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At least 577 records · Page 32

High-throughput screening of environmental polysaccharide-degrading bacteria using biomass containment and complex insoluble substrates

Abstract Carbohydrate degradation by microbes plays an important role in global nutrient cycling, human nutrition, and biotechnological applications. Studies that focus on the degradation of complex recalcitrant polysaccharides are challenging because of the insolubility of these substrates as found in their natural contexts. Specifically, current methods to examine carbohydrate-based biomass degradation using bacterial strains or purified enzymes are not compatible with high-throughput screening using complex insoluble materials. In this report, we developed a small 3D printed filter device that fits inside a microplate well that allows for the free movement of bacterial cells, media, and enzymes while containing insoluble biomass. These devices do not interfere with standard microplate readers and can be used for both short- (24–48 h) and long-duration (> 100 h) experiments using complex insoluble substrates. These devices were used to quantitatively screen in a high-throughput manner environmental isolates for their ability to grow using lignocellulose or rice grains as a sole nutrient source. Additionally, we determined that the microplate-based containment devices are compatible with existing enzymatic assays to measure activity against insoluble biomass. Overall, these microplate containment devices provide a platform to study the degradation of complex insoluble materials in a high-throughput manner and have the potential to help uncover ecologically important aspects of bacterial metabolism as well as to accelerate biotechnological innovation.

3D printing↗

Final Technical Report for DE-SC0021049: Manipulating interfacial reactivity with atomically layered heterostructures

This final technical report summarizes the work accomplished in this DOE Early Career Research Program project that has established moiré superlattice materials and two-dimensional (2D) heterostructures as a highly tunable platform for controlling heterogeneous charge transfer (ET) kinetics at solid-liquid interfaces. By precisely engineering van der Waals heterostructures of atomically thin 2D materials, particularly bilayer and trilayer graphene with controlled twist angles, this project demonstrated systematic control of interfacial charge transfer rates spanning three orders of magnitude. This research addresses fundamental questions about how electronic structure, charge localization, and atomic layer-dependent properties govern charge transfer at electrochemical interfaces, with broad implications for energy conversion, electrocatalysis, and next-generation electrochemical devices.

36 MATERIALS SCIENCE↗

Digital Platform Informed Certification of Components Derived from Advanced Manufacturing Technologies

The Transformational Challenge Reactor is being designed at Oak Ridge National Laboratory to demonstrate the feasibility of constructing a reactor core using advanced manufacturing technology. This technology includes additive manufacturing combined with machine learning, materials science, and data science technologies in an effort to facilitate the expansion of additive manufacturing into advanced nuclear energy systems and other applications requiring a high level of quality assurance. The Transformational Challenge Reactor is employing additive manufacturing and artificial intelligence to deliver a new approach. Beginning in FY21, the focus of the program has shifted away from demonstrating a reactor, and instead, towards delivering on four key thrust areas: (1) artificial intelligence-informed design, (2) advanced materials, (3) integrated sensing and control, and (4) the digital platform. Of these four thrust areas, the most pertinent to this report is the digital platform. The digital platform has the potential to be a key enabler for a paradigm shift in how components, those derived from advanced manufacturing technologies, are certified for use in nuclear applications. This is achieved primarily using machine learning to discover correlations from the abundance of data produced through additive manufacturing and those physical properties critical to the performance of the component.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Development of Long Fiber Filled Materials for Fused Filament Fabrication (FFF) (Phase 2)

ORNL worked with 3D Platform to develop new hardware and systems for their largest product, a medium format pellet system known as the WorkCenter 500. 3D Platform provided an Alpha version of the system for ORNL to begin development and systems integration. ORNL focused efforts on integrating a new dryer, safety enclosure, extruder, and build table for improved operation of the system.

