Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “cluster characterization”

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.

At least 901 records · Page 50

Electronic Structure of Atomically Dispersed Supported Iridium Catalyst Controls Iridium Aggregation

Supported iridium complexes, Ir(C 2 H 4 ) 2 /support, were characterized by X-ray absorption spectroscopy during a temperature ramp to 120 °C in flowing H 2 . Iridium in complexes bonded to weak and moderate electron-donor supports, SiO 2 and γ-Al 2 O 3 , underwent aggregation, forming nanoparticles and clusters, respectively. When the support was a strong electron-donor (MgO), iridium remained site-isolated. Density functional theory calculations confirm the dependence of iridium–support bond strength on the support’s electron-donor character. Coating the SiO 2 -supported complexes with 1-n-ethyl-3-methyl-imidazolium acetate enhanced electron density on the iridium, hindering its aggregation. Finally, these results demonstrate opportunities for stabilizing atomically dispersed supported noble metals under reducing conditions by choice of support/ionic liquid sheath combinations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Engineering Escherichia coli for Urease-Driven Synthesis of Metal Oxide Nanomaterials

The development of functional nanomaterials with controlled morphologies is essential for advancements in medicine, electronics and computing, energy, catalysis, and environmental applications. However, conventional synthesis methods often demand high energy input and pose significant environmental challenges. Urease-based biomineralization presents an efficient, eco-friendly alternative for nanomaterial production under mild conditions. In this study, we engineered Escherichia coli ( E. coli ) to express a urease gene cluster from Sporosarcina pasteurii using CRAGE-Duet technology. The engineered strain successfully synthesized calcium carbonate and calcium phosphate crystals. Expanding the approach, we synthesized metal oxide nanoparticles, including hematite (Fe 2 O 3 ), and nanocrystalline anatase titanium dioxide (TiO 2 ). These nanomaterials were characterized by electron microscopy, demonstrating the potential of E. coli as a sustainable and versatile platform for green nanomaterial synthesis.

bacteria↗

Cofactorless oxygenases guide anthraquinone-fused enediyne biosynthesis

The anthraquinone-fused enediynes (AFEs) combine an anthraquinone moiety and a ten-membered enediyne core capable of generating a cytotoxic diradical species. AFE cyclization is triggered by opening the F-ring epoxide, which is also the site of the most structural diversity. Previous studies of tiancimycin A, a heavily modified AFE, have revealed a cryptic aldehyde blocking installation of the epoxide, and no unassigned oxidases could be predicted within the tnm biosynthetic gene cluster. Here, in this study, we identify two consecutively acting cofactorless oxygenases derived from methyltransferase and α/β-hydrolase protein folds, TnmJ and TnmK2, respectively, that are responsible for F-ring tailoring in tiancimycin biosynthesis by comparative genomics. Further biochemical and structural characterizations reveal that the electron-rich AFE anthraquinone moiety assists in catalyzing deformylation, epoxidation and oxidative ring cleavage without exogenous cofactors. These enzymes therefore fill important knowledge gaps for the biosynthesis of this class of molecules and the underappreciated family of cofactorless oxygenases. Cofactorless oxygenases are rare in nature and natural product biosynthesis. Here the authors describe the biochemical and structural characterization of two such oxygenases catalyzing deformylation, ring cleavage and epoxidation in the biosynthesis of the enediyne natural product tiancimycin A.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning

Traumatic brain injury (TBI) affects how the brain functions in the short and long term. Resulting patient outcomes across physical, cognitive, and psychological domains are complex and often difficult to predict. Major challenges to developing personalized treatment for TBI include distilling large quantities of complex data and increasing the precision with which patient outcome prediction (prognoses) can be rendered. We developed and applied interpretable machine learning methods to TBI patient data. We show that complex data describing TBI patients' intake characteristics and outcome phenotypes can be distilled to smaller sets of clinically interpretable latent factors. We demonstrate that 19 clusters of TBI outcomes can be predicted from intake data, a ~ 6× improvement in precision over clinical standards. Finally, we show that 36% of the outcome variance across patients can be predicted. These results demonstrate the importance of interpretable machine learning applied to deeply characterized patients for data-driven distillation and precision prognosis.

