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Five structural domains in chain A and partial charge distribution in RBD with same orientation as of chain A.
Polymers are attractive membrane materials owing to their mechanical robustness and relatively inexpensive fabrication. An important indicator of membrane performance are free volume elements (FVE): microporous void spaces created by the inefficient packing of bulky groups along the polymer chain. FVEs tend to degrade over time, as polymer chains reorganize irreversibly. While it is widely accepted that polymer flexibility has an impact on membrane transport properties, the molecular nature of this impact is still not well understood. By the establishment of a correlation between local chain dynamics and the distribution of free volume elements (FVEs), penetrant transport can be regulated more efficiently in amorphous polymer membranes. In this work, we implement all-atom molecular dynamics (MD) simulations to explore the relationship between chain dynamics and free volume in three polymers with different levels of backbone flexibility: polymethylpentene (PMP), polystyrene (PS), and HAB-6FDA thermally rearranged polymer (TRP). We construct these polymers at different temperatures and examine how temperature impacts the FVE distribution and segmental mobility. Our analysis shows that chain segments near FVEs have higher mobility compared with the atoms in the bulk; the extent of this difference increases with chain flexibility. Increasing the chain flexibility by increasing the temperature results in a broader FVE distribution. Rigid polymers such as TRP show the most robust FVE distribution and are not significantly affected by the temperature change. To capture penetrant diffusion through the polymer matrix, hydrogen is inserted and the diffusion is measured at different temperatures; hydrogen mobility is influenced by the FVE structure and overall mobility of polymer chains. Lastly, at low temperatures, hydrogen mobility is influenced by void distribution, while at high temperatures, polymer dynamics dictate hydrogen transport.
Cyclic species in the interstellar medium have been searched for almost since the first detection of interstellar polyatomic molecules. Eleven different C3H2 rotational transitions were detected; 9 of which were studied in TMC-1, a nearby dark dust cloud, are shown. The 1 sub 10 yields 1 sub 01 and 2 sub 20 yields 2 sub 11 transitions were observed with the 43 m NRAO telescope, while the remaining transitions were detected with the 14 m antenna of the Five College Radio Observatory (FCRAO). The lines detected in TMC-1 have energies above the ground state ranging from 0.9 to 17.1 K and consist of both ortho and para species. Limited maps were made along the ridge for several of the transitions. The HC3N J = 2 yields 1 transition were mapped simultaneously with the C3H2 1 sub 10 yields 1 sub 01 line and therefore can compare the distribution of this ring with a carbon chain in TMC-1. C3H2 is distributed along a narrow ridge with a SE - NW extension which is slightly more extended than the HC2N J = 2 yields 1. Gaussian fits gives a FWHP extension of 8'5 for C3H2 while HC3N has a FWHP of 7'. The data show variations of the two velocity components along the ridge as a function of transition. Most of the transitions show a peak at the position of strongest HC3N emission while the 2 sub 21 yields 2 sub 10 transition shows a peak at the NH3 position.
The cost-effective production of sustainable aviation fuels (SAF) remains a major challenge within the energy sector. One approach to address this is the fermentation of biomass feedstocks into oxygenates followed by catalytic conversion to alkenes or other oligomerization precursors. 2,3-Butanediol (BDO) is a promising fermentation product due to its four-carbon nature, its decreased microorganism toxicity and associated higher maximum fermentation titers relative to other alcohols and oxygenates, and its capacity to be readily converted into butene isomers and longer chain alkenes. BDO conversion is currently constrained by separation challenges for BDO isolation due to its high boiling point and hydrophilicity. Here, this work expands upon previous BDO reactive separation via dioxolane formation over a solid acid catalyst by investigating the conversion of dioxolanes into alkene mixtures. Dioxolanes were formed from a range of aldehydes and subsequently converted over a Cu/ZSM-5 catalyst (448–523 K) via an ether cleavage, hydrogenation, and dehydration reaction network to form alkene-rich product mixtures (96% C 3+ alkene yield, 523 K). This selectivity is greater than that of direct BDO conversion to alkenes over an identical catalyst (89%, 523 K). C 3+ alkene selectivity is maximized between 498 and 523 K at complete dioxolane conversion without significant alkene hydrogenation to alkanes. The alkene product distributions can be tailored via both aldehyde selection during dioxolane formation and the dioxolane conversion reaction temperature. Alkene mixtures from dioxolane conversion predominantly reflect the carbon chain length and stereochemistry of BDO and the initial aldehyde at or below 498 K, yet higher reaction temperatures yield alkene mixtures of similar carbon chain distributions, regardless of initial aldehyde selection. Deactivation of the Cu/ZSM-5 catalyst is observed for multiple steps of the overall reaction network but can be minimized by facilitating the complete dioxolane-to-alkene reaction network at temperatures of at least 498 K.
