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Correlative chemical and elemental nano-imaging of morphology and disorder at the nacre-prismatic region interface in Pinctada margaritifera

Abstract Understanding biomineralization relies on imaging chemically heterogeneous organic–inorganic interfaces across a hierarchy of spatial scales. Further, organic minority phases are often responsible for emergent inorganic structures from the atomic arrangement of different polymorphs, to nano- and micrometer crystal dimensions, up to meter size mollusk shells. The desired simultaneous chemical and elemental imaging to identify sparse organic moieties across a large field-of-view with nanometer spatial resolution has not yet been achieved. Here, we combine nanoscale secondary ion mass spectroscopy (NanoSIMS) with spectroscopic IR s -SNOM imaging for simultaneous chemical, molecular, and elemental nanoimaging. At the example of Pinctada margaritifera mollusk shells we identify and resolve ~ 50 nm interlamellar protein sheets periodically arranged in regular ~ 600 nm intervals. The striations typically appear ~ 15 µm from the nacre-prism boundary at the interface between disordered neonacre to mature nacre. Using the polymorph distinctive IR-vibrational carbonate resonance, the nacre and prismatic regions are consistently identified as aragonite ( $${\overline{\nu }}_{a}=860$$ ν ¯ a = 860 cm −1 ) and calcite ( $${\overline{\nu }}_{c}=880$$ ν ¯ c = 880 cm −1 ), respectively. We observe previously unreported morphological features including aragonite subdomains encapsulated in extensions of the prism-covering organic membrane and regions of irregular nacre tablet formation coincident with dispersed organics. We also identify a ~ 200 nm region in the incipient nacre region with less well-defined crystal structure and integrated organics. These results show with the identification of the interlamellar protein layer how correlative nano-IR chemical and NanoSIMS elemental imaging can help distinguish different models proposed for shell growth in particular, and how organic function may relate to inorganic structure in other biomineralized systems in general.

54 ENVIRONMENTAL SCIENCES↗

Recombination smooths the time-signal disrupted by latency in within-host HIV phylogenies

Within-host HIV evolution involves several features that may disrupt standard phylogenetic reconstruction. One important feature is re-activation of latently integrated provirus, which has the potential to disrupt the temporal signal, leading to variation in the branch lengths and apparent evolutionary rates in a tree. Yet, real within-host HIV phylogenies tend to show clear, ladder-like trees structured by the time of sampling. Another important feature is recombination, which violates the fundamental assumption that evolutionary history can be represented by a single bifurcating tree. Thus, recombination complicates the within-host HIV dynamic by mixing genomes and creating evolutionary loop structures that cannot be represented in bifurcating trees. In this paper, we develop a coalescent-based simulator of within-host HIV evolution that includes latency, recombination, and effective population size dynamics that allows us to study the relationship between the true, complex genealogy of within-host HIV evolution, encoded as an Ancestral Recombination Graph (ARG), and the observed phylogenetic tree. To compare our ARG results to the familiar phylogeny format, we calculate the expected bifurcating tree after decomposing the ARG into all unique site trees, their combined distance matrix, and the overall corresponding bifurcating tree. While latency and recombination separately disrupt the phylogenetic signal, remarkably, we find that recombination recovers the temporal signal of within-host HIV evolution caused by latency by mixing fragments of old, latent genomes into the contemporary population. In effect, recombination averages over extant heterogeneity, whether it stems from mixed time-signals or population bottlenecks. Further, we establish that the signals of latency and recombination can be observed in phylogenetic trees despite being an incorrect representation of the true evolutionary history. Using an Approximate Bayesian Computation method, we develop a set of statistical probes to tune our simulation model to nine longitudinally-sampled within-host HIV phylogenies. Because ARGs are exceedingly difficult to infer from real HIV data, our simulation system allows investigating effects of latency, recombination, and population size bottlenecks by matching decomposed ARGs to real data as observed in standard phylogenies.

