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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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Big data science informs energy research: large-scale screening of crystal structures identifies unforeseen class of dyes for dye-sensitised solar cells.
Guidelines for incorporating aza-glycine residues in collagen peptides are presented, detailing their effects on triple-helical thermal stability.
A class of unconventional superhalogens by doping closo -boranes with selected metal atoms.
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The efficacy of strained layer threading dislocation filter structures in single crystal epitaxial layers is evaluated using numerical modeling for (001) face-centred cubic materials, such as GaAs or Si{sub 1−x}Ge{sub x}, and (0001) hexagonal materials such as GaN. We find that threading dislocation densities decay exponentially as a function of the strain relieved, irrespective of the fraction of threading dislocations that are mobile. Reactions between threading dislocations tend to produce a population that is a balanced mixture of mobile and sessile in (001) cubic materials. In contrast, mobile threading dislocations tend to be lost very rapidly in (0001) GaN, often with little or no reduction in the immobile dislocation density. The capture radius for threading dislocation interactions is estimated to be approximately 40 nm using cross section transmission electron microscopy of dislocation filtering structures in GaAs monolithically grown on Si. We find that the minimum threading dislocation density that can be obtained in any given structure is likely to be limited by kinetic effects to approximately 10{sup 4}–10{sup 5 }cm{sup −2}.
We investigate photoinduced charge transfer (CT) processes within dyads consisting of porphyrin derivatives in which one ring ligates a Zn metal center and where the rings vary by their degree of conjugation. Using a first-principles approach, we show that molecular-scale means can tune CT rates through stabilization affected by the polar environment. Such means of CT tuning are important for achieving high efficiency optoelectronic applications using organic semiconducting materials. Our fully quantum mechanical scheme is necessary for reliably modeling the CT process across different regimes, in contrast to the pervading semi-classical Marcus picture that grossly underestimates transfer in the far-inverted regime.
A simple equation describing the formation of plateaux induced by superelastic collisions in the electron energy distribution function (EEDF) of low temperature and afterglow plasmas is derived. The EEDFs predicted from this equation are in good agreement with those obtained from the numerical solution of the full Boltzmann equation in the presence of excited states.
We clarify the difference between the SiH{sub 4} consumption efficiency η and the SiH{sub 4} depletion fraction D, as measured in the pumping line and the actual reactor of an industrial plasma-enhanced chemical vapor deposition system. In the absence of significant polysilane and powder formation, η is proportional to the film growth rate. Above a certain powder formation threshold, any additional amount of SiH{sub 4} consumed translates into increased powder formation rather than into a faster growing Si film. In order to discuss a zero-dimensional analytical model and a two-dimensional numerical model, we measure η as a function of the radio frequency (RF) power density coupled into the plasma, the total gas flow rate, the input SiH{sub 4} concentration, and the reactor pressure. The adjunction of a small trimethylboron flow rate increases η and reduces the formation of powder, while the adjunction of a small disilane flow rate decreases η and favors the formation of powder. Unlike η, D is a location-dependent quantity. It is related to the SiH{sub 4} concentration in the plasma c{sub p}, and to the phase of the growing Si film, whether the substrate is glass or a c-Si wafer. In order to investigate transient effects due to the RF matching, the precoating of reactor walls, or the introduction of a purifier in the gas line, we measure the gas residence time and acquire time-resolved SiH{sub 4} density measurements throughout the ignition and the termination of a plasma.
The requirements on the material properties and growth control of silicon nitride (SiN{sub x}) spacer films in transistors are becoming ever more stringent as scaling of transistor structures continues. One method to deposit high-quality films with excellent control is atomic layer deposition (ALD). However, depositing SiN{sub x} by ALD has turned out to be very challenging. In this work, it is shown that the plasma gas residence time τ is a key parameter for the deposition of SiN{sub x} by plasma-assisted ALD and that this parameter can be linked to a so-called “redeposition effect”. This previously ignored effect, which takes place during the plasma step, is the dissociation of reaction products in the plasma and the subsequent redeposition of reaction-product fragments on the surface. For SiN{sub x} ALD using SiH{sub 2}(NH{sup t}Bu){sub 2} as precursor and N{sub 2} plasma as reactant, the gas residence time τ was found to determine both SiN{sub x} film quality and the resulting growth per cycle. It is shown that redeposition can be minimized by using a short residence time resulting in high-quality films with a high wet-etch resistance (i.e., a wet-etch rate of 0.5 nm/min in buffered HF solution). Due to the fundamental nature of the redeposition effect, it is expected to play a role in many more plasma-assisted ALD processes.
