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At least 649 records · Page 36

Revealing the Defect‐Driven Ferroelectric Mechanisms of Aluminum Nitride

Wurtzite III-nitride compounds are CMOS-compatible with widespread industrial interest to exercise ferroelectricity, despite their polar structure being highly resistant to polarization reversal. Here, we induce and tune ferroelectric properties in w-AlN via direct-write ion-beam processing, using nanoscale patterned defect engineering as a post-growth alternative to conventional cation substitution. Nanometric piezoresponse spectroscopy of the focused He + beam patterned defect concentrations in ferroelectric Al 0.92 B 0.08 N measures a localized 10x enhancement in effective piezoresponse and 40% reduction in switching barrier. The irradiation-induced point defects convert piezoelectric AlN into a ferroelectric system with site-saturated nucleation and raise the dielectric susceptibility, switched polarization, and effective piezoelectric coefficient. Enhanced defect-lattice interactions in AlN increase carrier conduction and phonon scattering loss but preserve long-range crystallinity. Here, based on atomistic analysis of nudged elastic band density functional theory calculations and reactive force field simulations, both nitrogen vacancies and defect complexes disrupt bond ordering, facilitating a line-by-line low-barrier switching of pristine AlN.

36 MATERIALS SCIENCE↗

Autonomous Aerosol and Plasma Co‐Jet Printing of Metallic Devices at Ambient Temperature

Abstract Additive manufacturing of metallic materials holds the potential to revolutionize the fabrication of functional devices unattainable via traditional methods. Despite recent advancements, printing metallic materials typically requires thermal processing at elevated temperatures to form dense structures with desired properties, which presents a major challenge for direct printing and integration with temperature‐sensitive materials. Herein, a unique co‐jet printing (CJP) method is reported integrating an aerosol jet and a non‐thermal, atmospheric pressure plasma jet to enable concurrent aerosol deposition of metal nanoparticle inks and in situ sintering at ambient temperature. A machine learning algorithm is integrated with the CJP to perform real‐time defect detection and autonomous correction, enhancing the yield of printed films with high electrical conductivity from 44% to 94%. Concurrent printing and sintering eliminate the need for post‐printing processing, reducing the overall manufacturing time by multiple folds depending on product size. CJP enables direct printing of functional devices on a variety of temperature‐sensitive materials including biological materials. Direct printing of hydration sensors on living plant leaves is demonstrated for long‐duration monitoring of hydration level in the plant. The versatile CJP method opens tremendous opportunities to harmoniously integrate abiotic and biotic materials for emerging applications in wearable/implantable devices and biohybrid systems.

Du, Yipu [Department of Aerospace and Mechanical E↗

A high-volume resonator for L-band DNP-NMR

DNP-NMR and EPR experiments that operate at or greater than L-band (i.e., ν 0 (e – ) = 1–2 GHz) are typically limited to maximum sample volumes of several hundred µL. These experiments rely on well-known resonator designs for DNP/EPR irradiation such as the loop-gap resonator and Alderman-Grant coil, where their maximum volumes limit further application to imaging experiments and high-throughput screening beyond L-band. Herein, we demonstrate a birdcage (BC) resonator design that can accommodate several mL of sample while operating around 1.5 GHz. The sample volume is maximized by using two identical BC resonators in a stacked configuration. Simulations are used to optimize the BC design and the performance is validated experimentally with liquid-state Overhauser-DNP-NMR experiments. This BC design exploits just the parasitic capacitance of conductive rings and features no fixed tuning capacitors. An enhancement of –77 is achieved on a 10 mM 4-Amino-TEMPO in H 2 O sample for a 5 mL sample volume. Finally, the associated sample heating is minimal due to the low-E-fields generated and the large sample mass with +3.4 K when driving 100 W for several seconds.

47 OTHER INSTRUMENTATION↗

Sensitivity and history match analysis of a carbon dioxide “huff-and-puff” injection test in a horizontal shale gas well in Tennessee

