SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Triphilic organochalcogen compounds are added to solid-state sulfur cathodes to establish intimate and durable interfacial contact among cathode components, enhancing ion and electron transport, active material utilization, and cycling stability.
The transport of conserved quantities like spin and charge is fundamental to characterizing the behavior of quantum many-body systems. Numerically simulating such dynamics is generically challenging, which motivates the consideration of quantum computing strategies. However, the relatively high gate errors and limited coherence times of today's quantum computers pose their own challenge, highlighting the need to be frugal with quantum resources. In this work we report simulations on quantum hardware of infinite-temperature energy transport in the mixed-field Ising chain, a paradigmatic many-body system that can exhibit a range of transport behaviors at intermediate times. We consider a chain with L = 12 sites and find results broadly consistent with those from ideal circuit simulators over 90 Trotter steps, containing up to 990 entangling gates. To obtain these results, we use two key problem-tailored insights. First, we identify a convenient basis – the Pauli Y basis – in which to sample the infinite-temperature trace and provide theoretical and numerical justifications for its efficiency relative to, e.g., the computational basis. Second, in addition to a variety of problem-agnostic error mitigation strategies, we employ a renormalization strategy that compensates for global nonconservation of energy due to device noise. We discuss the applicability of the proposed sampling approach beyond the mixed-field Ising chain and formulate a variational method to search for a sampling basis with small sample-to-sample fluctuations for an arbitrary Hamiltonian. This opens the door to applying these techniques in more general models.
The first conjugated polymers with functional Si–Si crosslinks are reported. In addition to complete structural characterization using solid-state NMR techniques, the electronic structure of the polymers are supported by electrochemical studies.
Preprint of manuscript describing database & physical properties of correlated electron molecular orbital materials.
In the United States, many impoundments at coal-fired power plants contain elevated contaminants like arsenic, boron, barium, and selenium. Zeolites synthesized from fly ash show promise as sorbents for these contaminants. However, optimizing sorption capacity is challenging due to numerous possible topologies, silicon to aluminum (Si/Al) ratios, and cation types. In this study, molecular simulations are used to design cationic zeolites for boric acid adsorption. Force field models based on quantum mechanical calculations (PBE + D2) for Na-, Ca-, Mn-, and Fe-exchanged chabazite and LTA are presented. The new D2FF force fields reproduce DFT energies with about half the error of UFF. Zeolite performance depends on Si/Al ratio and cation type, with low Si/Al ratio chabazite (CHA) and phillipsite (PHI) zeolite frameworks exchanged with Ca 2+ or Na + /Ca 2+ mixtures showing the highest adsorption. In conclusion, these findings suggest tailored fly ash-derived zeolites could provide effective boron removal from leachate ponds.
Ceria composite catalysts have long been used for ketonization reactions, which is a valuable chemistry for the upgrading of biomass-derived carboxylates. To better understand the interaction of zirconia with ceria in the context of ketonization, thin epitaxial films of ceria-zirconia mixed metal oxide Ce 1-x Zr x O 2-δ (x = 0-1) were grown on a Pt(111) substrate in ultrahigh vacuum conditions and studied with X-ray photoelectron spectroscopy (XPS). Core level and valence band XPS results suggest a strong interaction between ceria and zirconia cations, possibly due to increased filling of unoccupied 4f 0 orbitals of ceria from neighboring Zr cations in the lattice structure. This leads to a partial reduction of ceria from Ce 4+ to Ce 3+ , with Zr remaining predominantly in the 4+ oxidation state. Ketonization of acetic acid was studied using temperature programmed desorption (TPD) and high-resolution electron energy loss spectroscopy (HREELS). These results found ketonization over mixed Ce-Zr composite oxides exhibited lower activation energies than for pure CeO 2 and ZrO 2 , with Ce 0.38 Zr 0.62 O 2-δ exhibiting the highest yield of acetone among the studied surfaces. In conclusion, these results suggest the high activity of Ce-Zr catalysts appears to be a result of oxygen vacancy formation, stabilized by electron donation from Zr cations.
