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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Y-12 National Security Complex Biological Monitoring and Abatement Program—2024 Calendar Year Report

This report provides the results of the CY 2024 sampling of East Fork Poplar Creek (EFPC) as part of the Y-12 National Security Complex (Y-12) Biological Monitoring and Abatement Program (BMAP). The results are presented in the context of historical trends. The Y-12 BMAP was developed in 1985 to demonstrate that the effluent limits established for Y-12 protected the classified uses of the receiving stream, particularly the growth and propagation of aquatic life (Loar et al. 1989). Over the years, the BMAP has become an important and valuable long-term measure of stream conditions resulting from actions and activities at the Y-12 Complex. The BMAP currently consists of three tasks: (1) bioaccumulation monitoring, (2) benthic macroinvertebrate community monitoring, and (3) fish community monitoring. The benthic macroinvertebrate community monitoring task includes studies to evaluate the receiving stream’s biological integrity annually in comparison with Tennessee Water Quality Criteria following Tennessee Department of Environment and Conservation (TDEC) protocols. In addition to presenting the EFPC biological monitoring results, this report includes results from Comprehensive Environmental Response, Compensation, and Liability Act–funded BMAP programs in Bear Creek and McCoy Branch (presented in Appendixes A and B, respectively), as required in the Y-12 National Pollutant Discharge Elimination System (NPDES) permit. Additional biological testing at the Y-12 Complex includes toxicity testing of select storm drains as required in the NPDES permit. Although toxicity testing is not formally part of the BMAP, toxicity testing results from 2024 are provided in Appendix C.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Findings from Large Bench-Scale Testing of Denitration Electrolyzers for the EDCGe Project

This report highlights the key findings and outcomes relevant to the processability of waste supernatant at Hanford using a denitration electrolyzer. The Electrosynthesis Company issued a Phase 1 report to the Savannah River National Laboratory (SRNL), summarizing the evaluation of large bench-scale denitration electrolyzers to support the electrochemical denitration and caustic generation (EDCGe) project. The Electrosynthesis Company’s report (attached as Appendix A) provides insights into the initial steps required to implement an electrolyzer system at Hanford. Phase 1 experiments focused on validating the denitration electrolyzer’s performance, operating parameters, and reaction products. The robustness of the electrochemical denitration process was demonstrated by two electrolyzer flow cell systems (a 100 cm 2 ElectroCell MP and a 150 cm 2 NESI NS01 cell), both of which achieved significant nitrate and nitrite removal (>50%) with a current efficiency of ~95% for both systems. Higher current densities (500 mA cm –2 ) improved nitrate and nitrite removal rates compared to lower current densities (333 mA cm –2 ), while maintaining a current efficiency of ~94%. The NS01 cell achieved a nitrate species removal rate of ~0.41 mol h –1 at 5 kA m –2 (equiv. to 500 mA cm –2 ). The primary reaction product was ammonia (NH 3 ), constituting 78.3–91.6% of the products (excluding OH – formation). NH 3 was predominantly retained in the catholyte liquid phase rather than being off-gassed. Additionally, the NS01 cell reported an NH 3 generation rate of ~0.36 mol h –1 at 5 kA m –2 . Other gas formation included ~7% N 2 , ~7% H 2 , and trace amounts of N 2 O. The estimated power requirement (extrapolated from the 0.015 m 2 cell data) for a full-scale denitration electrolyzer is approximated to be ~1.6 MW (DC-only) to treat 50% of nitrate and nitrite in a waste stream and generates ~2.1 kmol h –1 of NH 3 with an initial concentration of 4 M NO 3 – /NO 2 – at 300 gal h –1 . Simulated waste containing aluminate, carbonate, oxalate, and halogens exhibited no adverse effects on denitration performance. A preliminary experiment comparing alkaline anolyte (5 M NaOH) with a nickel based anode to acidic media (2 M H 2 SO 4 ) with a DSA-O 2 anode showed a lower operating voltage and generated less H 2 than the acid media. Maintaining a stable 5 M OH – concentration in the anolyte through periodic additions of caustic did not significantly impact denitration performance. This operational mode will be required for long-term experiments. All the experiments demonstrated that electrochemical denitration is a promising approach for treating nitrate and nitrite in simulated waste streams, achieving significant conversion and robustness across varying experimental conditions and electrochemical cell configurations. Lastly, the ability to generate a nearly pure NH 3 stream may prove advantageous for processing at other locations within the Hanford site.