K-shell x-ray emission spectroscopy is a standard tool used to diagnose the plasma conditions created in high-energy-density physics experiments. In the simplest approach, the emissivity-weighted average temperature of the plasma can be extracted by fitting an emission spectrum to a single temperature condition. It is known, however, that a range of plasma conditions can contribute to the measured spectra due to a combination of the evolution of the sample and spatial gradients. In this work, we define a parameterized model of the temperature distribution and use Markov Chain Monte Carlo sampling of the input parameters, yielding uncertainties in the fit parameters to assess the uniqueness of the inferred temperature distribution. Here we present the analysis of time-integrated S and Fe x-ray spectroscopic data from the Orion laser facility and demonstrate that while fitting each spectral region to a single temperature yields two different temperatures, both spectra can be fit simultaneously with a single temperature distribution. We find that fitting both spectral regions together requires a maximum temperature of $1310^{+90}_{-70}$ eV with significant contributions from temperatures down to 200 eV.
We present an analysis of parton distribution functions (PDFs) of the proton using Markov chain Monte Carlo (MCMC) methods. The MCMC approach naturally implements Bayes’ theorem and, thus, provides a means to directly sample the underlying probability distribution—in this case, the probability distribution of the PDF parameters. This allows for a straightforward propagation of the resulting uncertainties into any PDF-dependent observable, preserving their simple probabilistic interpretation. In our analysis we include a broad set of deep inelastic scattering data from HERA, BCDMS and NMC experiments along with the Drell-Yan, 𝑊 and 𝑍 boson data from LHC and Tevatron experiments, which combined with theoretical calculations at next-to-next-to-leading order in QCD allow for realistic determination of PDFs. The main focus of this analysis is to explore alternative methods for PDF uncertainty estimation that are more firmly grounded in statistical principles. We show that the flexibility of the Bayes framework, allowing one, e.g., to account for non-Gaussianity or inconsistencies of datasets, is crucial to extract realistic uncertainties when such assumptions are not fulfilled. We also demonstrate that MCMC allows one to determine the Δ𝜒 2 value corresponding to a given confidence level in the sample, which can, in turn, be used as a statistically well-founded tolerance criterion used in the Hessian method, thus addressing one of its main long-standing drawbacks.
The organic compositions of carbonaceous chondrite meteorites have been extensively studied; however, there have been fewer reports of other meteorite classes, and almost none from iron meteorites, which contain much less carbon than carbonaceous chondrites but make up ~4% of observed meteorite falls. Here, we report the bulk amino acid content of three iron meteorites (Campo del Cielo, IAB; Canyon Diablo, IAB; and Cape York, IIIAB) and both the metal and silicate portions of a pallasite (Imilac). We developed a novel method to prepare the samples for analysis, followed by hot water extraction and analysis via liquid chromatography‐mass spectrometry. Free amino acid abundances ranging from 301 to 1216 pmol/g were observed in the meteorites, with the highest abundance in the silicate portion of the pallasite. Although some of the amino acid content could be attributed to terrestrial contamination, evidence suggests that some compounds are indigenous. A suite of C(5) amino acids was observed with a distinct distribution favoring a straight chain (n‐pentanoic acid) structure; this straight chain dominance is suggestive of that observed in thermally altered stony meteorites. Amino acids were also observed in terrestrial iron granules that were milled and analyzed in the same way as the meteorites, although the distribution of detected amino acids was different. It is possible that similar formation mechanisms existed in both the meteorites and the terrestrial iron, or that observed amino acids resulted from reactions of precursors during sample preparation. This work suggests that iron meteorites should not be overlooked for contributions of amino acids and likely other soluble organic molecules to the early Earth. Future studies of iron–nickel meteorites and asteroids, such as Psyche, may provide further insights into their potential organic inventory.
Comparison of monte carlo, percus-yevick integral equations and convolution-hypernetted-chain methods for computing radial distribution functions of gaussian model
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.
