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DOE OSTI2025

Energy efficiency in industrial drying: A hybrid ultrasonic system with a novel dynamic optimization framework

Drying processes are among the most energy-consuming operations in industrial and manufacturing settings, demanding strategic selection, design, and control for enhanced efficiency. Advancing drying technologies is critical for improving sustainability, lowering energy use, reducing carbon emissions, and minimizing waste. This study explores two innovative strategies aimed at transforming drying processes into sustainable, low-carbon systems by reducing energy consumption, minimizing waste, and maintaining a strong emphasis on preserving product quality. The first strategy showcases a sub-pilot scale hybrid ultrasonic-convective dryer for agrifood products. This technology, powered by electricity (process electrification), integrates non-thermal ultrasonic dehydration with convective heating and is presented as a sustainable and energy-efficient solution that enhances eco-friendly practices. The second strategy involves introducing and implementing a novel, multiobjective, mixed integer dynamic optimization technique to determine the optimal time-dependent process parameter values for the drying operation. This optimization technique yields operating conditions that are piecewise constant in time aiming to maximize the energy efficiency of the hybrid ultrasonic-convective dryer while ensuring strict adherence to product quality constraints. By adopting the hybrid ultrasonic-convective dryer, a notable 35% improvement in energy efficiency was achieved compared to conventional hot-air drying systems for drying apple slices. The proposed optimization framework further enhanced energy efficiency by nearly 14% over the most efficient process on the identical testbed, under static operating conditions. The reported enhancements have been experimentally validated. Regarding drying time (thereby improving production yield), the developed hybrid ultrasonic-convective dryer demonstrates as much as a 41% reduction in total processing time, which is further optimized by an additional 10% using our proposed optimization framework. The research outcomes have profound implications for the design and operation of drying systems, encompassing crucial aspects such as process electrification, cost-effectiveness, energy savings, time efficiency, product yield, product quality, and process automation.

Dynamic optimization↗

DOE OSTI2025

"Hidden" hydrothermal technical potential & technoeconomics: Revealing permeability & fluids with more data

Historical hydrothermal estimates have largely relied on temperature or heat flow estimates ignoring the need for natural flowing fluids. More accurate hydrothermal estimates require some indication of permeability and fluids that naturally exist in the subsurface. This paper describes a novel approach that includes proxies of permeability and fluids in hydrothermal estimates by leveraging the relatively data-rich Great Basin. Specifically, nameplate capacities (megawatts) of operating geothermal plants, negative (0 megawatt) locations and 48 geophysical and geologic features are used to used in eXtreme Gradient Boosting (XGBoost) regression to make hydrothermal capacity predictions. Additionally, this work inputs the XGBoost-based hydrothermal predictions into the Renewable Energy Potential (reV) model to quantify technical capacity, its uncertainty and techno-economics. Compared to historical hydrothermal estimates, these predictions adhere to the 37 operating geothermal plants and negative locations. We present a method for subsampling the negative sites to bring the labels into balance that uses the geologic domain knowledge to proportionally represent negatives. Overall, the distributions of the hydrothermal technical capacity and the site levelized cost of energy are respectively much tighter, lower and more accurate than the previous estimates for the Great Basin, as they include geological and geophysical surrogates for permeability and fluids. Percentile (50th and 90th, median and high estimate, respectively) models provide bookends for these metrics.

13 HYDRO ENERGY↗

DOE OSTI2026

High-throughput oxidation screening and down-selection of refractory high entropy alloys in the Al-Cr-Mo-Nb-Ta-Ti system

