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DOE OSTI2021

Harnessing Photoelectrochemistry for Wastewater Nitrate Treatment Coupled with Resource Recovery

Wastewater is a misplaced resource well suited to recover nutrients, value-added chemicals, energy, and clean water. A photoelectrochemical device is proposed to transform wastewater nitrates to ammonia and nitrous oxide, coupled with water oxidation. Numerical models were developed to quantify the dependence of process efficiencies and nitrogen-removal rates on light absorber band gaps, electrocatalytic kinetic parameters, competing oxygen reduction and hydrogen evolution reactions, and the reacting nitrate species concentrations that affect the mass-transfer limited current densities. With a single light-absorber and state-of-the-art catalysts, optimal solar-to-chemical efficiencies of 7% and 10% and nitrogen-removal rates of 260 and 395 gN m -2 day -1 are predicted for nitrate reduction to ammonia and nitrous oxide, respectively. The influence of competing reactions on the performance depends on the nitrate concentration and band gap of the light absorber modeled. Oxygen reduction is more dominant than hydrogen evolution to compete with the nitrate reduction reaction, but it is mass-transfer limited. Even with kinetic parameters that enhanced the driving forces for the competing reactions, the performance is only minimally affected by these reactions for optimally selected band gaps and nitrate concentrations larger than 100 mM. Here, theoretically predicted peak nitrogen removal rates and specific energy intensities are competitive with reported estimates for electrochemical and Sharon-Anammox processes for ammonia recovery and nitrogen removal, respectively. This result, together with the added benefit of harnessing sunlight to produce value-added products, indicates promise in the photoelectrochemical approach as a tertiary pathway to recover nutrients and energy from wastewater nitrates.

25 ENERGY STORAGE↗

DOE OSTI2018

Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm

To further improve fault diagnosis accuracy, a new hybrid feature selection approach combined with a genetic algorithm (GA) and support vector machine (SVM) is presented in this study. Adaptive synthetic technique and arctangent transformation method are adopted to improve the statistical property of the training set (IEC TC10 dataset). Five filter methods based on different evaluation metrics are employed to rank 48 input features derived from dissolved gas analysis (DGA). Then, feature combination methods are applied to aggregate feature ranks and form a lower-dimension candidate feature subset. The GA-SVM model is implemented to optimise parameters and select optimal feature subsets. 5-fold cross-validation accuracy of the GA-SVM is used to evaluate fault diagnosis capability of feature subsets and finally, a novel subset is determined as the optimal feature subset. Accuracy comparison manifests the superiority of the optimal feature subsets over that of conventional approaches. Besides, generalisation and robustness of the optimal subset are validated by testing DGA samples from the local power utility. Results indicate that the optimal feature subset obtained by the proposed method can significantly improve the accuracies of power transformer fault diagnosis.

Kari, Tusongjiang↗

DOE OSTI2021

Optimal renormalization of multiscale systems

While model order reduction is a promising approach in dealing with multi-scale time-dependent systems that are too large or too expensive to simulate for long times, the resulting reduced order models can suffer from instabilities. We have recently developed a time-dependent renormalization approach to stabilize such reduced models. In the current work, we extend this framework by introducing a parameter that controls the time-decay of the memory of such models and optimally selecting this parameter based on limited fully resolved simulations. First, we demonstrate our framework on the inviscid Burgers equation whose solution develops a finite-time singularity. Our renormalized reduced order models are stable and accurate for long times while using for their calibration only data from a full order simulation before the occurrence of the singularity. Furthermore, we apply this framework to the 3D Euler equations of incompressible fluid flow, where the problem of finite-time singularity formation is still open and where brute force simulation is only feasible for short times. Our approach allows us to obtain for the first time a perturbatively renormalizable model which is stable for long times and includes all the complex effects present in the 3D Euler dynamics. We find that, in each application, the renormalization coefficients display algebraic decay with increasing resolution, and that the parameter which controls the time-decay of the memory is problem-dependent.

