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At least 289 records · Page 16

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DOE OSTI2024

Thermally stable and self-healable lignin-based polyester

The increased use of plastics and the associated environmental impact has catalyzed research on the development of bio-derived polymers. Bio-based polyesters have gained increased attention due to the abundance of their starting materials and ease of processing. Lignin is naturally occurring in biomass with rich carbon content, whose functionality and rigidity make it an ideal bio-derived candidate for bio-based polyesters. Herein, a lignin-based polyester with good thermal stability and self-repairability was synthesized from carboxylated lignin and epoxidized soybean oil. The synthesized lignin/epoxidized soybean oil (ESO) vitrimer was brittle such that its mechanical performance could not be recorded. However, when polyethylene glycol (PEG) was incorporated as a plasticizer, polymer samples exhibited acceptable ductility. From thermomechanical analysis of the synthesized polyesters, the plasticizer did not impair thermal stability of polymers, but greatly enhanced mechanical properties. Notably, all samples exhibited stability at high temperatures, and good glass transition temperatures (51.0 ± 0.9–78.0 ± 1.2 °C). The highest tensile strength (3.983 ± 0.1 MPa) and storage modulus (1463.67 ± 12.6 MPa) were recorded for the polyester containing 6 % w/w PEG. Moreover, the polymer samples exhibited self-healing capability at 180 °C. This work expands on valorization of lignin through the synthesis of bio-derived materials.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Six-Letter DNA Nanotechnology: Incorporation of Z-P Base Pairs into Self-Assembling 3D Crystals

Artificially expanded genetic information systems (AEGIS) were developed to expand the diversity and functionality of biological systems. Recent experiments have shown that these expanded DNA molecular systems are robust platforms for information storage and retrieval as well as useful for basic biotechnologies. In tandem, nucleic acid nanotechnology has seen the use of information-based “semantomorphic” encoding to drive the self-assembly of a vast array of supramolecular devices. To establish the effectiveness of AEGIS toward nanotechnological applications, we investigated the ability of a six-letter alphabet composed of A:T, G:C and synthetic Z:P (Z, 6-amino-3-(1'-β- D-2'-deoxy ribofuranosyl)-5-nitro-(1H)-pyridin-2-one; P, 2-amino-8-(1'- β-D-2'-deoxyribofuranosyl)-imidazo-[1,2a]-1,3,5-triazin-(8H)-4-one) base pairs to engage in 3D self-assembly. We found that crystals could be programmably assembled from AEGIS oligomers. We conclude that unnatural base pairs can be used for the topological self-assembly of crystals. We anticipate the expansion of AEGISbased nucleic acid nanotechnologies to enable the development of novel nanomaterials, high-fidelity signal cascades, and dynamic nanoscale devices.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Multicaloric Cryocooling Using Heavy Rare-Earth Free La(Fe,Si) 13 -Based Compounds

The transition toward a carbon-neutral society based on renewable energies goes hand in hand with the availability of energy-efficient technologies. Here, magnetocaloric cooling is a very promising refrigeration technology to fulfill this role regarding cryogenic gas liquefaction. However, the current reliance on highly resource critical, heavy rare-earth-based compounds as magnetocaloric material makes global usage unsustainable. Here, we aim to mitigate this limitation through the utilization of a multicaloric cooling concept, which uses the external stimuli of isotropic pressure and magnetic field to tailor and induce magnetostructural phase transitions associated with large caloric effects. In this study, La 0.7 Ce 0.3 Fe 11.6 Si 1.4 is used as a nontoxic, low-cost, low-criticality multiferroic material to explore the potential, challenges, and peculiarities of multicaloric cryocooling, achieving maximum isothermal entropy changes up to −28 J (kg K) −1 in the temperature range from 190 K down to 30 K. Thus, the multicaloric cooling approach offers an additional degree of freedom to tailor the phase transition properties and may lead to energy-efficient and environmentally friendly gas liquefaction based on designed-for-purpose, noncritical multiferroic materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Copper-Based Two-Dimensional Conductive Metal–Organic Framework Thin Films for Ultrasensitive Detection of Perfluoroalkyls in Drinking Water

