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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Bead–Spring Simulation of Ionomer Melts—Studying the Effects of Chain-Length and Associating Group Fraction on Equilibrium Structure and Extensional Flow Behavior

Ionomers are associative polymers with diverse applications ranging from selective membranes and high-performance adhesives to abrasion- and chemical-resistant coatings, insulation layers, vacuum packaging, and foamed sheets. Within equilibrium melt, the ionic or associating groups are known to form thermally reversible, associative clusters whose presence can significantly affect the system’s mechanical, viscoelastic, and transport properties. It is, thus, of great interest to understand how to control such clusters’ size distribution, shape, and stability through the designed choice of polymer architecture and the ionic groups’ fraction, arrangement, and interaction strength. In this work, we represent linear associating polymers using a Kremer–Grest type bead–spring model and perform large-scale MD simulations to explore the effect of polymer chain-length (l) and fraction (f s ) of randomly placed associating groups on the size distribution and stability of formed clusters. We consider different chain-lengths (below and above entanglement), varying fractions of associating groups (represented by ‘sticky’ beads) between 5 and 20%, and a fixed sticky–sticky nonbond interaction strength of four times that between regular non-associating beads. For all melts containing associating groups the equilibrium structure factor S(q) displays a signature ionomer peak at low wave vector q whose intensity increases with increasing f s and l. The average cluster size Nc increases with f s . However, the effect of chain-length on Nc appears to be pronounced only at higher values of f s . Under extensional flows, the computed stress (and viscosity) is higher at higher f s and l regardless of strain rate. Beyond a critical strain rate, we observe fragmentation of the associative clusters, which has interesting effects on the stress/viscous response.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Deep learning potential of mean force between polymer grafted nanoparticles

Grafting polymer chains on the surfaces of nanoparticles is a well-known route to control their self-assembly and distribution in a polymer matrix. A wide variety of self-assembled structures are achieved by changing the grafting patterns on the surface of an individual nanoparticle. However, an accurate estimation of the effective potential of mean force between a pair of grafted nanoparticles that determines their assembly and distribution in a polymer matrix is an outstanding challenge in nanoscience. We address this problem via deep learning. As a proof of concept, here we report a deep learning framework that learns the interaction between a pair of single-chain grafted spherical nanoparticles from their molecular dynamics trajectory. Subsequently, we carry out the deep learning potential of mean force-based molecular simulation that predicts the self-assembly of a large number of single-chain grafted nanoparticles into various anisotropic superstructures, including percolating networks and bilayers depending on the nanoparticle concentration in three-dimensions. The deep learning potential of mean force-predicted self-assembled superstructures are consistent with the actual superstructures of single-chain polymer grafted spherical nanoparticles. This deep learning framework is very generic and extensible to more complex systems including multiple-chain grafted nanoparticles. Finally, we expect that this deep learning approach will accelerate the characterization and prediction of the self-assembly and phase behaviour of polymer-grafted and unfunctionalized nanoparticles in free space or a polymer matrix.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Induced Markov chain for wind farm generation forecasting

Systems and methods for forecasting power generation in a wind farm are disclosed. The systems and methods utilize an induced Markov chain model to generate a forecast of power generation of the wind farm. The forecast is at least one of a point forecast or a distributional forecast. Additionally, the systems and methods modify at least one of: (i) a generation of electricity at a power plant coupled to a common power grid as the wind farm; or (ii) a distribution of electricity in the common power grid based on the forecast of power generation of the wind farm. In an exemplary approach, utilizing the induced Markov chain model to generate the forecast may include determining a series of time adjacent power output measurements based on historical wind power measurements and calculating a time series of difference values based on the series of time adjacent power output measurements.