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

Tests of a High Temperature Sample Conditioner for the Waste Treatment Plant LV-S2, LV S3, HV S3A and HV-S3B Exhaust Systems

Tests were performed to evaluate a sample conditioning unit for stack monitoring at Hanford Tank Waste Treatment and Immobilization Plant (WTP) exhaust stacks with elevated air temperatures. The LV-S2, LV-S3, HV-S3A and HV-S3B exhaust stacks are expected to have elevated air temperature and dew point. At these emission points, exhaust temperatures are too high to deliver the air sample directly to the required stack monitoring equipment. As a result, a sample conditioning system is considered to cool and dry the air prior to its delivery to the stack monitoring system. The method proposed for the sample conditioning is a dilution system that will introduce cooler, dry air to the air sample stream. This method of sample conditioning is meant to reduce the sample temperature while avoiding condensation of moisture in the sample stream. An additional constraint is that the ANSI/HPS N13.1-1999 standard states that at least 50% of the 10 µm aerodynamic diameter (AD) particles present in the stack free stream must be delivered to the sample collector. In other words, depositional loss of particles should be limited to 50% in the sampling, transport, and conditioning systems. Based on estimates of particle penetration through the LV-S3 sampling system, the diluter should perform with about 80% penetration or better to ensure that the total sampling system passes the 50% or greater penetration criterion.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Techno-Economic, Feasibility, and Life Cycle Analysis of Renewable Propane: 2025 Update

To clarify the current and future landscape for renewable propane (RP) production, this work evaluates the value proposition of recovering RP from existing and planned hydroprocessed esters and fatty acids (HEFA) biorefineries and surveys emerging technologies under development or deployment. HEFA biorefineries co-produce a propane-rich fuel gas stream, normally used to meet HEFA process heat requirements, from which propane can be recovered and sold to create an additional revenue stream alongside liquid transportation fuels such as renewable diesel (RD) and sustainable aviation fuel (SAF). This report updates and extends a 2022 analysis of RP recovery from HEFA facilities by escalating capital and operating costs to 2024 prices, incorporating recent policy developments (including the Section 45Z Clean Fuel Production Credit), evaluating RP recovery for both RD- and SAF-focused HEFA facilities at two scales (3,000 and 75,000 barrels per day of feedstock), and quantifying the impact of RP recovery on HEFA liquid-fuel carbon intensity (CI) and associated tax credits using the 45ZCF-GREET model. For a 3,000 BPD RD-focused HEFA facility, approximately 3.5 million gallons per year (MGPY) of RP can be recovered; in this base case, the estimated payback period is 18 months based on the total installed cost of the RP recovery equipment and 36 months based on the total capital investment for the entire RP recovery project. The payback period is slightly shorter for the analogous SAF-focused configuration (approximately 4.3 MGPY RP). Sensitivity analysis shows that CAPEX magnitude, RP recovery plant scale, and CI-driven tax credit valuations are the dominant determinants of project viability. RP recovery may increase the CI of HEFA liquid fuels, which can reduce liquid-fuel tax credits (a key revenue stream for the HEFA biorefinery) and lengthen payback periods. However, RP recovery generally remains economically favorable across a wide range of plausible scenarios and market conditions. The report also summarizes emerging pathways that could expand future RP supply.

