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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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The formation of sparingly soluble ternary Na/Mg–Np( v )–borate(s) solid phases in alkaline, dilute-to-concentrated, NaCl and MgCl 2 solutions is confirmed by a multimethod experimental approach.
Structural, spectroscopic and theoretical analyses of mer-MBr3(OPcy3)3 (M = Am, Nd, Pr, Ce, La) reveal significant amounts of metal based p-orbital contribution.
Molybdenum disulfide (MoS 2 ) is a two-dimensional material widely used as a lubricant in many applications involving mechanical loading under a wide range of operating temperatures. Since many synthesis and processing techniques yield MoS 2 in its polycrystalline form, establishing grain boundary (GB) structure–property relations is key to designing MoS 2 microstructures with tailored properties. Here, we employ classical, reactive atomistic simulations to investigate the structure and mechanical behavior of a wide range of GBs in MoS 2 as a function of temperature. Using a bicrystal MoS 2 geometry, we characterize the atomic structure and calculate the energy of several low-angle GBs. Then, we simulate the tensile deformation behavior of MoS 2 bicrystals at several temperatures. Our results reveal that at temperatures above 100 K, the deformation of MoS 2 bicrystals is characterized by the nucleation of shear bands from GBs that grow, with subsequent loading, into the MoS 2 crystals. At low temperatures, the tensile deformation is characterized by the nucleation and propagation of deformation fronts, resulting in altered bond angles and bond lengths. Quantitative analysis reveals a decrease in the ultimate tensile stress and ultimate failure strain of MoS 2 bicrystals with the increase in temperature. Furthermore, our simulations of the mechanical behavior of metastable GBs reveal that the strength and ductility decrease with the increase in energy of these boundary structures. In broad terms, our work provides future avenues to employ GB engineering as a strategy to tailor the properties of MoS 2 microstructures.
In recent years, security monitoring of public places and critical infrastructure has heavily relied on the widespread use of cameras, raising concerns about personal privacy violations. To balance the need for effective security monitoring with the protection of personal privacy, we explore the potential of optical fiber sensors for this application. This article proposes FiberFlex, an intelligent and distributed fiber sensor system. Ultizing Field Programmable Gate Arrays (FPGA) high-level synthesis (HLS) acceleration, FiberFlex offers real-time pedestrian detection by co-designing the entire pipeline of optical signal acquisition, processing, and recognition networks based on the principles of optical fiber sensing. As a promising alternative to traditional camera-based monitoring systems, FiberFlex achieves pedestrian detection by analyzing the vibration patterns caused by pedestrian footsteps, enabling security monitoring while preserving individual privacy. FiberFlex comprises three modules: First , fiber-optic sensing system: A fiber-optic distributed acoustic sensing (DAS) system is built and used to measure the ground vibration waves generated by people walking. Second , algorithms: We first collect the training data by measuring the ground vibration waves, label the data, and use the data to train the neural network models to perform pedestrian recognition. Third , hardware accelerators: We use HLS tools to design hardware modules on FPGA for data collection and pre-processing and integrate them with the downstream neural network accelerators to perform in-line real-time pedestrian detection. The final detection results are sent back from FPGA to the host CPU. We implement our system FiberFlex with the in-house built DAS system and AMD/Xilinx Kintex7 FPGA KC705 board and verify the whole system using the real-world collected data. We conduct recognition tests on five test subjects of varying ages, heights, and weights in a fixed sensing area. Each subject experienced 20 real-time recognition tests using their daily walking habits, and the subjects were given adequate rest between tests. After 100 tests on five test subjects, the overall real-time recognition accuracy exceeded \(88.0\%\) . The whole system uses 55 W of power, 33 W in the optical DAS system and 22 W in the FPGA. Relying on its end-to-end interdisciplinary design, FiberFlex seamlessly combines fiber-optic sensors with FPGA accelerators to enable low-power real-time security monitoring without compromising privacy, making it a valuable addition to the existing security monitoring network. According to FiberFlex, more valuable research can be conducted in the future, such as fall monitoring for the elderly, migration of identification networks between different application scenarios, and improvement of anti-interference performance in more complex environments. In future perception networks, where the “eyes” are not feasible, let’s use fiber optic touch instead.
Relative intensity noise (RIN) inherent in fiber lasers poses a serious obstacle to their use in pump–probe spectroscopy and imaging. RIN can be removed through an analog balanced detector, or, as we have previously shown, software adaptive noise cancellation (ANC) on digitized signals. One major drawback to software ANC is the added time required for digitizing and post-processing. In this article, we describe a design for ANC on a field-programmable gate array (FPGA), making use of high-level synthesis tools and fixed-point arithmetic to achieve real-time laser RIN suppression at 25 MHz sample rates. Unlike the software-ANC approach, the FPGA-ANC device can serve as a dedicated drop-in denoiser, placed between the detectors and a commercial lock-in amplifier. We demonstrate its application to transient absorption spectroscopy and microscopy, lowering the noise floor to ~17 dB above the shot noise limit. Furthermore, we demonstrate a dramatic improvement in data acquisition time from ~6 h to ~5 min in a real-time imaging scenario.
