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DOE OSTI2025

Evaluation of microalgae cultivation at air-CO 2 equilibrium pH for improving carbon utilization efficiency

Microalgae are often cultivated at near-neutral pH to optimize growth. However, this results in significant CO 2 loss through outgassing in open-pond cultivation systems, leading to low CO 2 utilization efficiency and higher biomass production costs. One potential solution is algal cultivation at air-CO 2 equilibrium pH, which minimizes CO 2 outgassing but may inhibit growth due to stresses at higher pH levels. In this study, we evaluated the viability of this approach by growing Picochlorum celeri and Tetraselmis striata under outdoor relevant conditions at equilibrium pH. Compared to pH 7 cultures, biomass productivity declined by 35 % for P. celeri and 57 % for T. striata. Although CO 2 outgassing could be minimized, the significant loss in productivity led to a higher overall minimum biomass selling price (MBSP). Increased ammonia toxicity at the higher pH was found to be one of the growth-limiting factors and was mitigated by reducing ammonium fertilizer concentration. This adjustment resulted in a more moderate productivity decline of 13 % (instead of 35 %) for T. striata, and consequently, a lower MBSP was achieved. These findings suggest that equilibrium pH cultivation may be a viable method for reducing CO 2 loss without significantly compromising biomass productivity. Depending on the strain, strategies to mitigate stressors, such as ammonia toxicity, may be necessary.

09 BIOMASS FUELS↗

DOE OSTI2023

Effects of SO 2 poisoning and regeneration on spinel containing CH 4 oxidation catalysts

Methane oxidation under periodic conditions and the oxygen storage capacity of a bilayer Pt/Pd/Al 2 O 3 over a Mn 0.5 Fe 2.5 O 4 spinel catalyst were studied before and after SO 2 exposure, and after simulated regeneration conditions. Prior to sulfur exposure, improvement in CH 4 oxidation conversion under periodic conditions compared to steady-state conditions was observed. After sulfur exposure at 100 °C, there was a loss in CH 4 oxidation performance and a loss of oxygen storage capacity of the spinel material. Here, the extent of regeneration from sulfur poisoning depends on the ability to induce the decomposition of sulfate species, and while all regeneration methods tested in this study did improve CH 4 conversion, regeneration methods under periodic conditions induced greater sulfur species desorption from the catalyst surface leading to improved CH 4 con version. Key regeneration parameters – temperature, feed composition, modulation amplitude and frequency – were optimized to induce S species decomposition and correlated to CH 4 oxidation activity recovery.

03 NATURAL GAS↗

DOE OSTI2022

Data-driven modeling of power generation for a coal power plant under cycling

Increased penetration of renewables for power generation has negatively impacted the dynamics of conventional fossil fuel-based power plants. The power plants operating on the base load are forced to cycle, to adjust to the fluctuating power demands. This results in an inefficient operation of the coal power plants, which leads up to higher operating losses. To overcome such operational challenge associated with cycling and to develop an optimal process control, this work analyzes a set of models for predicting power generation. Moreover, the power generation is intrinsically affected by the state of the power plant components, and therefore our model development also incorporates additional power plant process variables while forecasting the power generation. We present and compare multiple state-of-the-art forecasting data-driven methods for power generation to determine the most adequate and accurate model. We also develop an interpretable attention-based transformer model to explain the importance of process variables during training and forecasting. The trained deep neural network (DNN) LSTM model has good accuracy in predicting gross power generation under various prediction horizons with/without cycling events and outperforms the other models for long-term forecasting. The DNN memory-based models show significant superiority over other state-of-the-art machine learning models for short, medium and long range predictions. The transformer-based model with attention enhances the selection of historical data for multi-horizon forecasting, and also allows to interpret the significance of internal power plant components on the power generation. This newly gained insights can be used by operation engineers to anticipate and monitor the health of power plant equipment during high cycling periods.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2024

CG-Kit: Code Generation Toolkit for performant and maintainable variants of source code applied to Flash-X hydrodynamics simulations