36 MATERIALS SCIENCE↗

MoorDyn V2: New Capabilities in Mooring System Components and Load Cases

MoorDyn, an open-source mooring dynamics model, is being expanded with capabilities for additional mooring system features and load cases. As floating wind turbine technology matures, mooring systems are becoming more sophisticated and more complex scenarios need to be considered in the design process. Mooring systems may have synthetic line materials, ballast/buoyancy bodies along the lines, or interconnections between platforms. Failure modes may involve multiple cascading line failures that depend on mooring system dynamics. Features recently added to MoorDyn aim to address these emerging needs. MoorDyn’s linear elasticity model has been supplemented to support user-defined stress-strain curves, which can be adjusted to represent synthetic mooring materials. Rigid six-degree-of-freedom bodies in the mooring system can now be modeled using two new model objects. “Rod” objects provide an option for rigid cylindrical bodies. They use the existing Morison equation-based hydrodynamics model and can be connected to mooring lines at either end. “Body” objects provide a generic six-degree-of-freedom rigid-body representation based on a lumped-parameter model of translational and rotational properties. Rod objects can be added to Body objects and mooring lines can be attached at any location, allowing a wide variety of submerged structures to be integrated into the mooring system. Lastly, a means of dynamically simulating mooring line failures has been implemented. These new features, currently in the C++ version of MoorDyn, are described and then demonstrated on a two-turbine shared-mooring array. A qualitative view of the results suggests the new features are functioning as expected.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

Mechanism-Resolved PFM of Ferroionic and Ferroelectric Responses in Thickness-Gradient Hf 0.5 Zr 0.5 O 2 Libraries

Resolving growth mechanisms and thickness evolution of functional properties is one of the key tasks in materials discovery and optimization involving thin-film materials, traditionally requiring significant experimental budgets. Here we introduce the combination of thickness-gradient libraries and automated scanning probe microscopy as a systematic pathway to elucidate growth modes and disentangle ferroelectric and electrochemical contributions in ferroelectric thin films. As a model system, we explore the Hf 0.5 Zr 0.5 O 2 (HZO) gradient thin films grown on La x Sr 1-x MnO 3 (LSMO) bottom electrode thin films. Automated piezoresponse force microscopy, spectroscopy, and lithography reveals that irreversible topographic deformation arises from electrochemical activity at the LSMO surface, whereas reversible phase inversion in HZO reflects ferroelectric switching. Automated topography height-map scans are used to further quantify nucleation density, particle-size evolution, and roughness correlations across the thickness-gradient, demonstrating that improved plume stabilization during growth suppresses interfacial reactions and promotes dense, fine-grained HZO conducive to ferroelectric phase formation. This combined materials-engineering and automated-SPM framework establishes a platform for high-throughput, mechanism-resolved characterization of ferroionic and ferroelectric responses in complex oxide films.

FOS: Physical sciences↗

MoorDyn V2: New Capabilities in Mooring System Components and Load Cases: Preprint

MoorDyn, an open-source mooring dynamics model, is being expanded with capabilities for additional mooring system features and load cases. As floating wind turbine technology matures, mooring systems are becoming more sophisticated and more complex scenarios need to be considered in the design process. Mooring systems may have synthetic line materials, ballast/buoyancy bodies along the lines, or interconnections between platforms. Failure modes may involve multiple cascading line failures that depend on mooring system dynamics. Features recently added to MoorDyn aim to address these emerging needs. MoorDyn’s linear elasticity model has been supplemented to support user-defined stress-strain curves, which can be adjusted to represent synthetic mooring materials. Rigid six-degree-of-freedom bodies in the mooring system can now be modeled using two new model objects. “Rod” objects provide an option for rigid cylindrical bodies. They use the existing Morison equation-based hydrodynamics model and can be connected to mooring lines at either end. “Body” objects provide a generic six-degree-of-freedom rigid-body representation based on a lumped-parameter model of translational and rotational properties. Rod objects can be added to Body objects and mooring lines can be attached at any location, allowing a wide variety of submerged structures to be integrated into the mooring system. Lastly, a means of dynamically simulating mooring line failures has been implemented. These new features, currently in the C++ version of MoorDyn, are described and then demonstrated on a two-turbine shared-mooring array. A qualitative view of the results suggests the new features are functioning as expected.

