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DOE OSTI2023

Targeting tissues via dynamic human systems modeling in generative design

Drug discovery is a complex, costly process with high failure rates. A successful drug should bind to a target, be deliverable to an intended site of activity, and promote a desired pharmacological effect without causing toxicity. Typically, these factors are evaluated in series over the course of a pipeline where the number of candidates is sequentially whittled down from a very large initial pool. One promise of AI-driven discovery is the opportunity to evaluate multiple facets of drug performance in parallel. However, despite ML-driven advancements, current models for pharmacological property prediction are exclusively trained to predict molecular properties, ignoring important, dynamic biodistribution and bioactivity effects. Here, we present our progress towards incorporating quantitative systems physiology models into an AI-enabled drug design and molecular generation pipeline. Within a genetic algorithm, we include human-relevant physiologically based pharmacokinetic (PBPK) models. These PBPK models leverage properties that are predicted by a fine-tuned molecular language model. Together, these models will aid in capturing the mapping between molecules and therapeutic outcomes that is necessary to accelerate the drug discovery process.

Fox, Zach↗

DOE OSTI2020

Assessment of Methane Generation from the Thermolysis of Organics in Caustic Waste

Methane generation has been detected during thermolytic Hydrogen Generation Rate (HGR) testing of SRS tank waste and in simulant samples. Simulant testing has shown methane generation in the presence of trimethylsilanol (TMS) and radioactive sample testing has shown methane generation in presence of methylmercury, both of which are found within SRS waste tanks. The 2019 report Evaluation of the Current State of Knowledge for Thermolysis of Organics within SRS Waste Forming Volatile Organic Compounds (VOCs) reviewed historical data and assessed literature pertaining to thermolytic methane generation. That review recommended undertaking thermolysis studies on simulated waste to detect and quantify VOCs. The purpose of this assessment is to review chemistry and current knowledge of organics in SRS waste to recommend which organics in SRS waste tanks, if any, should be tested for thermolytic methane generation. An evaluation of observed methane generation rates has been performed to assess the implication for current CSTF controls. Based on the inputs used in this report, it was concluded that no methane generation data observed to date has challenged the existing 5% organic contribution to the Composite Lower Flammability Limit (CLFL) assumption in the CSTF tank farm at temperatures below 100 °C. Evidence shows that thermolytic pathways to methane generation become significant at temperatures >100 °C in caustic waste. Currently, the influences of thermolytic methane production on the flammability of vessels at >100 °C (e.g., evaporators) is unclear. It is recommended that testing should be undertaken using the HGR experimental approach for methane generation. The initial simulant testing should obtain quantitative methane generation rates at elevated temperatures for methylated siloxanes/silanes and methylmercury with the goal of determining the current mechanism for thermolytic methane generation seen in SRS tank waste. The results of this testing should then be compiled and analyzed to ascertain the risk of challenging the current projected organic contribution of 5% to the CLFL at elevated temperatures. Following this testing, an evaluation of possible flammable gas generation in the 2H and 3H Evaporators should be performed to assess the risk of methane generation at the high temperatures employed by the evaporators.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2020

Quantitative encapsulation and retention of 227 Th and decay daughters in core–shell lanthanum phosphate nanoparticles

Targeted alpha therapy (TAT) offers great promise for treating recalcitrant tumors and micrometastatic cancers. One drawback of TAT is the potential damage to normal tissues and organs due to the relocation of decay daughters from the treatment site. As such, the present study evaluates La( 227 Th)PO 4 core (C) and core +2 shells (C2S) nanoparticles (NPs) as a delivery platform of 227 Th to minimize systemic distribution of decay daughters, 223 Ra and 211 Pb. In vitro retention of decay daughters within La( 227 Th)PO 4 C NPs was influenced by the concentration of reagents used during synthesis, in which the leakage of 223 Ra was between 0.4 ± 0.2% and 20.3 ± 1.1% in deionized water. Deposition of two nonradioactive LaPO 4 shells onto La( 227 Th)PO 4 C NPs increased the retention of decay daughters to >99.75%. The toxicity of the nonradioactive LaPO 4 C and C2S NP delivery platforms was examined in a mammalian breast cancer cell line, BT-474. No significant decrease in cell viability was observed for a monolayer of BT-474 cells for NP concentrations below 233.9 μg mL –1 , however cell viability decreased below 60% when BT-474 spheroids were incubated with either LaPO 4 C or C2S NPs at concentrations exceeding 29.2 μg mL –1 . La( 227 Th)PO 4 C2S NPs exhibit a high encapsulation and in vitro retention of radionuclides with limited contribution to cellular cytotoxicity for TAT applications.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2022