CG-Kit is a new Code Generation tool-Kit that we have developed as a part of the solution for portability and maintainability for multiphysics computing applications. The development of CG-Kit is rooted in the urgent need created by the shifting landscape of high-performance computing platforms and the algorithmic complexities of a particular large-scale multiphysics application: Flash-X. To efficiently use computing resources on a heterogeneous node, an application must have a map of computation to resources and a mechanism to move the data and computation to the resources according to the map. Most existing performance portability solutions are focussed on abstracting the expression of computations so that a unified source code can be specialized to run on different resources. However, such an approach is insufficient for a code like Flash-X, which has a multitude of code components that can be assembled in various permutations and combinations to form different instances of applications. Similar challenges apply to any code that has composability, where a single specified way of apportioning work among devices may not be optimal. Additionally, use cases arise where the optimal control flow of computation may differ for different devices while the underlying numerics remain identical. This combination leads to unique challenges including handling an existing large code base in Fortran and/or C/C++, subdivision of code into a great variety of units supporting a wide range of physics and numerical methods, different parallelization techniques for distributed and shared memory systems and accelerator devices, and heterogeneity of computing platforms requiring coexisting variants of parallel algorithms. All of these challenges demand that scientific software developers apply existing knowledge about domain applications, algorithms, and computing platforms to determine custom abstractions and granularity for code generation. There is a critical lack of tools to tackle those problems. CG-Kit is designed to fill this gap by providing a user with the ability to express their desired control flow and computation-to-resource map in the form a pseudocode-like recipe. It consists of standalone tools that can be combined into highly specific and, we argue, highly effective portability and maintainability toolchains. Here we present the design of our new tools: parametrized source trees, control flow graphs, and recipes. The tools are implemented in Python. They are agnostic to the programming language of the source code targeted for code generation. In conclusion, we demonstrate the capabilities of the toolkit with two examples, first, multithreaded variants of the basic AXPY operation, and second, variants of parallel algorithms within a hydrodynamics solver, called Spark, from Flash-X that operates on block-structured adaptive meshes.

Algorithmic portability↗

DOE OSTI2024

Trajectory Shaper: A Solution for Disrupted Cooperative Adaptive Cruise Control

Cooperative adaptive cruise control (CACC) can effectively reduce energy consumption, alleviate traffic congestion, and enhance safety. However, communication-related constraints and uncooperative vehicle users can disrupt CACC during real-world operations, significantly undermining the putative benefits of CACC. To alleviate the negative impacts of disrupted CACC, this study develops the trajectory shaper (TS) methods as backup solutions for two scenarios: (i) communication between vehicles is infeasible, and vehicles execute adaptive cruise control (ACC) using local sensor measurements; (ii) follower vehicles reject forming a cooperative platoon and execute their local distributed controllers using the information attained via communication. When communication is infeasible, a distributed TS is devised on each vehicle to modify the sensor measurements, enabling safe and efficient ACC operations. When communication is available but uncooperative agents are involved, the lead vehicle of the platoon executes a centralized TS to modify the information shared with uncooperative agents, achieving optimal platoon-level performance. The centralized and distributed TSs are implemented based on the model predictive control algorithms to yield optimal modifications on input information. Robustness is also factored to tackle model uncertainties during TS operations to ensure safety and efficiency. Numerical experiments validate the control performance of the proposed TSs.

Zhou, Anye [ORNL] (ORCID:0000000301455579)↗

DOE OSTI2020

Uncovering the Functional Role of Coherent Phonons during the Photoinduced Phase Transition in a Molecular Crystal

The atomic motions that make up phonons and molecular vibrations in molecular crystals influence their photophysical and electronic properties, including polaron formation, carrier mobility, and phase transitions. Discriminating between spectator and driving motions is a significant challenge hindering optimization. Unlocking this information and developing fine-tuned controls over actively participating phonon modes would not only lead to a stronger understanding of photochemistry but also provide a significant new tool in controlling solid state chemistry. We present a strategy using rationally designed double pulses to unveil the unique function of specific excited state phonon modes. Using ultrafast spectroscopy, we identified 50 and 90 cm –1 phonons involved in modulating the photoinduced spin-Peierls melting of potassium tetracyanoquinodimethane crystals. Here, we show that the 50 cm –1 phonon specifically corresponds to the coherent nuclear wavepacket involved in the charge transfer component of the overall spin-Peierls phase melting process, while the 90 cm –1 phonon facilitates the phase transition component.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2026