49 EE - Wind and Water Power Program - Wind (EE-4W↗

Volumetric Shaping of Nanoparticle-DNA Crystals by Light-Induced Milling

DNA-programmable self-assembly enables the formation of nanoparticle crystals with controlled lattice symmetry. While this approach offers the formation of complexly ordered nanostructures for optical, mechanical, and biological applications, a mesoscale control over such nanomaterials is limited. Directing the material formation process through the assembly pathway or external fields allows for modulating crystal morphology, but achieving arbitrary morphology remains challenging. Here, we present a photothermal method for shaping 3D DNA-programmable crystals of gold nanoparticles. Through local heating of nanoparticles due to plasmonic light absorption, we induce targeted volumetric dissolution of specifically defined crystal areas with micron-scale accuracy. This technique effectively prescribes crystal shaping and creates arbitrarily shaped voids within crystals. We further investigate both computationally and experimentally the key factors governing volumetric material subtraction. The developed automated light-milling platform enables the fabrication of nanomaterials exhibiting both DNA-programmable nanoscale order and custom-designed mesoscale architecture.

36 MATERIALS SCIENCE↗

A market-oriented database design for critical material research

Material databases are important tools to provide and store information from material research. Rising concerns about supply-chain risks to raw materials presents a need to incorporate raw-material market and end-use application data, beyond basic chemical and physical properties, into a material database. One key challenge for researchers working on critical materials is information scarcity and inconsistency. This paper introduces, as a result of a two-year project, a critical-material commodity database (CMCD) incorporated with a low-code web-based platform that allows easy access for users and simple updates for the authors. The main goal of this project was to educate material scientists on the applications having the most impact on the supply chain and current industrial specifications/markets for each application. The objective was to provide material researchers with harmonized information so that they could gain a better understanding of the market, focus their technologies on an application with a high potential for commercialization, and better contribute to supply-chain risk reduction. While the goal was met with high receptivity, several limitations stemmed from query design, distribution platform, and quality of data source. To overcome some of these limitations and expand on CMCD's potential, we are building a public webpage with an improved interface, better data organization, and higher extensibility.

36 MATERIALS SCIENCE↗

Direct 2,3-Butanediol Conversion to Butene-Rich C 3+ Olefins over Copper-Modified 2D Pillared MFI: Consequence of Reduced Diffusion Length

2,3-Butanediol (2,3-BDO), a critical C 4 platform chemical derived from biomass, syngas, or CO 2 , can be converted to C 3+ olefins, serving as important renewable feedstocks for producing sustainable aviation fuels to decarbonize the hard-to-electrify air transportation sector. Herein, we report a bifunctional Cu-modified diffusion-free 2D pillared MFI catalyst (Cu/PMFI) which can selectively catalyze 2,3-BDO conversion to butene-rich C 3+ olefins (95% selectivity at 97% conversion, 523 K). 2,3-BDO conversion to butenes over Cu/PMFI primarily occurs via methyl ethyl ketone intermediate while 2-methyl propanal is also observed as another minor dehydration product that leads to butene formation. In comparison with a control mesoporous Cu/ZSM-5 sample prepared by the postsynthetic approach, Cu/PMFI shows favorable C 3+ olefin selectivity (95% over Cu/PMFI vs 80% over Cu/ZSM-5 at ~5.1 h TOS). The coke formation over Cu/PMFI is dramatically suppressed by >50% in contrast to Cu/ZSM-5 in 90 h 2,3-BDO conversion due to the reduced diffusion length. Cu/PMFI also favors butene formation and minimizes nonbutene C 3+ olefins by inhibiting the downstream oligomerization and cracking reactions. This study highlights the usefulness of the diffusion-free 2D PMFI materials in catalytic conversion of biomass-derived platform molecules and the significance of diffusion impact on catalyst coke formation and product distributions.

2,3-butanediol↗

Rapid Evaluation of Amine-Functionalized Solvents for Biomass Deconstruction Using High-Throughput Screening and One-Pot Enzymatic Saccharification