Quantitative interpretation of time-lapse seismic data at Farnsworth field unit: Rock physics modeling, and calibration of simulated time-lapse velocity responses

Here, this study investigates the contribution of fluid saturation variation to the time-lapse velocity response by performing fluid substitution modeling. The methodology is exemplified by the time-lapse seismic monitoring of carbon dioxide at Farnsworth field unit (FWU). In order to evaluate the fluid distribution in a matured oil reservoir, the Southwest Regional Partnership (SWP) acquired multiple vertical seismic profile (VSP) surveys at different times during the CO 2 –water alternatinggas (WAG) injection period. In this work, we present a thorough methodology for computing the elastic response of the saturated rock for different fluid saturations using a site-specific petro-elastic model (PEM). The output from the PEM was combined with results from a fluid compositional model to compute the seismic velocities at times corresponding to each VSP survey. To produce a calibrated simulated response, the measured time-lapse seismic velocities were integrated into the numerical simulation model. The mismatches between the predicted and measured time-lapse velocities were minimized through an iterative calibration process using a trained artificial neural network proxy (ANN) coupled with a particle swarm optimizer (PSO). Our study indicates that the hybrid optimization workflow can effectively perform the history matching. With an accurate prediction of the hydrodynamic properties, the migration of CO 2 within the subsurface was modeled by predicting the spatial velocity distribution for a radius of 305 m around the injection well. The technology demonstrated and the expertise gained from this study can guide similar CO 2 -WAG projects.

58 GEOSCIENCES↗

DOE OSTI2025

BCARS Simulated Phantom Dataset for Evaluation of Processing Pipelines

Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy is a powerful label-free biological imaging technique, but the raw signal requires careful processing. The vibrationally resonant (Raman) fingerprint signal is usually small compared with instrumental noise sources and the nonresonant background (NRB) inherent in the BCARS signal. Fortunately, the NRB exhibits a systematic phase relationship with the coherent Raman response, acting as a heterodyne amplifier for the weak fingerprint signal. Due to this heterodyne effect, the Raman response can be recovered quantitatively and invariantly across different instruments, provided the NRB shape is known. Even with heterodyne amplification, the amplitudes of fingerprint signal components are often comparable to system noise. Singular value decomposition (SVD), which utilizes spatial information, is often employed for additional noise filtering. Consequently, finding optimal processing parameters to properly distinguish the NRB and Raman responses and suppress noise in the complex BCARS signal requires a reference system that realistically represents the spectral and spatial properties of BCARS signals obtained from biological samples. We present a digital tissue phantom that meets these criteria as a tool for testing candidate signal processing pipelines. The digital phantom is generated with simulated hyperspectral Raman images having system-specific noise and background characteristics. Here, we analyze phantom datasets with differing background and signal-to-noise conditions to evaluate their impact on the performance of multiple signal processing pipelines. Specifically, we investigate the application of a Butterworth filter-based routine to directly estimate the NRB from the BCARS signal. Additionally, we evaluate a Lorentzian wavelet transform as an alternative to the Hilbert transform for extracting the Raman spectrum from the BCARS signal. While we demonstrate this phantom for BCARS, it can be used for any spectroscopic Raman imaging approach.

Dixon, Jessica Z. [Georgia Institute of Technology↗

DOE OSTI2026

Optical diagnostic design for measuring the radiation front with a mid-leg pumped divertor on DIII-D

The design of an optical diagnostic system to localize the radiation front in a mid-leg pumped divertor configuration on DIII-D is presented. Divertor detachment is a key mechanism for handling power exhaust in tokamaks, and mid-leg pumping offers a promising approach to radiatively dissipate power while maintaining acceptable core performance. To predict the location of the radiation front and design a spectroscopic diagnostic to measure its position, a database of SOLPS-ITER simulations across a range of input powers and gas puffing rates representative of DIII-D operation was employed. These simulations provide self-consistent plasma backgrounds for a Cherab-Raysect synthetic diagnostic framework, which incorporates detailed tokamak geometry and physically accurate ray-tracing. Synthetic simulations of optical sightlines and viewing cone geometry were used to calculate line-integrated emission from the plasma, particularly of the C III 465 nm line, to serve as a proxy for the T e ≈ 7 − 10 eV temperature region associated with the onset and evolution of divertor detachment. Neutral deuterium emission is also evaluated for comparison. The synthetic diagnostic is used to assess the performance of existing DIII-D optical systems, including filterscopes and the Multichord Divertor Spectrometer, and to optimize line-of-sight placement within mechanical and installation constraints. The results provide quantitative guidance for diagnostic implementation on DIII-D and demonstrate the advantage of integrated synthetic diagnostics for divertor design studies and future advanced divertor concepts.

