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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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At least 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Hierarchical Self‐Assembly of Multidimensional Functional Materials from Sequence‐Defined Peptoids

Abstract Hierarchical self‐assembly represents a powerful strategy for the fabrication of functional materials across various length scales. However, achieving precise formation of functional hierarchical assemblies remains a significant challenge and requires a profound understanding of molecular assembly interactions. In this study, we present a molecular‐level understanding of the hierarchical assembly of sequence‐defined peptoids into multidimensional functional materials, including twisted nanotube bundles serving as a highly efficient artificial light harvesting system. By employing synchrotron‐based powder X‐ray diffraction and analyzing single crystal structures of model compounds, we elucidated the molecular packing and mechanisms underlying the assembly of peptoids into multidimensional nanostructures. Our findings demonstrate that incorporating aromatic functional groups, such as tetraphenyl ethylene (TPE), at the termini of assembling peptoid sequences promotes the formation of twisted bundles of nanotubes and nanosheets, thus enabling the creation of a highly efficient artificial light harvesting system. This research exemplifies the potential of leveraging sequence‐defined synthetic polymers to translate microscopic molecular structures into macroscopic assemblies. It holds promise for the development of functional materials with precisely controlled hierarchical structures and designed functions.

Shao, Li↗
DOE OSTI2024

Hierarchical Self‐Assembly of Multidimensional Functional Materials from Sequence‐Defined Peptoids

Abstract Hierarchical self‐assembly represents a powerful strategy for the fabrication of functional materials across various length scales. However, achieving precise formation of functional hierarchical assemblies remains a significant challenge and requires a profound understanding of molecular assembly interactions. In this study, we present a molecular‐level understanding of the hierarchical assembly of sequence‐defined peptoids into multidimensional functional materials, including twisted nanotube bundles serving as a highly efficient artificial light harvesting system. By employing synchrotron‐based powder X‐ray diffraction and analyzing single crystal structures of model compounds, we elucidated the molecular packing and mechanisms underlying the assembly of peptoids into multidimensional nanostructures. Our findings demonstrate that incorporating aromatic functional groups, such as tetraphenyl ethylene (TPE), at the termini of assembling peptoid sequences promotes the formation of twisted bundles of nanotubes and nanosheets, thus enabling the creation of a highly efficient artificial light harvesting system. This research exemplifies the potential of leveraging sequence‐defined synthetic polymers to translate microscopic molecular structures into macroscopic assemblies. It holds promise for the development of functional materials with precisely controlled hierarchical structures and designed functions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Submonomer synthesis of sequence defined peptoids with diverse side-chains.

Peptoids are a diverse family of sequence-defined oligomers of N-substituted glycine monomers, that can be readily accessed by the solid-phase submonomer synthesis method. Due to the versatility and efficiency of this chemistry, and the easy access to hundreds of potential monomers, there is an enormous potential sequence space that can be explored. This has enabled researchers from many different fields to custom-design peptoid sequences tailored to a wide variety of problems in biomedicine, nanoscience and polymer science. Here we provide detailed protocols for the synthesis of peptoids, using optimized protocols that can be performed by non-chemists. The submonomer method is fully compatible with Fmoc-peptide synthesis conditions, so the method is readily automated on existing automated peptide synthesizers using protocols provided here. Although the submonomer synthesis for peptoids is well established, there are special considerations required in order to access many of the most useful and desirable sidechains. Here we provide methods to include most of the amino-acid-like side chains, some of the most important non-natural monomer classes, as well as the creation of peptoid conjugates and peptide-peptoid hybrids.

Connolly, Michael D↗
DOE OSTI2020

Applicability of the Delplot method for the determination of catalytic reaction sequences: Hydrodeoxygenation of $\gamma$-valerolactone on Ni 2 P/MCM-41

Contact-time measurements based on sites are used to investigate the reaction sequence of the hydrodeoxygenation of γ-valerolactone (GVL) on Ni 2 P/MCM-41. Here the study is carried out at 300 °C and 0.5 MPa with site times of 0.29–6.1 s. A reaction network is proposed in which pentanoic acid and 2-methyltetrahydrofuran (2-MTHF) are formed in parallel initially and pentanal, butane and pentane are formed subsequently. A first-rank Delplot analysis (selectivity) gives a good fit, but higher-rank Delplots do not. The lack of applicability is explained by the magnitude of the rate constants, in which the rate constant for one of the later steps (the consumption of pentanal) is substantially larger than those for the preceding steps. For this reason, pentanal demonstrated the characteristics of a primary product and consequently affected the discrimination of higher rank products. A simulation of a model sequence with assumed rate constants confirms the reason why the Delplot method does not work for this reaction. Since the rate constants cannot be determined a priori to be of close magnitude, the Delplot method cannot be generally used to ascertain reaction sequences.