This paper examines hydrogen leakage and efficiency across the supply chain for liquid, gaseous, and mixed hydrogen systems. These factors are crucial for assessing hydrogen's role in mitigating emissions and facilitating a clean energy transition. Drawing on a comprehensive review of existing literature and model-based analysis, the study compiles leakage rates and efficiency metrics at each stage of the supply chain: production, storage, transmission, distribution, and end-use. These data inform system scenarios that estimate the impact of leakage on overall performance and climate benefits. The analysis also identifies persistent data gaps, particularly for liquid and mixed system configurations, and outlines priorities for future research. A comparison of hydrogen system types shows that gaseous pathways generally achieve the highest efficiencies (28 %–39 %) and the lowest leakage rates (∼4.5 %) across the supply chain. Liquid hydrogen systems, while favorable for long-distance and high-volume transport due to their higher energy density, exhibit lower efficiency (∼28 %) and a greater leakage potential (∼12 %). Mixed systems, which combine gaseous and liquid elements (e.g., pipeline transmission followed by liquefaction and truck distribution), show compounded energy losses and moderate-to-high leakage rates (6.8 %–9.4 %), highlighting trade-offs associated with added system complexity. The study highlights opportunities for technological advancements, including optimizing liquefaction, enhancing insulation for storage and transportation, and refining refueling equipment. These improvements are crucial for maximizing the climate benefits of hydrogen. The results offer actionable insights for researchers, industry, and policymakers working to develop low-leakage, high-efficiency hydrogen infrastructure.
Gravitational wave detectors in the mHz band (such as the Laser Interferometer Space Antenna, or LISA) will observe thousands of compact binaries in the galaxy which can be used to better understand the structure of the Milky Way. To test the effectiveness of LISA to measure the distribution of the galaxy, we simulated the Close White Dwarf Binary (CWDB) gravitational wave sky using different models for the Milky Way. To do so, we have developed a galaxy density distribution modeling code based on the Markov Chain Monte Carlo method. The code uses different distributions to construct realizations of the galaxy. We then use the Fisher Information Matrix to estimate the variance and covariance of the recovered parameters for each detected CWDB. This is the first step toward characterizing the capabilities of space-based gravitational wave detectors to constrain models for galactic structure, such as the size and orientation of the bar in the center of the Milky Way
Plants produce phylogenetically and spatially restricted, as well as structurally diverse specialized metabolites via multistep metabolic pathways. Hallmarks of specialized metabolic evolution include enzymatic promiscuity and recruitment of primary metabolic enzymes and examples of genomic clustering of pathway genes. Solanaceae glandular trichomes produce defensive acylsugars, with sidechains that vary in length across the family. We describe a tomato gene cluster on chromosome 7 involved in medium chain acylsugar accumulation due to trichome specific acyl-CoA synthetase and enoyl-CoA hydratase genes. This cluster co-localizes with a tomato steroidal alkaloid gene cluster and is syntenic to a chromosome 12 region containing another acylsugar pathway gene. We reconstructed the evolutionary events leading to this gene cluster and found that its phylogenetic distribution correlates with medium chain acylsugar accumulation across the Solanaceae. This work reveals insights into the dynamics behind gene cluster evolution and cell-type specific metabolite diversity.
This work presents a computationally-ecient approach for damage determination that quanti es uncertainty in the provided diagnosis. Given strain sensor data that are polluted with measurement errors, Bayesian inference is used to estimate the location, size, and orientation of damage. This approach uses Bayes' Theorem to combine any prior knowledge an analyst may have about the nature of the damage with information provided implicitly by the strain sensor data to form a posterior probability distribution over possible damage states. The unknown damage parameters are then estimated based on samples drawn numerically from this distribution using a Markov Chain Monte Carlo (MCMC) sampling algorithm. Several modi cations are made to the traditional Bayesian inference approach to provide signi cant computational speedup. First, an ecient surrogate model is constructed using sparse grid interpolation to replace a costly nite element model that must otherwise be evaluated for each sample drawn with MCMC. Next, the standard Bayesian posterior distribution is modi ed using a weighted likelihood formulation, which is shown to improve the convergence of the sampling process. Finally, a robust MCMC algorithm, Delayed Rejection Adaptive Metropolis (DRAM), is adopted to sample the probability distribution more eciently. Numerical examples demonstrate that the proposed framework e ectively provides damage estimates with uncertainty quanti cation and can yield orders of magnitude speedup over standard Bayesian approaches.