59 BASIC BIOLOGICAL SCIENCES↗

Navier: Dataflow Architecture for Computation Chemistry

Navier’s objectives were two evaluate the use of emerging technologies, especially dataflow accelerators, for high-performance computing (HPC) applications, specifically in the domain of chemistry, and to develop a prototype software stack to support such applications. Navier builds on capabilities previously developed by synergistic projects, such as PNNL Data Model Convergence (DMC) LDRD Hardware Advanced Workflows (HAW) and DuOMO, as well as DOE ARIAA. Throughout its 18 months, the Navier team developed new capabilities and artifacts at all levels of the HW/SW stack, provided a seamless way to integrate novel computing architectures (Sambanova SN10 and Xilinx Versal AI) into an existing software stack, developed chemistry workflows, data analytics tools, and HPC molecular dynamics workflows that leverage the developed stack and PNNL institutional investments in emerging architectures. Navier also explored the use of active learning to accelerate a computational chemistry workflow for organic molecules on PNNL Junction cluster (in collaboration with AMD/Xilinx). Navier developed tools, methodologies, and studies for hardware software co-design and (sparse) dataflow accelerators that are composable and can be used together or separately. These methodologies are now used in other projects, such as DOE AMAIS and HPDA. This report describes Navier’s achievement, the developed tools and methodologies, and the research findings and conclusions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Consistency and reproducibility in atomic layer deposition

Atomic layer deposition (ALD) is a thin film synthesis technique that can provide exquisite accuracy and precision in film thickness and composition even on complex, large area substrates. Based on self-limiting surface chemistry, ALD can be insensitive to process conditions and reactor designs, allowing an ALD process developed in one lab to be easily reproduced in other labs. In practice, however, ALD is sometimes difficult to reproduce or replicate, and the results can vary substantially between ALD reactors and between labs. This is exemplified by large deviations in reports on the growth of, e.g., Al2O3, FeOx, and TiO2 given the same precursors under similar conditions. Furthermore, the problem of irreproducibility seems to be growing as ALD is adopted by more researchers and integrated into new applications. In this article, the authors highlight some of the major sources of variations and errors and common misconceptions related to ALD. In particular, the authors focus on issues related to precursors, substrates, and deposition tools. The authors illustrate these problems through examples from the literature, and they present results from numerical simulations that describe how nonidealities would manifest in thickness profiles in a typical cross-flow reactor. They also describe how reproducibility in ALD is linked to consistent experimental practice and reporting between labs. The authors’ hope is that by educating newcomers to ALD and advocating for consistent reporting of deposition conditions, they can minimize irreproducibility and enable ALD practitioners to realize the full potential afforded by self-limiting surface chemistry.

36 MATERIALS SCIENCE↗

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), has typically limited machine learning (ML) in space studies and further study of radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNAseq) data from 6 mouse liver GeneLab datasets (GLDS) with a total of 113 spaceflight and ground-control samples to determine top features relevant to spaceflight including the effect of radiation exposure. Data was normalized within each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. The top MRMR features were used to predict spaceflight vs. ground-control samples using a Random Forest (RF) classifier with 5-fold cross validation (CV). The ML-based gene sets were further compared against differential gene expression results from individual GLDS. CV training using the top 100 MRMR genes show averages of 86% accuracy and 0.95 AUC value on the validation set over 5 folds (Figure 1A). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 811 or 68 DEGs overlapping between at least 2 or 3 studies, respectively (Figure 1B). Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism. Set analysis between the MRMR features and the DEGs showed 60 or 8 genes overlapping with at least 1 or 2 studies, respectively. MRMR feature selection and ensemble ML methods (e.g. RF) improve performance relative to a Naïve Bayes classifier when NGS data sets are analyzed. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise ratio. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from RNASeq analysis. Non-intersecting sets introduce opportunity to explore spaceflight relevant genes and implementing ML methods across existing NGS datasets may overcome sample size limitations. ML coupled with existing analytical methods enhances understanding of disease by revealing common underlying pathways across datasets.