We will discuss recent results from our group in reacting CO 2 and light alkanes to produce syngas, olefins, aromatics, and oxygenates. We will mainly use the simultaneous upgrading of CO 2 and ethane (SU-CO 2 Et) as examples to demonstrate the feasibility and reaction pathways of these processes. We will briefly discuss synthesis methods and essential structural characterization techniques that can be applied to SU-CO 2 Et catalysts. We will then illustrate how to identify and apply distinct active sites for different upgrading processes, using a combination of kinetic studies, in situ characterization, and density functional theory (DFT) calculations. We will conclude the Perspective by pointing out challenges and potential directions in catalyst design and synthesis, as well as structural characterization and mechanistic investigations, to further advance the simultaneous upgrading of CO 2 and light alkanes.
Near-infrared (NIR) luminescent materials are pivotal for advanced optoelectronic and biomedical applications, yet attaining efficient emission in the NIR-II region (950-1400 nm) remains challenging. Here, we introduce a ligand cationization strategy for designing copper(i) iodide-organic hybrid materials that emit in the NIR-II region (920-1120 nm) with PLQYs up to 8.58%. By incorporating rigid cationic ligands with CuI modules, we synergistically achieve bandgap narrowing (to 1.51 eV) and structural rigidification via ionic-dative bonding, effectively suppressing non-radiative decay while extending emission beyond 1100 nm. Coupled with solution processability-enabled by the successful synthesis of nanometer-sized nanoparticles in various shapes-and excellent thermal stability (≥210 °C), this work establishes ligand cationization as a universal approach for designing efficient NIR-II emitters.
The production of ammonia through the Haber–Bosch process is regarded as one of the most important inventions of the 20th century. Despite significant efforts in optimizing the process, it still consumes 1 to 2% of the worldwide annual energy for the high working temperatures and pressures. The design of a catalyst with a high activity at milder conditions represents another challenge for this reaction. Herein, we combine density functional theory and microkinetic modeling to illustrate a strategy to facilitate low-temperature and -pressure ammonia synthesis through modified energy-scaling relationships using a confined dual site. Our results suggest that an ammonia synthesis rate two to three orders of magnitude higher than the commercial Ru catalyst can be achieved under the same reaction conditions with the introduction of confinement. Such strategies will open pathways for the development of catalysts for the Haber–Bosch process that can operate at milder conditions and present more economically viable alternatives to current industrial solutions.
Silica polymerization, which involves the condensation reaction of silicic acid, is a fundamental process with wide-ranging implications in biological systems, material synthesis, and scale formation. The formation of a silica-based scale poses significant technological challenges to energy-efficient operations in various industrial processes, including heat exchangers and water treatment membranes. Despite the common strategy of applying functional polymers for inhibiting silica polymerization, the underlying mechanisms of inhibition remain elusive. In this study, we synthesized a series of nitrogen-containing polymers as silica inhibitors and elucidated the role of their molecular structures in stabilizing silicic acids. Polymers with both charged amine and uncharged amide groups in their backbones exhibit superior inhibition performance, retaining up to 430 ppm of reactive silica intact for 8 h under neutral pH conditions. In contrast, monomers of these amine/amide-containing polymers as well as polymers containing only amine or amide functionalities present insignificant inhibition. Molecular dynamics simulations reveal strong binding between the deprotonated silicic acid and a polymer when the amine groups in the polymer are protonated. Notably, an extended chain conformation of the polymer is crucial to prevent proximity between the interacting monomeric silica species, thereby facilitating effective silica inhibition. Furthermore, the hydrophobic nature of alkyl segments in polymer chains disrupts the hydration shell around the polymer, resulting in enhanced binding with ionized silicic acid precursors compared to monomers. Finally, our findings provide novel mechanistic insights into the stabilization of silicic acids with functional polymers, highlighting the molecular design principles of effective inhibitors for silica polymerization.
As Moore’s law approaches its limits, chalcogenides offer a promising route to next-generation computing and sensing, thanks to their topological, magnetoelectric, excitonic, and spintronic properties. Yet the same traits that make them appealing, e.g., exotic properties at monolayer thickness, clean van der Waals interfaces, and strong many-body effects, also heighten sensitivity to fabrication, hindering translation into scalable devices. Progress is further constrained by fragmented knowledge across synthesis, processing, and integration, and by the lack of systematic links between fabrication parameters and performance metrics. This Perspective examines key obstacles in controlling chalcogenide heterostructures and stresses the need for an integrated co-design framework, uniting materials growth, processing, and device architecture to accelerate practical technologies.