17 WIND ENERGY↗
DOE OSTI2026

Non-Markovian relaxation spectroscopy of fluxonium qubits

Recent studies have shown that parasitic two-level systems (TLS) in superconducting qubits, which are a leading source of decoherence, can have relaxation times longer than the qubits themselves. However, the standard techniques used to characterize qubit relaxation is only valid for measuring T 1 under the Born-Markov approximation and could mask environmental memory effects in practice. Here, we introduce two-timescale relaxometry, a technique to probe the qubit and environment relaxation simultaneously and efficiently. We apply it to high-coherence fluxonium qubits over a frequency range of 0.1-0.4 GHz, and reveal a discrete spectrum of TLS with millisecond lifetimes. Our analysis of the spectrum is consistent with a random distribution of TLS in the aluminum oxide tunnel barrier of the Josephson junction chain of the fluxonium, with a spectral and volumetric density and average electric dipole similar to previous TLS studies at much higher frequencies. Our study suggests that investigating and mitigating TLS in the junction chain is crucial to the development of various types of noise-protected qubits in circuit QED.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Two transitions in complex eigenvalue statistics: Hermiticity and integrability breaking

Open quantum systems have complex energy eigenvalues which are expected to follow non-Hermitian random matrix statistics, when chaotic, or two-dimensional (2d) Poisson statistics, when integrable. We investigate the spectral properties of a many-body quantum spin chain, i.e., the Hermitian Heisenberg model with imaginary disorder. Its rich complex eigenvalue statistics is found to separately break both Hermiticity and integrability at different scales of the disorder strength. With no disorder, the system is integrable and Hermitian, with spectral statistics corresponding to the 1d Poisson point process. At very small disorder, we find a transition from 1d Poisson statistics to an effective D -dimensional Poisson point process, showing Hermiticity breaking. At intermediate disorder, we find integrability breaking, as inferred from the statistics matching that of non-Hermitian complex symmetric random matrices in class AI † . For large disorder, as the spins align, we recover the expected integrability (now in the non-Hermitian setup), indicated by 2d Poisson statistics. These conclusions are based on fitting the spin-chain data of numerically generated nearest- and next-to-nearest-neighbor spacing distributions to an effective 2d Coulomb gas description at inverse temperature β . We confirm that such an effective description of random matrices also applies in classes AI † and AII † up to next-to-nearest-neighbor spacings. Published by the American Physical Society 2025

Akemann, Gernot (ORCID:0000000217104258)↗
DOE OSTI2025

On symmetry-resolved generalized entropies

Symmetry-resolved entanglement, capturing the refined structure of quantum entanglement in systems with global symmetries, has attracted a lot of attention recently. In this manuscript, introducing the notion of symmetry-resolved generalized entropies, we aim to develop a computational framework suitable for the study of excited state symmetry-resolved entanglement as well as the dynamical evolution of symmetry-resolved entanglement in symmetry-preserving out-of-equilibrium settings. We illustrate our framework using the example of (1+1)-d free massless compact boson theory, and benchmark our results using lattice computation in the XX chain. As a byproduct, our computational framework also provides access to the probability distribution of the symmetry charge contained within a subsystem and the corresponding full counting statistics.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Synthesis of Novel Hybrid Nanostructures Employing Advanced Structural Characterization Techniques

This proposal was for carrying out research on creating novel hybrid nanostructures with novel and interesting functional properties with the help of advanced structural on adding 0.001, 0.01 and 0.02 mg of Au nanoparticles. From the reflectivity measurements, and the intensities of the various order diffraction peaks from the multilayers, we were able to reconstruct the electron density profiles of the films normal to their surfaces. characterization techniques. There were 3 components to the research: (1) seeing if new hybrid nanostructures could be synthesized from Au nanoparticles and lipid multilayers (2) making artificial cells from Galactopyranose-Derived Single-Chain Amphiphiles and sponge phases of lipid/amphiphile mixtures, and (3) characterizing the pair distribution function for complex nanoparticles of melanin to understand/predict the structural colors seen in aggregates of such nanoparticles.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Multiscale Shear Properties and Flow Performance of Milled Woody Biomass