60 APPLIED LIFE SCIENCES↗

Metallization and spin fluctuations in Cu-doped lead apatite

An electronic structure and magnetic properties analysis of the recently proposed Cu-doped lead apatite is performed. Here, we show that electronic structures of differently Cu-substituted structures are characterized by localized molecular Cu-O bands at or near the Fermi level. The Cu substitutions can happen at both Pb1 and Pb2 sites, leading to metallic and semiconducting systems differently. The electronic systems in these bands are highly unstable magnetically and form clusters of rigidly ferromagnetically coupled magnetic moments on Cu and neighboring oxygen atoms with a total moment of about 1µ B . The ground state of uniformly Cu-doped lead apatite appears to be magnetic and semiconducting. The nonuniform distribution of two Cu atoms at the nearest Pb2 sites leads to an antiferromagnetic semiconducting state with formation energy close to uniformly distributed Cu configurations. The inclusion of quantum spin fluctuations confirms the stability of magnetic Cu-O clusters. Our calculations revealed the absence of the long-range magnetic order between uniformly distributed Cu-O clusters, creating the spin glass type of system.

36 MATERIALS SCIENCE↗

Exploiting Synergies Between Lensing and BAO Surveys for Improved Cosmological Constraints

Pinning down the nature of dark energy is one of the most pressing questions in modern physics and is one of the five key science drivers identified in the P5 report [P514]. Dark energy is thought to be either a cosmological constant with an equation of state w = P/ρ = −1 which remains constant at all times, a new type of fluid with an equation of state that varies with time (w ̸= constant), or dark energy might indicate a breakdown of Einstein’s Theory of General Relativity (GR). It is of critical importance to distinguish between these three scenarios. This can only be accomplished by ambitious and demanding measurements of both the expansion rate of the universe (to track the time evolution of dark energy) together with measurements of the rate at which cosmic structures, such as galaxies and clusters of galaxies, grow with time (the “growth rate”). Because of the challenging nature of such observations, and because no single probe simultaneously measures both expansion and growth, the Dark Energy Task Force [A+06] emphasized the importance of using multiple distinct methods to characterize dark energy.

79 ASTRONOMY AND ASTROPHYSICS↗

NuSTAR Observations of Abell 2163: Constraints on Non-thermal Emission

Since the first non-thermal reports of inverse Compton (IC) emission from the intracluster medium (ICM) of galaxy clusters at hard X-ray energies, we have yet to unambiguously confirm IC emission in observations with newer facilities. RXTE detected IC emission in one of the hottest known clusters, Abell 2163 (A2163), a massive merging cluster with a giant radio halo—the presumed source of relativistic electrons IC scattering CMB photons to X-ray energies. The cluster's redshift (z ~ 0.2) allows its thermal and non-thermal radio emission to fit the field of view of the Nuclear Spectroscopic Telescope Array (NuSTAR), permitting a deep observation capable of confirming or ruling out the RXTE report. The IC flux provides constraints on the average magnetic field strength in a cluster. To determine the global diffuse IC emission in A2163, we fit its global NuSTAR spectrum with four models: single- (1T) and two-temperature (2T), 1T+power-law component (T+IC), and multi-temperature+power law (9T+IC). Each represents different characterizations of the thermal ICM emission, with power-law components added to represent IC emission. We find that the 3–30 keV spectrum can be described by purely thermal emission, with a global average temperature of kT = (11.8 ± 0.2) keV. The IC flux is constrained to <4.0 × 10 –12 erg s –1 cm –2 using the 1T+IC model and <1.6 × 10 –12 erg s –1 cm –2 with the more physical 9T+IC model, both to 90% confidence levels. Here, combining these limits with 1.4 GHz diffuse radio data from the VLA, we find the average magnetic field strength to be >0.22 μG and >0.35 μG, respectively, providing the strongest constraints on these values in A2163 to date.