How to Minimize Faradaic Efficiency Error in Electrochemical CO 2 Reduction for Gas Products

Faradaic efficiency (FE) is an important metric for evaluating electrochemical processes, such as carbon dioxide reduction, that is often used for performance comparisons. Although the equation to calculate FE is well-known, details needed to yield accurate values are often overlooked, potentially leading to errors exceeding 100%. Avoiding any errors is crucial for drawing correct conclusions, especially during a catalysis optimization process. Factors with high potential for FE errors include incorrect mass flow rates and temperature values, imprecise gas chromatography calibrations, and uncorrected gas viscosities, while factors with less potential are also identified. In conclusion, this manuscript presents FE calculation guidelines intended for both newcomers and experts in the field of electrochemical fuel production.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Band Gap Engineering and Metastable Phase Discovery in Cu 2 BaSnS 4– x Se x Nanocrystals via Topotactic Anion Exchange

Earth-abundant multinary inorganic chalcogenides, such as Cu 2 BaSnS 4–x Se x (CBTSSe), have emerged as promising absorbers for next-generation solar cells. However, accessing colloidal CBTSSe nanocrystals, including metastable polymorphs with more favorable optoelectronic properties, remains a critical synthetic challenge. Herein, we report the first use of topotactic anion exchange to access metastable crystal structures in soft chemistry syntheses. By varying the stoichiometry of selenium precursors, we directly synthesize Cu 2 BaSnS 4–x Se x (0 ≤ x ≤ 4) nanocrystals with a broad band gap tunability of 1.51–2.04 eV. At x = 4, a new metastable trigonal phase of Cu 2 BaSnSe 4 is isolated. This phase arises via an in-situ topotactic anion exchange from sulfur-rich Cu 2 BaSnS 4–x Se x intermediates, while retaining both the cation sublattice and overall nanocrystal morphology. The trigonal Cu 2 BaSnSe 4 exhibits a nearly direct band gap that is 280 meV lower than its thermodynamically stable orthorhombic counterpart, aligning more closely with the optimal band gap for single-junction solar cells. In contrast, a post-synthetic anion exchange route leads to the thermodynamically preferred orthorhombic polymorph, pointing to the critical nature of the in-situ transformation. Our findings open a versatile pathway for both polymorphic and morphological control in colloidal nanocrystals, expanding the synthetic design space for new optoelectronic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Process innovations to enable viable enzymatic poly(ethylene terephthalate) recycling

Enzymatic depolymerization of poly(ethylene terephthalate) (PET) has received considerable attention for closed-loop polyester recycling. However, current approaches for enzymatic PET recycling face challenges to achieve commercial viability with lower environmental impacts compared with virgin polyester manufacturing. Here, in this study, we present multiple process innovations for enzymatic PET recycling that enable economic and environmental feasibility. We show that substrate amorphization through extrusion and quenching is energy-efficient and enables near-quantitative enzymatic conversion in 50 h. Using ammonium hydroxide for pH control and thermolysis of the isolated diammonium terephthalate salt reduces the acid and base consumption by >99%, lowering annual operating expenses by 74%. Fed-batch processing increased ethylene glycol concentration, leading to a 65% reduction in energy consumption for ethylene glycol recovery. These improvements were modeled in an optimal process, with recycled PET estimated to be US$\$$1.51 kg -1 relative to US domestic virgin PET at US$\$$1.87 kg -1 and eliminating key life cycle obstacles to scale this technology.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Boosting the cycling stability of Ni-rich layered oxide cathode by dry coating of ultrastable Li 3 V 2 (PO 4 ) 3 nanoparticles