We argue that statistical theories are generally unable to accurately predict state-resolved differential cross sections for triatomic bimolecular reactions studied in beam experiments, even in the idealized limit where the dynamics are fully chaotic. The basic reason is that quenching of interferences between partial waves is less efficient than intuitively expected, especially around the poles.
In this paper, the generation of multi-clustered structure of self-organized neural network with different neuronal firing patterns, i.e., bursting or spiking, has been investigated. The initially all-to-all-connected spiking neural network or bursting neural network can be self-organized into clustered structure through the symmetric spike-timing-dependent plasticity learning for both bursting and spiking neurons. However, the time consumption of this clustering procedure of the burst-based self-organized neural network (BSON) is much shorter than the spike-based self-organized neural network (SSON). Our results show that the BSON network has more obvious small-world properties, i.e., higher clustering coefficient and smaller shortest path length than the SSON network. Also, the results of larger structure entropy and activity entropy of the BSON network demonstrate that this network has higher topological complexity and dynamical diversity, which benefits for enhancing information transmission of neural circuits. Hence, we conclude that the burst firing can significantly enhance the efficiency of clustering procedure and the emergent clustered structure renders the whole network more synchronous and therefore more sensitive to weak input. This result is further confirmed from its improved performance on stochastic resonance. Therefore, we believe that the multi-clustered neural network which self-organized from the bursting dynamics has high efficiency in information processing.
The ability to dynamically learn and adapt to changes in the environment is one of the hallmarks of biological systems. In this work, we identify the subset of the design space of memristive materials that is optimal for dynamic learning applications. Using an architecture inspired on the learning center of the insect brain, we implement a model system consisting of a discrete implementation of spiking neurons where dynamic learning takes place on a set of plastic synapses formed by memristor pairs in a crossbar array. Using two separate benchmarks, one comprising the dynamic learning of the Modified National Institute of Standards and Technology dataset and another one targeting one shot learning, we have identified the key properties that memristive materials should have to be optimal dynamic learners. The results obtained show that a fine degree of control of the memristor internal state is key to achieve high classification accuracy during dynamic learning but that within this optimal region learning is extremely robust both to device variability and to errors in the writing of the internal state, in all cases allowing for 2 sigma variations greater than 40% without significant loss of accuracy, hence overcoming two of the perceived limitations of memristors. By additionally requiring that learning takes place concurrently to information processing, we are able to derive a set constraints to the memristor dynamics.
Here, we derive a new expression for the strength of a hydrogen bond (V HB ) in terms of the elongation of the covalent bond of the donor fragment participating in the hydrogen bond (Δr HB ) and the intermolecular coordinates R (separation between the heavy atoms) and θ (deviation of the hydrogen bond from linearity). The expression includes components describing the covalent D–H bond of the hydrogen bond donor via a Morse potential, the Pauli repulsion, and electrostatic interactions between the constituent fragments using a linear expansion of their dipole moment and a quadratic expansion of their polarizability tensor. We fitted the parameters of the model using ab initio electronic structure results for six hydrogen bonded dimers, namely, NH 3 –NH 3 , H 2 O–H 2 O, HF–HF, H 2 O–NH 3 , HF–H 2 O, and HF–NH 3 , and validated its performance for extended parts of their potential energy surfaces, resulting in a mean absolute error ranging from 0.07 to 0.31 kcal/mol. The derived expression describes the energy–structure relationship in terms of a single structural parameter, namely, the elongation of the donor’s covalent bond (Δr HB ), and suggests the novel relationship of 8.0 kcal/mol pm −1 (or 0.8 kcal/mol per 0.001 Å elongation). This structural parameter is easily obtained from theory and can serve as the single descriptor of the strength of individual hydrogen bonds.
