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At least 73 records · Page 4

Infrared triggered dwell and active cooling thermal control effects on microstructural uniformity in DED

Directed Energy Deposition (DED) offers rapid large scale fabrication, but difficulty in delivering consistent microstructures and properties hinders the use of DED fabricated components in safety or performance critical applications. Variability stems from the complex thermal cycles generated by the toolpath used to print the required geometry. Several practical methods have become established in DED to regulate overheating, such as active cooling of the baseplate structure or the use of an infrared camera to inject interlayer pauses to ensure the top layer of the component cools to a set temperature, which have been shown to affect microstructure. However, no critical assessment has been performed as to how effective these controls are in promoting microstructural uniformity in the context of complex layer timing commonly generated by non-prismatic geometries. Here we show how controls influence the thermal field, phase transformations, and dynamic annealing of a low-temperature transformation steel using infrared imaging and operando neutron diffraction. Counterintuitively, common thermal homogenization process controls can reduce microstructural uniformity because these approaches stabilize peak temperature while overlooking temperatures near the solid-state phase transformation fronts. Instead, the cyclic reheating induces spatially-variant dynamically annealed regions which can be modulated via control parameters. We show that these controls have spatially linked effects centimeters away from the active weld, which implies that microstructure control must co-optimize thermal input across many subsequent layers. In conclusion, our results demonstrate the pressing need for higher order controls that integrate predictive elements of simulation data to stabilize printed properties for future qualification of DED components.

Directed energy deposition

EMSL Community Science Campaign Meeting: Critical Minerals and Materials - Rhizo Critical Campaign Breakout Session Report Summary

The “Critical Minerals Biogeochemistry in the Rhizosphere – Ultramafic Soils (Rhizo Critical)” campaign breakout (BO) session was organized to identify major knowledge gaps and fundamental research needs in rhizosphere microbiology and geochemistry that, if addressed, could transform our ability to recover critical minerals from ultramafic soil systems. We sought to identify significant challenges that must be surmounted in the pursuit of deeper science knowledge. Our ultimate goal is to understand this landscape well enough to identify and prioritize opportunities for EMSL to make the greatest impact with Environmental Transformations and Interactions (ETI) science area research campaigns focused on the biogeochemical processes controlling the behavior of critical minerals and materials in the rhizosphere. The increasing demand for critical materials and minerals (CMM) in the U.S. has heightened interest in low-grade ores with much attention on ultramafic soils, which contain valuable metals such as nickel (Ni), chromium (Cr), manganese, cobalt (Co), and copper (Lee et al., 2025; DOE CMM Report, 2023) used in advanced battery, magnet, wiring and wind turbines, and stainless steel technologies. Metal hyperaccumulating plants grown in ultramafic soils can extract economically valuable concentrations of CMMs through the process of phytomining. This technology has evolved from phytoremediation, which involves using plants to cleanse contaminated environments by removing, detoxifying, or stabilizing pollutants like metals and organic compounds. Hyperaccumulator plants are capable of storing metals in their living tissues at concentrations hundreds to thousands of times higher than those found in 'normal' plants. For instance, while the average concentration of Ni in the dry matter of plants growing in typical soils is usually less than 5 µg g?¹, Ni hyperaccumulation is defined by concentrations exceeding 1,000 µg g?¹ (Corzo Remigio et al., 2020; Reeves et al., 2018). Phytomining research has primarily focused on Ni (Rylott and van der Ent, 2025), for which the U.S. has very limited conventional mines in operation. Most soils typically contain Ni concentrations ranging from 7 to 50 mg kg-1, whereas serpentine soils exhibit significantly higher levels, with Ni content often ranging between 700 and 8,000 mg kg-1 (Sobczyk et al., 2017). While more than 500 plant species in over 50 different families have been identified as Ni hyperaccumulators (Kidd et al., 2018), Ni phytomining (and phytominng in general) remains largely untested because most studies are short-term, small-scale, and conducted under simplified or artificially enriched conditions, so they fail to capture the low metal concentrations, environmental variability, and management constraints that would be needed for a field-scale demonstration. Few hyperaccumulator species have been validated as true “metal crops,” and their biomass production, stress tolerance, and rooting characteristics are usually too poor to yield economically meaningful metal outputs. Critically, the basic mechanisms of metal uptake, transport, and sequestration, especially as shaped by belowground processes such as root exudation, rhizosphere chemistry, and root–microbe interactions that control metal mobility and bioavailability (Montreemuk et al., 2023; Kidd et al., 2018; Durand et al., 2023; Alford et al., 2010), are still only partially understood, and downstream metal recovery from biomass is rarely optimized. Because these limitations stem from gaps in fundamental knowledge rather than from a failure of the concept itself (Rylott and van der Ent, 2025; van der Ent et al., 2015), there is a strong need for basic science that dissects plant metal homeostasis, rhizosphere and microbial processes, and their integration with soil chemistry and process engineering to design more robust, scalable phytomining systems.

