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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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A new FounDyn module in OpenFAST to consider foundation dynamics of monopile supported wind turbines using a site-specific soil reaction framework
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Large-scale multidirectional soil-foundation-structure interaction testing of renewable energy systems
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Assessing the foundation of the Trojan Horse Method
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Foundational insights into the mechanical and molecular evolution of porcine skin gelatin during gelation
Gelatin is a widely used material in biomedical fields, particularly in regenerative medicine and tissue engineering, due to its biocompatibility and versatile properties. While prior research has explored methods to enhance gelatin's mechanical strength and stability, fundamental studies on gelatin, specifically its curing process, mechanical stiffness, and chemical evolution during gelation, remain limited. This study uses ultrasonic testing and Fourier Transform Infrared Spectroscopy (FTIR) to examine gelatin's stiffness and molecular changes during gelation. Samples of 175 and 300 Porcine Skin Bloom Strength Gelatin at concentrations of 2% and 6% (w/v) were analyzed. Through transmission ultrasonic testing helped identify key transition points in gelation, with higher concentrations exhibiting delayed transitions. FTIR revealed that C-N bond formation peaks early while N-H bond deformation persists. A correlation emerged between sound speed and peak absorbance, suggesting that changes in molecular mobility may contribute to the observed sound speed behavior during periods of active bond formation. However, as gelation continues, fewer bonding components may be available, potentially decreasing molecular movement and contributing to the observed increase in sound speed. These findings provide insights into gelatin's mechanical and chemical evolution, offering a framework for improved control over its gelation kinetics. Swept-Frequency Acoustic Interferometry (SFAI) was performed at the end of the curing process to measure the sound speed, enabling the calculation of the bulk moduli of the gelatin samples. The combined use of ultrasonic and FTIR testing provides a non-destructive method for characterizing gelatin and other biomaterials. This approach advances understanding of gelatin curing behavior and supports the development of safer biomaterials with tailored mechanical properties for various applications such as tissue engineering and regenerative medicine.
A Photo-Labile Thioether Linkage to Phycoviolobilin Provides the Foundation for the Blue/Green Photocycles in DXCF-Cyanobacteriochromes
The phytochrome superfamily encompasses a diverse collection of photochromic photoreceptors in plants and microorganisms that employ a covalently linked bilin cradled in a cGMP-phosphodiesterase/adenylyl-cyclase/FhlA (GAF) domain to detect light. Whereas most interconvert between red- and far-red-light-absorbing states, cyanobacteria also express variants called cyanobacteriochromes (CBCRs) that modify bilin absorption to collectively perceive the entire visible spectrum. Here, we present two X-ray crystallographic structures of the GAF domain from the blue/green photochromic CBCR PixJ from Thermosynechococcus elongatus. Moreover, these structures confirm the hypothesis that CBCRs variably manipulate the chromophore π-conjugation system through isomerization and a second thioether linkage, in this case involving the bilin C10 carbon and Cys494 within a DXCF sequence characteristic of blue/green CBCRs. Biochemical studies support a mechanism for photoconversion whereby the second linkage ruptures on route to the green-light-absorbing state. All together, theTePixJ(GAF) models illustrate the remarkable structural and photochemical versatility among phytochromes and CBCRs in driving light perception.
The expanding field of plant virus ecology: Historical foundations, knowledge gaps, and research directions
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Coherence in Chemistry: Foundations and Frontiers
Coherence refers to correlations in waves. Because matter has a wave-particle nature, it is unsurprising that coherence has deep connections with the most contemporary issues in chemistry research (e.g., energy harvesting, femtosecond spectroscopy, molecular qubits and more). But what does the word "coherence" really mean in the context of molecules and other quantum systems? We provide a review of key concepts, definitions, and methodologies, surrounding coherence phenomena in chemistry, and we describe how the terms "coherence" and "quantum coherence" refer to many different phenomena in chemistry. Moreover, we show how these notions are related to the concept of an interference pattern. Coherence phenomena are indeed complex, and ambiguous definitions may spawn confusion. By describing the many definitions and contexts for coherence in the molecular sciences, we aim to enhance understanding and communication in this broad and active area of chemistry.