One dominant challenge facing the development of biorefineries is achieving consistent system throughput with highly variant biomass feedstock quality and handling performance. Current handling unit operations are adapted from other sectors (primarily agriculture), where some simplifying assumptions about granular mechanics and flow performance do not translate well to a highly compressible and anisotropic material with nonlinear time- and stress-dependent properties. This work explores the shear and frictional properties of loblolly pine at multiple experimental test apparatus and particle scales to elucidate a property window that defines the shear behavior over a range of material attributes (particle size, size distribution, moisture content, etc.). In general, it was observed that the bulk internal friction and apparent cohesion depend strongly on both the stress state of the sample in granular shear testers and the overall particle size and distribution span. For equipment designed to characterize the quasi-static shear stress failure of bulk materials ranging from 50 to 1,000 ml in test volume, similar test results were observed for finely milled particles (50% passing size of 1.4 mm) with a narrow size distribution (span between 10 and 90% passing size of 0.9 mm), while stress chaining and over-torque issues persisted for the bench-scale test apparatus for larger particle sizes or widely dispersed sample sizes. Measurement of the anisotropic particle–particle friction ranged from coefficients of approximately 0.20 to 0.45 and resulted in significantly higher and more variable friction measurements for larger particle sizes and in perpendicular alignment orientations. To supplement these laboratory-scale properties, this work explores the flow of loblolly pine and Douglas fir through a pilot-scale wedge-shaped hopper and a screw feeder. For the gravity-driven hopper flow, the critical arching distance and mass discharge rate ranged from approximately 10 to 30 mm and 2 to 16 tons/hour, respectively, for both materials, where the arching distance depends strongly on the overall particle size and depends less on the hopper inclination angle. Comparatively, the auger feeder was found to be much more impacted by the size of the particles, where smaller particles had a more consistent and stable flow while consuming less power.

09 BIOMASS FUELS↗
DOE OSTI2026

Assessing Photovoltaic Capacity Factor Variability Using Long-Term Satellite Derived Solar Resource Data Under Brazilian Climate

Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.

14 SOLAR ENERGY↗
DOE OSTI2020

Improving the picture of atomic structure in nonoriented polymer domains using the pair distribution function: A study of polyamide 6

Abstract Investigations of the atomic structures within polyamides started over 80 years ago and continue today. These weakly ordered materials diffract X‐rays poorly and typically require postprocessing to obtain idealized samples for structural studies. An important goal remains to develop techniques to study the local structure in its natural state, with atomic resolution, and with sensitivity to subtle changes due to synthesis conditions or other technologically relevant processing procedures. Here, we compare the structures of as‐produced, nonoriented polyamide 6 ([C 6 H 11 NO] n ) from both hydrolytic and anionic processes. A total scattering pair distribution function approach is used to elucidate information about the atomic bonding, molecular conformation, chain packing, crystallite size, and ratio of ordered to disordered domain content. The results are compared with those from standard analytical methods.

Terban, Maxwell W.↗
DOE OSTI2022

Dynamical Bond Formation in KNi 2 Se 2

Emphanisis, or the appearance out of nothing, has been used to describe the phenomena of spontaneous atom off-centering and dipole formation at elevated temperatures in lead chalcogenides. Here, we provide spectroscopic evidence of spontaneous formation of metal-metal bonds above T~50 K in the layered metal KNi 2 Se 2 . These bonds form zig-zag chains that lower the local symmetry from tetragonal to monoclinic. Energy-resolved pair distribution function measurements exclude a pure phonon origin of our observations, and instead imply the existence of extra, slowly fluctuating, Ni-Ni bonds above T=50 K. Density functional theory calculations support this lower symmetry configuration as an instability of tetragonal KNi 2 Se 2 . We thus demonstrate that the phenomena of emphansis is not limited to local electric dipole formation, but can also be driven by the formation of metal-metal bonds.