Due to improvements in well development, shale gas production has gained importance recently, especially in the United States. To improve gas production and develop a better understanding of shale reservoirs, researchers are conducting field tests to monitor CO 2 storage and enhanced gas recovery. Reservoir simulations can then utilize these field results for sensitivity studies, uncertainty analysis, history matching, gas production forecasts, and CO 2 storage capacity estimations. One such field test was performed for the Chattanooga Shale formation in Morgan County, Tennessee. Approximately 463 tonnes (510 tons) of CO 2 was successfully injected into a hydraulically fractured horizontal well over a thirteen-day period in March 2014. The injection test was achieved in four steps: pre-injection, injection, soaking, and flowback. In this paper, those steps were modeled with a reservoir simulator to match the historic production and injection rates with gas component compositions and forecast the production for five years. The reservoir fluid was modeled as a multi-component gas, including CH 4 , C 2 H 6 , C 3 H 8 , N 2 , and CO 2 . Langmuir constants, reservoir pressure, and fracture network volume were adjusted to match simulation results with observed production rates and gas composition. Results showed that, under the same reservoir conditions, each gas component behaves differently by way of compositional changes in production. CH4 behaves like N2, while CO 2 behaves like heavier hydrocarbons such as C 2 H 6 and C 3 H 8 . CO 2 plume results showed that, after injection, the produced CO 2 is mostly derived from fractured limestone (i.e., Fort Payne Formation) because the injected CO 2 is not adsorbed in limestone but rather by shale formations.

03 NATURAL GAS↗

Street-level temperature estimation using graph neural networks: Performance, feature embedding and interpretability

Estimating street-level air temperature is a challenging task due to the highly heterogeneous urban surfaces, canyon-like street morphology, and the diverse physical processes in the built environment. Though pioneering studies have embarked on investigations via data-driven approaches, many questions remain to be answered. Here, in this study, we leveraged an innovative framework and redefined the street-level temperature estimation problem using Graph Neural Networks (GNN) with spatial embedding techniques. The results showed that GNN models are more capable and consistent of estimating street-level temperature among tested locations, benefiting from its unique strength in handling extensive data over unstructured graph topology. In addition, we conducted in-depth analysis of feature importance to enhance the model interpretability. Among the urban features analyzed in this study, the time-variant canopy density and meter-level land use data emerge as crucial factors. Our findings highlight GNN 's high potential in capturing the complex dynamics between urban elements and their impacts on microclimate, thus offering valuable insights for comprehensive urban data collection and urban climate modeling in general. Collectively, this study also contributes to urban planning and policy by providing avenues to enhance city resilience against climate change, thereby advancing the agenda for environmental stewardship and urban sustainability.

54 ENVIRONMENTAL SCIENCES↗

Glass-fiber-reinforced polymeric film as an efficient protecting layer for stable Li metal electrodes

With numerous reports on protecting films for stable lithium (Li) metal electrodes, the key attributes for how to construct these efficient layers have rarely been fully investigated. Here, we report a rationally designed hybrid protective layer (HPL) with each component aligning with one key attribute; i.e., cross-linked poly(dimethylsiloxane) (PDMS) enhances flexibility, polyethylene glycol (PEG) provides homogeneous ion-conducting channels, and glass fiber (GF) affords mechanical robustness. A significant improvement of the electrochemical performance of HPL-modified electrodes can be achieved in Li/HPL@Cu half cells, HPL@Li/HPL@Li symmetric cells, and HPL@Li/LiFePO 4 full cells. Even with an industrial standard LiFePO 4 cathode (96.8 wt % active material), the assembled cell still exhibits a capacity retention of 90% after 100 cycles at 1 C. More importantly, the functionality of each component has been studied comprehensively via electrochemical and physical experiments and simulations, which will provide useful guidance on how to construct efficient protective layers for next-generation energy storage devices.

25 ENERGY STORAGE↗

In Situ Local Imaging of Ferromagnetism and Superconductivity in RbEuFe 4 As 4

The coexistence of superconductivity and ferromagnetism is an intrinsically interesting research focus in condensed matter physics but the study is limited by low superconducting (T c ) and magnetic (T m ) transition temperatures in related materials. Here, we used a scanning superconducting quantum interference device to image the in-situ diamagnetic and ferromagnetic responses of RbEuFe 4 As 4 with high T c and T m . We observed significant suppression of superfluid density in vicinity of the magnetic phase transition, signifying fluctuation-enhanced magnetic scatterings between Eu spins and Fe 3d conduction electrons. Intriguingly, we observed multiple ferromagnetic domains which should be absent in an ideal magnetic helical phase. The formation of these domains demonstrates a weak c-axis ferromagnetic component probably arising from Eu spin-canting effect, indicative of possible superconductivity-driven domain Meissner and domain vortex-antivortex phases as revealed in EuFe 2 (As 0.79 P 0.21 ) 2 . Finally, our observations highlight RbEuFe 4 As 4 is a unique system which includes multiple interplay channels between superconductivity and ferromagnetism.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Metal–Organic Framework-Based Microfluidic Impedance Sensor Platform for Ultrasensitive Detection of Perfluorooctanesulfonate

