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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

A primer on forest structure measurement with lidar for ecologists

Light detection and ranging (lidar) technology has fundamentally advanced the way we measure forest structure, facilitating new insights into ecological processes. Lidar for forest ecology applications is deployed on multiple types of platforms that operate from the ground, air, or space, and each has associated strengths and limitations. Ideally, the choice of what kind of lidar to use in a particular study should be guided by the ecological question of interest; however, practical considerations of cost, data availability, and processing tools can be equally important. This synthesis is a practical introduction to how different lidar platforms characterize forest structure (e.g., tree size/location, wood volume, branching structure, aboveground biomass, leaf properties), designed for a general audience of ecologists (not remote sensing scientists) seeking an accessible introduction to the use of lidar. We also provide examples of novel ecological insights from recent lidar research and describe current limitations and areas of expected improvement. Last, we include an appendix of data collected from terrestrial, mobile, unoccupied aerial system, airplane, and satellite lidar platforms within a common temperate forest area, with associated code to allow new lidar users to visualize and manipulate data in R.

Cushman, KC [ORNL] (ORCID:0000000234641151)↗
DOE OSTI2020

Neural networks for modeling and control of particle accelerators

Charged particle accelerators support a wide variety of scientific, industrial, and medical applications. They range in scale and complexity from systems with just a few components for beam acceleration and manipulation, to large scientific user facilities that span many kilometers and have hundreds-to-thousands of individually-controllable components. Specific operational requirements must be met by adjusting the many controllable variables of the accelerator. Meeting these requirements can be challenging, both in terms of the ability to achieve specific beam quality metrics in a reliable fashion and in terms of the time needed to set up and maintain the optimal operating conditions. One avenue toward addressing this challenge is to incorporate techniques from the fields of machine learning (ML) and artificial intelligence (AI) into the way particle accelerators are modeled and controlled. While many promising approaches within AI/ML could be used for particle accelerators, this dissertation focuses on approaches based on neural networks. Neural networks are particularly well-suited to modeling, control, and diagnostic analysis of nonlinear systems, as well as systems with large parameter spaces. They are also very appealing for their ability to process high-dimensional data types, such as images and time series (both of which are ubiquitous in particle accelerators). In this work, key studies that demonstrated the potential utility of modern neural network-based approaches to modeling and control of particle accelerators are presented. The context for this work is important: at the start of this work in 2012, there was little interest in AI/ML in the particle accelerator community, and many of the advances in neural networks and deep learning that enabled its present success had not yet been made at that time. As such, this work was both an exploration of possible application areas and a generator of proof-of-concept demonstrations in these areas.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Manipulating Photoconduction in Cu–Pyrene 1-D Coordination Nanosheets by Modulating Interlayer π–π Stacking

2D conductive metal organic frameworks (MOFs) have become promising candidates for applications in electronic and photoelectronic devices. However, the lack of a fundamental understanding of the charge transport mechanism and synthetic design to alter their conductivity limits their potential applications. In this work, we report the design and synthesis of two 1D Cu–pyrene coordination nanosheets, Cu–pyrene-4,5,9,10 tetrathiol (Cu-PTT) and Cu–2,7-di-tert-butylpyrene-4,5,9,10-tetrathiol (Cu– tBu PTT), as models of 2D conductive MOFs, where the introduction of two different functional groups (H and tert-butyl groups) at the 2 and 7 positions of the PTT ligands is used to tune the interlayer distance and π–π stacking of MOFs. Using optical pump terahertz probe spectroscopy, we show that both nanosheets exhibit appreciable photoconduction, but the photoconduction in Cu– tBu PTT is reduced with respect to that in Cu–PTT, which can be attributed to the larger interlayer distance in the former leading to the decoupling of the interlayer charge transport pathway. Furthermore, these results demonstrate a unique strategy to investigate the impact of out-of-plane extended conjugation on photoconduction in conductive conjugated polymers, MOFs, or coordination nanosheets through adjusting their interlayer spacing.