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

Forecasting Multi-Wave Epidemics Through Bayesian Inference

We present a simple, near-real-time Bayesian method to infer and forecast a multiwave outbreak, and demonstrate it on the COVID-19 pandemic. The approach uses timely epidemiological data that has been widely available for COVID-19. It provides short-term forecasts of the outbreak’s evolution, which can then be used for medical resource planning. The method postulates one- and multiwave infection models, which are convolved with the incubation-period distribution to yield competing disease models. The disease models’ parameters are estimated via Markov chain Monte Carlo sampling and information-theoretic criteria are used to select between them for use in forecasting. The method is demonstrated on two- and three-wave COVID-19 outbreaks in California, New Mexico and Florida, as observed during Summer-Winter 2020. We find that the method is robust to noise, provides useful forecasts (along with uncertainty bounds) and that it reliably detected when the initial single-wave COVID-19 outbreaks transformed into successive surges as containment efforts in these states failed by the end of Spring 2020.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Reaction chemistry of ethanol oligomerization to distillate-range molecules using low loading Cu/Mg x AlO y catalysts

We study ethanol oligomerization to higher alcohols and other oxygenates with a 0.3 wt. %Cu/Mg 2.9 AlO catalyst. This reaction involves more than 130 products in a complicated reaction network. The selectivity towards diesel fuel precursor compounds (hereafter ‘DFPC’) increased with conversion until reaching a plateau at an ethanol conversion of ~70 %. Alcohol selectivity was found to follow Schultz-Flory distribution at all studied conversions. Larger sized alcohols then are formed mostly by chain-growth mechanisms via surface reactions of adsorbed oligomers with ethanol-based monomers. Higher esters are formed from alcohols and aldehydes in a series reaction mechanism. Moreover, C 6+ ester and C 4+ ketones selectivities increase as conversion increases. We also found that C 4+ alcohols most likely undergo Guerbet coupling with the studied catalyst to form even higher alcohols once, and that the oxygen of these alcohols is active as a nucleophile, resulting in the selective formation of esters if the starting alcohol is branched. Finally, we performed several cofeed studies varying ethanol-to-H 2 inlet partial pressures, and adding acetaldehyde and ethyl acetate as cofeeds to ethanol at different concentrations. Here, we conclude from these experiments that acetaldehyde concentration controls reaction chemistry, with conditions favoring larger concentrations of the molecule promoting both alcohol coupling and ester formation, and conditions leading to lower concentrations of acetaldehyde resulting in higher alcohol selectivity at the expense of esters and higher aldehydes.

10 SYNTHETIC FUELS↗
DOE OSTI2024

Role of Bottlebrush Additives on the Structure of Block Copolymers in the Bulk and Thin Films

Blending block copolymers (BCP) with additives is a useful approach for controlling BCP morphology and properties. In athermal systems, blends of BCPs with polymer additives having very high molecular (M n ) mass generally result in macrophase separation. Bottlebrush polymers, which consist of a linear backbone and grafted side chains, present an interesting alternative where the overall M n of the system can be very large but the low M n side chains may drive miscibility with the BCP. Here, in this study, a bottlebrush with a polynorbornene backbone and polystyrene (PS) side chains is blended with PS-b-poly(methyl methacrylate) (PS-b-PMMA) of varying M n , and the resulting morphologies are examined in both the bulk and thin films. Two different M n of PS-b-PMMA were used in the bulk study, and the analysis of small-angle X-ray scattering data shows that the blends were miscible and lamellar at all concentrations. This deviates from reference series of both low and high M n linear polymer additives, which either showed morphological transitions from lamellae to cylinders (low M n ) or were immiscible at all mass fractions studied (high M n ). The relative molecular mass of the side chain (N SC ) and the corresponding component in the BCP (N A ) dictate the distribution of the bottlebrush throughout the BCP, analogous to BCP/linear blends or grafted nanoparticles in a homopolymer matrix. The studies on thin films show a thickness dependence for bottlebrush mass fractions at or above 0.17, a behavior which may be driven by conformational changes of the bottlebrush upon confinement.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Centrality dependence of Lévy-stable two-pion Bose-Einstein correlations in $\sqrt{𝑠{𝑁⁢𝑁}}$ = 200 GeV Au + Au collisions