Efficient and sustainable pretreatment of lignocellulosic biomass is critical for biofuel and biochemical production, yet its optimization is often hindered by slow, labor-intensive experimental methods. Here, we report the first demonstration of a custom-built, miniaturized, high-throughput screening platform integrated with one-pot enzymatic saccharification, enabling parallel evaluation of solvent type, feedstock, and temperature with minimal material use and high reproducibility. As a proof-of-concept, the HTX platform was used to screen five amine-functionalized solvents, including isopropanolamine, butylamine, N-methylbutylamine, ethanolamine, and ethanolamine acetate across three bioenergy crops (sorghum, poplar, and switchgrass) and pretreatment temperatures ranging from 80 to 140 °C. Vacuum drying successfully removed more than 99% of the solvents from the pretreated biomass, eliminating the need for water washing prior to saccharification. Isopropanolamine and N-methylbutylamine yielded the highest glucose (70–80%) and xylose (58–67%) release, with trends reflecting feedstock recalcitrance. The produced hydrolysates supported robust growth of an engineered strain of the yeast Rhodosporidium toruloides, confirming biocompatibility. This high-throughput platform provides a scalable, feedstock-agnostic framework for rapid pretreatment screening, accelerating solvent–feedstock pairing and process optimization. Its ability to integrate pretreatment, solvent removal, saccharification, and microbial conversion in a miniaturized format offers significant advantages for cost-competitive biorefinery development.

Biomass↗

QUOTAS: A New Research Platform for the Data-driven Discovery of Black Holes

We present QUOTAS, a novel research platform for the data-driven investigation of supermassive black hole (SMBH) populations. While SMBH data—observations and simulations—have grown in complexity and abundance, our computational environments and tools have not matured commensurately to exhaust opportunities for discovery. To explore the BH, host galaxy, and parent dark matter halo connection—in this pilot version—we assemble and colocate the high-redshift, z > 3 quasar population alongside simulated data at the same cosmic epochs. As a first demonstration of the utility of QUOTAS, we investigate correlations between observed Sloan Digital Sky Survey (SDSS) quasars and their hosts with those derived from simulations. Leveraging machine-learning algorithms (ML), to expand simulation volumes, we show that halo properties extracted from smaller dark-matter-only simulation boxes successfully replicate halo populations in larger boxes. Next, using the Illustris-TNG300 simulation that includes baryonic physics as the training set, we populate the larger LEGACY Expanse dark-matter-only box with quasars, and show that observed SDSS quasar occupation statistics are accurately replicated. First science results from QUOTAS comparing colocated observational and ML-trained simulated data at z3 are presented. QUOTAS demonstrates the power of ML, in analyzing and exploring large data sets, while also offering a unique opportunity to interrogate theoretical assumptions that underpin accretion and feedback models. QUOTAS and all related materials are publicly available at the Google Kaggle platform. (The full data set—observational data and simulation data—are available at: https://www.kaggle.com/ and the codes are available at:https://www.kaggle.com/datasets/quotasplatform/quotas)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

High-throughput screening of multifunctional nanocoatings based on combinations of polyphenols and catecholamines

Biomimetic surface coatings based on plant polyphenols and catecholamines have been used broadly in a variety of applications. However, the lack of a rational cost-effective platform for screening these coatings and their properties limits the true potential of these functional materials to be unleashed. Here, we investigated the oxidation behavior and coating formation ability of a library consisting of 45 phenolic compounds and catecholamines. UV–vis spectroscopy demonstrated significant acceleration of oxidation and polymerization under UV irradiation. We discovered that several binary mixtures resulted in non-additive behavior (synergistic or antagonistic effect) yielding much thicker or thinner coatings than individual compounds measured by ellipsometry. To investigate the properties of coatings derived from new combinations, we used a miniaturized high-throughput strategy to screen 2,532 spots coated with single, binary, and ternary combinations of coating precursors in one run. We evaluated the use of machine learning models to learn the relation between the chemical structure of the precursors and the thickness of the nanocoatings. Formation and stability of nanocoatings were investigated in a high-throughput manner via discontinuous dewetting. 30 stable combinations (hits) were used to tune the surface wettability and to form water droplet microarray and spot size gradients of water droplets on the coated surface. No toxicity was observed against eukaryotic HeLa cells and Pseudomonas aeruginosa (strain PA30) bacteria after 24 h incubation at 37 C. The strategy introduced here for high-throughput screening of nanocoatings derived from combinations of coating precursors enables the discovery of new functional materials for various applications in science and technology in a cost-effective miniaturized manner.