79 ASTRONOMY AND ASTROPHYSICS↗

Quantitative characterization of spatial distribution of particles in materials: Application to materials processing

Most engineering materials contain second phase particles or fibers which serve to reinforce the matrix phase. The effect of reinforcements on material properties is usually analyzed in terms of the average volume fraction and spacing of reinforcements, quantities which are global microstructural characteristics. However, material properties can also depend on local microstructural characteristics; for example, on how uniformly the reinforcing phase is distributed in the material. The analysis method will then be applied to a materials processing problem to discover how processing parameters can be selected to maximize redistribution of the reinforcing phase during processing. Several mathematical analysis methods could be adapted to the problem of characterizing the distribution of particles in materials. A tessellation-based method was selected. In the first phase of the investigation, a software package was written to automate the analysis. Typical results are shown. The analysis technique allows the degree to which particles are clustered together, the size and spacing of particle clusters, and the particle density in clusters to be found. The analysis methods were applied to computer-generated distributions and to a few real particle-containing materials. Methods for analyzing a nonuniform particle distribution in a material can be applied to two broad classes of materials science problems: understanding how the resulting particle distribution affects properties. The analysis method described is applied to a materials processing problem: how to select extrusion conditions to maximize the redistribution of reinforcing particles that are initially nonuniformly distributed. In addition, the tessellation-based method to analyze star distributions in spiral galaxies was adapted, illustrating the diverse types of problems to which the analysis method can be applied.

Parse, J. B.↗

Possibilistic clustering for shape recognition

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, we cast the clustering problem into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We constructed an appropriate objective function whose minimum will characterize a good possibilistic partition of the data, and we derived the membership and prototype update equations from necessary conditions for minimization of our criterion function. In this paper, we show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

Keller, James M.↗

Advanced Polymer Characterization: Modular Operations for Spectral Alignment by Iterative Compression (MOSAIC)

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry encodes structural information across diverse homo- and copolymer ensembles, yet decrypting these spectra requires a systematic analytical approach. We introduce Modular Operations for Spectral Alignment by Iterative Compression (MOSAIC)─a general cipher algorithm that applies modular arithmetic to filter monomer-derived mass contributions and cluster MALDI peaks by nonconstitutional repeating units (non-CRUs). MOSAIC performs sequential modular operations using monomer mass differences as base units to compress complex spectral data, revealing end-group distributions and comonomer incorporation. As a demonstration, we applied MOSAIC to five copolymers formed by two different polymerization mechanisms. Furthermore, the resulting remainder–mass plots clearly resolve polymer homologs with distinct non-CRUs into visually apparent clusters, enabling intuitive assignment of mass spectral features.

Wang, Hanlin M. [University of Illinois at Urbana−↗

Cryogenic “Iodide-Tagging” Photoelectron Spectroscopy: A Sensitive Probe for Specific Binding Sites of Amino Acids

This work showcases cryogenic and temperature-dependent “iodide-tagging” photoelectron spectroscopy to probe specific binding sites of amino acids using the glycine-iodide complex (Gly?I-) as a case study. Multiple Gly?I- isomers were generated from ambient electrospray ionization and kinetically isolated in a cryogenic ion trap. These structures were characterized with temperature-dependent “iodide-tagging” negative ion photoelectron spectroscopy (NIPES) where iodide was used as the “messenger” to interpret electronic energetics and structural information of various Gly?I- isomers. Accompanied by theoretical computations and Franck-Condon simulations, a total of five cluster structures have been identified along with their various binding motifs. This work demonstrates that “iodide-tagging” NIPES is a powerful general means for probing specific binding interactions in biological molecules of interest.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Surface warming in global cities is substantially more rapid than in rural background areas

Warming trends in cities are influenced both by large-scale climate processes and by local-scale urbanization. However, little is known about how surface warming trends of global cities differ from those characterized by weather observations in the rural background. Here, through statistical analyses of satellite land surface temperatures (2002 to 2021), we find that the mean surface warming trend is 0.50 ± 0.20 K·decade –1 (mean ± one S.D.) in the urban core of 2000-plus city clusters worldwide, and is 29% greater than the trend for the rural background. On average, background climate change is the largest contributor explaining 0.30 ± 0.11 K·decade –1 of the urban surface warming. In city clusters in China and India, however, more than 0.23 K·decade –1 of the mean trend is attributed to urban expansion. We also find evidence of urban greening in European cities, which offsets 0.13 ± 0.034 K·decade –1 of background surface warming.