09 BIOMASS FUELS↗
DOE OSTI2026

Intelligent Experiments through Real-Time AI: Fast Data Processing and Autonomous Detector Control for High-Energy Nuclear Experiments

The aim of this project is to develop software and hardware for fast real-time data processing and autonomous detector control and calibration for the sPHENIX and the future EIC experiments. Below summarizes Georgia Tech team efforts in the past year: 1. We developed a real-time clustering algorithm and FPGA-based pipeline architecture for processing fired pixel data from ALPIDE sensors in sPHENIX experiments. Our Columnar Clustering Co-Design introduces a hardware-aware, stream-friendly approach that segments pixel data by column pairs using a Column Pair Clustering (CPC) strategy, followed by Cluster Stitching to merge adjacent subclusters. Implemented in Vitis HLS, the pipeline comprises five stages—read-in, subclustering, stitching, analysis, and write-out—connected by tagged HLS streams with custom end-of-event signaling for robust synchronization. We designed a pipelined dataflow model optimized for throughput, low latency, and minimal buffering, enabling scalable clustering across events of arbitrary size. Our system maintains spatial precision via center-of-mass and shape key extraction and efficiently handles edge cases such as fragmented or nested clusters. Compared against DBSCAN in both software and hardware, our approach demonstrates competitive performance under FPGA constraints. 2. We also conducted a comprehensive algorithm-to-hardware co-design of connected component analysis tailored for sPHENIX experiments, focusing on real-time, low-latency processing using FPGAs and High-Level Synthesis (HLS). Starting from a Python-based particle tracking pipeline, the team translated the core logic—graph traversal via DFS and Union-Find—into an HLS-compatible C++ model, replacing dynamic memory and recursion with static arrays and pipelined control flow. The final design includes a fully streamed and dataflow-compatible Union-Find kernel optimized across five iterations, incorporating loop pipelining, array partitioning, AXI/FIFO interface tuning, and function flattening. Experimental results show up to 14.8× speedup over the CPU baseline, reducing per-graph latency to 1.58 μs and demonstrating strong resource efficiency with only ~7k LUTs and zero BRAM usage. The design maintains functional correctness against the Python reference using a Python-based C-simulation framework and Mean Squared Error metrics. This work validates the potential of HLS-driven FPGA designs for edge-level HEP data acquisition, laying a scalable foundation for future integration with real-time detector pipelines and multi-graph processing systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Multiplexed Inertial Coalescence Filters for High-Rate Liquid-Gas Chemistry

The aim of this project is to support the development of a disruptive method for deploying liquids in liquid-gas chemical processes to transform carbon dioxide capture from flue gas and ambient air streams. The proposed project is based on the development of a novel filtration method called the Helix MICRA™ (Multiplexed Inertial Coalescence Refining Apparatus) filters. Helix MICRA™ filters are a novel, patented filter that enable high efficiency, low-pressure drop capture of droplet streams. Liquid droplets have a large net-surface area per unit volume and have correspondingly rapid mass transfer rates. By effectively capturing these droplets after deployment, we enable high-rate carbon dioxide capture from air streams unlike any other technology. This project aims at using Helix MICRA™ filters to create efficient and compact carbon dioxide capture systems that would dramatically reduce system size and capital costs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

White-Rabbit-Disciplined FPGA Readout for Fermilab Timing Events

Fermilab's accelerator timing links broadcast short event codes to thousands of devices at once, but the links themselves carry no absolute notion of time; that comes separately from a White Rabbit reference. This work builds the piece that ties the two together on a single board. On a Xilinx Kria KR260 (Zynq UltraScale+), the programmable logic decodes a real Fermilab TCLK link, stamps every event with an absolute White-Rabbit \{sec, ns\} UTC time, and reads the timestamped stream out over AXI4-Lite; a thin Linux process on the same die publishes each event into a Redis stream on the control network. To exercise the full chain on one board, the decoded events are re-encoded as gigabit ACLK, transmitted out an SFP+ optical port, looped back over a short fiber jumper, and decoded again on the same timeline, and are additionally mirrored as an ACLK-Lite Manchester waveform for benchtop probing. Across sustained, multi-day testing against real Fermilab TCLK, the pipeline has decoded, timestamped, and published hundreds of millions of events with practically zero loss, and folding the timestamped stream on the 60-second accelerator supercycle recovers the machine's periodic structure directly from the published data.