14 SOLAR ENERGY↗
DOE OSTI2026

Nanoscale strain wave generation by a piezoelectric grating from polar vortices

Nanostructures formed by spontaneously broken symmetry have provided new ways to manipulate quantum states. Specifically, topological structures with periodic spatial ordering, such as polar vortices and skyrmions, can be ideal hosts for creating engineered responses in both spatial and frequency domains. So far, however, only a few examples of such hierarchical engineering have been reported in the literature. Here we demonstrate that the spatially modulated piezoelectric response of a polar vortex structure can create strain waves with a characteristic nanoscale wavefront. Using time-resolved pump–probe resonant X-ray scattering and diffraction measurements, coupled with dynamical phase-field simulations, we show that the piezoelectric modulation of the spontaneously formed polar vortex crystal functions as an acoustic diffraction grating. This system converts incoming laterally uniform strain waves into outgoing waves with a characteristic sub-terahertz frequency, driven by an intrinsic excitation of the polar vortex crystal. Moreover, our phase-field simulations suggest that the dynamic mechanical displacements exhibiting vortex textures are generated from both space- and time-varying piezoelectric responses. Our findings illustrate a new method for generating nanoscale strain waves with unique spatial textures by tuning the hierarchical order of polar topologies to engineer new collective modes, allowing for a wide range of control through the topological lattice.

ferroelectrics↗
DOE OSTI2026

Dynamical correlations and nonequilibrium sum rules in photodoped Hubbard ladders

Using matrix product state techniques we study the nonequilibrium dynamical response of the half-filled Hubbard ladder when subject to an optical pump. Optical pumping offers a way of producing and manipulating new strongly correlated phenomena by suppressing existing magnetic correlations. The ladder allows the effects of pump directionality to be investigated, and compared to a single chain it has strong spin-charge coupling and a fully gapped excitation spectrum, promising different nonequilibrium physics. We compute time-dependent correlations, including the nonequilibrium dynamical structure factors for spin and charge. By deriving a combined spin-charge sum rule that applies both in and out-of-equilibrium, we show that spectral weight is pumped directly from the antiferromagnetic spin response into a low energy 𝜔 ∼ 0 charge response below the Mott gap. The transfer of weight is pump direction dependent: pumping directed along the legs disrupts magnetic correlations more than pumping in the rung-direction, even if the post-pump energy density is similar. The charge correlation length is dramatically enhanced by the pump, whilst the spin correlations are most strongly suppressed at nearest- and next-nearest-neighbor spacings. After the pump the system is in a nonthermal correlated metallic state, with gapless charge excitations and approximately equal spin and charge correlation lengths, emphasizing the importance of treating these degrees of freedom on an equal footing in nonequilibrium systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Electrical Transport Interplay with Charge Density Waves, Magnetization, and Disorder Tuned by 2D van der Waals Interface Modification via Elemental Intercalation and Substitution in ZrTe 3 , 2H-TaS 2 , and Cr 2 Si 2 Te 6 Crystals

Electrical transport in 2D materials exhibits unique behaviors due to reduced dimensionality, broken symmetries, and quantum confinement. It serves as both a sensitive probe for the emergence of coherent electronic phases and a tool to actively manipulate many-body correlated states. Exploring their interplay and interdependence is crucial but remains underexplored. This review integratively cross-examines the atomic and electronic structures and transport properties of van der Waals-layered crystals ZrTe 3 , 2H-TaS 2 , and Cr 2 Si 2 Te 6 , providing a comprehensive understanding and uncovering new discoveries and insights. A common observation from these crystals is that modifying the atomic and electronic interface structures of 2D van der Waals interfaces using heteroatoms significantly influences the emergence and stability of coherent phases, as well as phase-sensitive transport responses. In ZrTe 3 , substitution and intercalation with Se, Hf, Cu, or Ni at the 2D vdW interface alter phonon–electron coupling, valence states, and the quasi-1D interface Fermi band, affecting the onset of CDW and SC, manifested as resistance upturns and zero-resistance states. We conclude here that these phenomena originate from dopant-induced variations in the lattice spacing of the quasi-1D Te chains of the 2D vdW interface, and propose an unconventional superconducting mechanism driven by valence fluctuations at the van Hove singularity, arising from quasi-1D lattice vibrations. Short-range in-plane electronic heterostructures at the vdW interface of Cr 2 Si 2 Te 6 result in a narrowed band gap. The sharp increase in in-plane resistance is found to be linked to the emergence and development of out-of-plane ferromagnetism. The insertion of 2D magnetic layers such as Mn, Fe, and Co into the vdW gap of 2H-TaS 2 induces anisotropic magnetism and associated transport responses to magnetic transitions. Overall, 2D vdW interface modification offers control over collective electronic behavior, transport properties, and their interplays, advancing fundamental science and nanoelectronic devices.