54 ENVIRONMENTAL SCIENCES↗

Estimating the electric vehicle charging demand of multi-unit dwelling residents in the United States

Abstract Early battery electric vehicle (EV) adopters can access home chargers for reliable charging. As the EV market grows, residents of multi-unit dwellings (MUDs) may face barriers in owning EVs and charging them without garage or parking availability. To investigate the mechanisms that can bridge existing disparities in home charging and station deployment, we characterized the travel behavior of MUD residents and estimated their EV residential charging demand. This study classifies the travel patterns of MUD residents by fusing trip diary data from the National Household Travel Survey and housing features from the American Housing Survey. A hierarchical agglomerative clustering method was used to cluster apartment complex residents’ travel profiles, considering attributes such as dwell time, daily vehicle miles traveled (VMT), income, and their residences’ US census division. We propose a charging decision model to determine the charging station placement demand in MUDs and the charging energy volume expected to be consumed, assuming that MUD drivers universally operate EVs in urban communities. Numerical experiments were conducted to gain insight into the charging demand of MUD residents in the US. We found that charging availability is indispensable for households that set out to meet 80% state of charge by the end of the day. When maintaining a 20% comfortable state of charge the entire day, the higher the VMT are, the greater the share of charging demand and the greater the energy use in MUD chargers. The upper-income group requires a greater share of MUD charging and greater daily kWh charged because of more VMT.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Decreases in polyunsaturated fatty acid content improve heat stress tolerance during flowering and silicle development in pennycress (Thlaspi arvense L.)

Introduction: Pennycress (Thlaspi arvense L.) is an emerging intermediate oilseed crop grown in the offseason between primary summer crops to produce three cash crops in two years. Previous efforts to improve seed oil quality produced Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) genome-edited lines with decreased polyunsaturated fatty acids (PUFAs) levels, through loss of function of the FATTY ACID DESATURASE 2 (FAD2), REDUCED OLEATE DESATURATION1 (ROD1), and FATTY ACID ELONGASE1 (FAE1) genes. While seed oil compositions were previously characterized, it remains unknown how vegetative and reproductive tissue compositions might differ and affect tolerance to high temperature (HT) conditions.Methods: In four growth chamber experiments, we explored HT tolerance during flowering and silicle development. Plants were subjected to a 34 °C day/28 °C night regime and compared to control plants maintained at 20 °C. Pollen grain viability at a range of temperatures, lipid peroxidation and proline content in leaves and silicles following HT, and seed yield were measured.Results: Both fad2 and rod1 mutant lines had relatively higher pollen viability (71% and 54% respectively) under moderately elevated temperature (28 °C) compared to wild-type controls (37%). They also showed smaller decreases in seed yield (0% and 40% for fad2 and rod1 respectively, compared to 61% for wild type), following HT exposure during late flowering and early silicle development. Silicles of fad2 plants experienced 65% less lipid peroxidation under HT and 55% less buildup of proline, signifying less stress.Discussion: The differential results of fad2 and rod1 are likely due to the role of FAD2 in membranes in all tissues, whereas ROD1 predominantly affects triacylglycerol (TAG) composition in oil-accumulating tissues including pollen. Our results indicate that decreasing PUFAs, through gene editing, can increase heat tolerance in reproductive tissues as an auxiliary benefit accompanying improved seed oil quality.

59 BASIC BIOLOGICAL SCIENCES↗

Effective many-body interactions in reduced-dimensionality spaces through neural network models

Accurately describing properties of challenging problems in physical sciences often requires complex mathematical models that are unmanageable to tackle head on. Therefore, developing reduced-dimensionality representations that encapsulate complex correlation effects in many-body systems is crucial to advance the understanding of these complicated problems. However, a numerical evaluation of these predictive models can still be associated with a significant computational overhead. To address this challenge, in this paper we discuss a combined framework that integrates recent advances in the development of active-space representations of coupled cluster (CC) downfolded Hamiltonians with neural network approaches. The primary objective of this effort is to train neural networks to eliminate the computationally expensive steps required for evaluating hundreds or thousands of Hugenholtz diagrams, which correspond to multidimensional tensor contractions necessary for evaluating a many-body form of downfolded effective Hamiltonians. Using small molecular systems (the H 2 O and HF molecules) as examples, we demonstrate that training neural networks employing effective Hamiltonians for a few nuclear geometries of molecules can accurately interpolate or extrapolate their forms to other geometrical configurations characterized by different intensities of correlation effects. We also discuss differences between effective interactions that define CC downfolded Hamiltonians with those of bare Hamiltonians defined by Coulomb interactions in the active spaces. Published by the American Physical Society 2024