Rossel, Jacob [Fermilab; UC, Berkeley (main)] (ORC↗
DOE OSTI2026

Mechanistic Modeling of TEG Dehydrator Emissions in Oil and Gas Industry

This work presents a mechanistic modeling approach for simulating methane emissions from triethylene glycol (TEG) dehydrators used in oil & gas (O&G) operations. The model was developed as a modular component of the Mechanistic Air Emissions Simulator (MAES) tool, incorporating species-specific absorption and emission dynamics through two-level, second-order polynomial regression (PR) models trained on ProMax simulation data: (1) species-level regression models that track the transfer rates of individual gas species within the dehydrator unit streams, and (2) outlet flow stream regression models that predict the fraction of inlet gas distributed among the outlet streams of the dehydrator unit. These behaviors were characterized over a range of glycol circulation ratios, wet gas pressures, and temperatures. The model was validated using root mean square error (RMSE) analysis. The species-level PR achieved low root mean square error (RMSE) values (<0.03) for light hydrocarbon species across all dehydrator components, ranging from 0.0009 for methane to 0.029 for normal pentane. Similarly, the outlet-level PR yielded RMSE values below 0.002 for the dry gas fraction, 0.001 for the flash tank fraction, and 0.002 for the still vent fraction, demonstrating strong agreement between predicted and reference ProMax values. When deployed at field facilities, the model significantly improved MAES-simulated dehydrator emissions, revealing that gas-assisted glycol pump emissions are the dominant contributors to both dehydrator-level and site-level methane emissions under uncontrolled conditions. Further analysis of the 154 dehydrator units reported by operators under the AMI 2024 project showed that 54 units (31%) used gas-driven glycol pumps, of which 6 units (11%) operated with uncontrolled flash tanks, and 22 units (40.7%) were identified as potentially oversized. Of the six dehydrator units with uncontrolled gas-assisted pumps, pump emissions accounted for 90.25% of total dehydrator emissions and 63.10% of total site-level emissions. These findings highlight substantial opportunities for emissions mitigation through equipment upgrades.

MAES↗
DOE OSTI2026

Recycling of Printed Circuit Boards to Recover Critical Materials

The printed circuit board (PCB), a central component of most electronic devices, represents a significant fraction of the electronic product waste stream. The complex composition of PCBs, consisting of metals, polymers, and fiberglass, requires specialized recovery steps to reclaim valuable and critical materials and the safe disposal of brominated compounds. In this review paper, we describe the current state of critical material recovery and traditional recycling technologies and identify key obstacles to large-scale implementation. Metals present at high concentrations, such as copper, lead, and iron, are conventionally recovered from PCBs using hydrometallurgical, pyrometallurgical, or electrometallurgical processes. Hydrometallurgical methods achieve high selectivity through chemical leaching but pose significant challenges for effluent and reagent recovery. Pyrometallurgical methods facilitate rapid metal separation through smelting but require substantial energy and may release harmful gases. Electrometallurgical techniques produce high-purity metals but are constrained by pretreatment requirements and the consumption of energy. The non-metallic fraction of PCB waste is recycled using thermochemical conversion, microwave-aided heating, and direct recycling of epoxy–fiberglass composites, enabling material or energy recovery. The recovered polymer from direct recycling may have reduced mechanical strength and poor compatibility with new polymer matrices, and the resulting products from the thermal conversion suffer from incomplete conversion, degradation of quality, and residual contamination, as compared to synthetic polymers. Recent process developments have focused on extracting rare earth and supply-critical materials present at lower concentrations in the waste stream. The literature on existing and emerging approaches for recycling PCB wastes is reviewed to identify sustainable, economically viable, and environmentally responsible strategies for the recovery and reuse of critical materials from waste streams.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The Impact of Alfvénic Shear Flow on Magnetic Reconnection and Turbulence