Perfluoroalkyls (PFAS) continue to emerge as a global health threat making their effective detection and capture extremely important. Though metal–organic frameworks (MOFs) have stood out as a promising class of porous materials for sensing PFAS, detection limits remain insufficient and a fundamental understanding of detection mechanisms warrants further investigation. Here, in this study, we show the use of a 2D conductive MOF film based on copper hexahydroxy triphenylene (Cu-HHTP) to fabricate chemiresistive sensing devices for detecting PFAS in drinking water. We further show ultrasensitive detection using electrochemical impedance spectroscopy. Owing to excellent electrostatic attractions and electrochemical interactions between the copper-based MOF and PFAS, confirmed by high-resolution spectroscopy and theoretical simulations, the MOF-based sensor reported herein exhibits excellent affinity and sensitivity toward perfluorinated acids at concentrations as low as 0.002 ng/L.

2D metal−organic frameworks↗

DOE OSTI2026

Patch-Based Convolutional Neural Networks for Multiple Microstructural Features Detection in FIB-SEM Micrographs of Irradiated Nuclear Fuel

Focused ion beam scanning electron microscopy (FIB-SEM) tomography has increasingly been utilized for acquiring three-dimensional (3D) microstructure features at the sub-micron scale in irradiated nuclear materials. This technique involves sequential ion beam slicing followed by electron beam imaging and compositional mapping using energy dispersive spectroscopy (EDS). Despite its growing use, several challenges persist. These include the time-intensive nature of data collection of EDS data, difficulties in distinguishing between various microstructures, and issues with image alignment. These challenges currently limit the broader application of FIB-SEM tomography in the field. To overcome these limitations, we propose using convolutional neural networks (CNNs) to automate microstructure identification in SEM images. Our study introduces a new framework for identifying microstructures in irradiated U-10Zr (wt. %) metallic fuel with limited annotated data. The framework includes the creation of a reliable annotated dataset with paired SEM and ground truth data from EDS maps, the applications of CNNs for microstructure identification, and the validation of model performance. Specifically, we employed the Segment Anything Model (SAM) to align SEM images with corresponding EDS maps and focused ion beam (FIB) tomography SEM data. We evaluate several models, including Patch-based U-Net, Attention U-Net, and Residual U-Net, finding that patch-based U-Net exhibits superior segmentation performance and consistency. This approach reduces reliance on EDS detectors and aids in accelerating nuclear material analysis process, highlighting the potential of advanced deep learning techniques to improve microstructural understanding in nuclear material. This is the first framework to integrate SAM and Patch-based CNN models for semantic segmentation of irradiated nuclear materials, with potential applicability to other tomography datasets.

36 - MATERIALS SCIENCE↗

DOE OSTI2025

Modelling pulsed field magnetization of iron-based bulk superconductors

Abstract Bulk superconductors can be used as super-strength quasi-permanent magnets capable of providing magnetic flux densities considerably superior to conventional permanent magnets. This makes them attractive for several engineering applications that rely on strong magnetic fields like rotating machines, NMR/MRI and magnetic drug delivery systems. Recently, the authors reported a record trapped magnetic field in an iron-based bulk superconductor: 2.83 T was trapped in potassium-doped barium iron arsenide (Ba, K)Fe 2 As 2 (or Ba122) at 5 K. Of particular significance is that the strength and temporal stability of this magnetic field exceeds the requirements of MRI machines, indicating iron-based bulks can now perform at levels demanded by engineering applications. One crucial challenge for their practical use, however, is the need to apply and remove an external magnetic field to magnetize them. Pulsed field magnetization (PFM) shows great promise as a practical method of magnetizing bulks, but the process generates heat in the bulk that is detrimental to its superconducting performance and ability to act as a super-strength magnet. In this paper, coupled electromagnetic–thermal numerical models are used to simulate the PFM of iron-based bulk superconductors. Here we focus on the recent-record-breaking, fine-grain polycrystalline K-doped Ba122 bulks. The impact that the specific J c ( B ) characteristics and thermal properties of the Ba122 material—all of which have been experimentally measured from state-of-the-art samples—have on the magnetic flux dynamics and thermal behaviour during PFM, including the final trapped field, is investigated. We show that because the thermal properties are similar to those of REBa 2 Cu 3 O 7 −δ bulks, a similar response to pulsed fields is obtained. A maximum trapped field of ∼0.81 T (∼43.3% of the maximum trapped field capability under ideal, field-cooling conditions) was simulated at 5 K, with a magnetization efficiency of ∼54%. The modelling framework provides a fast and flexible tool for optimising the practical PFM process at different operating temperatures to maximise the trapped field in state-of-the-art Ba122 bulks and to guide the design of future experiments.