The growing global concerns to public health from human exposure to perfluorooctanesulfonate (PFOS) requires rapid, sensitive, in-situ detection where current, state-of-the-art techniques are yet to adequately meet sensitivity and selectivity standards of the real-world. This work presents, for the first time, a synergistic approach for the targeted affinity-based capture of PFOS using a porous sorbent probe that enhances detection sensitivity by embedding it on a microfluidic conductive platform. This novel sorbent-containing platform functions as an electrochemical sensor to directly measure PFOS concentration through a proportional change in electrical response (increase in impedance). The extremely high surface area and pore volume of mesoporous metal-organic framework (MOF) Cr-MIL-101 is used as the probe for targeted PFOS capture based on the affinity of the chromium center towards both the fluorine tail groups as well as the sulfonate functionalities as demonstrated by spectroscopic (NMR and XPS) and microscopic (TEM) studies. Answering the need for an ultrasensitive PFOS detection technique, we are embedding these MOF capture probes inside a microfluidic channel, sandwiched between interdigitated microelectrodes (IDµE). The nanoporous geometry increases the signal to noise ratio of interdigitated microelectrodes tremendously increasing sensitivity; this in combination with the ability of the capture probes to interact with the PFOS at the molecular level and effectively transduce that response electrochemically has allowed us achieve a PFOS detection limit of 0.5 ng/L, which is unprecedented for in-situ analytical PFOS sensors and even comparable to quantification limits achieved using state-of-the-art ex situ techniques.

36 MATERIALS SCIENCE↗

Investigation of MgO additives on microstructure and properties of thin LLZO electrolytes for all-solid-state batteries

To realize high-energy density lithium lanthanum zirconate (LLZO)-based solid-state batteries (SSB), LLZO electrolytes should be fabricated with low thickness and high mechanical strength. An effective strategy for strengthening ceramic materials is to use additives. Here, we employed MgO nanopowders and fibers as additives for the thin LLZO electrolyte in order to improve the mechanical strength. The microstructure, mechanical properties, and electrochemical properties are characterized to investigate the effects of adding MgO and sintering time. The MgO remains at grain boundaries after sintering, making the microstructure of LLZO fine and uniform. The mechanical strength of the MgO-added LLZO was enhanced by more than 60% while maintaining high ionic conductivity (1 × 10 -4 S cm -1 ) at room temperature. Li symmetric cells using the MgO fiber–LLZO and MgO powder–LLZO exhibit 2 and 3 times higher critical current density (CCD) than those of pure LLZO, and a solid-state full cell exhibits stable cycling performance. Further, these results demonstrate that the use of MgO nanopowder or fiber as an additive for thin LLZO is beneficial for high-current density cycling, by improving mechanical properties and microstructure.

25 ENERGY STORAGE↗

The high intensity diffractometer for residual stress analysis (HIDRA), a third generation residual stress mapping neutron diffractometer at the high flux isotope reactor

This paper describes the hardware and software upgrades, operation, and performance of the high intensity diffractometer for residual stress analysis (HIDRA) instrument, a residual stress mapping neutron diffractometer located at the High Flux Isotope Reactor at Oak Ridge National Laboratory in Oak Ridge Tennessee, USA. Following a major upgrade in 2018, the new instrument has a single 3 He multiwire 2D 30 × 30 cm 2 position sensitive detector, yielding a field of view of 17° 2θ. Further, the increase in the field of view (from 4° 2θ) from the previous model instrument has contributed to the tremendous improvement in the out of plane solid angle such that the 3D count rate could be obtained easily. Accordingly, the hardware, software, Data Acquisition System (DAS), and so on have also been updated. Finally, all these enhanced features of HIDRA have been ably demonstrated by conducting multi directional diffraction measurements in the quenched 750-T74 aluminum, and the evolved and improved strain/stress mappings are presented.