Magnetic reconnection is a fundamental and omnipresent energy conversion process in plasma physics. Novel observations of fields and particles from Parker Solar Probe (PSP) have shown the absence of reconnection in a large number of current sheets in the near-Sun solar wind. Using near-Sun observations from PSP encounters 4–11 (2020 January–2022 March), we investigate whether reconnection onset might be suppressed by velocity shear. We compare estimates of the tearing mode growth rate in the presence of shear flow for time periods identified as containing reconnecting current sheets versus nonreconnecting times, finding systematically larger growth rates for reconnection periods. Upon examination of the parameters associated with reconnection onset, we find that 85% of the reconnection events are embedded in slow, non-Alfvénic wind streams. We compare with fast, slow non-Alfvénic, and slow Alfvénic streams, finding that the growth rate is suppressed in highly Alfvénic fast and slow wind, and reconnection is not seen in these wind types, as would be expected from our theoretical expressions. These wind streams have strong Alfvénic flow shear, consistent with the idea of reconnection suppression by such flows. This could help explain the frequent absence of reconnection events in the highly Alfvénic, near-Sun solar wind observed by PSP. Finally, we find a steepening of both the trace and magnitude magnetic field spectra within reconnection periods in comparison to ambient wind. We tie this to the dynamics of relatively balanced turbulence within these reconnection periods and the potential generation of compressible fluctuations.

slow solar wind↗
DOE OSTI2026

Learning to Trigger: Reinforcement Learning at the Large Hadron Collider

High-throughput scientific facilities such as the Large Hadron Collider depend on real-time event filtering (\textit{triggering}) under tight constraints on bandwidth, latency, and storage. In practice, trigger menus are largely static and hand-tuned and can become suboptimal as detector conditions, pileup, and background composition drift over time. We cast online threshold tuning as a sequential decision-making problem: a reinforcement learning agent ingests streaming summaries of recent rates and signal-sensitive features and updates trigger thresholds to maximize signal efficiency while tracking a target background rate within a tolerance band. We adapt Group-Filtered Policy Optimization (GFPO) to streaming control and introduce two variants (GFPO-F, GFPO-FR) that enforce background rate feasibility during training. On a benchmark that emulates realistic collider operation, we study two representative triggers: a total transverse energy ($H_{T}$) trigger sensitive to pileup variation, and an anomaly-detection (AD) trigger based on reconstruction loss for rare or non-standard signatures. On Monte Carlo streams, our agent increases the fraction of in-tolerance time intervals by 48% ($H_T$) and 28% (AD), with a cumulative gain of up to 2% in signal efficiency on those in-tolerance intervals. Transferring from simulation to \emph{real} collision data (CMS Run 283408), the same agent, without fine-tuning, achieves a 56% ($H_T$) and 28% (AD) in-tolerance improvement over baselines, with further signal-efficiency gain on both triggers. To our knowledge, this is the \emph{first} demonstration of RL-based trigger control on real Large Hadron Collider collision data. Code is available at https://github.com/Zixind/GFPO_LHC (see repo for details).

Ding, Zixin [Chicago U.]↗
DOE OSTI2026

A Single-Board Fermilab Timing Pipeline on the Xilinx KR260: Decode, Timestamp, Publish, Mirror

