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These files contain data from soil and root samples use in this publication. The R script uses this data to perform the statistical analysis used in the publication.
Distribution system state estimation requires reliable aggregation of the measured data. However, the large volume of the measured data imposes a significant stress on the underlying communication infrastructure. With the challenges associated with measurement availability, current distribution systems are typically unobservable. To cope with the unobservability issue, compressive sensing theory allows us to recover system state information from a small number of measurements provided the states of the distribution system exhibit sparsity. In this paper, we evaluate the robustness of an updated Kalman filtered modified compressive sensing (KF-ModCS) technique that dynamically estimates the grid states using a small fraction of measured data. In practice, measurements used for sparsity based state estimation may also be intermittent due to communication network induced losses. Further, to understand the effect of packet losses on KF-ModCS, we provide an upper bound for the expected variances of the state estimation error for a given rate of information loss. This upper bound is further improved if the support set of the sparse signal that characterizes the state dynamics does not change over time and/or the reduced model is observable. Simulations based on two practical data sets collected from actual customers in a distribution grid validate the theoretical results.
Supported tungsten oxides are widely used in a variety of catalytic reactions. Depending on the support, the cluster size, oxidation state, reducibility and speciation of the tungsten oxides can widely differ. When promoted with a platinum group metal, the resulting spillover of hydrogen may facilitate the reduction of supported tungsten oxide species, depending on the support. High resolution scanning transmission electron microscopy imaging showed nanometer scale WO x clusters were synthesized on SiO 2 whereas highly dispersed species were formed on TiO 2 . Results from H 2 -temperature-programmed reduction showed the presence of Pd lowered the initial reduction temperature of SiO 2 -supported WO x species but interestingly did not affect that of TiO 2 -supported WO x . X-ray photoelectron and absorption spectroscopies showed the W atoms in SiO 2 -supported WO x species reduce from a +6 oxidation state to primarily +5 after thermal treatment in 5% H 2 , while the fraction of W in the +5 oxidation state was relatively unaffected by reduction treatment of TiO 2 -supported WO x . The unusual behavior of TiO 2 -supported WO x was explained by quantum chemical calculations that reveal the lack of change in the oxidation state of W is attributed to charge delocalization on the surface atoms of the titania support, which does not occur on silica. Moreover, modeling results at <600 K in the presence of H 2 suggest the formation of Brønsted acid sites, and the absence of Lewis acid sites, on larger aggregates of WO x on silica and all cluster sizes on titania. These results provide experimental and theoretical insights into the nature of supported tungsten oxide clusters under conditions relevant to various catalytic reactions.
This dataset consist of annual (2020-2100) county-level population projections for the United States (U.S.) for Shared Socioeconomic Pathways (SSPs) 3 and 5. The original decadal state-level data is used as an input to the gridded population data model to downscale the state-level projections for each SSP to a 1 km grid. The 1 km data is then aggregated to the county-level using the 2020 TIGER/Line county shapefiles from the U.S. Census Bureau. The two data files are shared as .csv files with the following structure: Rows = Data for individual counties which are identified using their Federal Information Processing Standard (FIPS) code. The FIPS code is stored in the first column. Columns = Each year from 2020-2100 is an individual column. For easier reference, the state name associated with each row is stored in the last column. Values in each cell are the downscaled estimated population for that county-year combination. Values are fractional due to the weighting scheme used in the downscaling. The paper and model source code cited in the "Related Works" section describes the initial source of the population projections.
Associative polymers with precisely arranged stickers offer opportunities to program material properties with molecular precision. Yet, it remains unclear how the placement and fraction of stickers dictate structure, dynamics, and macroscopic properties. By developing a model unentangled polymer system with hydrogen-bonding stickers, we show that randomly distributed stickers neither form clusters nor change flow properties, whereas stickers placed at chain ends drive nanocluster formation even at low concentrations. Adding more end stickers produces a rubbery plateau spanning eight decades in frequency with two distinct relaxation timescales, in contrast to the single plateau predicted by the classic sticky Rouse model. These results demonstrate that sticker distribution dictates whether associative polymers undergo nanocluster formation or microphase separation, while substantial alterations in dynamics and viscoelasticity require both sticker aggregation and thermomechanical stability of associated domains. Our findings resolve a longstanding debate on associative polymer dynamics and provide molecular design rules for programmable soft materials.