bulk superconductors↗

DOE OSTI2026

Supercharging simulation-based inference for Bayesian optimal experimental design

Abstract Bayesian optimal experimental design (BOED) seeks to maximize the expected information gain (EIG) of experiments. This requires a likelihood estimate, which in many settings is intractable. Simulation-based inference (SBI) provides powerful tools for this regime. However, existing work explicitly connecting SBI and BOED is restricted to a single contrastive EIG bound. We show that the EIG admits multiple formulations which can directly leverage modern SBI density estimators, encompassing neural posterior, likelihood, and ratio estimation. Building on this perspective, we define a novel EIG estimator using neural likelihood estimation. Further, we identify optimization as a key bottleneck of gradient based EIG maximization and show that a simple multi-start parallel gradient ascent procedure can substantially improve reliability and performance. With these innovations, our SBI-based BOED methods are able to match or outperform by up to 22% existing state-of-the-art approaches across standard BOED benchmarks.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

A Review on Protection Challenges on Transmission Lines Connected to Inverter Based Resources

The increased penetration of inverter-based resources (IBRs) connected to transmission grids is challenging how traditional electric power systems operate. Such systems were originally designed based on the dominant presence of synchronous generators. Some main challenges include the increased concerns about system stability due to the reduced system inertia caused by IBRs and the reduced protection reliability caused by the non-deterministic fault current response of IBRs. The protection relays’ algorithms have been designed and validated based on well-known fault current characteristics of synchronous generations, while the short-circuit responses of IBRs heavily depend on their vendor-specific control schemes, which are mainly set to protect inverter semiconductor switches and DC components. The magnitude of short current that IBRs contribute is typically between 1.1–1.5 times the rated current that may behave unstably and incoherently with the voltages. The control scheme of the IBRs also controls the active (P) and reactive (Q) support during system voltage sag and swell conditions. These characteristics challenge not only protective relay schemes but also existing short-circuit software tools to correctly model IBRs for fault studies and protection coordination analyses. Utilities, university researchers, research institutes, and relay manufacturers have performed studies to investigate different aspects of high IBR penetration. Almost all studies support 1) creating international and national standards to regulate the IBRs, 2) revising engineering tools to address shortcomings, and 3) adjusting the protective relay settings to resolve protection problems. This paper summarizes the studies and research conducted in recent years to quantify the impact of IBR penetration on transmission line protection and to identify potential solutions to improve the dependability and reliability of grid protection systems.

14 SOLAR ENERGY↗

DOE OSTI2025

Efficient Signal Processing in BOTDA: Utilizing PCA and PCA-Based Neural Networks for Temperature Monitoring

This work presents a comparative analysis of the various signal processing techniques used in the Brillouin gain spectrum (BGS) peak estimation. Traditional fitting methods such as Lorentzian curve fitting (LCF) are slow and less effective in noisy data. PCA-based methods were tested on the experimental data: A Euclidian distance-based approach, and a probabilistic deep neural network (PDNN) based approach, both using 5 principal components to represent a single BGS. Both methods significantly reduce computational time with respect to LCF, whereas PDNN offers uncertainty insights along with the parameter value. Measuring a range of temperatures, analyzing accuracy, and speed, it can be concluded that PCA trained PDNN outperforms other methods, and appears to be helpful in scenario where large datasets are generated.