Cherab↗

DOE OSTI2020

Replicative Assessment of Spectroscopic Equipment

The Replicative Assessment of Spectrometric Equipment (RASE) is a software for evaluating the performance of radiation detectors and isotope identification algorithms. It uses a semi-empirical approach to rapidly generate synthetic spectra and inject into detector's software to obtain nuclide identification response. RASE facilitates studies of spectroscopic device performance and a quantitative assessment of its capabilities to correctly distinguish and identify isotopes of interest in realistic scenarios.

Chavez, JosephR.↗

DOE OSTI2020

Replicative Assessment of Spectroscopic Equipment v3.0

Sangiorgio, Samuele↗

DOE OSTI2025

Replicative Assessment of Spectroscopic Equipment v3.1

Sangiorgio, Samuele [Lawrence Livermore National L↗

DOE OSTI2023

Improvements of Nuclear Data Evaluations for Lead Isotopes in Support of Next Generation Lead-Cooled Fast Systems

The neutron evaluation of the isotopes that comprise natural lead were undertaken as a part of DOE-NEUP Project #19-16739. The goal of the project was to update the neutron cross sections to account for new differential measurements and incorporate the most up-to-date physics. Shortcomings in the lead cross sections was made known by several independent reports. The work performed here repeated the simulation of all the “benchmark” validation systems and concluded that the major issue is the scattering cross sections in the major lead isotopes above 100 keV. Re-evaluation of 206,207,208 Pb included both the resolved resonance region and fast region evaluations of the cross sections. Combined these regions cover energies from thermal to 20 MeV. The most drastic improvement is the resolved resonance region evaluation of 208 Pb which is now extended from 1.0 to 1.5 MeV. Parameterization of these resonances in the R-matrix provides a superior reconstruction of not only the experimental cross section but also the scattering distributions via the Blatt-Biedenharn formalism. Fast region evaluations of the three major isotopes were done to include new inelastic experimental data from the neutron Time-of-Flight facility at CERN. The culmination of all the changes to the cross section is a drastic improvement in the scattering kernel as shown in Rensselaer Polytechnic Institute (RPI) Quasi-Differential scattering measurements and improved prediction of keff for fast integral experiments. Alongside the new cross sections, new nuclear data covariance (uncertainties) have been computed and are included in the evaluation. Little is changed in the magnitude of the uncertainties but the correlations within the covariance display non-trivial changes. The new evaluations of 206,207,208 Pb have been submitted to the National Nuclear Data Center to be included in the ENDF/B-VIII.1 library. While the cross sections have been updated extensively, knowledge and modeling of the cross sections between 1.0 and 3.0 MeV for the isotopes remain a challenge. Most notably is the double differential elastic cross section for 208 Pb and 206 Pb. New quasi-differential measurements for pure samples of these two nuclei would go a long way in reducing compensating errors in the evaluations. Qualitatively, the project has produced a prosperous collaboration between the nuclear data group at RPI with staff scientists at Brookhaven National Laboratory, Naval Nuclear Laboratory, Oak Ridge National Laboratory, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Sandia National Laboratories. Quantitatively this is reflected in seven conference presentations, at least one journal submission, and one doctoral thesis.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Evaluation of Potential DAS Array Geometries for the Source Physics Experiment, Phase III

Distributed Acoustic Sensing (DAS) is a rapidly developing technology that can record acoustic wavefields at high sampling rates and with dense spatial spacing difficult to achieve with seismometers. However, the geophysical community has not fully explored DAS survey parameters to characterize the ideal array design. A better quantitative understanding of DAS array behavior prior to SPE Phase III acquisition can help improve the quality of the data recorded by guiding the DAS array design. Here we use array response functions as well as beamforming and backprojection results from forward modelling calculations to assess the performance of varying DAS array geometries to record regional and local sources. A seven-sided polygon DAS array demonstrated improved capabilities for recording regional sources over segmented linear arrays, with potential improvements in recording and locating local sources. These results help reveal DAS array performance as a function of geometry.