Nickel (Ni)-rich layered oxides such as LiNi 0.6 Co 0.2 Mn 0.2 O 2 (NCM622) represent one of the most promising candidates for next-generation high-energy lithium-ion batteries (LIBs). However, the pristine Ni-rich cathode materials usually suffer from poor structural stability during cycling. In this work, we demonstrate a simple but effective approach to improve the cycling stability of the NCM622 cathode by dry coating of ultrastable Li 3 V 2 (PO 4 ) 3 -carbon (LVP-C) nanoparticles, which leads to a robust composite cathode (NCM622/LVP-C) without sacrificing the specific energy density compared with pristine NCM622. The optimal NCM622/LVP-C composite presents a high specific capacity of 162 mA h g -1 at 0.5 C and excellent cycling performance with 85.0% capacity retention after 200 cycles at 2 C, higher than that of the pristine NCM622 (67.6%). Systematic characterization confirms that the LVP-C protective layer can effectively reduce the side reactions, restrict the cation mixing of NCM622 and improve its structural stability. Moreover, the NCM622/LVP-C||graphite full cells also show a commercial-level capacity of 3.2 mA h cm -2 and much improved cycling stability compared with NCM622/LVP-C||graphite full cells, indicating the great promise for low-cost, high-capacity and long-life LIBs.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Volatile fatty acid extraction from fermentation broth using a hydrophobic ionic liquid and in situ enzymatic esterification

Efficient recovery of volatile fatty acids (VFAs) from fermentation broth is a challenge due to low VFA titers and thus limits the commercialization of VFA production using biological routes. Liquid–liquid extraction using hydrophobic ionic liquids (ILs) shows great promise for the extraction and esterification of hydrophilic VFAs. In this study, several ILs were evaluated to select a water-immiscible and efficient extraction solvent. The selected IL, trihexyltetradecyl phosphonium dibutylphosphate ([P 666,14 ][DBP]), gave a cumulative VFA extraction of around 842.8 mg per g IL. The predicted excess enthalpy (H E ) and logarithmic activity coefficients ln(γ) using the COSMO-RS model were validated with the experimentally obtained VFA recovery from fermentation broth. To understand the extraction mechanism of VFAs, quantum theory of atoms in molecules (QTAIM) and noncovalent interaction (NCI) were performed. The results suggest that long chain fatty acids exhibit strong van der Waals interaction with the DBP anion leading to higher VFA extraction. The enzymatic esterification of VFAs with ethanol in [P 666,14 ][DBP] was optimized using the Box–Behnken response surface design of experiment. Under the optimized conditions, up to 83.7% of hexanoic acid was converted to ethyl esters, while other shorter chain VFAs have lower conversion efficiency (38.3–63.2%).

09 BIOMASS FUELS↗

DOE OSTI2025

SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression

Abstract Compact symbolic expressions have been shown to be more efficient than neural network (NN) models in terms of resource consumption and inference speed when implemented on custom hardware such as field-programmable gate arrays (FPGAs), while maintaining comparable accuracy (Tsoi et al 2024 EPJ Web Conf. 295 09036). These capabilities are highly valuable in environments with stringent computational resource constraints, such as high-energy physics experiments at the CERN Large Hadron Collider. However, finding compact expressions for high-dimensional datasets remains challenging due to the inherent limitations of genetic programming (GP), the search algorithm of most symbolic regression (SR) methods. Contrary to GP, the NN approach to SR offers scalability to high-dimensional inputs and leverages gradient methods for faster equation searching. Common ways of constraining expression complexity often involve multistage pruning with fine-tuning, which can result in significant performance loss. In this work, we propose S y m b o l N e t , a NN approach to SR specifically designed as a model compression technique, aimed at enabling low-latency inference for high-dimensional inputs on custom hardware such as FPGAs. This framework allows dynamic pruning of model weights, input features, and mathematical operators in a single training process, where both training loss and expression complexity are optimized simultaneously. We introduce a sparsity regularization term for each pruning type, which can adaptively adjust its strength, leading to convergence at a target sparsity ratio. Unlike most existing SR methods that struggle with datasets containing more than O ( 10 ) inputs, we demonstrate the effectiveness of our model on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs).