Machine Learning↗

Corrosion Testing Of Additively Manufactured Stainless Steel 316H In Molten Salt Environments

The development of a new ASTM standard for evaluating the corrosion resistance of additive manufactured (AM) stainless steel (SS) 316H in chloride molten salts is critical for the use of these materials in extreme environments such as molten salt reactors (MSRs). This report pertains to a work package of the Advaned Materials and Manufacturing Technologies (AMMT) developing a systematic methodology to link changes in AM fabrication parameters, namely surface finishing, porosity, microstructure, and chemical heterogeneity, to corrosion performance in NaCl-MgCl2 salt, a proposed secondary coolant for MSRs. During Fiscal Year 2024, Idaho National Laboratory investigators in the AMMT program, utilized SS316H bars, fabricated with laser bed powder fusion at Los Alamos National Laboratory, to establish and optimize the workflow for evaluating these process-to-performance relationships. Standard practices for specimen preparation were established using these specimens, with particular focus on descaling and sectioning methods that align with ASTM guidelines. A comprehensive experimental design for static corrosion testing was developed, with pre- and post-exposure analysis utilizing optical microscopy and scanning electron microscopy. So far standard descaling techniques were optimized, one static corrosion test was conducted on the LANL AM SS316H specimens, and pre- and post-corrosion practices were established. In addition, the work package yielded a review paper on corrosion testing gaps for AM materials in nuclear applications and submitted a proposal for a rapid-turnaround experiment to the Nuclear Science User Facilities Program to investigate the combined effects of proton irradiation and corrosion on AM SS316H. This work package establishes a foundation for evaluating processing-to-performance relationships for AM SS316H in harsh conditions, contributing to the safe and efficient design of components for next-generation nuclear reactors. The creation of a standardized methodology and the generation of relevant publications and future research pathways represent significant strides towards integrating AM materials into critical applications where corrosion resistance is paramount.

36 MATERIALS SCIENCE↗

Microreactor-liquid metal battery system in energy markets: An evaluation of potential costs, technology, and policy impacts

Microreactors represent an emerging innovation in the nuclear industry; yet have been overshadowed by their high capital costs. With the Inflation Reduction Act of 2022 (IRA), new opportunities have emerged to improve the economics of microreactor systems. This work examines liquid metal batteries (LMB) as a value-adding technology as part of microreactor-LMB systems within three U.S. electricity markets: ERCOT, PJM, and MISO. Our investigation considers key uncertainties: the cost of microreactors, the performance of LMBs, and the eligible tax credit levels. To this end, we use a dispatch optimization to trace not only the changes in system economics but also to provide a granular picture of energy delivery within the systems. We find that even with favorable costs for microreactors, significant regional variations in the project sizing and returns exist across the markets. Our heuristic method identifies their non-electric application potentials beyond electricity and technical requirements to maximize returns. The results suggest that 12–39 % of reactor heat could be cost-effectively diverted to produce more valuable by-products in U.S. markets. Including the impacts of tax credits, we establish the outcomes of each provision with varying rates. Coupling an LMB to a microreactor consistently improves the net present value of a microreactor compared to its standalone operation. In conclusion, for reasonable assumed conditions, we quantify a heterogeneous impact of round-trip efficiency (RTE) and extended LMB service life across the three markets—a one-year extension in LMB service life is roughly equivalent to a 2.11 % improvement in RTE for ERCOT, 1.16 % for PJM, and 1.04 % for MISO.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗

Paired Simulations and Experimental Investigations into the Calcium-Dependent Conformation of Albumin