36 MATERIALS SCIENCE↗

Assessment of the errors of high-fidelity two-qubit gates in silicon quantum dots

Achieving high-fidelity entangling operations between qubits consistently is essential for the performance of multi-qubit systems. Solid-state platforms are particularly exposed to errors arising from materials-induced variability between qubits, which leads to performance inconsistencies. Here we study the errors in a spin qubit processor, tying them to their physical origins. We use this knowledge to demonstrate consistent and repeatable operation with above 99% fidelity of two-qubit gates in the technologically important silicon metal-oxide-semiconductor quantum dot platform. Analysis of the physical errors and fidelities in multiple devices over extended periods allows us to ensure that we capture the variation and the most common error types. Physical error sources include the slow nuclear and electrical noise on single qubits and contextual noise that depends on the applied control sequence. Furthermore, we investigate the impact of qubit design, feedback systems and robust gate design to inform the design of future scalable, high-fidelity control strategies. Our results highlight both the capabilities and challenges for the scaling-up of silicon spin-based qubits into full-scale quantum processors.

Quantum dots↗

Improved Hydrogen Sensitivity and Selectivity in PdO with Metal-Organic Framework Membrane

Metal-organic frameworks (MOFs) are highly designable porous materials and are recognized for their exceptional selectivity as chemical sensors. However, they are not always suitable for incorporation with existing sensing platforms, especially sensing modes that rely on electronic changes in the sensing material (e.g., work-function response or conductometric response). One way that MOFs can be utilized is by growing them as a porous membrane on a sensing layer and using the MOF to affect the electronic structure of the sensing layer. In this paper, a proof-of-concept for electronic modulation with MOFs is demonstrated. A PdO nanoparticle sensing layer on a chemical-sensitive field-effect-transistor is made more sensitive to a reducing gas, hydrogen, and less sensitive to oxidizng molecules, like H 2 S and NO 2 , by growing a layer of the MOF “ZIF-8” over the nanoparticles. The proposed mechanism is supported by X-ray photoelectron spectroscopy showing that the ZIF-8 membrane partially reduces the PdO sensing layer.

Gardner, David W. (ORCID:0000000193518391)↗

Spin-Controllable Dynamics in Defect-Engineered Carbon Nanotubes as Single Photon Emitters: Data-Driven Modeling and Computations

Quantum technologies, such as quantum computing and sensing, require efficient single-photon emission (SPE) sources that operate at room temperature in telecom wavelengths. While several materials can serve as SPE sources, no single platform meets all the criteria for efficiency, ambient operation, and scalability. Single-walled carbon nanotubes (SWCNTs) with covalently attached molecules offer a promising solution. Their SPE can be easily tuned via modifications of the SWCNT's diameter, chirality, and bonded molecules, enabling emission across near-IR to telecom wavelengths at ambient conditions. However, to fully realize the potential of SWCNTs and unlock their quantum capabilities, a deeper understanding of how structural defects from molecular adducts affect their emission and competing photoexcited processes is essential. To address this gap in our knowledge, this project combined quantum chemistry calculations with data-driven methods of cheminformatics (QSAR) and machine learning (ML). The developed computational approaches have provided several design strategies for covalent functionalization of SWCNTs to improve their optical response. The collaboration with Los Alamos National Lab (LANL) enabled direct comparison of computational and experimental data, facilitating method validation. This partnership was enhanced through access to LANL's Center for Integrated Nanotechnologies (CINT) utilizing User Facility Program and summer internships, which provided three NDSU graduate students with hands-on experience at LANL. The outcomes of this project included (1) Advancing the current stage of computational methods in accurate modeling of non-adiabatic spin-dependent photoexcited dynamics and its applicability to nanosystems consisting of thousands of atoms, realized as open-access codes linked to existing DFT-based software; (2) Establishing the relationship between the structure of adducts and SWCNTs and intrinsic excitonic and spin properties of defect states for guiding novel synthetic strategies and experimental probes of chemically functionalized SWCNTs as near-IR emitting materials; (3) Generating virtual libraries of hypothetical functionalized SWCNTs for virtual screening of their chemical structures and optical properties, leveraging new functionalities of SWCNTs; (4) Offering a unique experience for NDSU graduate students that prepared them for future scientific careers related to materials modeling and big data processing. These results were summarized in 12 published journal papers and 3 recently submitted papers. One of a key finding is that the position of defect sites on the SWCNT surface primarily drives the emission redshift (up to 100 meV), while the polarity of the defect-inducing molecules has a much smaller effect (~10 meV). However, the electron-donating or withdrawing properties of a molecule influence selecting reactivity of defect sites. These insights important for optimizing synthetic protocols for desired emissions in SWCNTs. We also revealed that the interaction between two defects at various positions on the SWCNT enhances the redshift and optical activity of states, favoring strong near-IR emission. This suggests that manipulations in defect concentrations is a promising strategy for controlling efficient emission. Mostly important, the defect position was found controllable by the spin states of photoexcited intermediates: Excited aromatic molecules form ortho defects with SWCNTs at their singlet states in the presence of oxygen, while oxygen-free conditions favor para defects via the triplet-state mechanism. Additionally, a heat-activated [2+2] cycloaddition reaction facilitates divalent defect formation with fewer bonding positions that narrows emission bands. These groundbreaking findings have been experimentally validated and significantly advance our understanding of defect chemistry in SWCNTs. Using a novel encoding technique and 3D-MoRSE descriptors, we developed highly accurate ML/QSAR models to predict both the 3D structure and optical properties of SWCNTs with chemical defects. This model enabled the creation of a virtual library of 125,556 structures, providing new insights into the relationship between SWCNT-defect structure and emission.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