42 ENGINEERING↗
DOE OSTI2026

Hierarchical semi-Markov models with duration-aware dynamics for activity sequences

Residential electricity demand at granular scales is driven by what people do and for how long. Accurately forecasting this demand for applications like microgrid management and demand response therefore requires generative models for activities that can produce realistic daily activity sequences, capturing both the timing and duration of human behavior. This paper develops a generative model of human activity sequences using nationally representative time-use diaries at a 10-min resolution. We use this model to quantify which demographic factors are most critical for improving predictive performance. We propose a hierarchical semi-Markov framework that addresses two key modeling challenges. First, a time-inhomogeneous Markov router learns the patterns of “which activity comes next.” Second, a semi-Markov hazard component explicitly models activity durations, capturing “how long” activities realistically last. To ensure statistical stability when data are sparse, the model pools information across related demographic groups and time blocks. The entire framework is trained and evaluated using survey design weights to ensure our findings are representative of the U.S. population. On a held-out test set, we demonstrate that explicitly modeling durations with the hazard component provides a substantial and statistically significant improvement over purely Markovian models. Furthermore, our analysis reveals a clear hierarchy of demographic factors: Sex, Day-Type, and Household Size provide the largest predictive gains, while Region and Season, though important for energy calculations, contribute little to predicting the activity sequence itself. The result is an interpretable and robust generator of synthetic activity traces, providing a high-fidelity foundation for downstream energy systems modeling.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Data-driven prediction of geometry- and toolpath sequence-dependent intra-layer process conditions variations in laser powder bed fusion

Geometrical features and toolpath sequence are two important factors that cause process condition variations, such as variations in the meltpool temperature or meltpool size, that might lead to undesired material properties in the laser powder bed fusion (LPBF) process. Due to the high dynamics and complex physics of the LPBF process, it is difficult to predict variations in process conditions with simulations alone. Advances in measurement technology and computational technologies open up new possibilities for smart manufacturing. In this paper, a data-driven method to predict intra-layer variations in the processing conditions that source from the toolpath sequence and part geometry is presented. The approach is demonstrated using two-color on-axis pyrometer measurements. Three demonstration cases are presented in which it is demonstrated (1) how the trained predictive model can be used as a filter to ease the interpretation of process variations and discover patterns related to toolpath and part geometry, and (2) how to generate predictions that can be used for feedforward control, i.e., for adjusting laser power or scanning speed along the toolpath using a meltpool temperature prediction model generated based on on-axis measurements. Results show that the developed prediction model is able to meaningfully predict process variations resulted from toolpath sequence and geometry. Predictions are aligned with the results from the related work of others and for the case of 180° laser path turnarounds in our high-speed X-ray imaging experiments. In conclusion, the potential issues related to the current maturity status of the process and measuring equipment that could in practice affect the performance of the proposed solutions are also discussed.

42 ENGINEERING↗
DOE OSTI2024

Control over Conformational Landscapes of Polypeptoids by Monomer Sequence Patterning

The ability to program chain conformation and structure through control over the monomer sequence of synthetic polymers has broad implications for next-generation material design. While related problems of protein-folding and de novo design have generated accurate predictions of 3D folded chain structures, generalization to synthetic polymers remains intractable due to the requirement of large structural databases and the intrinsically disordered nature of polymer building blocks. In this work, polypeptoids, a class of peptidomimetic synthetic polymers, are utilized to build a general workflow for the study of relationships between monomer sequence and dynamic 3D chain structure in solution. Furthermore, this work demonstrates how control over the monomer sequence can alter the conformational landscape of synthetic polymers to deviate dramatically from classical chain statistics. Specifically, the distribution of end-to-end distances, as measured by double electron-electron resonance spectroscopy in dilute solvent, is systematically skewed towards shorter distances with an increasing number of hydrophobes and further refined by hydrophobe arrangement in amphiphilic polypeptoid chains.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Generation of Tunable Stochastic Sequences Using the Insulator–Metal Transition