Significance This work presents the genome-wide replacement of all rare AGR (AGA and AGG) arginine codons in the essential genes of Escherichia coli with synonymous CGN alternatives. Synonymous codon substitutions can lethally impact noncoding function by disrupting mRNA secondary structure and ribosomal binding site-like motifs. Here we quantitatively define the range of tolerable deviation in these metrics and use this relationship to provide critical insight into codon choice in recoded genomes. This work demonstrates that genome-wide removal of AGR is likely to be possible and provides a framework for designing genomes with radically altered genetic codes.
The plant cell wall is primarily a polysaccharide mesh of the most abundant biopolymers on earth. Although one of the richest sources of biorenewable materials, the biosynthesis of the plant polysaccharides is poorly understood. Structures of many essential plant glycosyltransferases are unknown and suitable substrates are often unavailable for in vitro analysis. The dearth of such information impedes the development of plants better suited for industrial applications. As such, presented here are structures of Arabidopsis xyloglucan xylosyltransferase 1 (XXT1) without ligands and in complexes with UDP and cellohexaose. XXT1 initiates side-chain extensions from a linear glucan polymer by transferring the xylosyl group from UDP-xylose during xyloglucan biosynthesis. XXT1, a homodimer and member of the GT-A fold family of glycosyltransferases, binds UDP analogously to other GT-A fold enzymes. Structures here and the properties of mutant XXT1s are consistent with a SN i -like catalytic mechanism. Distinct from other systems is the recognition of cellohexaose by way of an extended cleft. The XXT1 dimer alone cannot produce xylosylation patterns observed for native xyloglucans because of steric constraints imposed by the acceptor binding cleft. Homology modeling of XXT2 and XXT5, the other two xylosyltransferases involved in xyloglucan biosynthesis, reveals a structurally altered cleft in XXT5 that could accommodate a partially xylosylated glucan chain produced by XXT1 and/or XXT2. An assembly of the three XXTs can produce the xylosylation patterns of native xyloglucans, suggesting the involvement of an organized multienzyme complex in the xyloglucan biosynthesis.
Thymine DNA glycosylase (TDG) is a pivotal enzyme with dual roles in both genome maintenance and epigenetic regulation. TDG is involved in cytosine demethylation at CpG sites in DNA. Here we have used molecular modeling to delineate the lesion search and DNA base interrogation mechanisms of TDG. First, we examined the capacity of TDG to interrogate not only DNA substrates with 5-carboxyl cytosine modifications but also G:T mismatches and nonmismatched (A:T) base pairs using classical and accelerated molecular dynamics. To determine the kinetics, we constructed Markov state models. Base interrogation was found to be highly stochastic and proceeded through insertion of an arginine-containing loop into the DNA minor groove to transiently disrupt Watson–Crick pairing. Next, we employed chain-of-replicas path-sampling methodologies to compute minimum free energy paths for TDG base extrusion. We identified the key intermediates imparting selectivity and determined effective free energy profiles for the lesion search and base extrusion into the TDG active site. Here, our results show that DNA sculpting, dynamic glycosylase interactions, and stabilizing contacts collectively provide a powerful mechanism for the detection and discrimination of modified bases and epigenetic marks in DNA.
Severe plastic deformations under high pressure are used to produce nanostructured materials but were studied ex-situ. Rough diamond anvils are introduced to reach maximum friction equal to yield strength in shear and the first in-situ study of the evolution of the pressure-dependent yield strength and radial distribution of nanostructural parameters are performed for severely pre-deformed Zr. ω-Zr behaves like perfectly plastic, isotropic, and strain-path-independent and reaches steady values of the crystallite size and dislocation density, which are pressure-, strain- and strain-path-independent. However, steady states for a-Zr obtained with smooth and rough anvils are different, causing major challenge in plasticity theory. In-situ study of severe plastic deformation of α-Zr with rough diamond anvils revealed that pressure-dependent yield strength, crystallite size, and dislocation density are getting steady and plastic strain- and strain-path-independent.