Price, Jacob↗

DOE OSTI2023

Exceptional enhancement of mechanical properties in high-entropy alloys via thermodynamically guided local chemical ordering

Understanding the local chemical ordering propensity in random solid solutions, and tailoring its strength, can guide the design and discovery of complex, paradigm-shifting multicomponent alloys. First, we present a simple thermodynamic framework, based solely on binary enthalpies of mixing, to select optimal alloying elements to control the nature and extent of chemical ordering in high-entropy alloys (HEAs). Next, we couple high-resolution electron microscopy, atom probe tomography, hybrid Monte-Carlo, special quasirandom structures, and density functional theory calculations to demonstrate how controlled additions of Al and Ti and subsequent annealing drive chemical ordering in nearly random equiatomic face-centered cubic CoFeNi solid solution. We establish that short-range ordered domains, the precursors of long-range ordered precipitates, inform mechanical properties. Specifically, a progressively increasing local order boosts the tensile yield strengths of the parent CoFeNi alloy by a factor of four while also substantially improving ductility, which breaks the so-called strength–ductility paradox. Finally, we validate the generality of our approach by predicting and demonstrating that controlled additions of Al, which has large negative enthalpies of mixing with the constituent elements of another nearly random body-centered cubic refractory NbTaTi HEA, also introduces chemical ordering and enhances mechanical properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Assessing methods in fusion and fitting for time series construction in remote sensing-based earth observations

This study evaluates the comparative performance of spatiotemporal fusion and time-series fitting methods for constructing high-spatiotemporal-resolution remote sensing time-series data. Due to in-class similarity of fusion methods and fitting methods, we employ the Fit-FC (Fitting, spatial Filtering, and residual Compensation) model as a representative fusion method and the linear harmonic fitting model as a representative fitting method. Both Fit-FC and the linear harmonic fitting are widely used for high-spatiotemporal-resolution time-series data construction, and we modify the original Fit-FC model to enable automatic time-series fusion. To ensure data representativeness, we use 3 years (2019–2021) of Harmonized Landsat and Sentinel-2 surface reflectance datasets and Terra MCD43A4 products. Eight experimental regions are selected worldwide to guarantee generalization of the comparative performance between fusion and fitting methods, covering diverse land-use types (cropland, developed land, forest, and grassland) and varying climatological conditions. Time-series of NDVI and surface reflectance are analyzed under both actual observations and simulated data-missing scenarios. The constructed time-series data reveals that (1) the modified Fit-FC and linear harmonic fitting model achieve excellent performance in constructing high-resolution time-series images; (2) the fusion method outperforms the fitting method in constructing time-series of NDVI and surface reflectance images in cropland-, forest-, and grassland-dominated regions; (3) both methods achieve comparable performance in developed-dominated regions; (4) the fusion method is more robust to missing data, and better captures abrupt phenological transitions under conditions of continuous missing data; (5) the fitting method is computationally more efficient, making it suitable for large-scale time-series image reconstruction. This study provides valuable insights for selecting optimal strategies to generate high-resolution time-series images across diverse application scenarios and lays a foundation for extensions to other vegetation indices or land surface variables.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Using active learning to improve quasar identification for the DESI spectra processing pipeline

The Dark Energy Spectroscopic Instrument (DESI) survey uses an automatic spectral classification pipeline to classify spectra. QuasarNET is a convolutional neural network used as part of this pipeline originally trained using data from the Baryon Oscillation Spectroscopic Survey (BOSS). In this paper we implement an active learning algorithm to optimally select spectra to use for training a new version of the QuasarNET weights file using only DESI data, with the goal of improving classification accuracy. This active learning algorithm includes a novel outlier rejection step using a Self-Organizing Map to ensure we label spectra representative of the larger quasar sample observed in DESI. We perform two iterations of the active learning pipeline, assembling a final dataset of 5600 labeled spectra, a small subset of the approximately 1.3 million quasar targets in DESI's Data Release 1. When splitting the spectra into training and validation subsets we achieve similar performance to the previously trained weights file in completeness and purity calculated on the validation dataset but do so with less than one tenth of the amount of training data. The new weights also more consistently classify objects in the same way when used on unlabeled data compared to the old weights file. In the process of improving QuasarNET's classification accuracy we discovered a systemic error in QuasarNET's redshift estimation and used our findings to improve our understanding of QuasarNET's redshifts.