2D van der Waals interfaces↗
DOE OSTI2023

Evaluating Ion Accumulation and Storage in Traveling Wave Based Structures for Lossless Ion Manipulations

Structures for lossless ion manipulations (SLIM) technology has demonstrated high resolving power ion mobility separation and flexibility to integrate complex ion manipulations into a single experimental platform. To enable IMS separations, trapping/accumulating ions inside SLIM (or in-SLIM) prior to injection of a packet for separations provides ease of operation and reduces the need for dedicated ion traps external to SLIM. To fully characterize the ion accumulation process, we have evaluated the effect of TW amplitudes, ion collection times, and storage times on the “in-SLIM” accumulation process. The study utilized a SLIM module comprising 5 distinct tracks, each with a specific ion accumulation configuration. The effect of the TW conditions on the accumulation process was investigated for a 3-peptide mixture: kemptide, angiotensin II, and neurotensin at a TW speed of 106 m/s. The effect of ion accumulation time/collection time and storage time was investigated, in addition to TW amplitude. Overall, the signal of the analyte ions increased when the ion collection time increased from 49 to 163 ms but decreased when the ion collection time increased further to 652 ms due to the space charge effects. Ion losses were observed at high TW amplitudes (e.g., 15 V p–p and 20 V p–p ). In addition, under space charge conditions (e.g., collection times of 163 and 652 ms), the signal of the analyte ions decreased with an increase in storage times for all TW amplitudes applied to the trapping region. For ion accumulation, the data indicate that gentler TW conditions must be utilized to minimize ion losses and fragments to benefit from the “in-SLIM” accumulation process. Finally, wider SLIM tracks provided better performance than those with narrower tracks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

A chromosome-scale reference genome of trifoliate orange ( Poncirus trifoliata ) provides insights into disease resistance, cold tolerance and genome evolution in Citrus

Trifoliate orange (Poncirus trifoliata), a deciduous close relative of evergreen Citrus, has important traits for citrus production, including tolerance/resistance to citrus greening disease (Huanglongbing, HLB) and other major diseases, and cold tolerance. It has been one of the most important rootstocks, and one of the most valuable sources of resistance and tolerance genes for citrus. Here we present a high-quality, chromosomescale genome assembly of P. trifoliata. The 264.9-Mb assembly contains nine chromosomal pseudomolecules with 25 538 protein-coding genes, covering 97.2% of the estimated gene space. Comparative analyses of P. trifoliata and nine Citrus genomes revealed 605 species-specific genes and six rapidly evolving gene families in the P. trifoliata genome. Poncirus trifoliata has evolved specific adaptation in the C-repeat/ DREB binding factor (CBF)-dependent and CBF-independent cold signaling pathways to tolerate cold. We identified candidate genes within quantitative trait loci for HLB tolerance, and at the loci for resistance to citrus tristeza virus and citrus nematode. Genetic diversity analysis of Poncirus accessions and Poncirus/ Citrus hybrids shows a narrow genetic base in the US germplasm collection, and points to the importance of collecting and preserving more natural genetic variation. Two phenotypically divergent Poncirus accessions are found to be clonally related, supporting a previous conjecture that dwarf Flying Dragon originated as a mutant of a non-dwarfing type. The high-quality genome reveals features and evolutionary insights of Poncirus, and it will serve as a valuable resource for genetic, genomic and molecular research and manipulation in citrus.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A multiscale design method using interpretable machine learning for phononic materials with closely interacting scales

