Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Completeness”

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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Completing a Charge Transport Chain for Artificial Photosynthesis

A ruthenium polypyridyl chromophore with electronically isolated triarylamine substituents has been synthesized that models the role of tyrosine in the electron transport chain in photosystem II. When bound to the surface of a TiO 2 electrode, electron injection from a Ru(II) Metal-to-Ligand Charge Transfer (MLCT) excited state occurs from the complex to the electrode to give Ru(III). Subsequent rapid electron transfer from the pendant triarylamine to Ru(III) occurs with an observed rate constant of ~10 10 s –1 , which is limited by the rate of electron injection into the semiconductor. Finally, transfer of the oxidative equivalent away from the semiconductor surface results in dramatically reduced rates of back electron transfer, and a long-lived ($τ$ = ~165 μs) triarylamine radical cation that has been used to oxidize hydroquinone to quinone in solution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

Rapid mapping of polarization switching through complete information acquisition

Polarization switching in ferroelectric and multiferroic materials underpins a broad range of current and emergent applications, ranging from random access memories to field-effect transistors, and tunnelling devices. Switching in these materials is exquisitely sensitive to local defects and microstructure on the nanometre scale, necessitating spatially resolved high-resolution studies of these phenomena. Classical piezoresponse force microscopy and spectroscopy, although providing necessary spatial resolution, are fundamentally limited in data acquisition rates and energy resolution. This limitation stems from their two-tiered measurement protocol that combines slow (~1 s) switching and fast (~10 kHz–1 MHz) detection waveforms. Here we develop an approach for rapid probing of ferroelectric switching using direct strain detection of material response to probe bias. This approach, facilitated by high-sensitivity electronics and adaptive filtering, enables spectroscopic imaging at a rate 3,504 times faster the current state of the art, achieving high-veracity imaging of polarization dynamics in complex microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Complete information acquisition in scanning probe microscopy

In the last three decades, scanning probe microscopy (SPM) has emerged as a primary tool for exploring and controlling the nanoworld. A critical part of the SPM measurements is the information transfer from the tip-surface junction to a macroscopic measurement system. This process reduces the many degrees of freedom of a vibrating cantilever to relatively few parameters recorded as images. Similarly, the details of dynamic cantilever response at sub-microsecond time scales of transients, higher-order eigenmodes and harmonics are averaged out by transitioning to millisecond time scale of pixel acquisition. Hence, the amount of information available to the external observer is severely limited, and its selection is biased by the chosen data processing method. Here, we report a fundamentally new approach for SPM imaging based on information theory-type analysis of the data stream from the detector. This approach allows full exploration of complex tip-surface interactions, spatial mapping of multidimensional variability of material s properties and their mutual interactions, and SPM imaging at the information channel capacity limit.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2025

Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory

Abstract An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting physical insights from large datasets. However, atomistic ML often relies on unsupervised learning or model predictions to analyze information contents from simulation or training data. Here, we introduce a theoretical framework that provides a rigorous, model-free tool to quantify information contents in atomistic simulations. We demonstrate that the information entropy of a distribution of atom-centered environments explains known heuristics in ML potential developments, from training set sizes to dataset optimality. Using this tool, we propose a model-free UQ method that reliably predicts epistemic uncertainty and detects out-of-distribution samples, including rare events in systems such as nucleation. This method provides a general tool for data-driven atomistic modeling and combines efforts in ML, simulations, and physical explainability.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Optical second harmonic generation in anisotropic multilayers with complete multireflection of linear and nonlinear waves using ♯SHAARP.ml package