Full-width abstract \renewcommand{\maketitlehookd}{% \begin{abstract} \noindent Fermilab's accelerator timing links broadcast short event codes to thousands of devices at once, but the links themselves carry no absolute notion of time; that comes separately from a White Rabbit reference. This work builds the piece that ties the two together on a single board. On a Xilinx Kria KR260 (Zynq UltraScale+), the programmable logic decodes a real Fermilab TCLK link, stamps every event with an absolute White-Rabbit \{sec, ns\} UTC time, and reads the timestamped stream out over AXI4-Lite; a thin Linux process on the same die publishes each event into a Redis stream on the control network. To exercise the full chain on one board, the decoded events are re-encoded as gigabit ACLK, transmitted out an SFP+ optical port, looped back over a short fiber jumper, and decoded again on the same timeline, and are additionally mirrored as an ACLK-Lite Manchester waveform for benchtop probing. Across sustained, multi-day testing against real Fermilab TCLK, the pipeline has decoded, timestamped, and published hundreds of millions of events with practically zero loss, and folding the timestamped stream on the 60-second accelerator supercycle recovers the machine's periodic structure directly from the published data.

Rossel, Jacob [UC, Berkeley; Fermilab] (ORCID:0009↗
DOE OSTI2026

Impact of Fermentation-Derived Substrates on Hydrogen Production in Zero-Gap Microbial Electrolysis Cells

Zero-gap microbial electrolysis cells (MECs) represent a promising platform for hydrogen production from liquid waste streams due to reduced interelectrode spacing that lowers internal resistance and enhances mass transport. However, the performance and stability of zero-gap MECs treating chemically complex feedstocks remain insufficiently characterized. Here, we operated zero-gap MECs with real, unamended, corn stover dark fermentation effluent containing a wide range of organic substrates. The MECs fed fermentation effluent achieved a maximum current density of 24 A/m2 (15+-6 A/m2 over the cycle) and a hydrogen production rate of 75 L/L-d (42+-19 L/L-d over the cycle). The substrates were consumed at different rates, indicating substrate-selective utilization by the anodic microbial community. Acetate supported high and stable current generation, whereas ethanol, formate, lactate, and amino acids induced varying degrees of inhibition depending on their concentration. Residual sugars caused pronounced current fluctuations, consistent with ongoing fermentation and local pH changes. A diverse microbial community was crucial for efficiently utilizing complex organics and maximizing electrochemical performance. These results demonstrate how and to what extent substrate composition regulates zero-gap MEC performance and that microbial community and operational conditions can be leveraged to enhance performance. These novel findings provide practical guidance for achieving robust hydrogen recovery from chemically heterogeneous real liquid waste streams.

08 HYDROGEN↗
DOE OSTI2025

Multiple Sources of Riparian Wetland Suspended Solids during Episodic Rain Events: Influence on Uranium Transport

Suspended solids can be the primary vector for transporting contaminants in streams. The objective of this study was to determine whether changes in the properties of suspended solids during rain events impacted contaminant transport. Stream water was collected during five episodic events downstream from a U-contaminated wetland located in South Carolina, USA. The suspended particles were initially composed of Fe-flocs (particles formed in situ prior to the rain event) that had significantly greater Fe, Mn, organic-C, and U content than particles collected later during a sampling rain event. XANES and EXAFS revealed that U in the Fe-flocs was U(VI) and that it was not incorporated in a mineral structure but existed as inner- or outer-sphere adsorbed uranyl species associated with organic matter and Fe-oxides. The uranyl had an extraordinarily high affinity for the suspended solids, with solid to liquid U ratios of >72,000 (μg/kg)/(μg/L). After the initial flush of Fe-flocs, a greater fraction of the suspended solids had lower organic-C, Fe, Mn, and amorphous phases and were composed of more quartz, kaolinite, and gibbsite, resulting in lower U concentrations than those in the solids collected earlier in the rain event. This study highlights the importance of understanding suspended solids as transport vectors and their potential dynamic nature during rain events.

computer simulations↗
DOE OSTI2026

Field and Model Data Associated with the Manuscript “Drivers of Streamflow Intermittency in Humid Regions: 1. Evaluating Above- and Below-ground Controls of Flow Persistence in a Forested Catchment”