Machine learning↗

DOE OSTI2021

Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy

Abstract The majority of computational catalyst design focuses on the screening of material components and alloy composition to optimize selectivity and activity for a given reaction. However, predicting the metastability of the alloy catalyst surface at realistic operating conditions requires an extensive sampling of possible surface reconstructions and their associated kinetic pathways. We present CatGym, a deep reinforcement learning (DRL) environment for predicting the thermal surface reconstruction pathways and their associated kinetic barriers in crystalline solids under reaction conditions. The DRL agent iteratively changes the positions of atoms in the near-surface region to generate kinetic pathways to accessible local minima involving changes in the surface compositions. We showcase our agent by predicting the surface reconstruction pathways of a ternary Ni 3 Pd 3 Au 2 (111) alloy catalyst. Our results show that the DRL agent can not only explore more diverse surface compositions than the conventional minima hopping method, but also generate the kinetic surface reconstruction pathways. We further demonstrate that the kinetic pathway to a global minimum energy surface composition and its associated transition state predicted by our agent is in good agreement with the minimum energy path predicted by nudged elastic band calculations.

36 MATERIALS SCIENCE↗

DOE OSTI2021

An evolutionary algorithm for designing microbial communities via environmental modification

Despite a growing understanding of how environmental composition affects microbial communities, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbour thousands of different organisms and environmental substrates, making up a vast combinatorial space that precludes exhaustive experimental testing and computational prediction. Here, we present a method based on the combination of machine learning and metabolic modelling that selects optimal environmental compositions to produce target community phenotypes. In this framework, dynamic flux balance analysis is used to model the growth of a community in candidate environments. A genetic algorithm is then used to evaluate the behaviour of the community relative to a target phenotype, and subsequently adjust the environment to allow the organisms to approach this target. We apply this iterative process to thousands of in silico communities of varying sizes, showing how it can rapidly identify environments that yield desired taxonomic compositions and patterns of metabolic exchange. Moreover, this combination of approaches produces testable predictions for the assembly of experimental microbial communities with specific properties and can facilitate rational environmental design processes for complex microbiomes.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Mitigating Green's function Monte Carlo signal-to-noise problems using contour deformations

The Green's function Monte Carlo (GFMC) method provides accurate solutions to the nuclear many-body problem and predicts properties of light nuclei starting from realistic two- and three-body interactions. Controlling the GFMC fermion sign problem is crucial, as the signal-to-noise ratio decreases exponentially with imaginary time, requiring significant computing resources. Inspired by similar scenarios in lattice quantum field theory and spin systems, in this work, we employ integration contour deformations to improve the GFMC signal-to-noise ratio. Machine learning techniques are used to select optimal contours with minimal variance from parametrized families of deformations. As a proof of principle, we consider the deuteron binding energies and Euclidean density response functions. Here, we only observe mild signal-to-noise improvement for the binding energy case. On the other hand, we achieve an order of magnitude reduction of the variance for Euclidean density response functions, paving the way for computing electron- and neutrino-nucleus cross sections of larger nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Search for inelastic boosted dark matter with the ICARUS detector at the Gran Sasso Underground National Laboratory

We present the result of a search for inelastic boosted dark matter using the data corresponding to an exposure of 0.13 kton · year , collected by the ICARUS T-600 detector during its 2012–2013 operational period at the INFN Gran Sasso Underground National Laboratory. The benchmark boosted dark matter model features a multiparticle dark sector with a U ( 1 ) ′ gauge boson, the dark photon. The kinetic mixing of the dark photon with the Standard Model photon allows for a portal between the dark sector and the visible sector. The inelastic boosted dark matter interaction occurs when a dark matter particle inelastically scatters with an electron in the ICARUS detector, producing an outgoing, heavier dark sector state which subsequently decays back down to the dark matter particle, emitting a dark photon. The dark photon subsequently couples to a Standard Model photon through kinetic mixing. The Standard Model photon then converts to an electron-positron pair in the detector. This interaction process provides a distinct experimental signature that consists of a recoil electron from the primary interaction and an associated electron-positron pair from the secondary vertex. After analyzing 4,134 triggered events, the search results in zero observed events. Exclusion limits are set in the dark photon mass and coupling ( m X , ε ) parameter space for several selected optimal boosted dark matter mass sets and cover previously unexplored parameter space. Published by the American Physical Society 2025

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Measurement of branching fractions, C P asymmetry, and isospin asymmetry for B → ρ γ decays using Belle and Belle II data