58 GEOSCIENCES↗

DOE OSTI2020

GeoRePORT Case Study Examples: Reporting Using the Geothermal Resource Portfolio Optimization and Reporting Technique (GeoRePORT)

The Geothermal Research Portfolio Optimization & Reporting Technique (GeoRePORT) was developed with funding from the United States Department of Energy's Geothermal Technologies Office (GTO) to assist in identifying and pursuing long-term investment strategies through the development of a resource reporting protocol. GeoRePORT provides scientists and non-scientists a comprehensive and quantitative means of reporting: (1) features intrinsic to geothermal sites (project grade) and, (2) maturity of the development (project readiness). Because geothermal feasibility is not determined by any single factor (e.g. temperature, permeability, permitting), a site's project grade and readiness are evaluated on twelve independent attributes pertaining to geological, technical, or socio-economic feasibility. In this paper, we present case studies illustrating how GeoRePORT can be used to compare geological, technical, and socio-economic attributes between geothermal systems. The consistent and objective assessment protocols used in GeoRePORT allow for comparison of project attributes across unique locations and geological settings. GeoRePORT case studies outline the geological, socio-economic and technical features of four individual geothermal sites: Coso, Chena, Dixie Valley, and White Sands Missile Range. The case studies presented herein illustrate the usefulness of GeoRePORT in evaluating project risk/return, identifying gaps in reported data, evaluating R&D impact, and gathering insights on successes/failures as applicable to future projects.

40 EE - Geothermal Technologies Office (EE-4G)↗

DOE OSTI2024

Synthetic β-sheets mimicking fibrillar and oligomeric structures for evaluation of spectral X-ray scattering technique for biomarker quantification

Archetypical cross-β spines sharpen the boundary between functional and pathological proteins including β-amyloid, tau, α-synuclein and transthyretin are linked to many debilitating human neurodegenerative and non-neurodegenerative amyloidoses. An increased focus on development of pathogenic β-sheet specific fluid and imaging structural biomarkers and conformation-specific monoclonal antibodies in targeted therapies has been recently observed. Identification and quantification of pathogenic oligomers remain challenging for existing neuroimaging modalities. We propose two artificial β-sheets which can mimic the nanoscopic structural characteristics of pathogenic oligomers and fibrils for evaluating the performance of a label free, X-ray based biomarker detection and quantification technique. Highly similar structure with elliptical cross-section and parallel cross-β motif is observed among recombinant α-synuclein fibril, Aβ-42 fibril and artificial β-sheet fibrils. We then use these β-sheet models to assess the performance of spectral small angle X-ray scattering (sSAXS) technique for detecting β-sheet structures. sSAXS showed quantitatively accurate detection of antiparallel, cross-β artificial oligomers from a tissue mimicking environment and significant distinction between different oligomer packing densities such as diffuse and dense packings. The proposed synthetic β-sheet models mimicked the nanoscopic structural characteristics of β-sheets of fibrillar and oligomeric states of Aβ and α-synuclein based on the ATR-FTIR and SAXS data. The tunability of β-sheet proportions and shapes of structural motifs, and the low-cost of these β-sheet models can become useful test materials for evaluating β-sheet or amyloid specific biomarkers in a wide range of neurological diseases. By using the proposed synthetic β-sheet models, our study indicates that the sSAXS has potential to evaluate different stages of β-sheet-enriched structures including oligomers of pathogenic proteins.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Evaluating proteome allocation of Saccharomyces cerevisiae phenotypes with resource balance analysis

Saccharomyces cerevisiae is an important model organism and a workhorse in bioproduction. Here, we reconstructed a compact and tractable genome-scale resource balance analysis (RBA) model (i.e., named scRBA) to analyze metabolic fluxes and proteome allocation in a computationally efficient manner. Resource capacity models such as scRBA provide the quantitative means to identify bottlenecks in biosynthetic pathways due to enzyme, compartment size, and/or ribosome availability limitations. ATP maintenance rate and in vivo apparent turnover numbers (k app ) were regressed from metabolic flux and protein concentration data to capture observed physiological growth yield and proteome efficiency and allocation, respectively. Estimated parameter values were found to vary with oxygen and nutrient availability. Altogether, this work (i) provides condition-specific model parameters to recapitulate phenotypes corresponding to different extracellular environments, (ii) alludes to the enhancing effect of substrate channeling and post-translational activation on in vivo enzyme efficiency in glycolysis and electron transport chain, and (iii) reveals that the Crabtree effect is underpinned by specific limitations in mitochondrial proteome capacity and secondarily ribosome availability rather than overall proteome capacity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Processing aluminum alloy with hybrid wire arc additive manufacturing and ultrasonic nanocrystalline surface modification to improve porosity, surface finish, and hardness