Hierarchical assembly of building blocks via competing, orthogonal interactions is a hallmark of many of nature’s composite materials that do not require highly specific ligand-receptor interactions. To mimic this assembly mechanism requires the development of building blocks capable of tunable interactions. In the present work, we explored the interplay between repulsive (steric and electrostatic) and attractive hydrophobic forces. The designed building blocks allow hydrophobic forces to effectively act at controlled, large distances, to create and tune the assembly of membrane-based building blocks under dilute conditions and affect their interactions with cellular membranes via physical cross-bridges. Specifically, we employed double-end-anchored poly(ethylene glycol)s (DEA-PEGs)—hydrophilic PEG tethers with hydrophobic tails on both ends. Using differential-interference-contrast optical microscopy, synchrotron small angle X-ray scattering (SAXS), and cryogenic electron microscopy, we investigated the ability of DEA-PEGs to mediate assembly in the dilute regime on multiple length scales and on practical time scales. The PEG length, anchor hydrophobicity, and molar fraction of DEA-PEG molecules within a membrane strongly affect the assembly properties. Additional tuning of the intermembrane interactions can be achieved by adding repulsive interactions via PEG-lipids (steric) or cationic lipids to the DEA-PEG-mediated attractions. Further, while the optical and electron microscope imaging methods provided qualitative evidence of the ability of DEA-PEGs to assemble liposomes, the SAXS measurements and quantitative line-shape analysis in dilute preparations demonstrated that the ensemble average of loosely organized liposomal assemblies maintains DEA-PEG concentration-dependent tethering on defined nanometer length scales. For cationic liposome–DNA nanoparticles (CL–DNA NPs), aggregation induced by DEA-PEGs decreased internalization of NPs by cells, but tuning the DEA-PEG-induced attractions by adding repulsive steric interactions via PEG-lipids limited aggregation and increased NP uptake. However, confocal microscopy imaging together with colocalization studies with Rab11 and LysoTracker as markers of intracellular pathways showed that modifying CL–DNA NPs with DEA-PEGs alters their interactions with the plasma and endosomal membranes.
Abstract Earthquake triggering by seismic waves has been recognized as a phenomenon for nearly 30 years. However, our ability to study dynamic triggering has been limited by our ability to capture the triggering stresses accurately and record the resultant earthquakes. Here we use full waveforms from a dense seismic network and a modern, high‐resolution seismic catalog to measure triggering in Southern California from 2008 to 2017 based on interevent time ratios. We find that the fractional seismicity rate change, which we term triggering intensity or triggerability, as a function of peak strain change for the period of ∼20 s due to distant earthquakes is monotonically increasing and compatible with earlier measurements made with a disjoint data set from 1984 to 2008. A triggering strain of 1 microstrain is equivalent to the local productivity generated by an M 1.8 earthquakes. This result implies that a prediction of seismicity rate changes can be made based on recorded ground shaking using the same formalism as currently used for aftershock prediction. For a teleseismic event, this small level of triggering occurs throughout the region and thus aggregates to a regional effect. We find that the triggering rate decays after the triggerer follows an Omori‐Utsu law, but at a much slower rate than a typical aftershock sequence. The slow decay rate suggests that an ancillary process such as creep or fluid flow must be part of dynamic triggering. The prevalence of triggering in areas of creep or fluid involvement reinforces this inference. A triggering cascade of secondary earthquakes is insufficient to explain the data.