We present measurements of B + → ρ + γ and B 0 → ρ 0 γ decays using a combined data sample of 772 × 10 6 B B ¯ pairs collected by the Belle experiment and 387 × 10 6 B B ¯ pairs collected by the Belle II experiment in e + e − collisions at the ϒ ( 4 S ) resonance. After an optimized selection, a simultaneous fit to the Belle and Belle II datasets yields 114 ± 12 B + → ρ + γ and 99 ± 12 B 0 → ρ 0 γ decays. The measured branching fractions are ( 13.1 − 1.9 − 1.2 + 2.0 + 1.3 ) × 10 − 7 and ( 7.6 ± 1.3 − 0.8 + 1.0 ) × 10 − 7 for B + → ρ + γ and B 0 → ρ 0 γ decays, respectively, where the first uncertainty is statistical and the second is systematic. We also measure the isospin asymmetry A I ( B → ρ γ ) = ( 10.9 − 11.7 − 7.3 + 11.2 + 7.8 ) % and the direct C P asymmetry A C P ( B + → ρ + γ ) = ( − 8.2 ± 15.2 − 1.3 + 2.0 ) % . Published by the American Physical Society 2025

Adachi, I. (ORCID:0000000322870173)↗

DOE OSTI2026

Search for GeV-scale dark matter from the Galactic Center with IceCube-DeepCore

Models describing dark matter as a novel particle often predict that its annihilation or decay into Standard Model particles could produce a detectable neutrino flux in regions of high dark matter density, such as the Galactic Center. In this work, we search for these neutrinos using ∼9 years of IceCube-DeepCore data with an event selection optimized for energies between 15 to 200 GeV. We considered several annihilation and decay channels and dark matter masses ranging from 15 GeV up to 8 TeV. No significant deviation from the background expectation from atmospheric neutrinos and muons was found. The most significant result was found for a dark matter mass of 201.6 GeV annihilating into a pair of bb¯ quarks assuming the Navarro–Frenk–White halo profile with a post-trial significance of 1.08σ. We present upper limits on the thermally averaged annihilation cross section of the order of 10-24 cm3 s-1, as well as lower limits on the dark matter decay lifetime up to 1026 s for dark matter masses between 5 GeV up to 8 TeV. These results strengthen the current IceCube limits on dark matter masses above 20 GeV and provide an order of magnitude improvement at lower masses. In addition, they represent the strongest constraints from any neutrino telescope on GeV-scale dark matter and are among the world-leading limits for several dark matter scenarios.

Abbasi, R↗

DOE OSTI2021

Measuring Irradiance for Bifacial PV Systems

The advent of bifacial PV systems drives new requirements for irradiance measurement at PV projects for monitoring and assessment purposes. While there are several approaches, there is still no uniform guidance for what irradiance parameters to measure and for the optimal selection and placement of irradiance sensors at bifacial arrays. Standards are emerging to address these topics but are not yet available. In this paper we review approaches to bifacial irradiance monitoring which are being discussed in the research literature and pursued in early systems, to provide a preliminary guide and framework for developers planning bifacial projects.

bifacial↗

DOE OSTI2025

Lossy Compression: An Online Multi-Stage Technology for High-Fidelity Synchro- Waveform Measurements

Effective real-time monitoring and analysis of distributed grids necessitate the use of synchro-waveform measurements, which capture almost all high-frequency disturbances and transient phenomena. However, due to limitations in high-speed measurements and network bandwidth, it is challenging to transfer all high-fidelity synchro-waveforms losslessly and successfully. To cope with these challenges, a hybrid-based online multi-stage compression algorithm is proposed to significantly improve the compression efficiency for synchro-waveform measurements. Initially, the multiple discrete Wavelet transformation is deployed to deconstruct the waveform components. The delta encoding is further developed to decrease the magnitude. In conjunction with the Lempel-Ziv-Markov chain, the hybrid compression algorithm is implemented to achieve real-time compression for the synchro-waveform measurements. Moreover, an innovative error index that synergizes the time and frequency domain error and correlation is formulated to evaluate the waveform distortion. By integrating compression ratio, suitable parameters can be optimally selected. Finally, the simulation, laboratory experiments, as well as field tests across a spectrum of sampling frequencies and time intervals are conducted to substantiate the efficacy of the proposed method. Here, the outcomes demonstrated that a compression ratio of approximately 15.5 and 17.83 can be reached for 0.5 s and 1 s data under both offline and online scenarios, which equates to a substantial 93.5% to 94.39% reduction in data storage requirements.