Here, in this paper, a novel hybrid wire arc additive manufacturing (WAAM) and ultrasonic nanocrystal surface modification (UNSM) on porosity manipulation and surface properties of aluminum 5356 alloys was studied. The goal is to improve the quality of the WAAM-built part by eliminating bigger pores and reducing its size, reducing surface roughness, and increasing surface hardness. The as-built WAAM and WAAM-UNSM-treated samples were quantitatively studied for porosity using an X-ray micro-computed tomography (μ-CT). The surface roughness was measured on the surface profile of the same samples before and after UNSM treatment. Followed by the Vickers micro-hardness tests to evaluate the hardness modified by the influence of the UNSM treatment. It was found that the bigger pores in the as-built WAAM samples were eliminated and the medium-sized pores were shrunk to almost half the size after the UNSM treatment. Further, the UNSM treatment showed a significant improvement in both surface roughness and hardness on the WAAM Al5356 samples. This experimental work demonstrates the critical advantages of hybrid WAAM-UNSM in improving the qualities of the WAAM processed parts.

Hardness↗

DOE OSTI2025

Predictive numerical modeling of plasma-induced surface roughness and wettability evolution in LM-PAEK/CF tape

Plasma surface modification effectively enhances adhesion in thermoplastic composites, yet its impacts on high-performance polymers like low-melting polyaryletherketone (LM-PAEK) remain inadequately quantified. Here, this study integrates experimental analysis and numerical modeling to characterize surface roughness and wettability changes in LM-PAEK/carbon fiber composites treated with atmospheric plasma. Atomic Force Microscopy quantified surface topography (n = 10 per condition for contact angles), while static contact-angle assessments measured wettability. Roughness rapidly increased from ∼0.2 nm to 1.6 nm, and contact angle reduced from ∼90° to 24°, both stabilizing after 25–30 s of exposure. A semi-empirical, physics-informed framework was calibrated to these data, coupling surface chemistry via the Owens–Wendt decomposition with topography via the Wenzel roughness factor, and evaluated using out-of-sample (cross-validated) tests, while static contact angle assessments measured wettability. Numerical predictions matched experimental results closely (RMSE <5%, R 2 > 0.95). Incorporating material-specific parameters, the calibrated model supports plasma-treatment optimization and provides quantitative guidance for improving interfacial adhesion in thermoplastic composite manufacturing.

Atomic Force Microscopy↗

DOE OSTI2023

Quantifying neptunium oxidation states in nitric acid through spectroelectrochemistry and chemometrics

Controlled-potential in situ thin-layer spectropotentiometry was leveraged to generate visible/near-infrared (VIS/NIR) absorption spectral data sets for the development of chemometric models to quantify Np(III/IV/V/VI) oxidation states in HNO 3 . This technology would be valuable in laboratory studies and when monitoring process solutions to guide feed adjustments for radiochemical separations—the performance of which depends on oxidation state. This approach successfully isolated and stabilized Np species in pure (~99%) oxidation states without compromising solution optical properties. Multivariate curve resolution–alternating least squares models were evaluated to resolve spectral and component concentrations from a scan that sequentially produced Np(VI), Np(V), Np(IV), and Np(III) spectra with mixtures of two valences at a time. Although it provided a useful approximation, the method was not able to quantitively resolve each component likely because of rotational ambiguity. Additionally, partial least squares regression models were built from artificial and electrochemically generated VIS/NIR spectral training sets to study the effect of interionic interactions on spectral characteristics. Models built with true Bi-chemical mixtures of coexisting Np oxidation states and spectra generated from additive combinations of pure end points had similar prediction performance. This methodology can be used to directly quantify Np concentration and the ratio of Np oxidation states and other actinides in remote settings such as hot cells.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2024

Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods

Artificial intelligence (AI) and machine learning (ML) pose a challenge for achieving science that is both reproducible and replicable. The challenge is compounded in supervised models that depend on manually labeled training data, as they introduce additional decision-making and processes that require thorough documentation and reporting. We address these limitations by providing an approach to hand labeling training data for supervised ML that integrates quantitative content analysis (QCA)—a method from social science research. The QCA approach provides a rigorous and well-documented hand labeling procedure to improve the replicability and reproducibility of supervised ML applications in Earth systems science (ESS), as well as the ability to evaluate them. Specifically, the approach requires (a) the articulation and documentation of the exact decision-making process used for assigning hand labels in a “codebook” and (b) an empirical evaluation of the reliability” of the hand labelers. In this paper, we outline the contributions of QCA to the field, along with an overview of the general approach. We then provide a case study to further demonstrate how this framework has and can be applied when developing supervised ML models for applications in ESS. With this approach, we provide an actionable path forward for addressing ethical considerations and goals outlined by recent AGU work on ML ethics in ESS.

58 GEOSCIENCES↗