Brillouin optical time domain analysis↗

DOE OSTI2024

A review on protection challenges of Transmission Lines connected to Inverter Based Resources

14 SOLAR ENERGY↗

DOE OSTI2025

Aqueous-Based Granulation Method Towards Syngas Production

An aqueous-based granulation method is developed to combat the structural changes associated with binder-based granulation techniques. Microwave-assisted methane reforming studies showed an improved catalyst efficiency for these aqueous-based granules compared to the powder catalysts. This granulation method has been extended to other practical applications (chemical looping gasification and air separations).

aqueous-based granulation↗

DOE OSTI2025

An automated integrated web-based smart tool for open stope design

The Stability Graph is a widely used tool for the design of open stopes in underground mining. Many users of the Stability Graph still apply this design method manually. Although the manual approach has benefits, using multiple graphs and stability number computation charts for each stope surface is time-consuming, even for the experienced mining engineer. Current practice in the use of the method also limits data sharing. This paper presents a StopeSoft web-based tool for open stope stability prediction that is developed on the basis of the Stability Graph method and is available at openstope.com. StopeSoft incorporates flexibility in terms of Stability Graph options and incorporates additional critical factors often overlooked. As a web-based tool, StopeSoft encourages and makes data sharing possible globally, focused on expanding the database and improving the current limitations of the Stability Graph to provide practical, reliable solutions for mining engineers, consultants, and academics. The StopeSoft automated process facilitates the process of open stope stability prediction, saving time and minimizing potential human errors. Statistical treatment of the data accounts for the variability of input parameters to emphasize the probabilistic nature of the Stability Graph method. The probabilistic interpretation of the stability states of stope surfaces eliminates the false feeling of absolute stope performance based on its location on the Stability Graph , as implied by the deterministic approach.

58 GEOSCIENCES↗

DOE OSTI2024

A Review on Protection Challenges of Transmission Lines Connected to Inverter-Based Resources

14 SOLAR ENERGY↗

DOE OSTI2023

Hybrid Data‐Driven Discovery of High‐Performance Silver Selenide‐Based Thermoelectric Composites

Optimizing material compositions often enhances thermoelectric performances. However, the large selection of possible base elements and dopants results in a vast composition design space that is too large to systematically search using solely domain knowledge. To address this challenge, a hybrid data-driven strategy that integrates Bayesian optimization (BO) and Gaussian process regression (GPR) is proposed to optimize the composition of five elements (Ag, Se, S, Cu, and Te) in AgSe-based thermoelectric materials. Data is collected from the literature to provide prior knowledge for the initial GPR model, which is updated by actively collected experimental data during the iteration between BO and experiments. Within seven iterations, the optimized AgSe-based materials prepared using a simple high-throughput ink mixing and blade coating method deliver a high power factor of 2100 µW m −1 K −2 , which is a 75% improvement from the baseline composite (nominal composition of Ag 2 Se 1 ). In conclusion, the success of this study provides opportunities to generalize the demonstrated active machine learning technique to accelerate the development and optimization of a wide range of material systems with reduced experimental trials.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Rational Design of Methylated Triazine‐Based Linear Conjugated Polymers for Efficient CO 2 Photoreduction with Water

The development of semiconducting conjugated polymers for photoredox catalysis holds great promise for sustainable utilization of solar energy. In this work, a new family of porous methylated triazine‐based linear conjugated polymers is reported that enable efficient photoreduction of carbon dioxide (CO 2 ) with water (H 2 O) vapor, in the absence of any additional photosensitizer, sacrificial agents or cocatalysts. It is demonstrated that the key lies in the generation of methylated triazine linkages through a facile condensation reaction between benzamidine and acetic anhydride, which impedes the formation of conventional triazine‐based frameworks. It is also shown that regulating conjugated linear backbones with different lengths of electron‐donated benzyl units provides a facile means to modulate their optical properties and the exciton dissociation, thereby affording more long‐lived photogenerated charge carriers and boosting charge separation and transfer. A high‐performance carbon monoxide (CO) production rate of 218.9 µmol g −1 h −1 is achieved with ≈ 100% CO selectivity, which is accompanied by exceptional H 2 O oxidation to oxygen (O 2 ). It anticipates this new study will advance synthetic approaches toward polymeric semiconductors and facilitate new possibilities for triazine‐based conjugated polymers with promising potential in artificial photocatalysis.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Manganese‐Based Spinel Cathodes: A Promising Frontier for Solid‐State Lithium‐Ion Batteries