Proteomic investigations of Alzheimer’s and Parkinson’s disease have provided valuable insights into neurodegenerative disorders. Thus far, these investigations have largely been restricted to bottom-up approaches, hindering the degree to which one can characterize a protein’s “intact” state. Top-down proteomics (TDP) overcomes this limitation, however it is typically limited to observing only the most abundant proteoforms and of a relatively small size. Therefore, offline fractionation techniques are commonly used to reduce sample complexity, limiting throughput. A higher throughput alternative is online fractionation, such as gas phase high-field asymmetric waveform ion mobility spectrometry (FAIMS). Using a high complexity sample derived from Alzheimer’s disease brain tissue, we describe how the addition of FAIMS to TDP can robustly improve the depth of proteome coverage. For example, implementation of FAIMS at -50 compensation voltage (CV) more than doubled the mean number of non-redundant proteoforms observed (1,833 ± 17, n = 3), compared to without (754 ± 35 proteoforms). We also found FAIMS can influence the transmission of proteoforms and their charge envelopes based on their size. Importantly, FAIMS enabled the identification of intact amyloid beta (Aß) proteoforms, including the aggregation-prone Aß1-42 variant which is strongly linked to Alzheimer’s disease.
Nanocomposites integrate functional nanofillers into viscoelastic matrices for electronics, lightweight structural materials, and tissue engineering. Herein, the effect of methacrylate-functionalized (MA-SiO 2 ) and vinyl-functionalized (V-SiO 2 ) silica nanoparticles on the thermal, mechanical, physical, and morphological characteristics of poly(ethylene glycol) (PEG) nanocomposites was investigated. The gel fraction of V-SiO 2 composites decreases upon addition of 3.8 wt% but increases with further addition (>7.4 wt%) until it reaches a plateau at 10.7 wt%. The MA-SiO 2 induced no significant changes in gel fraction and both V-SiO 2 and MA-SiO 2 nanoparticles had a negligible impact on the nanocomposite glass transition temperature and water absorption. The Young's modulus and ultimate compressive stress increased with increasing nanoparticle concentration for both nanoparticles. Due to the higher crosslink density, MA-SiO 2 composites reached a maximum mechanical stress at a concentration of 7.4 wt%, while V-SiO 2 composites reached a maximum at a concentration of 10.7 wt%. Scanning electron microscopy, transmission electron microscopy, and small-angle X-ray scattering revealed a bimodal size distribution for V-SiO 2 and a monomodal size distribution for MA-SiO 2 . Although aggregates were observed for both nanoparticle surface treatments, V-SiO 2 dispersion was poor while MA-SiO 2 were generally well-dispersed. Finally, these findings lay the framework for silica nanofillers in PEG-based nanocomposites for advanced manufacturing applications.
In this study, the amphiphilic salt lithium trifluoromethanesulfonylimide octadecane (C18LiTFSI) was used as a basis to investigate the effects of anion density and cation coordination sites within blended electrolytes with strong ionic aggregation. C18LiTFSI was previously reported as a single-component, ion-condensed electrolyte with a wide layered liquid crystalline phase regime. Three additive molecules with varyingly sized polar sulfonyl groups attached to an octodecane-tail were synthesized and mixed with C18LiTFSI. The thermal properties, morphology, and ionic conductivity of the blended electrolytes were characterized. It was found that the blended electrolytes exhibited layered liquid crystalline morphology over a narrower temperature range than the pure salt, and the ionic conductivity of the blended liquid crystalline electrolytes were generally lower than that of the pure salt. Surprising, the additives were found to have the greatest effect on the bulk ionic conductivity of the semicrystalline phase of the electrolytes. Addition of minor fractions of methylsulfonyloctadecane to C18LiTFSI resulted in increases in conductivity of over two orders of magnitude at room temperature, while addition of ethylsulfonyloctadecane or isopropylsulfonyloctadecane with the larger head group resulted in decreased ionic conductivity over the entire composition space and temperature range investigated.