Serum albumin is the most abundant protein in blood plasma, and it is involved in multiple biological processes. Serum albumin has recently been adapted for improving biomaterial integration with bone tissue, and studies have shown the importance of this protein in bone repair and regeneration. However, the mechanism of action is not yet clear. In stark contrast, other studies have demonstrated that albumin blocks cell adhesion to surfaces, which is seen as a limitation to its bone healing role. These apparent contradictions suggest that the conformation of albumin facilitates its bioactivity, leading to enhanced bone repair. Serum albumin is known to play a major role in maintaining the calcium ion concentration in blood plasma. Due to the prevalence of calcium at bone repair and regeneration sites, it has been hypothesized that calcium binding to serum albumin triggers a conformational change, leading to bioactivity. In the current study, molecular modeling approaches including molecular docking, atomic molecular dynamics (MD) simulation, and coarse-grained MD simulation were used to test this hypothesis by investigating the conformational changes induced in bovine serum albumin by interaction with calcium ions. The computational results were qualitatively validated with experimental Fourier-transform infrared spectroscopy analysis. We find that free calcium ions in solution transiently bind with the three major loops in albumin, triggering a conformational change where N-terminal and C-terminal domains separate from each other in a partial unfolding process. The separation distance between these domains was found to correlate with the calcium ion concentration. The experimental data support the simulation results showing that albumin has enhanced conformational heterogeneity upon exposure to intermediate levels of calcium, without any significant secondary structure changes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Mechanical and Thermophysical Properties of 3D-Printed SiC before and after Neutron Irradiation – FY21

This report presents the mechanical and thermophysical properties of 3D-printed SiC before and after neutron irradiation that have been evaluated to assess the fuel matrix material for the Transformational Challenge Reactor (TCR). The TCR fuel form consists of an additively-manufactured silicon carbide (SiC) matrix and uranium nitride tristructural isotropic (UN TRISO) fuel particles, which is manufactured through a newly developed processing route combining binderjet 3D printing, TRISO fuel particle loading, and chemical vapor infiltration (CVI). Because the fuel matrix is a primary component of the TCR core and its response to mechanical and thermal loads during operation is one of the most influential factors on the integrity of TCR core, testing and evaluation have focused on producing mechanical and thermophysical properties data for the binderjet/CVI SiC. Baseline mechanical and thermophysical properties were measured from the disk specimens printed for different and sizes orientations, which included equibiaxial flexural failure strength, elastic constants, thermal diffusivity and conductivity, density, and the coefficient of thermal expansion. Flexural failure strength datasets showed similar Weibull distributions regardless of sample variants including different orientations. The mean failure strengths of the 3D-printed SiC variants were in the range of 280–310 MPa, which are slightly lower than that of the chemical vapor deposition (CVD) SiC. Thermophysical test results showed that specific heat and thermal expansion are not sensitive to the build direction of SiC samples, while thermal conductivity is highly dependent on the build direction and can be correlated to the anisotropic character of the 3D-printed SiC. Neutron irradiation tests were carried out on the 3D-printed 6-mm diameter SiC disk specimens. Irradiation was carried to 2.3 dpa over a temperature range of 360–880°C. No significant degradation in strength was observed in SiC after irradiations in various conditions and with different orientations. Anisotropy that had been observed in the thermal conductivity of 3D-printed SiC prior to irradiation vanished after irradiation as the irradiation defect thermal resistivity accumulated in the material. Electron microscopy of the microstructure after neutron irradiation showed distinct defect morphologies in the heterogenous material, but no evidence for irradiation-induced cracking or degradation in the microstructure was observed.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Cognitive IoT and Edge Computing for Intrusion Detection with Federated TinyML

Internet of Things (IoT) and Edge Computing (EC) are rapidly becoming an integral part of the modern society. By 2030, there is estimated to be over 40 billion active and connected IoT devices [1]. This rapid progress also comes with a significant implication on cybersecurity. Back-end infrastructure and systems have a much broader attack than they did previously due to vulnerable IoT/EC devices being connected to wireless networks. This expanding attack surface is a growing concern because IoT/EC are increasingly being used in critical systems such as power grids, health care, and smart homes. To effectively address a problem of this scale, cognitive cyber methods—which can autonomously detect and react to cyber attacks as they develop—are needed. To address this, we bring Artificial Intelligence (AI) and Machine Learning (ML) to IoT/EC devices, using tinyML to monitor voluminous IoT data against cyber threats, and using Federated Learning (FL) to share local detection knowledge across the system while preserving privacy. We propose a novel three-layer architecture: (1) an IoT layer for tinyML-based inference, (2) an edge layer for ML model training, and (3) a cloud layer for FL operations. Using the publicly available 11-class N-BaIoT dataset [2], we demonstrate that this architecture mitigates resource constraints at the IoT layer while improving detection accuracy over standard two-layer designs. An outlier-resistant scaler, feature reduction, and quantization enable the tinyML model to maintain detection accuracy with a reduced model size. Additionally, federated learning that only utilizes the intersection (across heterogenous devices) of the reduced feature set achieves superior detection accuracy compared to locally trained models.