Rapid experimentation and characterization are ever-present needs in the discovery of high entropy alloys. High entropy alloy systems are difficult to survey with systematic composition sweeps using traditional synthesis methods. The number of distinct compositions in even a four-element system is experimentally intractable. Exploration of these, and higher-element systems, necessitates thermodynamic prediction coupled with an automated sample creation method and a rapid screening methodology to effectively down-select alloys with targeted properties. As a result, a high-throughput method for evaluating the oxidation performance of refractory high entropy alloys was developed and tested. The six-element system of aluminum, chromium, molybdenum, niobium, tantalum, and titanium was evaluated for single phase stability and short-duration oxidation resistance. Target compositions were initially determined via thermodynamic predictions of single-phase stability across a wide temperature range. A twenty-five-sample build plate was produced using directed energy deposition additive manufacturing. After fabrication, the twenty-five 1 cm 3 samples were heat treated and characterized for composition and phase identification. The build plate was exposed to a high temperature oxidizing environment at 1000 °C for three hours. After oxidation, the composition, morphology, and chemistry of the oxides formed were characterized. Of the twenty-five samples produced, nine exhibited a favorable oxidation response, from which a single-phase BCC alloy at a composition of Al 13 Cr 7 Mo 19 Nb 18 Ta 26 Ti 17 was identified as the alloy with the most protective oxidation coating with a thin, adherent oxide scale. Finally, the complete experimental down-selection—from machine setup to final alloy identification—required approximately 45 labor hours, demonstrating a rapid validation for alloy discovery.

Additive manufacturing↗

DOE OSTI2025

Sensitivity analysis of a parameterization of coagulation in an aqueous transport model

A three-dimensional aqueous transport model, ALGE, was developed at the Savannah River National Laboratory (SRNL) as a tool for emergency response. Recently, coagulation and break-up of sediment and particulate (contaminant adhered to sediment) matter has been added as a new model capability, representing both processes as functions of salinity and turbulence. A sensitivity analysis was performed by comparing time series of sediment and particulate concentrations at various locations when the model’s parameters (particle size and density) are altered. The results show significant differences between experiments in both freshwater and saltwater systems parameters vary (P-values < 0.05). From this sensitivity study, we have concluded the importance of incorporating both salinity and turbulent effects, as they have a large role in the physical process of coagulation and breakup of sediment. By accounting for these physical processes, we have made the ALGE model a more robust tool for emergency response.

Aqueous fate and transport↗

DOE OSTI2024

A model for trapping and re-solution regarding intra-granular bubbles in UO 2 , linked to atomic-scale simulations

In the literature, a clear definition of the irradiation re-solution frequency of gas from bubbles in the UO 2 fuel is absent. Moreover, for intra-granular bubbles, a detailed calculation of the cumulated displaced gas quantities in function of the distance from the radius of the bubble after a re-solution event has never been published. The assessment of these two elements is very useful if we want to increase the adherence of fission gas release codes to our present knowledge of the behavior of fission gases. Hence, we suggest to link the definition of the re-solution frequency to atomic-scale simulations. Furthermore, we present the cumulated displaced gas quantities obtained from Molecular Dynamics calculations, from which we have derived a re-solution profile that can be exploited to better consider the irradiation re-solution phenomenon inside Fission Gas Release codes. On top of that, we have built a new trapping/re-solution model for intra-granular bubbles linked to Molecular Dynamics simulations that can be easily incorporated into Fission Gas Release codes. In conclusion, we also check that the model is properly built through the comparison of the new model against a reference.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2024

Beam energy dependence of net-hyperon yield and its implication on baryon transport mechanism

In the constituent quark model, each quark inside a baryon carries 1/3 unit of the baryon number. An alternative picture exists where the center of a Y-shaped topology of gluon fields, called the baryon junction, carries a unit baryon number. Studying baryon transport over a large rapidity gap (δy) in nuclear collisions provides a possible tool to distinguish these two pictures. A recent analysis of global data on net-proton yield at mid-rapidity in Au+Au collisions showed an exponential dependence on δy and the exponential slope does not vary with event centrality, favoring the baryon junction picture. Since junctions are flavor blind, hyperons – baryons containing valence strange quarks – are expected to exhibit a similar behavior as the proton. This study aims to test this prediction by analyzing hyperon yields in Au+Au collisions at various energies. We observe that net-hyperon yields, after correcting for the strangeness production suppression, adhere to the expected exponential form. The extracted slope parameters for net-Λ, net-$Ξ$ and net-Ω are consistent with each other and with those of net-proton within uncertainties, and exhibit no centrality dependence. Various implementations of the PYTHIA event generator, primarily based on valence quarks for baryon transport, are unable to simultaneously describe the slope parameters for all baryons.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