Probabilistic computing is a paradigm in which data are not represented by stable bits, but rather by the probability of a metastable bit to be in a particular state. The development of this technology has been hindered by the availability of hardware capable of generating stochastic and tunable sequences of “1s” and “0s”. The options are currently limited to complex CMOS circuitry and, recently, magnetic tunnel junctions. Here, we demonstrate that metal–insulator transitions can also be used for this purpose. We use an electrical pump/probe protocol and take advantage of the stochastic relaxation dynamics in VO 2 to induce random metallization events. A simple latch circuit converts the metallization sequence into a random stream of 1s and 0s. The resetting pulse in between probes decorrelates successive events, providing a true stochastic digital sequence.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Sequence-selective dynamic covalent assembly of information-bearing oligomers

Relatively robust dynamic covalent interactions have been employed extensively to mediate molecular self-assembly reactions; however, these assembly processes often do not converge to a thermodynamic equilibrium, instead yielding mixtures of kinetically-trapped species. Here, we report a dynamic covalent self-assembly process that mitigates kinetic trapping such that multiple unique oligomers bearing covalently coreactive pendant groups are able to undergo simultaneous, sequence-selective hybridization with their complementary strands to afford biomimetic, in-registry molecular ladders with covalent rungs. Analogous to the thermal cycling commonly employed for nucleic acid melting and annealing, this is achieved by raising and lowering the concentration of a multi-role reagent to effect quantitative dissociation and subsequently catalyze covalent bond rearrangement, affording selective assembly of the oligomeric sequences. The hybridization specificity afforded by this process further enabled information encoded in oligomers to be retrieved through selective hybridization with complementary, mass-labeled sequences.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network

Using the Cap Analysis of Gene Expression (CAGE) technology, the FANTOM5 consortium provided one of the most comprehensive maps of transcription start sites (TSSs) in several species. Strikingly, ~72% of them could not be assigned to a specific gene and initiate at unconventional regions, outside promoters or enhancers. Here, we probe these unassigned TSSs and show that, in all species studied, a significant fraction of CAGE peaks initiate at microsatellites, also called short tandem repeats (STRs). To confirm this transcription, we develop Cap Trap RNA-seq, a technology which combines cap trapping and long read MinION sequencing. We train sequence-based deep learning models able to predict CAGE signal at STRs with high accuracy. These models unveil the importance of STR surrounding sequences not only to distinguish STR classes, but also to predict the level of transcription initiation. Importantly, genetic variants linked to human diseases are preferentially found at STRs with high transcription initiation level, supporting the biological and clinical relevance of transcription initiation at STRs. Together, our results extend the repertoire of non-coding transcription associated with DNA tandem repeats and complexify STR polymorphism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The influence of Holliday junction sequence and dynamics on DNA crystal self-assembly

The programmable synthesis of rationally engineered crystal architectures for the precise arrangement of molecular species is a foundational goal in nanotechnology, and DNA has become one of the most prominent molecules for the construction of these materials. In particular, branched DNA junctions have been used as the central building block for the assembly of 3D lattices. Here, crystallography is used to probe the effect of all 36 immobile Holliday junction sequences on self-assembling DNA crystals. Contrary to the established paradigm in the field, most junctions yield crystals, with some enhancing the resolution or resulting in unique crystal symmetries. Unexpectedly, even the sequence adjacent to the junction has a significant effect on the crystal assemblies. Six of the immobile junction sequences are completely resistant to crystallization and thus deemed “fatal,” and molecular dynamics simulations reveal that these junctions invariably lack two discrete ion binding sites that are pivotal for crystal formation. The structures and dynamics detailed here could be used to inform future designs of both crystals and DNA nanostructures more broadly, and have potential implications for the molecular engineering of applied nanoelectronics, nanophotonics, and catalysis within the crystalline context.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

The interplay of DNA repair context with target sequence predictably biases Cas9-generated mutations

Abstract Repair of double-stranded breaks generated by CRISPR/Cas9 is highly dependent on the flanking DNA sequence. To learn about interactions between DNA repair and target sequence, we measure frequencies of over 236,000 distinct Cas9-generated mutational outcomes at over 2800 synthetic target sequences in 18 DNA repair deficient mouse embryonic stem cells lines. We classify the outcomes in an unbiased way, finding a specialised role forPrkdc(DNA-PKcs protein) andPolmin creating 1 bp insertions matching the nucleotide on the protospacer-adjacent motif side of the break, a variable involvement ofNbnandPolqin the creation of different deletion outcomes, and uni-directional deletions dependent on both end-protection and end-resection. Using our dataset, we build predictive models of the mutagenic outcomes of Cas9 scission that outperform the current standards. This work improves our understanding of DNA repair gene function, and provides avenues for more precise modulation of Cas9-generated mutations.