Charged colloidal particles neutralized by a single counterion are increasingly important for many emerging technologies. Attention here is paid specifically to hydrogen fuel cells and water electrolyzers whose catalyst layers are manufactured from a perfluorinated sulfonic acid polymer (PFSA) suspended in aqueous/alcohol solutions. Partially dissolved PFSA aggregates, known collectively as ionomers, are stabilized by the electrostatic repulsion of overlapping diffuse double layers consisting of only protons dissociated from the suspended polymer. We denote such double layers containing no added electrolyte as "single ion". Size-distribution predictions build upon interparticle interaction potential energies from the Derjaguin-Landau-Verwey-Overbeek (DLVO) formalism. However, when only a single counterion is present in solution, classical DLVO electrostatic potential energies no longer apply. Accordingly, here a new formulation is proposed to describe how single-counterion diffuse double layers interact in colloidal suspensions. Part II (Srivastav, H.; Weber, A. Z.; Radke, C. J. Langmuir 2024 DOI: 10.1021/acs.langmuir.3c03904) of this contribution uses the new single-ion interaction energies to predict aggregated size distributions and the resulting solution pH of PFSA in mixtures of n-propanol and water. A single-counterion diffuse layer cannot reach an electrically neutral concentration far from a charged particle. Consequently, nowhere in the dispersion is the solvent neutral, and the diffuse layer emanating from one particle always experiences the presence of other particles (or walls). Thus, in addition to an intervening interparticle repulsive force, a backside osmotic force is always present. With this new construction, we establish that single-ion repulsive pair interaction energies are much larger than those of classical DLVO electrostatic potentials. The proposed single-ion electrostatic pair potential governs dramatic new dispersion behavior, including dispersions that are stable at a low volume fraction but unstable at a high volume fraction and finite volume-fraction dispersions that are unstable with fine particles but stable with coarse particles. Finally, the proposed single-counterion electrostatic pair potential provides a general expression for predicting colloidal behavior for any charged particle dispersion in ionizing solvents with no added electrolyte.
This paper presents a multi-level crystal plasticity-based simulation framework for modeling mechanical response and microstructure evolution of Ti-6Al-4V with α-lath/lamellar microstructures. Here, the model is a correlated structure visco-plastic self-consistent (CS-VPSC) formulation linking three scales: a single crystals micro-scale, a lath/lamellar colony meso-scale, and a lath/lamellar aggregate macro-scale. A selected hardening law for the evolution of critical resolved shear stress per slip system used in CS-VPSC is phenomenological. However, it adjusts the resistances of basal and prismatic slip systems based on the geometry of slip transfer between adjacent lamellae. Consistent with experimental evidences, the resolved shear stress on the pyramidal slip planes is dependent not only on the stress in the direction of slip but also on the two orthogonal shear stress components and the three normal stress components (non-Schmid effects). Electron backscatter diffraction (EBSD) data in conjunction with a procedure relying on α→β phase transformation is used to construct paired variants of α-laths/lamellae satisfying their local crystallographic correlations. The procedure fits volume fractions of individual laths/lamellae with the experimental EBSD data and selects a distribution of habit planes between adjacent variants with respect to the loading direction. The simulation framework is applied to interpret the deformation behavior in tension and compression along two sample directions of Ti-6Al-4V fabricated via laser powder bed fusion. Moreover, the model is used to simulate texture evolution during rolling of the material to large strains. It is demonstrated that the model is capable of predicting plastic anisotropy/asymmetry and the concomitant texture evolution. While the model reveals a significant effect of habit plane inclination with respect to the loading direction on yield stress, the comparison of the data and model predictions shows that a random distribution of habit planes fits the flow response. It is further inferred that the tension-compression asymmetry arises from the non-Schmid effects.
In this work, we present a systematic investigation of the impact of silica nanoparticle (SiNP) size and surface chemistry on the nanoparticle dispersion state and the resulting morphology and vanadium ion permeability of the composite ionomer membranes. Specifically, Nafion containing a mass fraction of 5% silica particles, ranging in nominal diameters from 10 nm to >1 μm and with both sulfonic acid- and amine-functionalized surfaces, was fabricated. Most notably, an 80% reduction in vanadium ion permeability was observed for ionomer membranes containing amine-functionalized SiNPs at a nominal diameter of 200 nm. Further, these membranes exhibited an almost 400% increase in proton selectivity when compared to pristine Nafion. Trends in vanadium ion permeability within a particular nominal diameter were seen to be a function of the surface chemistry, where, for example, vanadyl ion permeability was observed to increase with increasing particle size for membranes containing unfunctionalized SiNPs, while it was seen to remain relatively constant for membranes containing amine-functionalized SiNPs. In general, the silica particles tended to exhibit a higher extent of aggregation as the size of the particles was increased. From small-angle neutron scattering experiments, an increase in the spacing of the hydrophobic domains was observed for all composite membranes, though particle size and surface chemistry were seen to have varying impacts on the spacing of the ionic domains of the ionomer.