Ahkami, Amirhossein

Using the optimal combined index weight ratio to improve the probability of anomaly detection in big area additive manufacturing

Big Area Additive Manufacturing (BAAM) of composites requires significant time, energy, and material, so it is critical to reduce production inefficiencies to make functional parts without multiple iterations. Statistical process control coupled with Principal Component Analysis (PCA) is a powerful technique that provides a quick, computationally inexpensive, and intuitive way for operators to detect defects that form in a manufacturing process without massive datasets. Recently, a combined index that is a weighted sum of the Hotelling's T 2 and squared residual error statistics has been proposed that can be monitored in one chart, improving interpretation accuracy and simplicity. However, the literature does not offer a formal method to optimise the weights. Here, we introduce two new approaches to the traditional weight selection approach using simulated and BAAM image data. Approach 1 uses a theoretically motivated optimum inspired by probabilistic principal component analysis. Approach 2 systematically varies the ratio of the weights to find the optimum. We show that approach 1 delivers optimal anomaly detection performance in select cases while approach 2 fares better in practice. Surprisingly, we also show that choosing a more complex PCA model has a minimal negative impact on anomaly detection performance compared to a more simplistic model.

3-dimensional printing

Phase-field predictions of the influence of cooling rates during AM on the Evolution of Microstructures in Nickel-Based Single Crystal Superalloys

Additive manufacturing of single crystals made of Ni-based superalloys offers major cost savings for gas turbine engines with the inclusion of internal cooling channels. However, the lack of understanding of the effect of transient thermal conditions on solidification grain structure during additive manufacturing hinders the potential for process control to maintain the single crystal quality. The use of high-fidelity simulations through high performance computing to predict the evolution of the solidification microstructure will enhance the abilities to tailor the microstructures through process optimization. Phase field simulations are used to determine the effect local thermal conditions and defects on the stability of the solidification morphology, specifically with respect to the onset of columnar-to-equiaxed transition that results in the loss of the single crystal. The results are expected to be instrumental for developing future surrogate models to speed up the integration of design and manufacturing of turbine blades under the harsh in-service conditions.

36 MATERIALS SCIENCE

Controlled electrochemical nutrient delivery to enhance marine primary productivity