3D printing↗

Vibrational properties and thermal transport in quaternary chalcogenides: The case of Te-based compositions

Vibrational thermal properties of CuZn 2 InTe 4 , AgZn 2 InTe 4 , and Cu 2 CdSnTe 4 , derived from binary II-VI zinc-blendes, are reported based on first-principles calculations. While the chalcogenide atoms in these materials have the same lattice positions, the cation atom arrangements vary, resulting in different crystal symmetries and subsequent properties. The compositional differences have important effects on the vibrational thermal characteristics of the studied materials, which demonstrate that low-frequency optical phonons hybridize with acoustic phonons and lead to enhanced phonon-phonon scattering and low lattice thermal conductivities. The phonon density of states, mode Grüneisen parameters, and phonon scattering rates are also calculated, enabling deeper insight into the microscopic thermal conduction processes in these materials. Compositional variations drive differences among the three materials considered here; nonetheless, their structural similarities and generally low thermal conductivities (0.5–4 W/m K at room temperature) suggest that other similar II-VI zinc-blende derived materials will also exhibit similarly low values, as also corroborated by experimental data. Finally, this, combined with the versatility in designing a variety of motifs on the overall structure, makes quaternary chalcogenides interesting for thermal management and energy conversion applications that require low thermal conductivity.

36 MATERIALS SCIENCE↗

Accelerating Hydraulic Fracture Imaging by Deep Transfer Learning

Deep transfer learning has a great success story in computer vision (CV), natural language processing, and many other fields. In this communication, we are going to push forward the deep transfer learning to the hydraulic fracture imaging problem by proposing a two-step approach: 1) train a convolutional neural network (CNN) to reconstruct target geometries by a relatively large amount of approximated field patterns generated from a simplified model and 2) fine-tune the top layers of transferred CNN by a small amount of true field patterns generated through a full model. The advantages include the rapid generation of large amount of data through the simplified model and the high reconstruction accuracy through a careful design of the deep transfer learning. Here, the CNN trained through the deep transfer learning can accurately reconstruct the lateral extent and direction of fractures with unseen conductivity and white Gaussian noise, showing a notable acceleration/accuracy enhancement over the previous CNN trained by mixing a small number of true data into the approximated data as data augmentation.

02 PETROLEUM↗

Oil-impregnated densified wood veneer with high electrical insulation enabled by nanosized oil channels

Growing energy demands and renewable integration are stressing the aging power grid infrastructure. Lignocellulosic oil-impregnated paper is widely used in power transformers but suffers from critical limitations, such as low dielectric strength, mechanical strength, and thermal conductivity, causing premature transformer failures. Here, we demonstrate a superior electrically insulating oil-impregnated paper design using the naturally anisotropic structure of densified wood veneer to achieve nanosized channels of oil that efficiently disrupt electrical breakdown pathways. The developed oil-impregnated densified wood (ODW) creates aligned cellulose fibers with 166 ± 87–nanometer oil nanochannels, achieving record dielectric strength of 105 kilovolts per millimeter. The structure also delivers a mechanical strength of up to 384 megapascals and a thermal conductivity of 0.33 watts per meter per kelvin, enabling enhanced longevity upon thermal aging tests. The ODW could replace conventional transformer insulation to enhance power transformer performance and improve lifetime. Moreover, its anisotropic oil-filled nanochannel design offers a general strategy for hybrid dielectrics in medium- and high-voltage applications.

36 MATERIALS SCIENCE↗

Performance Testing of Enhanced Linear Variable Integrated Sensor (ELVIS III) for LVDTs

The Enhanced Linear Variable Intrinsic Sensor (ELVIS) was a 2024 breakthrough discovery by Idaho National Laboratory (INL) in terms of further innovating the usage of all linear variable differential transformers (LVDTs) and enhancing their performance in harsh environments. In irradiation tests conducted within material test reactors (MTRs), LVDTs with an internal temperature sensing capability can address several critical challenges. This report focuses on performance testing of the ELVIS III device with new hybrid LVDTs provided by the Institute for Energy Technology (IFE), which is the world’s sole supplier of nuclear-grade LVDTs. The ELVIS III device was evaluated in terms of temperature showcasing promising results comparable to a typical type-K thermocouple (TC).