Ultrafast (1‐5 sec) Lamination of Perovskite Solar Cells With Self‐Encapsulation Using Rapid Joule Heating

Perovskite solar cells (PSCs) are traditionally fabricated using sequential layer‐by‐layer deposition, in which each layer of the device is processed on top of the preceding layer. This constrains the processing techniques and selection of transport layer materials that can be used in the solar cell. To overcome these challenges, two half‐cells can be processed independently and then diffusion‐bonded through a lamination process. However, current lamination processes for perovskite solar cells suffer from relatively long process times, which can limit throughput when moving toward high‐volume manufacturing. In this study, a custom platform was designed for rapid‐joule heating of perovskite materials and devices. This enabled more than a 99% reduction in lamination time from 26 min to 1 s. Perovskite samples that were laminated in 1 s exhibited comparable values of percent bonded area, interfacial toughness, grain domain size, and X‐ray diffraction spectra to those laminated in greater than 10 min. As a proof‐of‐concept, 18.3% efficient devices were successfully laminated in 5 s. A transient heat transfer model was developed to describe the relationship between the perovskite temperature and the electrical power supplied to the heaters, establishing a baseline for predicting processing conditions in large‐scale manufacturing systems. Ultra‐fast lamination provides a pathway toward scalable roll‐to‐roll or sheet‐to‐sheet manufacturing of PSCs.

heat transfer↗

Dynamic Diketoenamine Crosslinking Unlocks Vinyl Polymer Vitrimers From β ‐Triketone Chemistry

Covalent adaptable networks (CANs) offer a compelling strategy to unite the mechanical robustness of thermosets with the reprocessability of thermoplastics, yet achieving simultaneous durability, processability, and true recyclability remains challenging. We introduce diketoenamine (DKE) vitrimers derived from β-triketone methacrylate monomers and demonstrate how rational monomer design dictates network processability, viscoelasticity, and recyclability. By systematically varying the spacer length between the β-triketone (TK) moiety and the polymer backbone, we identify a key structure–property relationship that dictates vitrimer behavior. Networks bearing TK pendants minimally displaced from the backbone suppress creep but exhibit limited stress relaxation, whereas extended spacers yield lower glass transition temperatures, higher effective crosslink densities, and efficient stress dissipation, enabling optical transparency and reprocessability. Extending this platform to ultra-high molecular-weight prepolymers introduces physical entanglements as secondary crosslinks, further enhancing dimensional stability without compromising processability. Both mechanical and chemical recycling validate the closed-loop circularity of these materials. Furthermore, these results establish TK methacrylates as a versatile platform for designing high-performance vitrimers that integrate durability, reprocessability, and true closed-loop recyclability.

closed-loop recyclability↗