Abstract Optical second harmonic generation (SHG) is a nonlinear optical effect widely used for nonlinear optical microscopy and laser frequency conversion. Closed-form analytical solution of the nonlinear optical responses is essential for evaluating materials whose optical properties are unknown a priori. A recent open-source code, ♯SHAARP. si , can provide such closed form solutions for crystals with arbitrary symmetries, orientations, and anisotropic properties at a single interface. However, optical components are often in the form of slabs, thin films on substrates, and multilayer heterostructures with multiple reflections of both the fundamental and up to ten different SHG waves at each interface, adding significant complexity. Many approximations have therefore been employed in the existing analytical approaches, such as slowly varying approximation, weak reflection of the nonlinear polarization, transparent medium, high crystallographic symmetry, Kleinman symmetry, easy crystal orientation along a high-symmetry direction, phase matching conditions and negligible interference among nonlinear waves, which may lead to large errors in the reported material properties. To avoid these approximations, we have developed an open-source package named Second Harmonic Analysis of Anisotropic Rotational Polarimetry in Multilayers (♯SHAARP. ml ). The reliability and accuracy are established by experimentally benchmarking with both the SHG polarimetry and Maker fringes using standard and commonly used nonlinear optical materials as well as twisted 2-dimensional heterostructures.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Developing a complete AI-accelerated workflow for superconductor discovery

The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed T c > 5 K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery.

Gibson, Jason B. [Quantum Formatics, Cambridge, MA↗
DOE OSTI2023

Complete field-induced spectral response of the spin-1/2 triangular-lattice antiferromagnet CsYbSe 2

Fifty years after Anderson’s resonating valence-bond proposal, the spin-1/2 triangular-lattice Heisenberg antiferromagnet (TLHAF) remains the ultimate platform to explore highly entangled quantum spin states in proximity to magnetic order. Yb-based delafossites are ideal candidate TLHAF materials, which allow experimental access to the full range of applied in-plane magnetic fields. We perform a systematic neutron scattering study of CsYbSe 2 , first proving the Heisenberg character of the interactions and quantifying the second-neighbor coupling. We then measure the complex evolution of the excitation spectrum, finding extensive continuum features near the 120°-ordered state, throughout the 1/3-magnetization plateau and beyond this up to saturation. We perform cylinder matrix-product-state (MPS) calculations to obtain an unbiased numerical benchmark for the TLHAF and spectacular agreement with the experimental spectra. The measured and calculated longitudinal spectral functions reflect the role of multi-magnon bound and scattering states. These results provide valuable insight into unconventional field-induced spin excitations in frustrated quantum materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Complete integration of carbene-transfer chemistry into biosynthesis

Biosynthesis is an environmentally benign and renewable approach that can be used to produce a broad range of natural and, in some cases, new-to-nature products. However, biology lacks many of the reactions that are available to synthetic chemists, resulting in a narrower scope of accessible products when using biosynthesis rather than synthetic chemistry. A prime example of such chemistry is carbene-transfer reactions. Although it was recently shown that carbene-transfer reactions can be performed in a cell and used for biosynthesis, carbene donors and unnatural cofactors needed to be added exogenously and transported into cells to effect the desired reactions, precluding cost-effective scale-up of the biosynthesis process with these reactions. Here, in this paper, we report the access to a diazo ester carbene precursor by cellular metabolism and a microbial platform for introducing unnatural carbene-transfer reactions into biosynthesis. The α-diazoester azaserine was produced by expressing a biosynthetic gene cluster in Streptomyces albus. The intracellularly produced azaserine was used as a carbene donor to cyclopropanate another intracellularly produced molecule—styrene. The reaction was catalysed by engineered P450 mutants containing a native cofactor with excellent diastereoselectivity and a moderate yield. Our study establishes a scalable, microbial platform for conducting intracellular abiological carbene-transfer reactions to functionalize a range of natural and new-to-nature products and expands the scope of organic products that can be produced by cellular metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Author Correction: Microbial Metagenomes Across a Complete Phytoplankton Bloom Cycle: High-Resolution Sampling Every 4 Hours Over 22 Days