Sputter deposition of ultrathick Bi coatings onto rotating substrates for inertial confinement fusion

Sputter-deposited Bi is a potential candidate material for the production of inertial confinement fusion (ICF) hohlraums, which are sphero-cylindrical canisters that serve as the housing for hydrogen-fuel-filled capsules in ICF implosions. The fabrication of hohlraums involves deposition onto rotating sphero-cylindrical substrates and, hence, requires an understanding of oblique angle deposition phenomena. Here, we systematically study the effect of dynamic substrate tilt on properties of Bi films deposited onto rotating planar substrates. Results show that film morphology, porosity, and electrical resistivity depend on substrate tilt. In comparison with films deposited onto substrates adhered to a stationary sample holder, films deposited on rotating substrates have more uniform microstructure with fewer void defects. Based on Monte Carlo simulations, these changes in film morphology and microstructure are attributed to averaging and geometric effects related to sample position and rotation. In conclusion, the fabrication of an ∼ 50-µm-thick Bi film on a rotating sphero-cylindrical mandrel demonstrates a significant step toward the production of Bi hohlraums.

Bismuth↗

DOE OSTI2025

Evolution of Ni-Mo/MgO during catalytic methane pyrolysis to produce base-growth nanotubes

Catalytic pyrolysis of methane is a promising approach for affordable hydrogen production without CO 2 emissions. While this process is thermodynamically appealing compared to steam reforming, the high stability of methane requires severe conditions, making catalyst stability challenging. Here, we report the behavior of a highly promising Ni-Mo/MgO catalyst, which greatly outperforms its Ni/MgO, Mo/MgO, and Ni-Mo/SiO 2 counterparts at atmospheric pressure. At 800°C, nearly 229 g of carbon nanotubes per gram of Ni are produced. We propose that this superior performance results from the phase evolution of the catalyst, which exsolves stable nickel catalyst particles under reaction conditions. We further reveal that molybdenum carbide formation reduces sintering and adheres the active catalytic particles to the support throughout the reaction, enabling catalyst reuse. Several characterization techniques (same-spot TEM, XPS, XRD, and Raman) are employed to examine catalyst morphology at every step, fostering a deeper understanding of its catalytic activity and stability.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Optimising the processing and storage of visibilities using lossy compression

The next-generation radio astronomy instruments are providing a massive increase in sensitivity and coverage, largely through increasing the number of stations in the array and the frequency span sampled. The two primary problems encountered when processing the resultant avalanche of data are the need for abundant storage and the constraints imposed by I/O, as I/O bandwidths drop significantly on cold storage. An example of this is the data deluge expected from the SKA Telescopes of more than 60 PB per day, all to be stored on the buffer filesystem. While compressing the data is an obvious solution, the impacts on the final data products are hard to predict. In this paper, we chose an error-controlled compressor – MGARD – and applied it to simulated SKA-Mid and real pathfinder visibility data, in noise-free and noise-dominated regimes. As the data have an implicit error level in the system temperature, using an error bound in compression provides a natural metric for compression. MGARD ensures the compression incurred errors adhere to the user-prescribed tolerance. To measure the degradation of images reconstructed using the lossy compressed data, we proposed a list of diagnostic measures, exploring the trade-off between these error bounds and the corresponding compression ratios, as well as the impact on science quality derived from the lossy compressed data products through a series of experiments. We studied the global and local impacts on the output images for continuum and spectral line examples. We found relative error bounds of as much as 10%, which provide compression ratios of about 20, have a limited impact on the continuum imaging as the increased noise is less than the image RMS, whereas a 1% error bound (compression ratio of 8) introduces an increase in noise of about an order of magnitude less than the image RMS. For extremely sensitive observations and for very precious data, we would recommend a 0.1% error bound with compression ratios of about 4. These have noise impacts two orders of magnitude less than the image RMS levels. At these levels, the limits are due to instabilities in the deconvolution methods. We compared the results to the alternative compression tool DYSCO, in both the impacts on the images and in the relative flexibility. MGARD provides better compression for similar error bounds and has a host of potentially powerful additional features.