The immobilization of molecular electrocatalysts on conductive electrodes is an appealing strategy for enhancing their overall activity relative to those of analogous molecular compounds. Here, in this study, we report on the interfacial electrochemistry of self-assembled two-dimensional nanosheets of graphene nanoribbons (GNR-2DNS) and analogs containing a Rh-based hydrogen evolution reaction (HER) catalyst (RhGNR-2DNS) immobilized on conductive electrodes. Proton-coupled electron transfer (PCET) taking place at N-centers of the nanoribbons was utilized as an indirect reporter of the interfacial electric fields experienced by the monolayer nanosheet located within the electric double layer. The experimental Pourbaix diagrams were compared with a theoretical model, which derives the experimental Pourbaix slopes as a function of parameter f, a fraction of the interfacial potential drop experienced by the redox-active group. Interestingly, our study revealed that GNR-2DNS was strongly coupled to glassy carbon electrodes (f = 1), while RhGNR-2DNS was not (f = 0.15). We further investigated the HER mechanism by RhGNR-2DNS using electrochemical and X-ray absorption spectroelectrochemical methods and compared it to homogeneous molecular model compounds. RhGNR-2DNS was found to be an active HER electrocatalyst over a broader set of aqueous pH conditions than its molecular analogs. We find that the improved HER performance in the immobilized catalyst arises due to two factors. First, redox-active bipyrimidine-based ligands were shown to dramatically alter the activity of Rh sites by increasing the electron density at the active Rh center and providing RhGNR-2DNS with improved catalysis. Second, catalyst immobilization was found to prevent catalyst aggregation that was found to occur for the molecular analog in the basic pH. Overall, this study provides valuable insights into the mechanism by which catalyst immobilization can affect the overall electrocatalytic performance.
Here, we use a combination of experiments and coarse-grained molecular dynamics simulations to elucidate the structure–property relationships in polymer electrolytes obtained by the copolymerization of poly(vinyl ethylene carbonate─lithium styrene bis(trifluoromethanesulfonyl)imide) or p(VEC-LiSTFSI). Experiments show that the conductivity reduces with increasing anion (i.e., STFSI) fraction on the chain, and the cation transference number (t + ) is found to be dependent on the anion fraction. Furthermore, a significant fraction of unpolymerized VEC monomers are observed. Since it is inherently difficult to experimentally control the chain architecture and the amount of unpolymerized VEC in these systems, we perform coarse-grained molecular dynamics simulations on model polymer systems with different chain architectures to mimic the plausible experimental systems. Specifically, we look at the differences in transference numbers arising from (i) a random copolymer of VEC and STFSI monomers; (ii) a blend of VEC-STFSI copolymer with VEC monomers; and (iii) a ternary blend of the VEC homopolymer, STFSI homopolymers, and VEC monomers. The ternary blend model demonstrates the closest resemblance with the experimental transference numbers and diffusivities. The lithium diffusivity obtained from the coarse-grained models with VEC monomers (plasticizers) is about 1.5 times that of the model without VEC monomers, showing that the plasticizing effect of VEC monomers is modest. We rationalize the experimental observations based on aggregate and cluster analyses obtained from molecular simulations. This work reveals that polymer electrolyte chain architecture and plasticizers can critically influence the transport properties, and these parameters should be considered when designing single ion conducting polymeric electrolytes.