[(MeCN)Ni(CF 3 ) 3 ] - and [Ni(CF 3 ) 4 ] 2– : Foundations toward the Development of Trifluoromethylations at Unsupported Nickel
Nickel anions [(MeCN)Ni(CF 3 ) 3 ] - and [Ni(CF 3 ) 4 ] 2– were prepared by the formal addition of 3 and 4 equiv, respectively, of AgCF 3 to [(dme)NiBr 2 ] in the presence of the [PPh 4 ] + counterion. Detailed insights into the electronic properties of these new compounds were obtained through the use of density functional theory (DFT) calculations, spectroscopy-oriented configuration interaction (SORCI) calculations, X-ray absorption spectroscopy, and cyclic voltammetry. The data collectively show that trifluoromethyl complexes of nickel, even in the most common oxidation state of nickel(II), are highly covalent systems whereby a hole is distributed on the trifluoromethyl ligands, surprisingly rendering the metal to a physically more reduced state. In the cases of [(MeCN)Ni(CF 3 ) 3 ] - and [Ni(CF 3 ) 4 ] 2- , these complexes are better physically described as d 9 metal complexes. [(MeCN)Ni(CF 3 ) 3 ] - is electrophilic and reacts with other nucleophiles such as phenoxide to yield the unsupported [(PhO)Ni(CF 3 ) 3 ] 2– salt, revealing the broader potential of [(MeCN)Ni(CF 3 ) 3 ] - in the development of “ligandless” trifluoromethylations at nickel. Proof-in-principle experiments show that the reaction of [(MeCN)Ni(CF 3 ) 3 ] - with an aryl iodonium salt yields trifluoromethylated arene, presumably via a high-valent, unsupported, and formal organonickel(IV) intermediate. Evidence of the feasibility of such intermediates is provided with the structurally characterized [PPh 4 ] 2 [Ni(CF 3 ) 4 (SO 4 )], which was derived through the two-electron oxidation of [Ni(CF 3 ) 4 ] 2– .
Introduction to Electron Transfer: Theoretical Foundations and Pedagogical Examples
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Transferring a Molecular Foundation Model for Polymer Property Predictions
Transformer-based large language models have remarkable potential to accelerate design optimization for applications such as drug development and material discovery. Self-supervised pretraining of transformer models requires large-scale data sets, which are often sparsely populated in topical areas such as polymer science. Further, state-of-the-art approaches for polymers conduct data augmentation to generate additional samples but unavoidably incur extra computational costs. In contrast, large-scale open-source data sets are available for small molecules and provide a potential solution to data scarcity through transfer learning. In this work, we show that using transformers pretrained on small molecules and fine-tuned on polymer properties achieves comparable accuracy to those trained on augmented polymer data sets for a series of benchmark prediction tasks.
Multireference Equation-of-Motion Driven Similarity Renormalization Group: Theoretical Foundations and Applications to Ionized States
We present a formulation and implementation of an equation-of-motion (EOM) extension of the multireference driven similarity renormalization group (MR-DSRG) formalism for ionization potentials (IP-EOM-DSRG). The IP-EOM-DSRG formalism results in a Hermitian generalized eigenvalue problem, delivering accurate ionization potentials for strongly correlated systems. The EOM step scales as O(N 5 ) with the basis set size N, allowing for efficient calculation of spectroscopic properties, such as transition energies and intensities. The IP-EOM-DSRG formalism is combined with three truncation schemes of the parent MR-DSRG theory: an iterative nonperturbative method with up to two-body excitations [MR-LDSRG(2)] and second- and third-order perturbative approximations [DSRG-MRPT2/3]. We benchmark these variants by computing (1) the vertical valence ionization potentials of a series of small molecules at both equilibrium and stretched geometries; (2) the spectroscopic constants of several low-lying electronic states of the OH, CN, N 2 + , and CO + radicals; and (3) the binding curves of low-lying electronic states of the CN radical. A comparison with experimental data and theoretical results shows that all three IP-EOM-DSRG methods accurately reproduce the vertical ionization potentials and spectroscopic constants of these systems. Notably, the DSRG-MRPT3 and MR-LDSRG(2) versions outperform several state-of-the-art multireference methods of comparable or higher cost.
Proteomics Goes to Court: A Statistical Foundation for Forensic Toxin/Organism Identification Using Bottom-Up Proteomics
Bottom-up proteomics is increasingly being used to characterize unknown environmental, clinical, and forensic samples. Proteomics-based bacterial identification typically proceeds by tabulating peptide “hits” (i.e. confidently identified peptides) associated with the organisms in a database; those organisms with enough hits are declared present in the sample. This approach has proven successful in laboratory studies, however, important research gaps remain. First, the common-practice reliance on unique peptides for identification is susceptible to a phenomenon known as signal erosion. Second, no general guidelines are available for determining how many hits are needed to make a confident identification. These gaps inhibit the transition of this approach to real-world samples where conditions vary and large databases may be needed. We propose statistical criteria for organism identification that applies regardless of sample quality or data analysis pipeline. These criteria are straightforward, producing a p-value on the result. We test the criteria on LC-MS/MS datasets representing 919 different bacterial and toxin datasets acquired with multiple data collection platforms. Results reveal a >95% correct species-level identification rate, demonstrating the effectiveness and robustness of proteomics-based organism/toxin identification.
Data Sets Are Foundational to Research. Why Don’t We Cite Them?
The lack of clear references to and descriptions of data sets in published literature limits the usefulness of data, as well as the reproducibility and credibility of scientific findings.
Variational consistent histories as a hybrid algorithm for quantum foundations
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