Achieving brain-like efficiency in computing requires a co-design between the development of neural algorithms, brain-inspired circuit design, and careful consideration of how to use emerging devices. The recognition that leveraging device-level noise as a source of controlled stochasticity represents an exciting prospect of achieving brain-like capabilities in probabilistic neural algorithms, but the reality of integrating stochastic devices with deterministic devices in an already-challenging neuromorphic circuit design process is formidable. Here, we explore how the brain combines different signaling modalities into its neural circuits as well as consider the implications of more tightly integrated stochastic, analog, and digital circuits. Further, by acknowledging that a fully CMOS implementation is the appropriate baseline, we conclude that if mixing modalities is going to be successful for neuromorphic computing, it will be critical that device choices consider strengths and limitations at the overall circuit level.
Global demand for methanol as both a chemical precursor and a fuel additive is rising. At the same time, numerous renewable methanol production pathways are under development, which, if commercialized, could provide significant environmental benefits over traditional methanol synthesis pathways. However, it is difficult to compare technologies at different maturity levels, with differing feedstocks, and with significant differences in overall process design. Thus, there is a need to harmonize the analyses of renewable pathways using a consistent techno-economic approach to evaluate the potential for commercialization of various pathways. This analysis uses a novel cross-comparison method to assess near-term and long-term viability of both low- and high-maturity level technologies. Furthermore, the techno-economic assessment considers cost factors critical to market acceptance combined with carbon- and energy-efficiency assessments of three renewable pathways compared with a commercial baseline. We find that biomass gasification to methanol represents a near-term viable pathway with a high technology readiness level and commercially competitive market price. If cost-reducing technological improvements can be realized and scaled up in the CO 2 electrolysis pathways, the potential for higher carbon efficiencies may help drive market adoption of these more modular, direct conversion pathways in future markets as they present an opportunity to better support global decarbonization efforts through efficient waste carbon utilization.
We have established a scalable and economically viable process to convert lignin into ionic liquids for use in biopolymer processing. We have studied the four main topics: 1) lignin depolymerization to produce vanillin through oxidation, 2) lignin reduction to produce guaiacols, 3) oxidation process scale up, and 4) ionic liquid synthesis and application for biopolymer processing. The main accomplishment is summarized below: A maximal vanillin yield of 4.9% (8.8% total product yield) was achieved under optimal oxidation conditions using design of experiments (DOE); Aminophenol with 94.5% purity was synthesized via reductive amination using vanillin isolated from ion-exchange chromatography; At a higher lignin loading (10%), the vanillin yield in the scale up reaction was lower than the bench scale reaction; The filtration-based downstream separation of the scale-up process was able to recover 76% of vanillin; A maximal lignin reduction product (guaiacols) yield of 6.5% was achieved in the presence of Ru/C, formic acid, and methanol; The lignin reduction product yield and composition were affected by the use of different solvent and catalytic reagents (methanol in combination of Ru/C and formic acid gives the highest product yield); Lignin oxidation and reduction products: vanillin, acetovanillone, guaiacols, and eugenol are successfully used for IL synthesis; XX based ILs showed cellulose solubility with up to xx%; Technoeconomic analysis results indicate the potential of producing affordable ILs from kraft lignin (MSP: $14/kg); Life-cycle assessment results show the potential to reduce GHG emissions by up to 85% relative to existing ILs ([Ch][Lys]) with GHG emission: 1.2 kg CO 2e /kg of IL produced.
Efforts to develop polymer precursor electrolytes that offer properties anticipated to be similar or superior to (lithium phosphorus oxynitride, LiPON) glasses are reported. Such precursors offer the potential to be used to process LiPON-like thin glass/ceramic coatings for use in all solid state batteries, ASBs. Here, LiPON glasses provide a design basis for the synthesis of sets of oligomers/polymers by lithiation of OP(NH 2 ) 3–x (NH) x [from OP(NH) 3 ],OP(NH 2 ) 3-x (NHSiMe 3 ) x and [P=N] 3 (NHSiMe 3 ) 6–x (NH) x . Furthermore, the resulting systems have degrees of polymerization of 5–20. Treatment with selected amounts of LiNH 2 provides varying degrees of lithiation and Li + conducting properties commensurate with Li + content. Polymer electrolytes impregnated in/on Celgard exhibit Li + conductivities up to ~1 × 10 –5 S cm –1 at room temperature and are thermally stable to ~150 °C. A Li–S battery assembled using a Li 6 SiPON composition polymer electrolyte exhibits an initial reversible capacity of 1500 mAh g sulfur –1 and excellent cycle performance at 0.25 and 0.5 C rate over 120 cycles at room temperature.