Tsoi, Ho Fung (ORCID:0000000225502184)↗

DOE OSTI2022

Dark Energy Survey Year 3 results: Cosmological constraints from galaxy clustering and galaxy-galaxy lensing using the MagLim lens sample

The cosmological information extracted from photometric surveys is most robust when multiple probes of the large scale structure of the Universe are used. Two of the most sensitive probes are the clustering of galaxies and the tangential shear of background galaxy shapes produced by those foreground galaxies, so-called galaxy-galaxy lensing. Combining the measurements of these two two-point functions leads to cosmological constraints that are independent of the way galaxies trace matter (the galaxy bias factor). The optimal choice of foreground, or lens, galaxies is governed by the joint, but conflicting requirements to obtain accurate redshift information and large statistics. We present cosmological results from the full 5000 deg 2 of the Dark Energy Survey’s first three years of observations (Y3) combining those two-point functions, using for the first time a magnitude-limited lens sample (MagLim) of 11 million galaxies, especially selected to optimize such combination, and 100 million background shapes. We consider two flat cosmological models, the Standard Model with dark energy and cold dark matter ( Λ CDM ) a variation with a free parameter for the dark energy equation of state ( w CDM ). Both models are marginalized over 25 astrophysical and systematic nuisance parameters. In Λ CDM we obtain for the matter density Ω m = 0.320 - 0.034 + 0.041 and for the clustering amplitude S 8 ≡ σ 8 ( Ω m / 0.3 ) 0.5 = 0.778 - 0.031 + 0.037 , at 68% C.L. The latter is only 1 σ smaller than the prediction in this model informed by measurements of the cosmic microwave background by the Planck satellite. In w CDM we find Ω m = 0.32 - 0.046 + 0.044 , S 8 = 0.777 - 0.051 + 0.049 and dark energy equation of state w = - 1.031 - 0.379 + 0.218 . We find that including smaller scales, while marginalizing over nonlinear galaxy bias, improves the constraining power in the Ω m - S 8 plane by 31% and in the Ω m - w plane by 41% while yielding consistent cosmological parameters from those in the linear bias case. These results are combined with those from cosmic shear in a companion paper to present full DES-Y3 constraints from the three two-point functions ( 3 × 2 pt ).

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Active nematic ratchet in asymmetric obstacle arrays

We numerically investigate the effect of an asymmetric periodic obstacle array in a two-dimensional active nematic. We find that activity in conjunction with the asymmetry leads to a ratchet effect or unidirectional flow of the fluid along the asymmetry direction. The directional flow is still present even in the active turbulent phase when the gap between obstacles is sufficiently small. We demonstrate that the dynamics of the topological defects transition from flow mirroring to smectic-like as the gap between obstacles is made smaller, and explain this transition in terms of the pinning of negative winding number defects between obstacles. This also leads to a nonmonotonic ratchet effect magnitude as a function of obstacle size, so that there is an optimal obstacle size for ratcheting at fixed activity.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Thermophysical Property Measurements on Salt Mixture

Thermal conductivity, specific heat, viscosity, and density are critical properties for understanding thermal hydraulics of heat transfer fluids (HTFs) like molten salts. These properties are important for design and safe operation of any HTF loop. These characterizations are required to benchmark computational chemical models that are used to predict properties, but existent data are limited. These measurements are extremely difficult to make due to the operating temperature range and corrosive properties of the salt.Density measurements are needed to predict volumetric behavior. Thermal expansion mismatches can cause mechanical failure of structural materials and important components that are in contact with the molten salt. All of these properties are related to heat transfer in HTFs. The heat generated in a reactor must be high enough to derive power; however it should not be so hot that it cannot be safely removed via the HTF at an appropriate rate. It is important to understand the thermal hydraulics required to operate at the desired power generation level and to avoid situations in which thermal shifts can damage components,cause failure of structural materials, and/or lead to accidents. Understanding these thermal properties can aid in component and process design and can contribute to optimized operational and safety parameters.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Efficient high-dimensional variational data assimilation with machine-learned reduced-order models