Oceanic photosynthesis contributes to approximately half of the Earth’s net annual primary productivity. Marine photosynthetic productivity has a high degree of heterogeneity due to spatial and temporal co-limitations of light, temperature, and/or nutrients. Across coastal, near-shore, and open ocean regions, insufficient concentrations of key nutrients (e.g., N, P, Fe) can limit primary productivity. Although studies have shown a significant increase in primary productivity with the addition of low doses of trace nutrients, a sustainable approach to reliably deliver and maintain low doses of nutrients and ensure their bioavailability, remains a challenge. Chemical nutrient addition has relied on the use of chelating agents to ensure nutrient bioavailability, but synthetic chelators are persistent environmental pollutants. In this study, we demonstrate for the first time the use of a controlled electrochemical nutrient delivery (CEND) approach to accelerate the growth of phytoplankton without the need for such chelators. Our study uses commercial stainless-steel electrodes to deliver low concentrations of iron to enhance growth rates in the microalga Picochlorum celeri TG2. To demonstrate the process control offered by the CEND method, we evaluate iron delivery as a function of pulse time, pulse frequency, and rest time between pulses. Our data show that at the same total Fe dose of 163 ppb, electrochemical iron delivery can achieve 9.54 ± 0.58 mg biomass/µg Fe, which is comparable to 9.14 ± 0.17 mg biomass/µg Fe achieved with chemical iron additions that include the synthetic chelating agent ethylenediaminetetraacetic acid (EDTA). Further, when different total iron doses (163 ppb, 325 ppb, and 650 ppb) were delivered over 72 h using CEND, biomass yield per iron dose was higher at lower doses: 9.54 ± 0.58 mg biomass/µg Fe at 163 ppb vs. 4.32 ± 0.32 mg biomass/µg Fe at 650 ppb. This highlights the benefits of CEND in delivering frequent and low doses of nutrients for improved process efficiency. Preliminary assessments show both lower cost and reduced greenhouse gas emissions from electrochemical over chemical iron additions with EDTA. The CEND approach opens new pathways to enhance marine primary productivity, without the unintended environmental impacts of synthetic chelators.

analytical chemistry

Controls Status for Fermilab’s PIP-II Project

Testing of Cryomodule first articles at PIP-II's on-site Cryomodule Test Facility (CMTF), where EPICS based controls are already being used, has been invaluable to the project. In addition to validating hardware and controls components, it has provided opportunities to build buy-in and illicit meaningful feedback early-on. A device naming convention has also been significantly refined with an internal working group and the PIP-II integration team. Compressed air and Process Control Water (PCW) systems have been installed, and operations has been engaged in Phoebus display development in preparation for a transition to operations for these systems by 2026. Cryoplant hardware has been put in place, and collaboration between controls and PIP-IIs cryo group for IOC and display development is well underway for commissioning of cryoplant systems to begin early 2026. The project has also received beneficial occupancy of the High-Bay Building in October 2025, keeping us on schedule for Warm Front End commissioning to begin in 2027.

Crisp, Dan [Fermilab]

Real Time, In-line Monitoring of Hanford Tank Wastes - Year 1 Report

The team comprised of students, postdocs, early, mid and senior career scientists from Los Alamos National Laboratory, Savanah River National Laboratory, Georgia Tech and Florida International University, with the guidance of H2C, is developing a suite of in-line instruments for the Hanford high level waste (HLW) and low active waste (LAW) processes to provide near-real-time analysis of waste form physical properties and composition. The work builds on results from the recent DOE-ORP, EM Technology Development and other projects that demonstrated promise for the use of real-time in-line monitoring (RTIM) to measure chemical compositions of slurries of up to 20 weight % solids. The goal is for this instrument suite is to substantially reduce the need for sampling for process control. Sample waste, exposure associated with sample analysis, and the demand for an external laboratory facility would be greatly reduced. The throughput of waste treatment systems would be improved by elimination of the downtime caused by waiting for sample results. This translates into reduced process storage as process knowledge will be continuously updated in near-real-time. These breakthrough technologies would significantly reduce the life cycle cost and accelerate the schedule for the Hanford tank waste mission.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W

Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.