Carbon fiber (CF)-reinforced epoxy composites are widely used in vehicle applications, where early damage detection is crucial for reliability and safety. To address this need, we developed a self-sensing epoxy/CF composite by embedding a PVDF-MoS 2 nanosensor via an embedded 3D printing method. By harnessing the intrinsic curing kinetics of epoxy, we tailored its rheological properties to optimize the embedded printing process, enabling precise and reliable support for sensor filaments without compromising the composite’s structural and functional integrity. Through comprehensive rheological and kinetic analysis, we established a quantitative relationship among curing temperature, conversion rate, and resulting yield modulus─defining a narrow processing window essential for successful sensor integration. Specifically, we identified that an epoxy yield modulus range of 180–294 Pa and a conversion rate below 10% are critical to support the PVDF-MoS 2 filament architecture. Here, this embedded 3D printing method produces complex and multimaterial PVDF-MoS 2 sensors within an epoxy matrix with minimal deformation and reduced postprocessing, which is scalable and adaptable for industrial applications. Under cyclic loading, the embedded sensors exhibited stable signals under constant loads and increased voltage signals in response to crack formation (17–35% higher) and catastrophic failure (1 order of magnitude higher), effectively capturing structural changes in real time. This study demonstrates the potential of PVDF-MoS 2 nanocomposite sensor materials for real-time structural health monitoring in epoxy–CF composite systems, enabling early detection of defects and stress anomalies, significantly reducing the risk of unexpected failures, and enhancing structural reliability.
Explore the source record for details and available documents.
The Baltimore Social-Environmental Collaborative (BSEC) Urban Integrated Field Laboratory seeks a new paradigm for urban climate research. Motivated by deep uncertainties in urban climate and the future of urban systems, BSEC works collaboratively across institutions and stakeholder groups to co-generate the science needed to advance energy security and resilience to extreme events across the city of Baltimore, Maryland, USA, and to do so in a manner that can inform similar efforts in other cities. BSEC begins with stakeholder priorities (health, affordable energy, etc) and designs observation networks and models to deliver climate science to address them. This takes the form of an iterative collaborative cycle, in which an initial research strategy is repeatedly updated in conversation with community partners, and researchers and stakeholders learn from each other. To date, this cycle has included multiple rounds of collaborative deliberation on urban heat mitigation, in which a multicriteria decision tool has been updated with more community-relevant spatial structure and modified optimization metrics. The guiding objective of this cycle is to inform potential ‘secure and resilient pathways’ for energy and infrastructure. In doing so, BSEC addresses fundamental urban science questions in natural and social sciences. It also tests our ability to integrate this science in a manner that advances participatory decision-making for urban resilience.
Electrification refers to transitioning from fuel-powered systems to electrically-powered alternatives. In an industrial context, this primarily refers to the electrification of process heat, but there are also widespread opportunities to electrify space heating and cooling as well as onsite transportation (e.g., forklifts). Electrification does not refer to one particular technology, but a wide range of technologies that use different methods to transfer electrical energy into a usable form – usually heat.
Electrification refers to the process of transitioning from fuel-powered systems to electric-powered systems, or electrotechnologies. This document first provides an overview of industrial electrification, including possible advantages and common applications. Next, it provides a comprehensive, step-by-step framework for assessing the potential for electrification at an individual industrial facility. The steps in this framework include inventorying current fuel-powered systems, evaluating a manufacturing facility’s electrification readiness, and identifying, evaluating, and prioritizing strategies and technologies. This document also contains information about project implementation, and the appendices provide tools and resources to support organizations pursuing electrification.
A recommendation for adjustments to the initial LSST survey strategy is delivered to the Observatory Director. These adjustments respond to improved knowledge and understanding of the system performance and completeness of science verification activities at the start of LSST.
JSNS 2 (J-PARC Sterile Neutrino Search at J-PARC Spallation Neutron Source) is an experiment that is searching for sterile neutrinos via the observation of $\overline{ν}$ μ → $\overline{ν}$ e appearance oscillations using neutrinos from muon decay-at-rest. For this search, rejecting cosmic-ray-induced neutron events by Pulse Shape Discrimination (PSD) is essential because the JSNS2 detector is located above ground, on the third floor of the building. We have achieved 94.95% ± 0.15% rejection of neutron events while keeping 92.82% ± 1.77% of signal, electron-like events using a data-driven likelihood method. This article will report the PSD technique using the full fiducial volume of the JSNS2 detector.