Manipulating the dispersive characteristics of vibrational waves is beneficial for many applications, e.g., high-precision instruments. architected hierarchical phononic materials have sparked promise tunability of elastodynamic waves and vibrations over multiple frequency ranges. In this article, hierarchical unit-cells are obtained, where features at each length scale result in a band gap within a targeted frequency range. Our novel approach, the ‘‘hierarchical unit-cell template method,’’ is an interpretable machine-learning approach that uncovers global unit-cell shape/topology patterns corresponding to predefined band-gap objectives. A scale-separation effect is observed where the coarse-scale band-gap objective is mostly unaffected by the fine-scale features despite the closeness of their length scales, thus enabling an efficient hierarchical algorithm. Moreover, the hierarchical patterns revealed are not predefined or self-similar hierarchies as common in current hierarchical phononic materials. Furthermore, our approach offers a flexible and efficient method for the exploration of new regions in the hierarchical design space, extracting minimal effective patterns for inverse design in applications targeting multiple frequency ranges.

Architected materials↗
DOE OSTI2024

Tailoring Local Chemical Ordering via Elemental Tuning in High-Entropy Alloys

Due to the large multi-elemental space desired for property screening and optimization, high-entropy alloys (HEAs) hold greater potential over conventional alloys for a range of applications, such as structural materials, energy conversion, and catalysis. However, the relationship between the HEA composition and its local structural/elemental configuration is not well understood, particularly in noble-metal-based HEA nanomaterials, hindering the design and development of nano-HEAs in energy conversion and catalysis applications. In this work, we determined precise atomic-level structural and elemental arrangements in model HEAs composed of RhPtPdFeCo and RuPtPdFeCo to unveil their local characteristics. Notably, by changing just one constituent element in the HEA (Rh to Ru), we found dramatic changes in the elemental arrangement from complete random mixing to a local single elemental ordering feature. Additionally, we demonstrate that the local ordering in RuPtPdFeCo can be further controlled by varying the Ru concentration, allowing us to toggle between local Ru clustering and distinct heterostructures in multicomponent systems. Overall, our study presents a practical approach for manipulating local atomic structures and elemental arrangements in noble-metal-based HEA systems, which could provide in-depth knowledge to mechanistically understand the functionality of noble-metal-based HEA nanomaterials in practical applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Observation of a phase transition within the domain walls of ferromagnetic Co3Sn2S2

Abstract The ferromagnetic phase of Co 3 Sn 2 S 2 is widely considered to be a topological Weyl semimetal, with evidence for momentum-space monopoles of Berry curvature from transport and spectroscopic probes. As the bandstructure is highly sensitive to the magnetic order, attention has focused on anomalies in magnetization, susceptibility and transport measurements that are seen well below the Curie temperature, leading to speculation that a “hidden” phase coexists with ferromagnetism. Here we report spatially-resolved measurements by Kerr effect microscopy that identify this phase. We find that the anomalies coincide with a deep minimum in domain wall (DW) mobility, indicating a crossover between two regimes of DW propagation. We demonstrate that this crossover is a manifestation of a 2D phase transition that occurs within the DW, in which the magnetization texture changes from continuous rotation to unidirectional variation. We propose that the existence of this 2D transition deep within the ferromagnetic state of the bulk is a consequence of a giant quality factor for magnetocrystalline anisotropy unique to this compound. This work broadens the horizon of the conventional binary classification of DWs into Bloch and Néel walls, and suggests new strategies for manipulation of domain walls and their role in electron and spin transport.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dimensional Evolution Guides Property Control in the A n Cu 4– n TiS 4 Semiconductor Series