High-fidelity synchro-waveform measurements↗

DOE OSTI2020

Investigation of Various Transformer Topologies for HF Isolation Applications

High-frequency (HF) transformers are an essential part of many power electronic devices. The performance and behavior of HF transformers can greatly affect the efficiency and performance of all systems, particularly, from a parasitic parameter point of view. In this article, HF transformers' parasitic parameters, such as leakage inductances and parasitic capacitances, are analyzed using a novel analytical method, finite element method (FEM), and experimental measurements of different structures and winding arrangements. Also, the magnetic field, electric field, electric displacement field, and electric potential distribution within the transformers are simulated and analyzed. Four different HF transformers with E and U cores with different windings are designed and analyzed. Investigation outcomes help to classify structures according to the trade-off between leakage inductances and series parasitic capacitances. This information can later be used for the optimal selection and design of transformers as a function of their operating frequency for any power rating and voltage level. Moreover, 3-D FEM and experimental results validate the proposed methodology to be used for designing HF transformers in high-voltage/power applications.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Effect of pattern transfer process on roughness of block copolymer patterns from directed self-assembly

Block copolymer-directed self-assembly (DSA) remains promising for improving pattern quality and reducing the stochastic variations that challenge high numerical aperture extreme ultraviolet lithography. Equally critical is refining pattern transfer methods for accurately transferring the rectified DSA features to the underlying substrate. We compare two atomic layer deposition (ALD)-based techniques: sequential infiltration synthesis (SIS) and dry liftoff, applied to polystyrene-block-poly(methyl methacrylate) (PS-b-PMMA) DSA patterns. Both methods utilize aluminum oxide hard masks, with one synthesized through infiltration into the PMMA domains and the other through conformal ALD coating. High-resolution scanning electron micrographs were analyzed to measure line edge, width, and placement roughness for both the line (PMMA) and space (PS) features. Although both methods yielded similar overall 3σ rms roughness, they differed significantly in the frequency-dependent power spectral density (PSD) profiles. SIS reduced line placement roughness at length scales associated with the polymer pitch, but increased space width roughness at low frequencies, whereas dry liftoff mimicked the frequency content of the original guiding pattern. This study underscores the importance of PSD evaluation in selecting optimal pattern transfer strategies for specific applications.

Block copolymers↗

DOE OSTI2023

Mechanisms of ultrafast GHz burst fs laser ablation

Gigahertz (GHz) femtosecond (fs) lasers have opened possibilities for enhancing and controlling the laser machining quality to engineer the physicochemical properties of materials. However, fundamental understanding of laser-material interactions by GHz fs laser has remained unsolved due to the complexity of associated ablation dynamics. Here, we study the ablation dynamics of copper (Cu) by GHz fs bursts using in situ multimodal diagnostics, time-resolved scattering imaging, emission imaging, and emission spectroscopy. A combination of probing techniques reveals that GHz fs bursts rapidly remove molten Cu from the irradiated spot due to the recoil pressure exerted by following fs pulses. Material ejection essentially stops right after the burst irradiation due to the limited amount of remnant matter, combined with the suppressed heat conduction into the target material. Our work provides insights into the complex ablation mechanisms incurred by GHz fs bursts, which are critical in selecting optimal laser conditions in cross-cutting processing, micro/nano-fabrication, and spectroscopy applications.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

GenomeFace v1.0

GenomeFace is meta-genome binning software. Metagenomic binning, the process of grouping DNA sequences into taxonomic units, is critical for understanding the functions, interactions, and evolutionary dynamics of microbial communities. We propose a deep learning approach to binning using two neural networks, one based on composition and another on environmental abundance, dynamically weighting the contribution of each based on characteristics of the input data. Trained on over 43,000 prokaryotic genomes, our network for composition-based binning is inspired by metric learning techniques used for facial recognition. Using a task-specific, multi-GPU accelerated algorithm to cluster the embeddings produced by our network, our binner leverages marker genes observed to be universally present in nearly all taxa to grade and select optimal clusters of sequences from a hierarchy of candidates. We evaluate our approach on four simulated datasets with known ground truth. Our linear time integration of marker genes recovers more near complete genomes than state of the art but computationally infeasible solutions using them, while being over an order of magnitude faster. Finally, we demonstrate the scalability and acuity of our approach by testing it on three of the largest metagenome assemblies ever performed. Compared to other binners, we produced 47%-183% more near complete genomes. From these datasets, we find over the genomes of over 3000 new candidate species which have never been previously cataloged, representing a potential 4% expansion of the known bacterial tree of life.

Lettich, Richard [Lawrence Berkeley National Labor↗