In the version of this article initially published, two errors in authorship were made. First, Tatiana Rynearson of the School of Oceanography, University of Rhode Island was mistakenly omitted from the final author list. Second, Kurt LaButti of the Joint Genome Institute was mistakenly omitted from the final author list and replaces Alicia Clum due to a staffing change at the Joint Genome Institute. These authorship omissions were not identified until after the work had been published. We are updating the authorship to appropriately recognize the contributions of these authors. The error has been corrected in the PDF and HTML versions of the article.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Analyses of transcriptomes and the first complete genome of Leucocalocybe mongolica provide new insights into phylogenetic relationships and conservation

In this study, we report a de novo assembly of the first high-quality genome for a wild mushroom species Leucocalocybe mongolica (LM). We performed high-throughput transcriptome sequencing to analyze the genetic basis for the life history of LM. Our results show that the genome size of LM is 46.0 Mb, including 26 contigs with a contig N50 size of 3.6 Mb. In total, we predicted 11,599 protein-coding genes, of which 65.7% (7630) could be aligned with high confidence to annotated homologous genes in other species. We performed phylogenetic analyses using genes form 3269 single-copy gene families and showed support for distinguishing LM from the genus Tricholoma (L.) P.Kumm., in which it is sometimes circumscribed. We believe that one reason for limited wild occurrences of LM may be the loss of key metabolic genes, especially carbohydrate-active enzymes (CAZymes), based on comparisons with other closely related species. The results of our transcriptome analyses between vegetative (mycelia) and reproductive (fruiting bodies) organs indicated that changes in gene expression among some key CAZyme genes may help to determine the switch from asexual to sexual reproduction. Taken together, our genomic and transcriptome data for LM comprise a valuable resource for both understanding the evolutionary and life history of this species.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Short RNA indicator sequences are not completely degraded by autoclaving

Short indicator RNA sequences (,100 bp) persist after autoclaving and are recovered intact by molecular amplification. Primers targeting longer sequences are most likely to produce false positives due to amplification errors easily verified by melting curves analyses. If short indicator RNA sequences are used for virus identification and quantification then post autoclave RNA degradation methodology should be employed, which may include further autoclaving.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

A high-density SNP genetic map consisting of a complete set of homologous groups in autohexaploid sweetpotato (Ipomoea batatas)

Sweetpotato (Ipomoea batatas) is an autohexaploid species with 90 chromosomes (2n = 6x = 90) and a basic chromosome number of 15, and is therefore regarded as one of the most challenging species for high-density genetic map construction. Here, we used single nucleotide polymorphisms (SNPs) identified by double-digest restriction site-associated DNA sequencing based on next-generation sequencing technology to construct a map for sweetpotato. We then aligned the sequence reads onto the reference genome sequence of I. trifida, a likely diploid ancestor of sweetpotato, to detect SNPs. In addition, to simplify analysis of the complex genetic mode of autohexaploidy, we used an S1 mapping population derived from self-pollination of a single parent. As a result, 28,087 double-simplex SNPs showing a Mendelian segregation ratio in the S1 progeny could be mapped onto 96 linkage groups (LGs), covering a total distance of 33,020.4 cM. Based on the positions of the SNPs on the I. trifida genome, the LGs were classified into 15 groups, each with roughly six LGs and six small extra groups. The molecular genetic techniques used in this study are applicable to high-density mapping of other polyploid plant species, including important crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Complete fold annotation of the human proteome using a novel structural feature space

Recognition of protein structural fold is the starting point for many structure prediction tools and protein function inference. Fold prediction is computationally demanding and recognizing novel folds is difficult such that the majority of proteins have not been annotated for fold classification. Here we describe a new machine learning approach using a novel feature space that can be used for accurate recognition of all 1,221 currently known folds and inference of unknown novel folds. We show that our method achieves better than 94% accuracy even when many folds have only one training example. We demonstrate the utility of this method by predicting the folds of 34,330 human protein domains and showing that these predictions can yield useful insights into potential biological function, such as prediction of RNA-binding ability. Finally, our method can be applied to de novo fold prediction of entire proteomes and identify candidate novel fold families.

59 BASIC BIOLOGICAL SCIENCES↗