Through progressive reduction of the three-dimensional (3D) covalent network of Cu 4 TiS 4 , we isolate seven new members of the A n Cu 4–n TiS 4 family (A = alkali metal; n = 0–4), spanning 3D, 2D, 1D, and 0D structural fragments. The dimensional reduction is rational, as it preserves the edge-sharing connectivity between [CuS 4 ] 7– and [TiS 4 ] 4– tetrahedra across the series. This structural evolution is driven by the stepwise substitution of Cu with alkali metals, guiding the formation of fragments with reduced dimensionality. The effects of “n” and “A” on the crystal structures, stabilities, electronic structures, and optoelectronic properties are profound, demonstrating that the manipulation of alkali metal size and A n Cu 4–n TiS 4 stoichiometry enables predictable variations in structure and properties. For example, the n = 0 and n = 4 end members of the A n Cu 4–n TiS 4 family set the range of achievable band gaps with 2.00 eV for Cu 4 TiS 4 , 2.60 eV for Na 4 TiS 4 , and intermediate values for the n = 1–3 members. Notably, CsCu 3 TiS 4 exhibits exceptional air stability and congruent melting, with density functional theory (DFT) calculating moderate hole and electron effective masses in specific crystallographic directions (mh = 1.24m 0 , me = 0.87m 0 ). Additionally, A 3 CuTiS 4 (A = Na, K, Rb) displays direct band gap behavior and long photoluminescence lifetimes of 2.3–8.6 μs, and K 3 CuTiS 4 has a PLQY of 5.19%. These findings underscore the potential of the A n Cu 4–n TiS 4 family for applications in optoelectronics and demonstrate widely applicable design concepts that unveil rational stoichiometries within a given composition space to generate a series of crystal structures related through an evolving covalent dimensionality that corresponds to a predictable electronic structure and property progression.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Chemical Framework to Design Linear-like Relaxors toward Capacitive Energy Storage

ABO 3 -type perovskite relaxor ferroelectrics (RFEs) have emerged as the preferred option for dielectric capacitive energy storage. However, the compositional design of RFEs with high energy density and efficiency poses significant challenges owing to the vast compositional space and the absence of general rules. Here, we present an atomic-level chemical framework that captures inherent characteristics in terms of radius and ferroelectric activity of ions. By categorizing A/B-site ions as host framework, rattling, ferroelectrically active, and blocking ions and assembling these four types of ions with specific criteria, linear-like relaxors with weak locally correlated and highly extendable unit-cell polarization vectors can be constructed. As example, we demonstrate two new compositions of Bi 0.5 K 0.5 TiO 3- based and BaTiO 3- based relaxors, showing extremely high recoverable energy densities of 17.3 and 12.1 J cm –3 , respectively, both with a high efficiency of about 90%. Further, the role of different types of ions in forming heterogeneous polar structures is identified through element-specific local structure analysis using neutron total scattering combined with reverse Monte Carlo modeling. Further, our work not only opens up new avenues toward rational compositional design of high energy storage performance lead-free RFEs but also sheds light on atomic-level manipulation of functional properties in compositionally complex ferroelectrics.

25 ENERGY STORAGE↗
DOE OSTI2020

Experiences with Mobile Units for Liquid RAW Management in Slovak and Czech Nuclear Power Plants - 20089

In Nuclear Power Plants VVER 440, Russian type of reactor there was designed auxiliary building where according to basic design all operational liquid radioactive waste (RAW) should be stored in large capacity tanks during the whole operational period. That's why there are in the auxiliary building several types of large storage tanks with different internal volume from 110 m{sup 3}, 460 m{sup 3} to 550 m{sup 3}. Storage tanks are usually situated in rooms with limited access and minimal handling space. Operational waste was here transported by the system of pipes from the reactor building. There is possibility to handle with the waste and set the pipeline in to the chosen storage tanks. Nobody takes care about the quality of waste in the storage tanks and waste waits for the period of decommissioning when it should be treated. The storage capacity was designed for the whole lifetime of nuclear power plant. Currently the life time of Nuclear Power Plant's (NPP) is extended and there is necessity to retrieve and treat the RAW in advance. Another reason why the stakeholder, who operates the NPPs, decides to treat the operational RAW during the operational period of NPP, is that there are still new requirements from safety point of view which need to have free available space for example because of potential accident. So treating the operational RAW during the life time of NPP is present issue. Because in basic design nobody cares how the RAW will be retrieved and treated in the future, there is usually no technology for that in existing NPP. Some of the NPPs build an additional building with suitable technologies for treatment or hire the company which offer mobile technology for retrieving the waste from the storage tanks. Wood Nuclear Slovakia PLC provides all activities within liquid RAW management projects by its own staff including management, design, construction and deployment of equipment, engineering support, as well as all RAW retrieval and solidification activities. We offer RAW management as a service without any significant changes in existing NPP's buildings and technology. It is always a technical challenge to conduct successful retrieval, pumping and treatment of RAW. The complex RAW retrieval system can be designed as tailored solution for any RAW storage according to known boundary conditions and specific needs. These systems are based on years of experience with RAW manipulation, processing and treatment and the whole RAW management knowledge by Wood Nuclear Slovakia LTD. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Developing And Scaling an OpenFOAM Model to Study Turbulent Flow in a HFIR Coolant Channel