Transient binding between associating macromolecules can cause qualitative changes to chain dynamics, including modes of conformational relaxation and diffusion, through tethering effects imparted by long-range connectivity. Here, in this work, the role of binding on short-time segmental dynamics in associative polymer gels is investigated by neutron spin-echo (NSE) measurements on a class of model artificial coiled-coil proteins with a systematically varied architecture, probing timescales of 0.1–130 ns, and length scales close to the molecular radius of gyration. The results illustrate effects of transient cross-linking on chain dynamics on different timescales, manifested in changes in segmental relaxation behavior with variations in strand length, chain concentration, and sticker distribution (endblock- vs midblock-functionalized). In all gels, a short-time cooperative diffusion mode is seen over all wave vectors, analogous to a semidilute solution, with no transitions seen at any known structural length scale. However, the diffusion coefficients are found to decrease with increasing junction density across all gels, with the strand length and number of stickers per chain in each gel appearing to play a relatively minor role. The slowing of cooperative diffusion with junction density contrasts with classical predictions of a greater restoring force for fluctuation dissipation due to the increased elasticity, suggesting additional effects of the coiled-coil junctions such as an enhancement in local viscosity that slows dynamics. Notably, the relaxation rates for all gels can be rescaled by the interjunction spacing inferred from small-angle neutron scattering, where they collapse onto a master curve suggestive of self-similar dynamics even in networks with different strand lengths and chain architectures. On long timescales (but shorter than the junction exchange time), a slowing of network relaxation is observed, resulting in a nondecaying plateau in the spin-echo amplitude attributed to a freezing of chain dynamics due to tethering. A characteristic length scale corresponding to the extent of dynamic fluctuations is estimated for each gel, which appears to be smaller than the interjunction spacing but similar to the correlation blob size of the overlapping strands. The results indicate an important role of transient binding on molecular-scale dynamics in associative polymer gels, even on timescales shorter than the junction exchange time, in addition to its effects on long-range self-diffusion previously observed.
This report documents a statistical method for the "real-time" characterization of partially observed epidemics. Observations consist of daily counts of symptomatic patients, diagnosed with the disease. Characterization, in this context, refers to estimation of epidemiological parameters that can be used to provide short-term forecasts of the ongoing epidemic, as well as to provide gross information for the time-dependent infection rate. The characterization problem is formulated as a Bayesian inverse problem, and is predicated on a model for the distribution of the incubation period. The model parameters are estimated as distributions using a Markov Chain Monte Carlo (MCMC) method, thus quantifying the uncertainty in the estimates. The method is applied to the COVID-19 pandemic of 2020, using data at the country, provincial (e.g., states) and regional (e.g. county) levels. The epidemiological model includes a stochastic component due to uncertainties in the incubation period. This model-form uncertainty is accommodated by a pseudo-marginal Metropolis-Hastings MCMC sampler, which produces posterior distributions that reflect this uncertainty. We approximate the discrepancy between the data and the epidemiological model using Gaussian and negative binomial error models; the latter was motivated by the over-dispersed count data. For small daily counts we find the performance of the calibrated models to be similar for the two error models. For large daily counts the negative-binomial approximation is numerically unstable unlike the Gaussian error model. Application of the model at the country level (for the United States, Germany, Italy, etc.) generally provided accurate forecasts, as the data consisted of large counts which suppressed the day-to-day variations in the observations. Further, the bulk of the data is sourced over the duration before the relaxation of the curbs on population mixing, and is not confounded by any discernible country-wide second wave of infections. At the state-level, where reporting was poor or which evinced few infections (e.g., New Mexico), the variance in the data posed some, though not insurmountable, difficulties, and forecasts were able to capture the data with large uncertainty bounds. The method was found to be sufficiently sensitive to discern the flattening of the infection and epidemic curve due to shelter-in-place orders after around 90% quantile for the incubation distribution (about 10 days for COVID-19). The proposed model was also used at a regional level to compare the forecasts for the central and north-west regions of New Mexico. Modeling the data for these regions illustrated different disease spread dynamics captured by the model. While in the central region the daily counts peaked in the late April, in the north-west region the ramp-up continued for approximately three more weeks.
The β-oxidation pathway, normally involved in the catabolism of fatty acids, can be functionally made to act as a fermentative, iterative, elongation pathway when driven by the activity of a trans-enoyl-CoA reductase (TER). Here, the terminal acyl-CoA reduction to alcohol can occur on substrates with varied chain lengths, leading to a broad distribution of fermentation products in vivo. Tight control of the average chain length and product profile is desirable, as the chain length greatly influences the molecular properties and the commercial value. Lacking a termination enzyme with a narrow chain length preference, we sought alternative factors that could influence the product profile and pathway flux in the iterative pathway. In this study, we reconstituted the reversed β-oxidation (R-βox) pathway in vitro with a purified tri-functional complex (FadBA) responsible for the thiolase, enoyl-CoA hydratase, and hydroxyacyl-CoA dehydrogenase activities, a TER, and an acyl-CoA reductase. Using this system, we determined the rate-limiting step of the elongation cycle and demonstrated that by controlling the ratio of these three enzymes and the ratio of NADH and NADPH, we can influence the average chain length of the alcohol product profile.