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗

Preservation of Microalgae, Lignocellulosic Biomass Blends by Ensiling to Enable Consistent Year-Round Feedstock Supply for Thermochemical Conversion to Biofuels

Seasonal variation in microalgae productivity is a significant barrier to economical production of algae biofuels and chemicals. Summer production can be 3–5 times higher than in the winter resulting in uneven feedstock supplies at algae biorefineries. A portion of the summer production must be preserved for conversion in the winter in order to maintain a biorefinery running at capacity. Ensiling, a preservation process that utilizes lactic acid fermentation to limit microbial degradation, has been demonstrated to successfully stabilize algae biomass (20% solids) and algae-lignocellulosic blends (40% algae-60% lignocellulosic biomass, dry basis) for over 6 months, resulting in fuel production cost savings with fewer emissions. Preservation of algae as blends could be beneficial to biorefineries that utilize thermochemical approaches to fuel production as co-processing of algae and lignocellulosic biomass has been observed to enhance biocrude yield and improve oil quality. This study conducts a resource assessment of biomass residues in the southern United States to identify materials available during peak algae productivity and in sufficient quantity to meet the algae storage needs of an algae biofuel industry. Eight feedstocks met the quantity threshold but only three, distillers grains, haylage, and yard waste, were also available in season. Storage experiments utilizing both freshwater and marine strains of microalgae – Scenedesmus acutus, Chlorella vulgaris, Chlorella zofingiensis, Nannochloropsis gaditana, and Porphyridium purpureum – and yard waste were conducted for 30 days. Storage losses were less than 10% in all but one case, and the pH of all but one blend was reduced to less than 4.7, indicating that yard waste is a suitable feedstock for blending with algae prior to storage. To better understand whether the benefits to conversion realized by processing blends might be affected by storage, elemental analysis and bomb calorimetry of pre- and post-storage algae-yard waste blends were conducted to characterize changes occurring during storage. Storing algae biomass as blends with lignocellulosic biomass could be an effective method of mitigating seasonal variability in algae biomass production while retaining the synergistic effect of co-processing algae blends in thermochemical conversion.

09 BIOMASS FUELS↗

Latent Tracks in Ion-Irradiated LiTaO 3 Crystals: Damage Morphology Characterization and Thermal Spike Analysis

Systematic research on the response of crystal materials to the deposition of irradiation energy to electrons and atomic nuclei has attracted considerable attention since it is fundamental to understanding the behavior of various materials in natural and manmade radiation environments. This work examines and compares track formation in LiTaO3 induced by separate and combined effects of electronic excitation and nuclear collision. Under 0.71–6.17 MeV/u ion irradiation with electronic energy loss ranging from 6.0 to 13.8 keV/nm, the track damage morphologies evolve from discontinuous to continuous cylindrical zone. Based on the irradiation energy deposited via electronic energy loss, the subsequently induced energy exchange and temperature evolution processes in electron and lattice subsystems are calculated through the inelastic thermal spike model, demonstrating the formation of track damage and relevant thresholds of lattice energy and temperature. Combined with a disorder accumulation model, the damage accumulation in LiTaO3 produced by nuclear energy loss is also experimentally determined. The damage characterizations and inelastic thermal spike calculations further demonstrate that compared to damage-free LiTaO3, nuclear-collision-damaged LiTaO3 presents a more intense thermal spike response to electronic energy loss owing to the decrease in thermal conductivity and increase in electron–phonon coupling, which further enhance track damage.

36 MATERIALS SCIENCE↗

Digital Twin Empowered PV Power Prediction

The accurate prediction of photovoltaic (PV) power generation is significant to ensure the economic and safe operation of power systems. To this end, the paper proposes a new digital twin (DT) empowered PV power prediction framework that is capable of ensuring reliable data transmission and employing the DT to achieve high accuracy of power prediction. With this framework, considering potential data contamination in the collected PV data, a generative adversarial network is employed to restore the historical data set, which offers a prerequisite to ensure accurate mapping from the physical space to the digital space. Further, a new DT empowered PV power prediction method is proposed. Therein, we model a DT that encompasses a digital physical model for reflecting the physical operation mechanism and a neural network model (i.e., a parallel network of convolution and bidirectional long-short-term memory model) for capturing the hidden spatial-temporal features. The proposed method enables the use of the DT to take advantages of the digital physical model and the neural network model, resulting in enhanced prediction accuracy. Finally, a real data set is conducted to access the effectiveness of the proposed method.

14 SOLAR ENERGY↗

Cellulosic gels, films and composites including the gels, and methods of forming same

Disclosed are cellulose-based 5 flexible aerogels and xerogels containing bacterial cellulose nanorods, ribbons, fibers, and the like, wherein the gels have tunable optical, heat transfer, and stiffness properties. Further disclosed are highly transparent and flexible cellulose nanofiber-polysiloxane composite aerogels featuring enhanced mechanical robustness, tunable optical anisotropy, and low thermal conductivity.

Smalyukh, Ivan I.↗