Improving the understanding of how computational fluid dynamics (CFD) direct numerical simulations (DNS) of flows in the High Flux Isotope Reactor (HFIR) perform when run in parallel using the high performance computing (HPC) platform Summit at the Oak Ridge Leadership Computing Facility (OLCF) is of particular importance to boost the computational tools used to support HFIR conversion to low enriched fuel (LEU). Evaluation of scaling performance was driven by the increasing importance of graphics processing unit (GPU) usage in HPC, which is becoming the standard for modern supercomputers such as Summit. The desired results are to obtain a strong positive correlation between the computational resources dedicated to a problem and the relative speed-up of the simulation in comparison to a benchmark. This capability will allow substantially improvement in HFIR flow analytical capabilities, specifically when predicting turbulence properties at high Reynolds numbers. The study leverages previous simulation results performed with code PHASTA (finite element) on HPC platforms Cori (NERSC) and Theta (ALCF) [1] with computing options provided in the computing platform OpenFOAM (finite volume) at OLCF. Transitioning from PHASTA to OpenFOAM will (1) eliminate dependence on third-party software for mesh generation and manipulation, (2) reduce resource needs by employing modern architectures, and (3) build expertise for future modeling of HFIR-specific problems like heat transfer in involute geometry, entrance effects, flow structure in channel corners, and so on—all important issues when defining the available thermal margins in the transition to LEU. CPUs and GPUs differ significantly in their architecture and utilization, as discussed in the literature [2]. The most important differences are in the approach to computations and their memory. A single GPU contains a large quantity of cores, enabling it to perform with a much higher throughput than a CPU, but execution requires a different approach. GPU codes execute instructions using the Single-Instruction Multiple-Thread (SIMT) approach in which a single instruction is used for groups of threads called warps. A warp typically consists of 32 threads which must execute the same set of instructions, although on separate threads. Alternately, a CPU has far fewer cores that are much more flexible in their operation, excelling at quickly performing more complex serial computations. This is why GPUs have greater throughput when properly utilized. The second important difference is seen when comparing their memory spaces. Limited memory allocations and CPU–GPU communications cause a significant bottleneck in GPU-accelerated programs. Further study was required to properly take advantage of GPU resources. A comprehensive analysis of code performance and the model-specific features of turbulence constitutes the core of this work. In this study, a DNS simulation of HFIR channel turbulence was performed with the finite volume CFD code OpenFOAM v2112 and CUDA v11.0 on Red Hat Enterprise Linux v8.2. The OpenFOAM installation had AMGx integrated to enable GPU acceleration and utilizes the PETSc4FOAM library. The computational resources and the problem size were scaled on CPU and CPU + GPU architectures to gain a better understanding of the performance of a DNS problem on modern computing hardware. The study aimed to analyze the scaling of the code exclusively on CPUs and then to examine the scaling of the codes with GPU acceleration enabled. Scaling studies included CPU and GPU acceleration on a mesh of varying resolution to analyze the impact of problem size relative to computational resources. In the course of preparing the GPU configuration on Summit, mainly using the AMGX solvers, difficulties were encountered stemming from constant changes resulting from extensive ongoing development activities and the changing environment. This resulted in the inability to complete the GPU portion of the work. The code was compiled and tested, but production runs to assess acceleration were not performed because the used discretional compute time allocation expired as year-end approached. The Summit HPC platform is scheduled for decommissioning in 2024, making it unattractive for future use with Nvidia-based GPUs. Therefore, the work will be moved onto NERSC machines in FY24. An application was prepared and submitted, and sufficient node-hours were awarded to continue the research in the next calendar year. This report summarizes work performed thus far, which mostly focused on CPU OpenFOAM computing.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗
DOE OSTI2026

COMPASS-FME Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) Experiment Level 2 Sensor Data v2-1