Science & Technology - Other Topics↗
DOE OSTI2022

CRISPR–Cas9 bends and twists DNA to read its sequence

In bacterial defense and genome editing applications, the CRISPR-associated protein Cas9 searches millions of DNA base pairs to locate a 20-nucleotide, guide RNA-complementary target sequence that abuts a protospacer-adjacent motif (PAM). Target capture requires Cas9 to unwind DNA at candidate sequences using an unknown ATP-independent mechanism. Here we show that Cas9 sharply bends and undertwists DNA on PAM binding, thereby flipping DNA nucleotides out of the duplex and toward the guide RNA for sequence interrogation. Cryogenic-electron microscopy (cryo-EM) structures of Cas9–RNA–DNA complexes trapped at different states of the interrogation pathway, together with solution conformational probing, reveal that global protein rearrangement accompanies formation of an unstacked DNA hinge. Bend-induced base flipping explains how Cas9 ‘reads’ snippets of DNA to locate target sites within a vast excess of nontarget DNA, a process crucial to both bacterial antiviral immunity and genome editing. This mechanism establishes a physical solution to the problem of complementarity-guided DNA search and shows how interrogation speed and local DNA geometry may influence genome editing efficiency.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Sequence specificity analysis of the SETD2 protein lysine methyltransferase and discovery of a SETD2 super-substrate

SETD2 catalyzes methylation at lysine 36 of histone H3 and it has many disease connections. We investigated the substrate sequence specificity of SETD2 and identified nine additional peptide and one protein (FBN1) substrates. Our data showed that SETD2 strongly prefers amino acids different from those in the H3K36 sequence at several positions of its specificity profile. Based on this, we designed an optimized super-substrate containing four amino acid exchanges and show by quantitative methylation assays with SETD2 that the super-substrate peptide is methylated about 290-fold more efficiently than the H3K36 peptide. Protein methylation studies confirmed very strong SETD2 methylation of the super-substrate in vitro and in cells. We solved the structure of SETD2 with bound super-substrate peptide containing a target lysine to methionine mutation, which revealed better interactions involving three of the substituted residues. Our data illustrate that substrate sequence design can strongly increase the activity of protein lysine methyltransferases.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Decoding co-/post-transcriptional complexities of plant transcriptomes and epitranscriptome using next-generation sequencing technologies

Next-generation sequencing (NGS) technologies - Illumina RNA-seq, Pacific Biosciences isoform sequencing (PacBio Iso-seq), and Oxford Nanopore direct RNA sequencing (DRS) - have revealed the complexity of plant transcriptomes and their regulation at the co-/post-transcriptional level. Global analysis of mature mRNAs, transcripts from nuclear run-on assays, and nascent chromatin-bound mRNAs using short as well as full-length and single-molecule DRS reads have uncovered potential roles of different forms of RNA polymerase II during the transcription process, and the extent of co-transcriptional pre-mRNA splicing and polyadenylation. These tools have also allowed mapping of transcriptome-wide start sites in cap-containing RNAs, poly(A) site choice, poly(A) tail length, and RNA base modifications. The emerging theme from recent studies is that reprogramming of gene expression in response to developmental cues and stresses at the co-/post-transcriptional level likely plays a crucial role in eliciting appropriate responses for optimal growth and plant survival under adverse conditions. Although the mechanisms by which developmental cues and different stresses regulate co-/post-transcriptional splicing are largely unknown, a few recent studies indicate that the external cues target spliceosomal and splicing regulatory proteins to modulate alternative splicing. In this review, we provide an overview of recent discoveries on the dynamics and complexities of plant transcriptomes, mechanistic insights into splicing regulation, and discuss critical gaps in co-/post-transcriptional research that need to be addressed using diverse genomic and biochemical approaches.