Techniques: interferometric↗

DOE OSTI2024

Trust Not Verify? The Critical Need for Data Curation Standards in Materials Informatics

The importance of data curation has been recognized in multiple areas of research; however, the discussion of this important issue is only beginning to emerge in materials science. In this Perspective, we highlight the benefits of using the standardized data curation protocols in materials science and discuss current gaps in accurate and reproducible data reporting using case studies drawn from high-impact materials science papers and well-known databases such as the Crystallography Open Database (COD) and the Cambridge Structural Database (CSD). We argue that both experimental and computational materials scientists need to embrace a culture of rigorous data curation as part of modern research data management. We propose a sample data curation pipeline for materials chemistry and illustrate its use by creating two new materials chemistry databases. Here, we hope that this perspective will serve to catalyze further discussion and promote the continuous development of rigorous data curation practices within the materials science research community. We posit that adherence to best practices of data curation will promote and enhance the reliability, reproducibility, and integrity of materials research and enable the development of reliable AI and machine learning models that critically depend on the use of quality data.

Chemical structure↗

DOE OSTI2026

Understanding and Mitigating Stickiness in Biochar Produced through Acid Hydrolysis and Dehydration

Levulinic acid is a platform chemical with significant potential for conversion into a wide range of biobased chemicals and fuels. A common process for producing levulinic acid from lignocellulosic feedstocks involves acid hydrolysis and dehydration (AHDH), where hexose polymers are hydrolyzed into monomeric sugars and subsequently dehydrated to levulinic acid and formic acid in the presence of dilute sulfuric acid. However, scaling the AHDH process is challenging because of the formation of byproducts such as sticky biochar, which accumulates in continuous-flow reactors, reducing effective reaction volume and increasing process downtime. This study investigates the effect of a chemical preconditioning step on mitigating sticky biochar formation. Woody biomass was preconditioned at 170 °C with 0.26 wt % sulfuric acid for 30 min, resulting in substantial removal of hemicellulose and acid-soluble lignin. AHDH of these preconditioned solids produced biochar that did not adhere to reactor surfaces. TGA analysis confirmed that the chemical preconditioning step minimized interactions between hemicellulose-derived degradation products and lignin side chains, reducing sticky char formation. Additionally, the study observed a 6% higher yield of organic acids from softwood species compared to hardwoods, with bark content shown to negatively impact yield. These findings suggest that targeted preconditioning of lignocellulosic biomass can enhance reactor operability and improve organic acid production efficiency in AHDH processes.

biopolymers↗

DOE OSTI2025

Tuning Molecular Interactions between Peptoids and Substrates to Achieve Surface-Agnostic Coating

Achieving programmable and robust coatings that maintain functionality while adhering to various surface types with molecular-level tunability and programmable features remains challenging. In this study, we develop adaptable and stable surface-agnostic coatings (SACs) based on crystalline peptoid membranes by tuning interpeptoid and peptoid-substrate interactions. We utilize two complementary methods: (1) surfaceinduced assembly, where peptoid membranes form directly on substrates, and (2) depositing preformed peptoid crystalline membranes via an aqueous layer-by-layer (LbL) assembly technique. These strategies are applied to substrates with diverse surface chemistries and topographies, including mica, highly ordered pyrolytic graphite (HOPG), MoS 2 , sapphire, and porous membranes like porous alumina and polysulfide. Atomic force microscopy confirms the formation of peptoid coatings and reveals differences in assembly behavior across surfaces. Moisture vapor transport measurements serve as a proof-of-concept test for membrane continuity and tunable permeance. Together, these findings demonstrate the adaptability and programmability of peptoid-based SACs, enabling rational coating design on surfaces with diverse chemical and topographical features. Furthermore, this work opens pathways for using peptoid membranes as programmable surface modifiers in functional interfaces, protective coatings, and membrane platforms.