Recently, all-solid-state lithium-ion batteries (ASSLIBs), which exhibit improved safety and enhanced energy density compared to conventional commercialized lithium-ion batteries (LIBs), thereby have garnered extensive research interest. Among the promising cathode candidates, Mn-based spinel cathodes LiMn 2 O 4 (LMO) and LiNi 0.5 Mn 1.5 O 4 (LNMO), with the unique characteristics of low cost, structural stability, and 3D Li-ion diffusion channels, have demonstrated excellent performance in LIBs and presented great potential in ASSLIBs applications. However, several challenges, including structural degradations, poor interfacial contact, large interfacial resistance, and Mn-dissolution/diffusion during the electrochemical cycling, hinder their practical applications and commercialization in the ASSLIBs. Particularly, the high-voltage LNMO cathodes suffer from the challenge of electrochemical incompatibility with most of the solid-state electrolytes (SSEs). Herein, the spinel structure, the electrochemical behavior, and the structural degradation of the LMO/LNMO are explored. The characteristics and recent progress of the mitigating strategies to the challenges of various SSEs, including polymer-, oxide-, composite-, sulfide-, halide-, and LiPON-based SSEs, are introduced when paired with LMO/LNMO. Finally, the directions for future research to advance Mn-based spinel cathodes and fulfill the requirements of the next-generation ASSLIBs are also discussed.

Dou, Yu [Concordia University, Montreal, QC (Canad↗

DOE OSTI2024

3D Ferroelectric Phase Field Simulations of Polycrystalline Multi‐Phase Hafnia and Zirconia Based Ultra‐Thin Films

Abstract HfO 2 – and ZrO 2 –based ferroelectric thin films have emerged as promising candidates for the gate oxides of next‐generation electronic devices. Recent work has experimentally demonstrated that a tetragonal/orthorhombic (t/o‐) phase mixture with partially in‐plane polarization can lead to negative capacitance (NC) stabilization. However, there is a discrepancy between experiments and the theoretical understanding of domain formation and domain wall motion in these multi‐phase, polycrystalline materials. Furthermore, the effect of anisotropic domain wall coupling on NC has not been studied so far. Here, 3D phase field simulations of HfO 2 – and ZrO 2 –based mixed‐phase ultra‐thin films on silicon are applied to understand the necessary and beneficial conditions for NC stabilization. It is found that smaller ferroelectric grains and a larger angle of the polar axis with respect to the out‐of‐plane direction enhances the NC effect. Furthermore, it is shown that theoretically predicted negative domain wall coupling even along only one axis prevents NC stabilization. Therefore, it is concluded that topological domain walls play a critical role in experimentally observed NC phenomena in HfO 2 – and ZrO 2 –based ferroelectrics.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2024

A Sulfide‐Based Solid Electrolyte With High Humid Air Tolerance for Long Lifespan All‐Solid‐State Sodium Batteries

Abstract Sulfide‐based superionic conductors present great promise to achieve high energy density and safety for all‐solid‐state sodium batteries (ASSSBs). However, the poor electrolyte/electrode interface compatibility and humid air stability seriously hinder their deployment in ASSSBs. Herein, a series of high‐performance Na 3‐□ Sb 1‐4x (SnWCaTi) x S 4 sulfide‐based solid electrolytes (SSEs) are reported by coupling the vacancy effect with configurational entropy, which displays an excellent interface stability against sodium metal and an extraordinary tolerance toward the moist atmosphere, even for water. The optimized electrolyte effectively inhibits the detrimental mixed ion‐electron conducting interphase formation, achieving the ultra‐stable operation of Na–Na symmetric cell up to 1000 h. Furthermore, the Na + diffusion kinetics is obviously enhanced by increasing the Na sites local anisotropy and Na vacancies. Eventually, the assembled TiS 2 //Na 5 Sn ASSSBs deliver a remarkable reversible capacity of 211.6 mAh g −1 at 0.5C with a long‐term cycling performance of 450 cycles at room temperature. More importantly, it achieves a steady running up to 100 cycles at 1C even if this electrolyte is placed in the air with a dew temperature of 13.8 °C for 30 min, the highest values in the state‐of‐the‐art sulfide‐based ASSSBs. The well‐designed SSEs open a new avenue for realizing the advanced and powerful ASSSBs.

Guo, Yayu↗