Contrastive Learning

Elucidating the Radiation-Induced Redox Chemistry of Plutonium Under Used Nuclear Fuel Reprocessing Conditions

Plutonium plays a critical role in the development of sustainable nuclear fuel cycles, and yet, our fundamental understanding of this element’s inherent radiation-induced redox chemistry and associated impacts on nuclear fuel cycle technologies is limited. Unanticipated changes in oxidation state distribution can influence the speciation and transport of plutonium in a given process. Control of these parameters is especially important for used nuclear fuel reprocessing technologies, wherein the separation and recovery of plutonium is typically achieved by the selective formation, maintenance, and complexation of specific oxidation states. Furthermore, plutonium’s inherent radiation-induced redox chemistry has the capacity to influence the radiolytic behavior of its complexes, the longevity of which are critical in the design of efficient and cost-effective advanced reprocessing technologies. These radiation-induced processes are unavoidable under fuel cycle conditions owing to the inherency of ionizing radiation fields to the decay of plutonium’s isotopes and to the various other radioisotopes generated by nuclear fission and neutron-capture process and the subsequent radioactive decay of their products. As such, mechanistically understanding the response of plutonium’s multiple oxidation states to multi-component ionizing radiation fields is essential for predicting the behavior of this critical element under used nuclear fuel reprocessing conditions. Here, through a combination of time-resolved (electron pulse) and steady-state (alpha and gamma) irradiation experiments complemented by quantitative, multiscale modeling calculations, we present advances in our understanding of radiation-induced plutonium redox chemistry!

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

A high fidelity and user-friendly equation-oriented optimization model for carbon capture using a novel water-lean solvent

Research Triangle Institute (RTI) International and SLB have developed a novel water-lean solvent technology for carbon capture, demonstrating low specific reboiler duty (SRD) values at capture rates exceeding 90%. At the Technology Centre Mongstad (TCM) pilot plant, the technology achieved an SRD of 2.55 GJ/t-CO2 at 95% capture, utilizing an intercooler and a 5°C temperature approach in the lean/rich solvent cross exchanger. To meet varying carbon capture targets for Front End Engineering and Design (FEED) studies and to enable real-time optimization and advanced process control, an efficient optimization model is required. This model needs to minimize energy demand for a given capture rate and determine optimal operating parameters in response to fluctuating flue gas conditions. While an existing Aspen Plus simulation model, developed by RTI and SLB, accurately matches TCM plant data, its sequential modular (SM) strategy is too slow for real-time applications due to recycle streams and tight heat integration inherent in solvent-based carbon capture processes. Although an equation-oriented (EO) modeling strategy is more suitable for optimizing these processes, its adoption has been limited by several factors: feature limitations in Aspen Plus EO mode (e.g., lack of balance block support), a less user-friendly interface for variable identification and loop solving, complex troubleshooting of convergence issues, and the necessity for accurate initial values.

carbon capture

Operando Surface-Enhanced Infrared Spectroscopy Connects Interfacial Dynamics with Reaction Kinetics During Electrochemical CO 2 Reduction on Copper

The reaction microenvironment plays a key role in dictating the selectivity of electrochemical CO 2 reduction. However, understanding the chemical nature of this microenvironment under operating conditions remains a substantial challenge. For this study, we employed attenuated total reflectance surface-enhanced infrared absorption spectroscopy (ATR-SEIRAS) in operando for simultaneous measurements of reaction kinetics and concentrations of reactants and intermediates at the reaction interface, all under controlled mass transport conditions. These operando measurements enable direct correlations between the reaction microenvironment, mass transport, and kinetics for a Cu electrocatalyst, such as higher local concentrations of CO 2 under faster mass transport corresponding to higher rates of CO 2 reduction. We observed that faster mass transport decreased the *CO coverage at less negative potentials (-0.6 V RHE ) and increased the *CO coverage at more negative potentials (-1.1 V RHE ). We developed a transport-coupled kinetic model that captures these spectroscopic observations and provides insight into the processes controlling interfacial concentrations of reactants and intermediates, aiding future efforts toward tailoring reaction microenvironments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

RLGBS: Reinforcement Learning-Guided Beam Search for process optimization in a paper machine dryer section