Li, Mingyan [ORNL] (ORCID:0009000569532640)↗

Global Radiative Forcing of Coupled Tropospheric Ozone and Aerosols in a Unified General Circulation Model

Global simulations of sea salt and mineral dust aerosols are integrated into a previously developed unified general circulation model (GCM), the Goddard Institute for Space Studies (GISS) GCM II', that simulates coupled tropospheric ozone-NOx-hydrocarbon chemistry and sulfate, nitrate, ammonium, black carbon, primary organic carbon, and secondary organic carbon aerosols. The fully coupled gas-aerosol unified GCM allows one to evaluate the extent to which global burdens, radiative forcing, and eventually climate feedbacks of ozone and aerosols are influenced by gas-aerosol chemical interactions. Estimated present-day global burdens of sea salt and mineral dust are 6.93 and 18.1 Tg with lifetimes of 0.4 and 3.9 days, respectively. The GCM is applied to estimate current top of atmosphere (TOA) and surface radiative forcing by tropospheric ozone and all natural and anthropogenic aerosol components. The global annual mean value of the radiative forcing by tropospheric ozone is estimated to be +0.53 W m(sup -2) at TOA and +0.07 W m(sup -2) at the Earth's surface. Global, annual average TOA and surface radiative forcing by all aerosols are estimated as -0.72 and -4.04 W m(sup -2), respectively. While the predicted highest aerosol cooling and heating at TOA are -10 and +12 W m(sup -2) respectively, surface forcing can reach values as high as -30 W m(sup -2), mainly caused by the absorption by black carbon, mineral dust, and OC. We also estimate the effects of chemistry-aerosol coupling on forcing estimates based on currently available understanding of heterogeneous reactions on aerosols. Through altering the burdens of sulfate, nitrate, and ozone, heterogeneous reactions are predicted to change the global mean TOA forcing of aerosols by 17% and influence global mean TOA forcing of tropospheric ozone by 15%.

Liao, Hong↗

Predicting Switchgrass Biomass Yields Using a Spectral Vegetation Index Derived from Multispectral Satellite Imagery

Successful scaling of perennial bioenergy crop production requires a landscape design that optimizes the benefits of finite lands for people, communities, and environments. Utilizing marginal areas is the key to sustainable bioenergy crop production (Ssegane et al., 2015, 2016). Marginal areas are often small-sized lands and unevenly distributed across the agricultural landscape (Ssegane et al., 2016); thus, a systematic, semi-automated remote sensing method is needed as an effective means of estimating bioenergy crop yields across landscapes. Argonne National Laboratory (Argonne) is currently developing a tool, Scaling Up Perennial Bioenergy Economics and Ecosystem Services Tool (SUPERBEEST), to identify marginal agricultural lands and quantify environmental and economic effects of perennial bioenergy crop production systems. The tool aims to provide users a path to foster the sustainable and productive integration of bioenergy crops in the Midwestern agricultural landscape. Reliable, cost-effective, and timely estimation of bioenergy crop yields using remote sensing would help calculate and track the success of integrated bioenergy crops in the landscape for those communities. Argonne previously conducted feasibility studies for estimating biomass yields for bioenergy feedstock, corn and perennial grass using spectral vegetation indices (SVIs)1 derived from optical imagery (Hamada et al., 2015, 2021). In both studies, SVIs, more specifically those sensitive to plant chlorophyll or nitrogen contents, showed potential for estimating or predicting biomass yields with a correlation of determination (R2) ranging from 0.54 to 0.96, indicating a value for further investigation as a viable means of quantifying bioenergy feedstock production across large landscapes. Thus, the goal of this study is to evaluate the feasibility of use of SVIs as a means of estimating or predicting switchgrass biomass yields at harvest using publicly available multispectral satellite imagery. The feasibility analysis was performed using four study areas of mature switchgrass located in Virginia. Objectives are to (1) examine the SVIs and establish their relationships with switchgrass biomass yields at harvest, (2) develop a parsimonious image processing model for predicting at-harvest yields by applying the relationships with the most promising spectral index and (3) map switchgrass yields predicted by the image processing model across the study sites. The calibration to field data will rely on switchgrass biomass yields determined by the baling method, representing a potential challenge to the analysis but an important practical aspect for future applications. With this research design, the study aimed to gain insights into enabling remote sensing-based estimation of bioenergy crop yields in a reliable, cost- effective, and timely manner across large, heterogeneous landscapes.

