined, exploring how each can benefit LIBS analysis and highlighting their respective advantages and shortcomings. This structured approach will provide a comprehensive understanding of the integration of ML in the context of LIBS analysis.

47 OTHER INSTRUMENTATION↗
DOE OSTI2026

Database-Agnostic Log Analysis and Monitoring Framework

Prior to my internship, I was informed that a previous intern had built a tool to analyse MongoDB logs and look for invalid access attempts, which served as a great reference point for my project. I was initially tasked with expanding on her prototype and filling in the gaps such as integrating it with the main monitoring tool the lab uses. Eventually, the scope grew, expanding to support other databases and a growing collection of tools. I organized the framework around an observer pattern, meaning one point in the program sending updates to the rest of the framework. Every time a log was read and parsed, it was sent to be processed by the tools, using the type of event as a means to determine which tools should get a chance to act on the log. This decouples the tools from the log reader, making future updates and additions much easier. The framework processes MongoDB logs at ~135,000 entries per second and PostgreSQL logs at ~170,500 entries per second, accurately detecting anomalies such as slow queries and connections from unknown addresses. This framework serves to fill gaps in database monitoring tools currently implemented at the lab, such as tracking failed authentication for PostgreSQL and MongoDB which had very minimal or none before this framework. National labs such as Fermilab hold sensitive data and valuable computing resources, making them attractive targets. Monitoring intrusion attempts on databases is made much easier by this comprehensive monitoring suite.

Clark, Dylan [Unlisted, US, IL; Fermilab]↗
DOE OSTI2019

Biomass Electrochemical Reactor for Upgrading Biorefinery Waste to Industrial Chemicals and Hydrogen

The goal of this project was to develop an electrochemical process for depolymerization of biorefinery lignin to industrial chemicals, most notably as replacements in resins. The project team consisted of Ohio University (lead organization), Hexion, Inc. and Lakehead University. There were six distinct project tasks, consisting of Initial Validation, Development of TiO 2 Nanowire-supported Ni-Co Electrocatalysts, Electrochemical Characterization of TiO 2 Nanowire-supported Ni-Co Electrocatalysts, Development of Electrochemical Flow Reactor for Production of Industrial Chemicals and Hydrogen, Product Formulations and Integrated Biorefinery Analysis. The project was divided into two budget periods. After intermediate validation near the end of Budget Period 1, the project was discontinued. This final report is intended to highlight major project objectives and results. Technical metrics were developed between the project team and the DOE validation team during the initial validation period. Critical technical metrics included conversion of lignin to products and product selectivity. Desired products were low molecular weight aromatic compounds in the 150-300 MW range. We targeted this class of compounds because of their potential to replace petroleum derivatives in resin formulations. Electrochemical oxidation of biorefinery lignin can lead to significant lignin depolymerization and conversion, with >40% of the lignin reacted. However, product stream analysis is extremely difficult. We were not able to adequately quantify the concentration of the primary oxidation products, or to determine with any degree of certainty what the product selectivity was. The project team initially proposed analytical techniques such as GC-MS to identify product distributions. However, due to 1) difficulty in extracting products into an organic phase suitable for GC analysis and 2) the wide range of product compounds with similar structures, GC-MS analysis was difficult to perform. The project team did have some success with NMR spectroscopic analysis, but not enough so to adequately identify product selectivity. By UV-vis spectroscopy and statistical analysis, we were able to confirm achieving the technical milestone for lignin conversion, and achieved at least 40% conversion by the electrochemical oxidation technique. However, we were not able to confirm product selectivity using GC-MS analysis, as originally postulated. Despite these difficulties, we were able to incorporate the product stream into a resin, and observed better resin synthesis behavior using the oxidized lignin versus the unoxidized lignin. However, no resin synthesized with oxidized biorefinery lignin met the commercial standards for resin quality. One potential problem with electrochemical oxidation of biomass is the competing oxygen evolution reaction (OER) at the reactor anode. We observed that, especially at higher anode potentials, the OER can consume up to 50% of the energy input to the system, meaning that much of the energy goes toward generation of an essentially worthless product (O 2 ). However, we also observed that the OER can be avoided almost entirely at lower potentials. These uncertainties made assigning a value to the electrochemical reactor product stream difficult. Any product generated electrochemically would have to be valuable enough to offset the additional cost of electricity incurred, including electricity to run the electrochemical reactor and the additional electricity that must be purchased because some of the lignin (which is currently burned in biorefineries to recover energy) is diverted to the electrochemical reactor. It is not clear at this time whether the product stream has sufficient economic value to offset these additional electricity costs. The two most promising areas for this project in terms of demonstrating economic feasibility are product stream analysis and resin synthesis. In particular, product stream analysis has proven to be difficult. With additional time, we would refine analytical techniques to better understand key product stream characteristics, such as aromatic compound content. Evaluating the product streams in resin synthesis would likely provide more information on the economic value of the product stream. If we could determine that value, then we would be able to better evaluate whether the electrochemical conversion approach is feasible. The details of this work are presented in the following final report. The report is organized into individual tasks for clarity, although all tasks were integrated and many occurred simultaneously.

09 BIOMASS FUELS↗