Biochemistry & Molecular Biology↗
DOE OSTI2023

High-redshift quasars along the main sequence

The 4D Eigenvector 1 empirical formalism (4DE1) and its main sequence (MS) for quasars has emerged as a powerful tool for organising the diversity among quasar populations, as several key observational measures and physical parameters are systematically changing along it. Trends revealed by 4DE1 are very well established to explain all the diverse characteristics seen in low-redshift quasar samples. Nevertheless, the situation is far less clear when dealing with high-luminosity and high-redshift sources. Here, we aim to evaluate the behaviour of our sample of 22 quasars at high redshift (2.2 ≤ z ≤ 3.7) and high luminosity (47.39 ≤ L bol ≤ 48.36) in the context of the 4DE1. Our approach involves studying quasar physics through a spectroscopic exploration of UV and optical emission line diagnostics. We used new observations from the ISAAC instrument at ESO-VLT and primarily from the SDSS to cover the optical and the UV rest-frames, respectively. The emission lines were characterised both via a quantitative parametrisation of the line profiles and a decomposition of the emission line profiles using multicomponent fitting routines. We provide spectrophotometric properties and line profile measurements for Hβ+[O III]λλ4959,5007, as well as Si IVλ1397+O IV]λ1402, C IVλ1549+He IIλ1640, and the 1900 Å blend (including Al IIIλ1860, Si III]λ1892, and C III]λ1909). For six out of the 22 objects, a significantly blueshifted component on the Hβ profile is present. In 14 out of 22 cases, an Hβ outflowing component associated with [O III] is detected. The majority of [O III]λλ4959,5007 emission line profiles show blueshifted velocities higher than 250 km s -1 . We find extremely broad [O III]λλ4959,5007 emission that is comparable to the width of Hβ broad profile in some highly accreting quasars. The [O III]λλ4959,5007 and C IVλ1549 blueshifts show very high amplitudes and a high degree of correlation. The line widths and shifts are correlated for both [O III]λλ4959,5007 and C IVλ1549, suggesting that emission from outflowing gas is providing a substantial broadening effect to both lines. Otherwise, the links between C IVλ1549 centroid velocity at half intensity (c(1/2)), Eddington ratio (L/L Edd ), and bolometric luminosity are found to be in agreement with previous studies of high-luminosity quasars. Our analysis suggests that the behaviour of quasars of very high luminosity all along the main sequence is strongly affected by powerful outflows involving a broad range of spatial scales. The main sequence correlations remain valid at high redshift and high luminosity even if a systematic increase in line width is observed. Scaling laws based on UV Al IIIλ1860 and Hβ emission lines are equally reliable estimators of M BH .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Towards a self-consistent model of the convective core boundary in upper main sequence stars: I. 2.5D and 3D simulations

There is strong observational evidence that the convective cores of intermediate-mass and massive main sequence stars are substantially larger than those predicted by standard stellar-evolution models. However, it is unclear what physical processes cause this phenomenon or how to predict the extent and stratification of stellar convective boundary layers. Convective penetration is a thermal-timescale process that is likely to be particularly relevant during the slow evolution on the main sequence. We use our low-Mach-number S EVEN -L EAGUE H YDRO code to study this process in 2.5D and 3D geometries. Starting with a chemically homogeneous model of a 15 M ⊙ zero-age main sequence star, we construct a series of simulations with the luminosity increased and opacity decreased by the same factor, ranging from 10 3 to 10 6 . After reaching thermal equilibrium, all of our models show a clear penetration layer; its thickness becomes statistically constant in time and it is shown to converge upon grid refinement. The penetration layer becomes nearly adiabatic with a steep transition to a radiative stratification in simulations at the lower end of our luminosity range. This structure corresponds to the adiabatic ‘step overshoot’ model often employed in stellar-evolution calculations. The simulations with the highest and lowest luminosity differ by less than a factor of two in the penetration distance. The high computational cost of 3D simulations makes our current 3D data set rather sparse. Depending on how we extrapolate the 3D data to the actual luminosity of the initial stellar model, we obtain penetration distances ranging from 0.09 to 0.44 pressure scale heights, which is broadly compatible with observations.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

End-to-end learning of multiple sequence alignments with differentiable Smith–Waterman

Abstract Motivation Multiple sequence alignments (MSAs) of homologous sequences contain information on structural and functional constraints and their evolutionary histories. Despite their importance for many downstream tasks, such as structure prediction, MSA generation is often treated as a separate pre-processing step, without any guidance from the application it will be used for. Results Here, we implement a smooth and differentiable version of the Smith–Waterman pairwise alignment algorithm that enables jointly learning an MSA and a downstream machine learning system in an end-to-end fashion. To demonstrate its utility, we introduce SMURF (Smooth Markov Unaligned Random Field), a new method that jointly learns an alignment and the parameters of a Markov Random Field for unsupervised contact prediction. We find that SMURF learns MSAs that mildly improve contact prediction on a diverse set of protein and RNA families. As a proof of concept, we demonstrate that by connecting our differentiable alignment module to AlphaFold2 and maximizing predicted confidence, we can learn MSAs that improve structure predictions over the initial MSAs. Interestingly, the alignments that improve AlphaFold predictions are self-inconsistent and can be viewed as adversarial. This work highlights the potential of differentiable dynamic programming to improve neural network pipelines that rely on an alignment and the potential dangers of optimizing predictions of protein sequences with methods that are not fully understood. Availability and implementation Our code and examples are available at: https://github.com/spetti/SMURF. Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