biomimetic polymers↗

DOE OSTI2025

Adhesion of Self-Complementary, Sinusoidal Surfaces Fabricated Using Two-Photon Polymerization

Microscale, pick-and-place assembly is a non-lithographic assembly method poised to impact diverse fields including flexible electronics, microfluidics and robotics. However, a major technological challenge is the need to deterministically control adhesion between parts. Here, switchable adhesion involving 3D-printed, self-complementary surfaces is demonstrated. Mechanical properties of metasurfaces pressed against flat, rigid substrates are modeled using finite element methods. A series of flat slabs and metastructured slabs with 2D sinusoidal surfaces are printed using two-photon polymerization (2PP) of a shape-memory resin. The surface frequency of featured slabs was varied between $3.\bar3$ mm −1 and $26.\bar6$ mm −1 with similar amplitudes. Adhesion between printed metasurfaces and glass and between printed, self-complementary metasurfaces is studied above and below the cured resin’s glass transition temperature (∼45 °C). Simple heating of adhering surfaces to above 60 °C lowers adhesion, and compression of surfaces while above the glass transition temperature followed by cooling to room temperature elevates adhesion. The nominal adhesive strength between printed, self-complementary surfaces, as determined by the maximum observable pull-off stress, exceeds 3 MPa. Further tailoring complementary surfaces for adhesion control may facilitate microscale disassembly for recovery of components or precious metals.

amorphous materials↗

DOE OSTI2026

A Prodrug Strategy to Conditionally Trap Therapeutic Payloads for Improved Tumor Retention

Altered extracellular proteolysis has been exploited to selectively activate therapeutics in diseases such as cancer; however, once activated, extracellular drugs can diffuse away, limiting efficacy. We address this challenge by coupling proteolytic activation with membrane tethering to retain drugs within diseased tissue. To accomplish this, we developed “restricted interaction peptides” (RIPs), a delivery platform that leverages elevated proteolytic activity to activate membrane-interacting peptides, localizing cargos near the site of proteolysis. We demonstrate that RIPs can deliver diverse therapeutic cargos, including cytotoxins and radioisotopes. As proof of concept, we engineered “FRIP,” a RIP designed for cleavage by fibroblast activation protein (FAP), an endoprotease upregulated in solid tumors and fibrosis. Efficient P4–P4’ substrate sequences were identified and incorporated into FRIPs. Cell-based studies showed that, upon activation, the peptide adhered to membranes rapidly internalized and successfully delivered therapeutic cargos. Consistent with this, FRIPs delivering MMAE inhibited proliferation in an FAP-dependent manner. Imaging studies confirmed tumor targeting with minimal uptake in normal tissues. Finally, FRIPs delivering MMAE or Cu-67 exhibited potent antitumor effects. These findings establish membrane tethering as a strategy to enhance drug retention.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Accelerated Self-Healing and Property Recovery in Brush Particle Solids Featuring Brush Dispersity

Brush particles, hybrid materials consisting of polymer chains tethered to particle surfaces, offer tunable properties that make them promising candidates for advanced functional materials. This study investigated the role of chain dispersity in the viscoelastic self-healing of poly (methyl acrylate) (PMA)-based brush particle solids. Increasing the molecular weight dispersity of grafted chains significantly enhanced both strain-to-fracture and toughness of brush particle solids, while the elastic modulus and glass transition temperature were independent of chain dispersity. Cut-and-adhere testing revealed a significant acceleration of the rate of toughness recovery in high-dispersity systems as compared to low-dispersity analogs for which toughness recovery markedly lagged the recovery of Young’s modulus. The results suggest that structure and property recovery in brush particle solids are sensitive to the dynamical heterogeneity of brush canopies and highlight the role of molecular weight dispersity as a design parameter to enable hybrid materials with advanced self-healing ability.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Catalytic Borylation of Poly(vinyl chloride) Produces Adhesive Materials