Paper drying is responsible for over two-thirds of energy consumption in the U.S. pulp and paper industry, presenting significant potential for energy savings through optimization of process parameters. Current approaches often assume fixed operating conditions, neglecting dynamic ambient and process variations that limit achievable savings and real-world applicability. To this end, we develop a physics-based simulation environment for a paper machine dryer section and propose a reinforcement learning (RL) framework to minimize overall energy consumption by optimizing drying process parameters under diverse operating conditions. To mitigate overdrying and numerical instabilities caused by suboptimal local RL actions, we introduce Reinforcement Learning-Guided Beam Search (RLGBS), which explores multiple action sequences in parallel using beam search. Instead of making step-by-step decisions, RLGBS prioritizes solutions based on cumulative probability, reducing the impact of individual suboptimal actions. Experiments demonstrate that RLGBS achieves consistent energy savings under unseen operating conditions not encountered during training, outperforming conventional RL methods. While validated in drying optimization, this framework is broadly applicable to other RL-based industrial process control problems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI

Fabrication and Evaluation of Large Alumina Crucibles by Vat Photopolymerization Additive Manufacturing for High-Temperature Actinide Chemistry

Additive manufacturing (AM) offers opportunities to advance the design and function of ceramic tooling in high temperature actinide pyrochemistry. In technical ceramics such as alumina, conventional forming techniques often restrict design flexibility and can limit experimental progress. In this study, we investigate the use of vat photopolymerization (VP) with commercial resins to fabricate large-scale alumina crucibles, reaching dimensions up to 125 mm, which is significantly larger than typically reported for dense VP ceramics. Notably, these additively manufactured components are produced using consumer-grade hardware, which limits process control, but offers significant upside in scalability and accessibility. Using microscopy and X-ray computed tomography, the VP alumina parts have high bulk densities above 95%, but also the prevalence of AM-induced artifacts and surface defects. Mechanical testing showed these defects to significantly reduce flexural strength and compromise part reliability. Electrorefining trials under sustained exposure to molten salts and metals reveal mixed results, with the AM material exhibiting high chemical compatibility, but mechanical failures due to the reduced strength were prevalent. Our findings illustrate both the promise and current limitations of AM ceramics for actinide chemistry, and point toward future improvements in process optimization, design strategies, and part screening to enhance performance and reliability.

Materials science

Bridging length scales in hard materials with ultra-small angle X-ray scattering – a critical review

Owing to their exceptional properties, hard materials such as advanced ceramics, metals and composites have enormous economic and societal value, with applications across numerous industries. Understanding their microstructural characteristics is crucial for enhancing their performance, materials development and unleashing their potential for future innovative applications. However, their microstructures are unambiguously hierarchical and typically span several length scales, from sub-ångstrom to micrometres, posing demanding challenges for their characterization, especially for in situ characterization which is critical to understanding the kinetic processes controlling microstructure formation. This review provides a comprehensive description of the rapidly developing technique of ultra-small angle X-ray scattering (USAXS), a nondestructive method for probing the nano-to-micrometre scale features of hard materials. USAXS and its complementary techniques, when developed for and applied to hard materials, offer valuable insights into their porosity, grain size, phase composition and inhomogeneities. We discuss the fundamental principles, instrumentation, advantages, challenges and global status of USAXS for hard materials. Using selected examples, we demonstrate the potential of this technique for unveiling the microstructural characteristics of hard materials and its relevance to advanced materials development and manufacturing process optimization. We also provide our perspective on the opportunities and challenges for the continued development of USAXS, including multimodal characterization, coherent scattering, time-resolved studies, machine learning and autonomous experiments. Our goal is to stimulate further implementation and exploration of USAXS techniques and inspire their broader adoption across various domains of hard materials science, thereby driving the field toward discoveries and further developments.