97 MATHEMATICS AND COMPUTING↗

Adsorption of Water on JSC-1A Lunar Simulant Samples

Remote sensing probes sent to the moon in the 1990s indicated that water may exist in areas such as the bottoms of deep, permanently shadowed craters at the lunar poles, buried under regolith. Water is of paramount importance for any lunar exploration and colonization project which would require self-sustainable systems. Therefore, investigating the interaction of water with lunar regolith is pertinent to future exploration. The lunar environment can be approximated in ultra-high vacuum systems such as those used in thermal desorption spectroscopy (TDS). Questions about water dissociation, surface wetting, degree of crystallization, details of water-ice transitions, and cluster formation kinetics can be addressed by TDS. Lunar regolith specimens collected during the Apollo missions are still available though precious, so testing with simulant is required before applying to use lunar regolith samples. Hence, we used for these studies JSC-1a, mostly an aluminosilicate glass and basaltic material containing substantial amounts of plagioclase, some olivine and traces of other minerals. Objectives of this project include: 1) Manufacturing samples using as little raw material as possible, allowing the use of surface chemistry and kinetics tools to determine the feasibility of parallel studies on regolith, and 2) Characterizing the adsorption kinetics of water on the regolith simulant. This has implications for the probability of finding water on the moon and, if present, for recovery techniques. For condensed water films, complex TDS data were obtained containing multiple features, which are related to subtle rearrangements of the water adlayer. Results from JSC-1a TDS studies indicate: 1) Water dissociation on JSC-1a at low exposures, with features detected at temperatures as high as 450 K and 2) The formation of 3D water clusters and a rather porous condensed water film. It appears plausible that the sub- m sized particles act as nucleation centers.

Goering, John↗

Understanding the Scalability of Bayesian Network Inference Using Clique Tree Growth Curves

One of the main approaches to performing computation in Bayesian networks (BNs) is clique tree clustering and propagation. The clique tree approach consists of propagation in a clique tree compiled from a Bayesian network, and while it was introduced in the 1980s, there is still a lack of understanding of how clique tree computation time depends on variations in BN size and structure. In this article, we improve this understanding by developing an approach to characterizing clique tree growth as a function of parameters that can be computed in polynomial time from BNs, specifically: (i) the ratio of the number of a BN s non-root nodes to the number of root nodes, and (ii) the expected number of moral edges in their moral graphs. Analytically, we partition the set of cliques in a clique tree into different sets, and introduce a growth curve for the total size of each set. For the special case of bipartite BNs, there are two sets and two growth curves, a mixed clique growth curve and a root clique growth curve. In experiments, where random bipartite BNs generated using the BPART algorithm are studied, we systematically increase the out-degree of the root nodes in bipartite Bayesian networks, by increasing the number of leaf nodes. Surprisingly, root clique growth is well-approximated by Gompertz growth curves, an S-shaped family of curves that has previously been used to describe growth processes in biology, medicine, and neuroscience. We believe that this research improves the understanding of the scaling behavior of clique tree clustering for a certain class of Bayesian networks; presents an aid for trade-off studies of clique tree clustering using growth curves; and ultimately provides a foundation for benchmarking and developing improved BN inference and machine learning algorithms.

Mengshoel, Ole J.↗

Anion–Cation–Anion Ion Triplet Characterization by Computation and Photoelectron Spectroscopy

Ion triplets of the chloride salts of two commonly used weakly coordinating cations are reported (i.e., Cl – ·NMe 4 + Cl – (1 – ) and Cl – ·PPh 4 + Cl – (2 – )). Negative ion photoelectron spectra at 20 K afford vertical and adiabatic detachment energies of 5.18 and 5.00 eV (1 – ) and 5.03 and 4.70 eV (2 – ), respectively. These results are well reproduced by coupled cluster calculations with single, double, and perturbative triple excitations (CCSD(T)) whereas M06-2X is systematically too small by ~0.3 eV (i.e., 7 kcal mol –1 ). The structures of both 1 – and 2 – have five or six C–H···Cl – interactions that stabilize these cluster anions by 32 (1 – ) and 27 (2 – ) kcal mol –1 as given by their chloride dissociation enthalpies. In conclusion, these values drop to 7.4 and 3.8 kcal mol –1 in dichloromethane based up conductor-like polarizable continuum model calculations and suggest that X – ·M + X – ion triplets with a weakly coordinating cation maybe the reactive form of salts under some conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