Postpolymerization functionalization of polymers can create new applications for existing materials, while retaining their most favorable, intrinsic properties. Polyvinyl chloride (PVC) is a widely used, commodity polymer that is particularly challenging to modify. We report a copper-catalyzed protocol that replaces a small fraction of the C-Cl bonds in PVC with C-B bonds to boronic esters. The reaction occurs with an inexpensive catalyst comprising copper(II) chloride and an NHC ligand derived from a common ionic liquid that is distinct from the N-heterocyclic carbene (NHC) used for the borylation of small alkyl halides. The resulting materials adhere strongly to common surfaces, such as glass and metals, even more strongly than do commercial glues.

D’Angelo, Kyan A↗

DOE OSTI2026

Photoluminescent Cu(I) HETPHENs Featuring Bulky Alkyl-Substituted Phenanthrolines

Successful excited-state-enhancing substituent effect strategies applied in homoleptic Cu(I) metal-to-ligand charge transfer (MLCT) chromophores have been adapted to the heteroleptic phenanthroline (HETPHEN) platform, leveraging 2,9-mesityl-1,10-phenanthroline (mesPhen) in conjunction with a series of seven distinct 2,9- and 2,3,4,7,8,9-substituted phenanthrolines. The newly conceived CuHETPHEN complexes feature MLCT lifetimes ranging from 62 to 443 ns, all of which exhibit unprecedented room-temperature photoluminescence and demonstrate quantitative adherence to the energy gap law. TD-DFT calculations successfully modeled the systematic variation in electronic transition intensities, accounting for the experimentally observed UV–vis absorption bands across the entire series of molecules while providing detailed structural explanations for the variations in spectral profiles. These heteroleptic Cu(I) diimine chromophores exhibit thermally activated delayed fluorescence (TADF), with delayed fluorescence occurring between closely spaced 1 MLCT and 3 MLCT excited manifolds, having energetic separations of ΔE = 713–1009 cm –1 , as demonstrated here for the first time. Nanosecond and ultrafast transient absorption spectroscopy verified the MLCT nature of these excited states and extracted the time constants for the initial pseudo-Jahn–Teller distortion and intersystem crossing throughout the entire series of molecules. Lastly, the triplet photosensitization properties of these Cu(I) HETPHENs are demonstrated in a number of model photochemical transformations.

Chromophores↗

DOE OSTI2025

Accelerating charge estimation in molecular dynamics simulations using physics-informed neural networks: corrosion applications

Molecular Dynamics (MD) simulations are used to understand the effects of corrosion on metallic materials in salt brine. Reactive force fields in classical MD enable accurate modeling of bond formation and breakage in the aqueous medium and at the metal-electrolyte interface, while also facilitating dynamic partial charge equilibration. However, MD simulations are computationally intensive and unsuitable for modeling the long time scales characteristic of corrosive phenomena. To address this, we develop reduced-order machine learning models that provide accurate and efficient predictions of charge density in corrosive environments. Specifically, we use Long Short-Term Memory (LSTM) networks to forecast charge density evolution based on atomic environments represented by Smooth Overlap of Atomic Positions (SOAP) descriptors. A physics-informed loss function enforces charge neutrality and electronegativity equivalence. The atomic charges predicted by the deep learning model trained on this work were obtained two orders of magnitude faster than those from molecular dynamics (MD) simulations, with an error of less than 3% compared to the MD-obtained charges, even in extrapolative scenarios, while adhering to physical constraints. This demonstrates the excellent accuracy, computational efficiency, and validity of the developed model. Lastly, even though developed for corrosion, these protocols are formulated in a phenomenon-agnostic manner, allowing application to various variable-charge interatomic potentials and related fields.

Atomistic models↗