09 BIOMASS FUELS↗

Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River

Hydrologic exchange between river channels and adjacent subsurface environments is a key process that influences water quality and ecosystem function in river corridors. High-resolution numerical models were often used to resolve the spatial and temporal variations of exchange flows, which are computationally expensive. In this study, we adopt Random Forest (RF) and Extreme Gradient Boosting (XGB) approaches for deriving reduced order models of hydrologic exchange flows and associated transit time distributions, with integrated field observations (e.g., bathymetry) and hydrodynamic simulation data (e.g., river velocity, depth). The setup allows an improved understanding of the influences of various physical, spatial, and temporal factors on the hydrologic exchange flows and transit times. The predictors also contain those derived using hybrid clustering, leveraging our previous work on river corridor system hydromorphic classification. The machine learning-based predictive models are developed and validated along the Columbia River Corridor, and the results show that the top parameters are the thickness of the top geological formation layer, the flow regime, river velocity, and river depth; the RF and XGB models can achieve 70% to 80% accuracy and therefore are effective alternatives to the computational demanding numerical models of exchange flows and transit time distributions. Each machine learning model with its favorable configuration and setup have been evaluated. The transferability of the models to other river reaches and larger scales, which mostly depends on data availability, is also discussed.

97 MATHEMATICS AND COMPUTING↗

An adaptive Hessian approximated stochastic gradient MCMC method

Bayesian approaches have been successfully integrated into training deep neural networks. One popular family is stochastic gradient Markov chain Monte Carlo methods (SG-MCMC), which have gained increasing interest due to their ability to handle large datasets and the potential to avoid overfitting. Although standard SG-MCMC methods have shown great performance in a variety of problems, they may be inefficient when the random variables in the target posterior densities have scale differences or are highly correlated. Here, we present an adaptive Hessian approximated stochastic gradient MCMC method to incorporate local geometric information while sampling from the posterior. The idea is to apply stochastic approximation (SA) to sequentially update a preconditioning matrix at each iteration. The preconditioner possesses second-order information and can guide the random walk of a sampler efficiently. Instead of computing and saving the full Hessian of the log posterior, we use limited memory of the samples and their stochastic gradients to approximate the inverse Hessian-vector multiplication in the updating formula. Moreover, by smoothly optimizing the preconditioning matrix via SA, our proposed algorithm can asymptotically converge to the target distribution with a controllable bias under mild conditions. To reduce the training and testing computational burden, we adopt a magnitude-based weight pruning method to enforce the sparsity of the network. Our method is user-friendly and demonstrates better learning results compared to standard SG-MCMC updating rules. The approximation of inverse Hessian alleviates storage and computational complexities for large dimensional models. Numerical experiments are performed on several problems, including sampling from 2D correlated distribution, synthetic regression problems, and learning the numerical solutions of heterogeneous elliptic PDE. The numerical results demonstrate great improvement in both the convergence rate and accuracy.