Anoxic, sulfidic conditions have been prevalent since the early Proterozoic and favor aqueous iron-sulfur (FeS aq ) clusters as a major fraction of the soluble, reduced iron and sulfur pool. FeS aq cluster formation and nucleation is driven by the high affinity between ferrous iron (Fe(II)) and sulfide (HS − ), ultimately yielding particles that precipitate as iron sulfide minerals. FeS aq clusters were recently shown to be bioavailable sources of iron and sulfur for a variety of anaerobes, yet little is known of the factors that influence the kinetics of their formation and nucleation. Here we apply computational and spectroscopic approaches to investigate the dynamics of FeS aq nucleation, cluster growth, precipitation, and redissolution as a function of Fe(II)/HS − concentration, temperature, and pH. Experiments were conducted under excess HS − to mimic euxinic conditions common to contemporary anaerobic aquatic ecosystems and those of the Proterozoic. Density functional theory calculations reveal the key role of water oxygen-iron interactions in stabilizing small FeS aq clusters and promoting solubility. Dynamic light scattering revealed a concentration-dependent increase in the kinetics of FeS aq nucleation and cluster aggregation. Increasing temperature promoted FeS aq cluster nucleation and aggregation while also enhancing dissolution. Alkaline pH also promoted FeS aq nucleation and cluster aggregation. At 25 °C, pH 7.0, and at reactant concentrations of 30 µM, FeS aq clusters < 10 nm in diameter remained in solution for > 2 h. These results underscore the importance of temperature, pH, and reactant concentration in the kinetics of FeS aq nucleation and cluster growth that, in turn, influence their bioavailability in anaerobic ecosystems.
A particularly promising approach to deconstructing and fractionating lignocellulosic biomass to produce green renewable fuels and high-value chemicals pretreats the biomass with organic solvents in aqueous solution. Here, neutron scattering and molecular-dynamics simulations reveal the temperature-dependent morphological changes in poplar wood biomass during tetrahydrofuran (THF):water pretreatment and provide a mechanism by which the solvent components drive efficient biomass breakdown. Whereas lignin dissociates over a wide temperature range (>25 °C) cellulose disruption occurs only above 150 °C. Neutron scattering with contrast variation provides direct evidence for the formation of THF-rich nanoclusters (R g ∼ 0.5 nm) on the nonpolar cellulose surfaces and on hydrophobic lignin, and equivalent water-rich nanoclusters on polar cellulose surfaces. The disassembly of the amphiphilic biomass is thus enabled through the local demixing of highly functional cosolvents, THF and water, which preferentially solvate specific biomass surfaces so as to match the local solute polarity. A multiscale description of the efficiency of THF:water pretreatment is provided: matching polarity at the atomic scale prevents lignin aggregation and disrupts cellulose, leading to improvements in deconstruction at the macroscopic scale.
The Orbiting Carbon Observatory 2 (OCO-2) is NASA's first Earth observation satellite mission dedicated to studying the sources and sinks of carbon dioxide (CO 2 ) on a global scale. The observations of reflected sunlight are inverted in a retrieval algorithm to produce estimates of the dry air mole-fractions of CO 2 (X CO2 ). The OCO-2 Level 2 data release, version 11.1 (v11.1) retrievals from the Atmospheric Carbon Observations from Space (ACOS) algorithm, includes significant improvements in the X CO2 data product compared to older OCO-2 data versions. This work compares the v11.1 X CO2 from OCO-2 against X CO2 estimates collected from a global ground-based network known as the Total Carbon Column Observing Network (TCCON), OCO-2's primary validation source. The OCO-2 project provides a version of the Level 2 data product, called “lite” files that include calibrated and bias-corrected XCO2 values, accessible together with all OCO-2 data products through the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). This work shows that OCO-2 X CO2 observations made between September 2014 and December 2023, after quality filtering and the application of an averaging kernel correction, agree well with coincident TCCON data for all OCO-2 observational modes of land (nadir, glint, target) and ocean (glint). The aggregated, bias-corrected, and quality-filtered absolute average bias values are less than or equal to 0.20 parts per million (ppm) globally for all OCO-2 observation modes, where the biases do not indicate a statistically significant time dependence. The land nadir/glint mode has the lowest bias value of −0.03 ± 0.85 ppm.