Bottlebrush random copolymers (BRCPs), consisting of a random distribution of two homopolymer chains along a backbone, can segregate to the interface between two immiscible homopolymers. BRCPs undergo a reconfiguration, where each block segregates to one of the homopolymer phases, adopting a Janus-type structure, reducing the interfacial tension and promoting adhesion between the two homopolymers, thereby serving as a Janus bottlebrush copolymer (JBCP) compatibilizer. Here, we synthesized a series of JBCPs by copolymerizing deuterated or hydrogenated polystyrene (DPS/PS) and poly(tert-butyl acrylate) (PtBA) macromonomers using ruthenium benzylidene-initiated ring-opening metathesis polymerization (ROMP). Subsequent acid-catalyzed hydrolysis converted the PtBA brushes to poly(acrylic acid) (PAA). The JBCPs were then placed at the interface between DPS/PS homopolymers and poly(2-vinyl pyridine) (P2VP) homopolymers, where the degree of polymerization of the backbone (N BB ) and the grafting density (GD) of the JBCPs were varied. Neutron reflectivity (NR) was used to determine the interfacial width and segmental density distributions (including PS homopolymer, PS block, PAA block and P2VP homopolymer) across the polymer-polymer interface. Our findings indicate that the star-like JBCP with N BB = 6 produces the largest interfacial broadening. Increasing N BB to 100 (rod-like shape) and 250 (worm-like shape) reduced the interfacial broadening due to a decrease in the interactions between blocks and homopolymers by stretching of blocks. Decreasing the GD from 100% to 80% at N BB = 100 caused an increase the interfacial width, yet further decreasing the GD to 50% and 20% reduced the interfacial width, as 80% of GD may efficiently increase the flexibility of blocks and promote interactions between homopolymers, while maintaining relatively high number of blocks attached to one molecule. The interfacial conformation of JBCPs was further translated into compatibilization efficiency. Thin film morphology studies showed that only the lower N BB values (N BB = 6 and N BB = 24) and the 80% GD of N BB = 100 had bicontinuous morphologies, due to a sufficient binding energy that arrested phase separation, supported by mechanical testing using asymmetric double cantilever beam (ADCB) tests. These provide fundamental insights into the assembly behavior of JBCPs compatibilizers at homopolymer interfaces, opening strategies for the design of new BCP compatibilizers.
Application packaging and distribution are the final steps for delivering software to end-users; both are frequently neglected when creating scientific software. Commercial businesses rely on electronic distribution systems that have rendered disk drives obsolete. Still, national laboratories continue to rely heavily on removable media to distribute and limit access to controlled applications. With increasing concerns of unauthorized copying of sensitive applications, a modern distribution system that utilizes cryptographically secure communication and authentication protocols has been developed. This new distribution system will secure the chain of custody for nuclear software while simultaneously simplifying access to these tools. This report summarizes four primary advancements made toward the secure distribution of Nuclear Energy Advanced Modeling and Simulation (NEAMS)-developed, Multiphysics Object Oriented Simulation Environment (MOOSE)-based applications: application installation, package distribution, automated package building, and distribution of documentation. NEAMS is currently developing more than ten separate applications based on the open-source MOOSE Framework. Distribution of these applications has primarily been accomplished by distributing source code, with end-users compiling the applications themselves. This work created a mechanism where MOOSE applications can be installed in a similar way to any other software. This allows both administrators and end-users simplified access to runnable executables. With this new installation capability, it was then possible to rethink distribution. A new, secure capability for delivering MOOSE-based applications over the internet has been created. This system requires unique cryptographic tokens for authentication, greatly securing the custody chain for software. Once granted access, installation of any NEAMS code can be accomplished with these terminal commands: "conda install ncrc" "ncrc install ncrc-bison." After these two commands (and authenticating) the BISON application will be securely down- loaded from Idaho National Laboratory (INL)’s servers, installed, and ready to use. To enable this new distribution capability to be successful, the open-source Continuous Integration, Verification, Enhancement, and Testing (CIVET) Continuous Integration (CI) capability was augmented to add Continuous Delivery (CD). CD enables the automated building and packaging of MOOSE-based applications as they are modified by development teams, ensuring that our customers can obtain up-to-date versions of the software at any time. The need for instruction on how to use these applications was addressed through modifications to the MOOSE documentation system. The MooseDocs capability, which enables robust documentation of MOOSE-based applications, has been extended to allow both for the installation of documentation and the packaging of documentation with installed applications. Together, these enhancements form the core of a new, secure distribution mechanism for nuclear simulation tools. In concert with the Nuclear Computational Resource Center (NCRC), NEAMS- developed applications will now be straightforward to obtain securely.