This is the version v2-1 Level 2 (L2) data release for COMPASS-FME environmental sensors located at our Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experimental site. This manipulative, ecosystem-scale TEMPEST experiment addresses the potential for freshwater and estuarine-water disturbance events to alter tree function, species composition, and ecosystem processes in a deciduous coastal forest in MD, USA. The experiment uses a large-unit (2000 m2), un-replicated experimental design, with three 50 m × 40 m plots serving as control, freshwater, and estuarine-water treatments. Level 2 (L2) data consist of sensor observations from the COMPASS-FME synoptic sites, TEMPEST, and DELUGE. Compared to the L1 data, these are more consistent (always 15-minute timestamps for the entire year); better QA/QC’d (out of bounds, out of service, and extreme outlier values are removed); and more complete, with a gap-filled time series available alongside the main observations, and additional derived (calculated) variables. L2 data are intended to be rapidly and easily usable in analyses and simulations. However, algorithmic outlier identification always carries the risk of removing valid data, and Level 1 data may be more suitable for analyses that focus on variability or extreme events. This dataset includes: - An overall dataset README file that describes the current version, gives citation and contact information, etc. - Site- and year-specific folders, each holding variable-specific Parquet (a high performance, space efficient format; see https://parquet.apache.org) data files for each site and plot in that year. - Metadata files within each site-year folder provide full information on data units, expected ranges, contact information, detailed flood times, as well as a general description of the site. - Environmental sensor types that appear in the data files include weather (ClimaVUE50, CS, RM Young, and LI instruments in the graphs below); soil conditions (TEROS12); soil redox state (Redox); groundwater variables (AquaTROLL200 and AquaTROLL600); open water sondes (Exo); tree sap velocity (Sapflow); and system voltage and state (Datalogger). Data are reported every 15 minutes. Please see v2-1 TEMPEST L2 Sensor Package Quick Start.pdf for detailed information on data package structure, temporal coverage, and versioning. Data files are in Apache Parquet, a high performance, space efficient format for tabular data. These files can be read using R's `arrow` package (https://arrow.apache.org/docs/r/), with similar tools available in other languages. The TEMPEST flood events occurred on the following dates. They lasted for ~10 hours each day and delivered ~80,000 gallons to each plot; many data streams are available at 1 or 5 minute frequency during these periods. * Tests: Aug 25 (fresh plot) and Sep 9 (salt plot), 2021 * TEMPEST 1: June 22, 2022 * TEMPEST 2: June 6-7, 2023 * TEMPEST 3: June 11-13, 2024

EARTH SCIENCE > ATMOSPHERE > ATMOSPHERIC TEMPERATU↗
DOE OSTI2022

Electronic energy loss and ion velocity correlation effects in track production in swift-ion-irradiated LiNbO3: A quantitative assessment between structural damage morphology and energy deposition

The primary motivation for studying how irradiation modifies the structures and properties of solid materials involves the understanding of undesirable phenomena, including irradiation-induced degradation of components in nuclear reactors and space exploration, and beneficial applications, including material performance tailoring through ion beam modification and defect engineering. In this work, the formation mechanism of latent tracks with different damage morphologies in LiNbO 3 crystals under 0.09–6.17 MeV/u ion irradiation with an electronic energy loss from 2.6–13.2 keV/nm is analyzed by experimental characterizations and numerical calculations. Irradiation-induced damage is preliminarily evaluated via the prism coupling technique to analyze the correlation between the dark-mode spectra and energy loss profiles of irradiated regions. Under the irradiation conditions of different ion velocities and electronic energy losses, different damage morphologies, from individual spherical defects to discontinuous and continuous tracks, are experimentally characterized. During ion penetration process, the ion velocity determines the spatiotemporal distribution of deposited irradiation energy induced by electronic energy loss, meaning that the two essential factors including electronic energy loss and ion velocity co-affect the track damage. The inelastic thermal spike model is used to numerically calculate the spatiotemporal evolutions of energy deposition and the corresponding atomic temperature under different irradiation conditions, and a quantitative relationship is proposed by comparison with corresponding experimentally observed track damage morphologies. Additionally, the obtained quantitative relationship between irradiation conditions and track damage provides deep insight and guidance for understanding the damage behavior of crystal materials in extreme radiation environments and selecting irradiation parameters, including ion species and energies, for ion beam technique application in atomic-level defect manipulation, material modification, and micro/nanofabrication.

36 MATERIALS SCIENCE↗