ABSTRACT Development of transgenic bioenergy sorghum [ Sorghum bicolor (L.) Moench] with increased contents of mixed‐linkage (1,3;1,4)‐β‐glucan (MLG) is an important step towards enhancing quality of bioenergy feedstocks. Since MLG‐enhancement leads to greater biomass digestibility, our overarching hypothesis is that root residues of MLG‐enhanced plants may be more readily decomposed in the soil, potentially creating new opportunities for optimizing soil carbon (C) sequestration, nutrient cycling, and overall agricultural sustainability. The study examined morphological, chemical, and enzymatic characteristics of fine and coarse roots of four bioenergy sorghum genotypes. Then, we incubated the roots within soils with contrasting vegetation histories while measuring C mineralization, microbial biomass C (MBC), and activity of hydrolytic enzymes and calculating vector length and vector angle enzymatic stoichiometry parameters. The results indicated that MLG‐enhancing transformations increased root total nitrogen (N) contents, decreased C/N ratios, and were associated with higher MLG concentrations in fine than in coarse roots. Incubations with transgenic roots led to 16%–38% higher MBC and 19%–41% lower microbial metabolic quotient (qCO 2 ). While enzyme activity differed markedly among the studied genotypes, it did not directly respond to MLG levels in root tissues. The increase in MBC without concurrent increases in C mineralization or hydrolytic enzyme activities in transgenic genotypes suggests that MLG enhancement promoted microbial anabolic retention of root‐derived C rather than stimulating catabolic decomposition. Enzymatic vector results indicated that these parameters reflect a variety of drivers behind microbial enzyme production, including availability of specific substrates, such as MLG here, and/or deficiency in specific nutrients, such as phosphorus (P). The study confirms the positive impacts from the roots of engineered MLG‐enhanced bioenergy plants on soil microbial activity and highlights the interactive influences on the MLG‐enhancement effects from root size and inherent soil properties.
This study examines the vapochromic and vapoluminescent behaviors of [Pt(tpy)Cl]PF 6 host molecules (tpy = 2,2′:6′,2′′-terpyridine) in acetonitrile (CH 3 CN) vapor, questioning the notion that these effects arise only from direct host–guest interactions.
The graphic illustrates how an external acoustic field stabilizes the SEI layer by enhancing lithium-ion mass transfer at slip lines and kinks, reducing pit formation and promoting a more uniform SEI, ultimately improving battery performance.
The rectangular cyclobutadiene (CBD, C 4 H 4 ) is a unique moiety for building nonbenzenoid polycyclic conjugated hydrocarbons with interesting electron‐accepting properties. Herein, the investigation on chemical reduction of several CBD‐containing polycyclic hydrocarbons with increasing conjugation length is reported: biphenylene (C 12 H 8 ), dimethyl[2]naphthalene (C 22 H 16 ), and tetramethyl‐dibenzo‐[3]phenylene (C 30 H 22 ). The two‐step sequential reduction is first demonstrated by in situ spectroscopic investigation and then confirmed by the isolation of single crystals of the reduced products. The X‐ray crystallographic analysis reveals the formation of several mono‐ and doubly reduced products in solvent‐separated and complexed forms. The crystal structures for both neutral parents and corresponding reduced products unravel the changes in bond alternation in each ring of the fused systems. Density functional theory (DFT) and nucleus‐independent chemical shift (NICS) scan calculations reveal that the two‐electron addition reduces the aromatic character in the benzenoid rings but has minor influence on the antiaromatic CBD rings.
Thermal-based remote sensing of surface energy fluxes has traditionally relied on high spatial resolution satellite data with revisit frequencies on the order of weeks. In this study, we evaluate a biophysics-based analytical surface energy balance model for predicting latent energy (LE) and sensible heat (H) fluxes using proximal sensing observations. The Surface Temperature Initiated Closure (STIC1.2) model has been extensively validated across a wide range of spatial and temporal scales using various satellite-derived thermal datasets. Here we extend this validation by applying STIC at sub-hourly temporal resolution over multiple growing seasons for four distinct agricultural systems. We further develop and evaluate novel STIC variants that incorporate machine learning (ML) techniques to eliminate the need for specific surface energy balance observations, specifically net radiation and soil heat flux, thereby enhancing model applicability in data-sparse settings. The integration of an ML component to estimate surface available energy is shown to have strong predictive performance for both LE (R2 = 0.81-0.94) and H (R2 = 0.46-0.72) across all agricultural systems examined here, demonstrating the potential of hybrid biophysical – machine learning approaches for surface energy balance modeling with minimal data requirements. This study concludes with a novel application of explainable machine learning (exML) to diagnose sources of model error. This exML framework attributes residual prediction errors to both model input variables and environmental drivers not explicitly included in the simulation experiments. This approach provides a new pathway for improving model design and integrating previously overlooked yet influential variables into future model iterations.