36 MATERIALS SCIENCE

The Effects of Beta Irradiation on the Performance of ATR Fiber Probes

The use of real-time inline monitoring to track the chemical composition of slurries is recognized as a promising method to reduce the processing time of Hanford tank waste. The conversion of waste into glass suitable for long-term storage requires adherence to waste acceptance criteria and process control limits that respectively ensure safe operation of the vitrification facility and suitable glass quality. Real-time measurements of blended waste materials can ensure that the various criteria are met or can be addressed in a timely manner.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W

Accurate Ultrasonic Thickness Measurement for Arbitrary Time-Variant Thermal Profile

Ultrasonic thickness measurement of mechanical structures is one of the most popular and commonly used nondestructive methods for various kinds of process control and corrosion monitoring. With ultrasonic propagation speed being temperature-dependent, the thickness measurement can be performed reliably only when the thermal profile is completely known. Most conventional techniques assume the temperature of the test structure is uniform and at room temperature across its thickness. Such assumptions may lead to large errors in the thickness measurement, especially when there are significant temperature variations across the thickness. State-of-the-art techniques use external temperature measurements or implement iterative methods to compensate for the unknown thermal profiles. However, such techniques produce unsatisfactory results when the heat distribution is complex or varies rapidly with time. In this work, we propose a two-sensors technique, using both compressive and shear excitations, with a non-iterative rapid data processing method for accurate thickness measurement under arbitrary time-variant thermal profile. The independent behavior of shear and compressive waves is used to formulate a real-time thickness estimation technique. The developed technique is experimentally validated on a steel plate with fixed acoustic sensors. Test results show that the error in thickness estimation can be reduced by up to 98% compared to conventional thickness gauging methods.

36 MATERIALS SCIENCE

Development of Data-Driven Models for Performance Prediction and Chemical Dosing of a Full-Scale Controlled Phosphorus Precipitation Reactor

This study evaluated the use of data-driven models to improve control of a struvite precipitation reactor that removes phosphorus from wastewater while producing a fertilizer product. The researchers developed predictive models for influent orthophosphate concentration, effluent orthophosphate concentration, and phosphorus removal using operational data from a full-scale MagPrex™ reactor at a water resource recovery facility in Denver, Colorado. Model predictions were used to recommend magnesium chloride dosing adjustments needed to achieve a target effluent phosphorus concentration. Several machine learning approaches were tested, with ridge regression providing the best predictions for influent orthophosphate concentration and phosphorus removal, and XGBoost providing the best predictions for effluent orthophosphate concentration. Simulation results indicated that the decision-support approach could correctly identify dosing adjustments in most cases and reduce chemical use. Full-scale implementation achieved lower accuracy due to changing operating conditions and limited historical data in some operating ranges. Here, the results demonstrate the potential of data-driven tools to support phosphorus recovery process control while also identifying practical limitations that affect deployment in full-scale systems.

42 ENGINEERING

In situ x-ray imaging to understand subsurface behavior during continuous wave laser drilling

A limited understanding regarding the underlying dynamics and mechanisms of material removal during continuous wave laser drilling has presented significant challenges in achieving precision and process control. Here, to address this, we employed high-fidelity, in situ synchrotron x-ray imaging to reveal previously unknown material behaviors during continuous wave laser drilling with power modulation. Our findings highlight that high-aspect ratio drill holes are achieved when the laser modulation frequency falls within the range of 8–12 kHz, provided that the laser average power and modulation amplitude levels meet the specified limits. Under these conditions, we identified a material removal mechanism driven by incremental accumulation of recoil pressure that gradually pushes material upward from deep within the substrate to the surface. This mechanism manifested as a low-frequency fluctuation in the vapor depression depth, resulting in periodic instances of material ejection. Furthermore, our study underscores that rapid expansion of the melt pool and the widening of the drill hole opening can impede effective material removal by redirecting energy from material ejection to increasing the melt pool size. This investigation contributes essential insights into the subsurface dynamics involved in the drilling of high-aspect ratio holes, furthering our fundamental understanding of this intricate process.

47 OTHER INSTRUMENTATION