This package contains field data, modeling files, and scripts supporting the investigation of the drivers of streamflow intermittency in a forested catchment. It includes the field data collected from electrical resistivity tomography (ERT) surveys, ground penetrating radar (GPR), continuous self-potential (SP) monitoring, electromagnetic (EM) imaging, groundwater and stilling well. In addition, it contains the data and results of the coupled water- and electrical-flow model developed using the COMSOL Multiphysics and Advanced Terrestrial Simulator (ATS), as well as software files and Jupyter notebooks used to process the data and generate figures in the manuscript submitted for peer review. The data archive is organized in the following directories: 1) Climate Includes hourly precipitation and daily evapotranspiration time series (2024 – 2025) provided as CSV files, alongside a text file detailing dataset units. 2) Coupled_model Contains two subfolders: Synthetic and Field_Application subfolder. Synthetic subfolder contains the ATS XML input script (can be opened using any code editor) for the four synthetic hydrological cases tested (Connected and gaining, Connected and losing, Disconnected and losing, and dry stream). It also includes other experimental cases to test the influence of precipitation and concentration gradient. For each synthetic case, the flow model simulation is executed using the ATS XML scripts and the included Python script (generate_data_set.py) to convert ATS output to COMSOL-ready input. COMSOL Multiphysics template (.mph can be opened with the commercial software COMSOL and requires a license) is executed using the ATS output data to simulate the potential field. It also includes the Synthetic_model_plot.ipynb (can be opened using any code editor) to visualize the SP result and generate manuscript figures. The data subfolder contains mesh files to run both the ATS (.exo and .stl files can be viewed using Paraview; .h5 files can be opened using HDFView software and h5py Python package) and COMSOL models. Field_Application subfolder contains two subfolders: ES_MDA_inversion and Final_Model. ES_MDA_inversion contains the Python script (.py can be opened using any code editor) and SP observation data used to run the Ensemble Smoother with Multiple Data Assimilation (ES-MDA) inversion sequence to get the optimal model parameters. The Final_model subfolder contains the ATS XML input scripts, data files, output data for the two SP sites. The same workflow steps outlined for the Synthetic subfolder apply here. It also contains the Jupyter notebook (Plot_final_calib.ipynb) to visualize the results of the modeled SP, stream-groundwater exchange and moisture content. 3) Discharge Includes the electrical conductivity (EC) time series (provided as CSV files) from salt slug injections. It also includes the Jupyter notebook (Discharge_process.ipynyb) used to estimate discharge. All discharge measurements collated into rating_curve_processed.csv 4) EM Contains the CSV file of the EM data from the DUALEM-42, including spatial coordinates (x, y, z), apparent conductivity, and in-phase measurements at 2 m coil separations for horizontal coplanar (HCP) and perpendicular (PRP) geometries. 5) ERT Contains raw resistivity data (provided as CSV files), spatial location of each of the electrodes (provided as CSV files), and files used for the resistivity inversion (.resipy can be opened with the open-source ResIPy software). 6) GPR Includes GPR field datasets collected at 100 MHz and 250 MHz antenna frequencies, along with the processing/interpretation project file (GPR_process.gpz can be viewed using EKKO_Project 6, a commercial software by Sensors & Software that requires a license). 7) Slug_test Includes the slug test data at all the groundwater wells provided as CSV files, as well as the Jupyter notebook (Slug_test.ipynb) for calculating hydraulic conductivity. 8) SP Contains the SP data collected in field at the two SP sites (one in the perennial reach and the other in the intermittent reach), provided as DAT files. 9) Well_data Contains two subfolders: 1) Raw, which provides unprocessed pressure, electrical conductivity and temperature timeseries downloaded from the loggers in all the groundwater and stilling wells, and 2) Processed, which contains sorted, QA/QC timeseries data for each well. The data archive also contains data_process.ipynb, a Jupyter notebook used for field data analysis and generating figures (plotting well, SP, climate, and discharge data, as well as calculating head gradient at sites with nested groundwater wells). It also includes DTW.ipynb, a Jupyter notebook containing the code for the dynamic time warping (DTW) with sliding window to evaluate SP signal synchronicity.