97 MATHEMATICS AND COMPUTING↗

Use of Multi-Site Radiation Therapy for Systemic Disease Control

Metastatic cancer is a heterogeneous entity, some of which could benefit from local consolidative radiation therapy (RT). Although randomized evidence is growing in support of using RT for oligometastatic disease, a highly active area of investigation relates to whether RT could benefit patients with polymetastatic disease. This article highlights the preclinical and clinical rationale for using RT for polymetastatic disease, proposes an exploratory framework for selecting patients best suited for these types of treatments, and briefly reviews potential challenges. The goal of this hypothesis-generating review is to address personalized multimodality systemic treatment for patients with metastatic cancer. The rationale for using high-dose RT is primarily for local control and immune activation in either oligometastatic or polymetastatic disease. However, the primary application of low-dose RT is to activate distinct antitumor immune pathways and modulate the tumor stroma in efforts to better facilitate T cell infiltration. We explore clinical cases involving high- and low-dose RT to demonstrate the potential efficacy of such treatment. We then group patients by extent of disease burden to implement high- and/or low-dose RT. Patients with low-volume disease may receive high-dose RT to all sites as part of an oligometastatic paradigm. Subjects with high-volume disease (for whom standard of care remains palliative RT only) could be treated with a combination of high-dose RT to a few sites for immune activation, while receiving low-dose RT to several remaining lesions to enhance systemic responses from high-dose RT and immunotherapy. We further discuss how emerging but speculative concepts such as immune function may be integrated into this approach and examine therapies currently under investigation that may help address immune deficiencies. The review concludes by addressing challenges in using RT for polymetastatic disease, such as concerns about treatment planning workflows, treatment times, dose constraints for multiple-isocenter treatments, and economic considerations.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Integrated CO 2 capture and hydrogenation in presence of Ru–Na 2 ZrO 3 : An in-situ study

Integrated CO 2 capture and conversion (ICCC) by hydrogenation is a promising strategy to utilize carbon dioxide and this work add to the effort to elucidate the catalytic hydrogenation mechanism using Ru based dual functional materials (DFM). Ru-Na 2 ZrO 3 DFMs, obtained through different wet methods, were evaluated for the first time and the relationship between Ru and support systematically investigated. The thermally stable and cyclable Ru-Na 2 ZrO 3 -a (obtained without filtration step) exhibited CO 2 conversion of 80% and a higher yield of CO at 400°C compared to previously tested DFM, while the Na depleted/Zr rich Ru-Na 2 ZrO 3 -b resulted in 90% selectivity to CH 4 with yield of 1.11 mmol/g at the same temperature. The in-situ experiments have provided conclusive evidence showing that CO 2 hydrogenation on the two Ru DFMs is fundamentally different. In Ru-Na 2 ZrO 3 -a, the monoclinic Na 2 ZrO 3 support acted as the active centre (not as promoter) for CO 2 bridging binding and hydrogenation to CH 4 at the metal-support interface through associative formate pathway with limited further reduction to methane due to lack of H 2 spillover from the small and well dispersed Ru NPs, which results in CO desorption. Conversely, abundant clusters of larger Ru NPs in Ru-Na 2 ZrO 3 -b, led to CH 4 production due to co-existent Ru on-top direct dissociation of CO 2 (preferential) and monodentate formate adsorption and further methanation. Alkali zirconates doped metals, and their synthesis method could thus play a crucial role in designing tuneable heterogeneous catalysis in C 1 chemistry, which could significantly benefit the environment by lowering CO 2 levels, encouraging cleaner industrial practices, supporting a circular economy, and converting waste CO 2 into valuable products.

36 MATERIALS SCIENCE↗

Precision Agriculture using Networks of Degradable Analytical Sensors (PANDAS) (Final Technical Report)