Here, in this Accounts article, we summarize our recent work on truncating conjugated two-dimensional framework materials down to a single pore, or a single macrocycle. Conjugated 2D architectures have emerged as one of the most synthetically adaptable motifs for coupling semiconductivity and porosity in metal–organic frameworks (MOFs) and covalent organic frameworks (COFs). However, despite their prevalence, 2D architectures have several limitations. In particular, the strong interlayer π–π stacking can limit both processability and the accessibility of internal active sites. We have found that simple macrocycles preserve key aspects of 2D framework structure and function, including porosity and out-of-plane electrical conductivity, while providing improved processability, surface tunability, and mass transport properties. In this article, we first describe our synthetic approach and general design considerations. Specifically, we show how ditopic analogues of the tritopic ligands commonly found in the synthesis of 2D MOFs and COFs can be used to achieve a diverse library of conjugated macrocycles that resemble fragments of semiconducting frameworks in both form and function. The length of the peripheral side chains, the size of the aromatic core, and the solubility of intermediates are all key variables in favoring selective macrocycle formation over undesired linear polymers and oligomers. Next, we highlight the unique advantages that macrocycles provide, including improved processability, atomically precise surface tunability, and greater active site accessibility. In particular, the identity of the peripheral side chains dramatically impacts both solubility and colloidal stability as well as crystal size and morphology. We further show how the solution processability and nanoscale dimensions of macrocycles can simplify electronic device fabrication and improve electrochemical performance. Finally, we end with a forward-looking discussion on how macrocycles offer a unique bridge between conjugated molecules and extended frameworks, enabling new application areas and fundamental science.
Domain-specific systems improve the performance of a specific set of applications compared to general-purpose processing systems by deploying custom hardware accelerators. These hardware accelerators are generated using high-level synthesis (HLS) tools. The HLS tools enable a comprehensive design space exploration to optimize the compute performance of the generated accelerators. However, they often ignore the challenges of implementing the accelerators in a system-on-chip, particularly how the accelerators access memory. Our work introduces a buffering system design that improves accelerators' memory accesses by intelligently employing burst transactions to prefetch useful data from external memory to on-chip local buffers. Our design is dynamic, parametric, and transparent to the accelerators generated by HLS tools. We derive the buffering system parameters using appropriate compiler-based analysis passes and memory channel latency constraints. The proposed buffering system design results in, on average, 8.8x performance improvements while lowering memory channel utilization on average by 53.2% for a set of PolyBench kernels.
Atomic layer deposition (ALD) modification of ultra-high-aspect-ratio structures (>10,000:1) is a powerful platform with applications in catalysis, filtration, and energy conversion. However, the deposition of conformal and tunable ALD coatings at these aspect ratios remains challenging, resulting in empirical tradeoffs between precursor utilization and reaction time. Here, we demonstrate tunable control of the ALD infiltration depth into an aerogel monolith (AM) and develop a reaction-diffusion model to accurately describe the coating process. Specifically, we investigate the ALD exposure time and precursor dose needed to conformally coat a silica AM with pore sizes of ~20 nm, a monolith thickness of ~2.5 mm, and aspect ratios exceeding 60,000:1. We demonstrate complete infiltration into the AM, which is quantified by elemental mapping. A reaction-diffusion model is developed, which accounts for multiple doses and precursor depletion in the ALD chamber during an exposure step. The experimentally validated model enables the prediction and tuning of infiltration depth into a tortuous, high-aspect-ratio structure such as an AM, allowing for the synthesis of rationally designed material architectures. Additionally, the model allows for co-optimization of the total deposition time and percentage of unreacted precursor, which are important for the manufacturability and sustainability of ALD processing. Lastly, we demonstrate that ultra-thin ALD Al 2 O 3 coatings can be used to stabilize silica AMs against structural degradation under high-temperature annealing conditions (700-800 °C), by limiting changes in surface area and monolith volume. As a result, this improved high-temperature stability has implications for numerous aerogel applications, including catalysis and thermal insulation.
The project sought to explore development of novel polymer separator materials and additives for Li-S and Li-ion batteries that would solve critical failure modes and performance bottlenecks for these chemistries. This technology presented significant advanced manufacturing challenges and opportunities in getting to scale, including materials synthesis scale-up, large-area polymer processing techniques, electrode formulation, and the development of coin and pouch cells for electrochemical energy storage. Specific accomplishments included synthesis of organic molecular and polymer materials designed to solve outstanding challenges in electrochemical cells. The primary focus of new materials design was aimed at improving longevity, power, cost reduction, and environmental sustainability of said electrochemical devices. The project also involved assembly and characterization of electrochemical devices with standard galvanostatic and potentiostatic techniques as well as electrochemical impedance spectroscopy.