ATS↗
DOE OSTI2026

BOTTLE: Hybrid Chemical-Mechanical Separation and Upcycling of Mixed Plastic Waste

The main objective of this project is to develop a hybrid mechanical-chemical recycling technology for multilayered and laminated plastics. We aimed to separate and upcycle up to more than 80% of the two main constituents of such structures, polyolefins and polyesters, for a significantly lower cost and at higher energetic efficiency and much larger throughputs than chemical recycling. At the end of the project, the team was able to: a) Develop an extrusion-based separation technology that resorts to polyester depolymerizaton and extraction and allows for more than 90% of the polyester to be separated in the melt from the main polyolefin stream. b) Depolymerize the separated PET to more than 90%, which facilitates its posterior repolymerization and guarantees its retention in the polymer value-chain. c) Develop a zeolite-induced extrusion-based technology able to conduct continuous catalytic cracking of polyolefins, including highly contaminated PCR streams, at temperatures as low as 350 OC. d) Show, using LCA/TEA analysis that the two technologies are much more advantageous techno-economically and over the material’s life cycle than existing recycling technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Accuracy optimized neural networks do not effectively model optic flow tuning in brain area MSTd

Accuracy-optimized convolutional neural networks (CNNs) have emerged as highly effective models at predicting neural responses in brain areas along the primate ventral stream, but it is largely unknown whether they effectively model neurons in the complementary primate dorsal stream. We explored how well CNNs model the optic flow tuning properties of neurons in dorsal area MSTd and we compared our results with the Non-Negative Matrix Factorization (NNMF) model, which successfully models many tuning properties of MSTd neurons. To better understand the role of computational properties in the NNMF model that give rise to optic flow tuning that resembles that of MSTd neurons, we created additional CNN model variants that implement key NNMF constraints – non-negative weights and sparse coding of optic flow. While the CNNs and NNMF models both accurately estimate the observer's self-motion from purely translational or rotational optic flow, NNMF and the CNNs with nonnegative weights yield substantially less accurate estimates than the other CNNs when tested on more complex optic flow that combines observer translation and rotation. Despite its poor accuracy, NNMF gives rise to tuning properties that align more closely with those observed in primate MSTd than any of the accuracy-optimized CNNs. This work offers a step toward a deeper understanding of the computational properties and constraints that describe the optic flow tuning of primate area MSTd.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Wireless Frequency‐Multiplexed Acoustic Array‐Based Acoustofluidics

Abstract Acoustofluidics has shown great potential in enabling on‐chip technologies for driving liquid flows and manipulating particles and cells for engineering, chemical, and biomedical applications. To introduce on‐demand liquid sample processing and micro/nano‐object manipulation functions to wearable and embeddable electronics, wireless acoustofluidic chips are highly desired. This paper presents wireless acoustofluidic chips to generate acoustic waves carrying sufficient energy and achieve key acoustofluidic functions, including arranging particles and cells, generating fluid streaming, and enriching in‐droplet particles. To enable these functions, the wireless acoustofluidic chips leverage mechanisms, including inductive coupling‐based wireless power transfer (WPT), frequency multiplexing‐based control of multiple acoustic waves, and the resultant acoustic radiation and drag forces. For validation, the wirelessly generated acoustic waves are measured using laser vibrometry when different materials (e.g., bone, tissue, and hand) are inserted between the WPT transmitter and receiver. Moreover, the wireless acoustofluidic chips successfully arrange nanoparticles into different patterns, align cells into parallel pearl chains, generate streaming, and enrich in‐droplet microparticles. This research is anticipated to facilitate the development of embeddable wireless on‐chip flow generators, wearable sensors with liquid sample processing functions, and implantable devices with flow generation and acoustic stimulation abilities for engineering, veterinary, and biomedical applications.

Li, Jiali↗