Precision agriculture, where sensing of soil, environment and crop conditions are used to precisely synchronize inputs (such as water and fertilizer) to crop needs enhances input use efficiency. This can improve yields and farm profitability while mitigating environmental losses, improving soil carbon content and substantially decreasing energy use for food, feed and fuel crops. Unfortunately, farmers are not yet able to harness the full potential of these management technologies as there is a lack of available management information, and there is therefore a need for sensors that are able to economically measure spatio-temporal variability in soil and crop properties of extremely heterogeneous farm fields precisely at high resolution and at low cost. Real-time, in-situ monitoring of agricultural soil conditions is today carried out using devices that limit the total number of nodes that can be used economically to typically one per acre or less. Higher spatio-temporal resolution sensing would enable more precise agricultural input optimization, with significant benefits to the farmer and the environment. In order to address this issue, this project focused on developing additively manufactured, biodegradable, soil sensors with predicted costs of < $\$$1 per unit to monitor crop inputs (such as water and fertilizer) that predictably, harmlessly degrade away into the soil when no longer needed. These sensor nodes should be easy to place, accurately and continuously monitor soil and crop conditions for an entire season, be read remotely using existing farm equipment, require no ongoing maintenance, not impede farm operations and produce no persistent waste. This approach could enable a >100× increase in information density over current solutions for precision farming of row and other crops, and lead to significant reductions in input energy use and provide increased yield for biofuel crops. Over the course of this project the team at the University of Colorado Boulder, University of California Berkeley, and Colorado State University/Kansas State University investigated a wide range of printable biodegradable electronic materials and sensor designs for determining soil moisture and soil nitrate concentration. These efforts expanded the available materials set for printed soil degradable electronic materials, particularly for conductors, enabling high conductivity and stability. Printed soil moisture and nitrate sensors with suitable sensitivity and selectivity were developed and characterized. Low power and passive wireless electronic systems were integrated with the soil sensors, and testing was carried out with completed sensors to understand their functionality under agricultural conditions. Additionally, other sensor types enabled by the biodegradable materials set created during this project, such as soil microbial activity sensors, were also developed and demonstrated. Project outputs include 10 peer reviewed publications, 4 patent applications, 21 technical presentations, 3 PhD thesis, 10 media reports, 8 additional grants worth over $\$$6M, and the formation of 3 start-up companies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Mechanoadaptive strain and functional osseointegration of dental implants in rats

Spatiotemporal implant-bone biomechanics and mechanoadaptive strains in peri-implant tissue are poorly understood. Physical and chemical characteristics of an implant-bone complex (IBC) were correlated in three-dimensional space (along the length and around a dental implant) to gather insights into time related integration of the implant with the cortical portion of a jaw bone in a rat. Rats (N = 9) were divided into three experimental groups with three rats per time point; 3-, 11-, and 24-day. All rats were fed crumbled hard pellets mixed with water (soft-food diet) for the first 3 days followed by a hard-food diet with intact hard-food pellets (groups of 11- and 24-day only). Biomechanics of the IBCs harvested from rats at each time point was evaluated by performing mechanical testing in situ in tandem with X-ray imaging. The effect of physical association (contact area) of a loaded implant with adapting peri-implant tissue, and resulting strain within was mapped by using digital volume correlation (DVC) technique. The IBC stiffness at respective time points was correlated with mechanical strain in peri-implant tissue. Results illustrated that IBC stiffness at 11-day was lower than that observed at 3-day. However, at 24-day, IBC stiffness recovered to that which was observed at 3-day. Furthermore, correlative microscopy and spectroscopy illustrated that the lower IBC stiffness was constituted by softer and less mineralized peri-implant tissue that contained varying expressions of osteoconductive elements. Lower IBC stiffness observed at 11-day was constituted by less mineralized peri-implant tissue with osteoconductive elements that included phosphorus (P) which was co-localized with higher expression of zinc (Zn), and lower expression of calcium (Ca). Higher IBC stiffness at 24-day was constituted by mineralized peri-implant tissue with higher expressions of osteoconductive elements including Ca and P, and lower expressions of Zn. These spatiotemporal correlative maps of peri-implant tissue architecture, heterogeneous distribution of mineral density, and elemental colocalization underscore mechanoadaptive physicochemical properties of peri-implant tissue that facilitate functional osseointegration of an implant. These results provided insights into 1) plausible "prescription" of mechanical loads as an osteoinductive "therapeutic dose" to encourage osteoconductive elements in the peri-implant tissue that would facilitate functional osseointegration of the implant; 2) a "critical temporal window" between 3 and 11 days, and perhaps it is this acute phase during which key candidate regenerative molecules can be harnessed to accelerate osseointegration of an implant under load.

60 APPLIED LIFE SCIENCES↗