Abstract. Data assimilation (DA) in geophysical sciences remains the cornerstone of robust forecasts from numerical models. Indeed, DA plays a crucial role in the quality of numerical weather prediction and is a crucial building block that has allowed dramatic improvements in weather forecasting over the past few decades. DA is commonly framed in a variational setting, where one solves an optimization problem within a Bayesian formulation using raw model forecasts as a prior and observations as likelihood. This leads to a DA objective function that needs to be minimized, where the decision variables are the initial conditions specified to the model. In traditional DA, the forward model is numerically and computationally expensive. Here we replace the forward model with a low-dimensional, data-driven, and differentiable emulator. Consequently, gradients of our DA objective function with respect to the decision variables are obtained rapidly via automatic differentiation. We demonstrate our approach by performing an emulator-assisted DA forecast of geopotential height. Our results indicate that emulator-assisted DA is faster than traditional equation-based DA forecasts by 4 orders of magnitude, allowing computations to be performed on a workstation rather than a dedicated high-performance computer. In addition, we describe accuracy benefits of emulator-assisted DA when compared to simply using the emulator for forecasting (i.e., without DA). Our overall formulation is denoted AIEADA (Artificial Intelligence Emulator-Assisted Data Assimilation).

58 GEOSCIENCES↗

DOE OSTI2025

Loss Landscape Analysis for Reliable Quantized ML Models for Scientific Sensing

In this paper, we propose a method to perform empirical analysis of the loss landscape of machine learning (ML) models. The method is applied to two ML models for scientific sensing, which necessitates quantization to be deployed and are subject to noise and perturbations due to experimental conditions. Our method allows assessing the robustness of ML models to such effects as a function of quantization precision and under different regularization techniques -- two crucial concerns that remained underexplored so far. By investigating the interplay between performance, efficiency, and robustness by means of loss landscape analysis, we both established a strong correlation between gently-shaped landscapes and robustness to input and weight perturbations and observed other intriguing and non-obvious phenomena. Our method allows a systematic exploration of such trade-offs a priori, i.e., without training and testing multiple models, leading to more efficient development workflows. This work also highlights the importance of incorporating robustness into the Pareto optimization of ML models, enabling more reliable and adaptive scientific sensing systems.

Baldi, Tommaso [Pisa, Scuola Normale Superiore]↗

DOE OSTI2025

Optimization of edge bead removal (EBR) process to enhance defect reduction in optical lithography

Defect reduction remains a critical objective in the integrated circuit manufacturing process, particularly within the highly re-entrant lithography modules where minimizing defects is crucial. Defects at the wafer edge can contaminate lithography modules and downstream processing equipment, leading to redistribution onto the wafer surface and adversely affecting overall device yield. A persistent challenge in the resist coating process is the formation of resist edge beads, driven by the strong Van der Waals attraction of excess photoresist (PR) to itself and the underlying substrate. The edge bead removal (EBR) process is a standard cleaning step designed to eliminate these edge beads and prevent potential contamination. Here, in this study, we identify the sources of EBR induced defects and additional EBR process encroachment toward edge patterning during the EBR cleaning process. This study provides a comprehensive study aimed at optimizing the EBR cleaning process to effectively eliminate EBR-induced defects, thereby enhancing overall device yield. Specifically, we identify three primary defects induced by the EBR cleaning process: rainbow-type, finger-shaped, and teardrop-type defects. Our experimental study reveals that in addition to EBR rinse time, PR cast time is crucial parameters contributing to the formation of these defects. By properly optimizing the PR cast time and EBR rinse time, we were able to remove nearly 100 % of dense clusters of defects that were easily visible even at low magnification optical microscopy throughout the wafer edge. We observed that shorter PR casting times shows edge defects caused by inefficient EBR process because of insufficient time for PR to fully settle causing superfluous PR to continue flowing toward wafer edge during EBR clearing step, leading to partial removal of PR at the wafer edge and the formation of rainbow defects. Proper optimization of both PR casting time and EBR chemistries dispense time is essential to resolve these defects, ensuring efficient EBR cleaning process and improved overall device yield.

42 ENGINEERING↗