The PHENIX experiment measured the centrality dependence of two-pion Bose-Einstein correlation functions in $\sqrt{𝑠{𝑁⁢𝑁}}$ = 200 GeV Au + Au collisions at the Relativistic Heavy Ion Collider at Brookhaven National Laboratory. The data are well represented by Lévy-stable source distributions. The extracted source parameters are the correlation-strength parameter 𝜆, the Lévy index of stability 𝛼, and the Lévy-scale parameter 𝑅 as a function of transverse mass 𝑚 𝑇 and centrality. The 𝜆⁡(𝑚 𝑇 ) parameter is constant at larger values of 𝑚 𝑇 , but decreases as 𝑚 𝑇 decreases. The Lévy-scale parameter 𝑅⁡(𝑚 𝑇 ) decreases with 𝑚 𝑇 and exhibits proportionality to the length scale of the nuclear overlap region. The Lévy exponent 𝛼⁡(𝑚 𝑇 ) is independent of 𝑚 𝑇 within uncertainties in each investigated centrality bin, but shows a clear centrality dependence. At all centralities, the Lévy exponent 𝛼 is significantly different from that of Gaussian (𝛼 = 2) or Cauchy (𝛼 = 1) source distributions. Comparisons to the predictions of Monte-Carlo simulations of resonance-decay chains show that, in all but the most peripheral centrality class (50%–60%), the obtained results are inconsistent with the measurements, unless a significant reduction of the in-medium mass of the 𝜂′ meson is included. Finally, in each centrality class, the best value of the in-medium 𝜂′ mass is compared to the mass of the 𝜂 meson, as well as to several theoretical predictions that consider restoration of U 𝐴⁢ (1) symmetry in hot hadronic matter.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Models to Incorporate Reaction Mechanisms into DG-OSPREY: Fixed-Bed Simulations for Organic Iodides Capture Using Ag 0 Z

As one of the most potent radioisotopes released during spent nuclear fuel reprocessing, 129 I is strictly regulated and must be removed before discharge. Organic iodides (primarily alkyl iodides with different chain lengths, i.e., CH 3 I, C 4 H 9 I, and C 12 H 25 I) comprise ~2% of the total iodine in the reprocessing off-gases and are primarily present in vessel off-gas (VOG). Reduced silver mordenite (Ag 0 Z) is predominantly considered for the removal of radioiodine; however, its capture performance and underlying interaction processes with long-chain organic iodides are not fully understood. Two major tasks were accomplished in this study. First, to improve upon the previous experimental studies where Ag 0 Z was used to capture CH 3 I, C 4 H 9 I, and C 12 H 25 I at different concentrations, we comprehensively investigated the corresponding capture mechanisms by characterizing fully loaded Ag 0 Z samples. Second, computational codes were implemented to perform fixed-bed simulations that account for transport and reaction mechanisms. Scanning electron microscopy with energy dispersive X-ray analysis (SEM-EDX), powder X-ray diffraction (PXRD), UV-visible diffuse reflectance spectroscopy (UV-vis DRS), and thermogravimetric analysis (TGA) were conducted on Ag 0 Z samples that are saturated with I 2 , CH 3 I, C 4 H 9 I, and C 12 H 25 I), respectively. Results indicate that AgI is the predominant adsorption product regardless of the adsorbed iodine species, yet alkyl iodides with different carbon chain lengths may have different compositions of α- and γ-AgI. Synchrotron pair distribution function (PDF) measurements and TGA coupled with a Fourier transformed infrared detector (TGA FTIR) have been performed, and experimental data are currently analyzed. Results are expected to provide further insights into the adsorption mechanisms. The fixed-bed capture performance for CH 3 I was successfully simulated by solving mathematical equations that describe the underlying transport processes and adsorption reactions. The computational framework, Catalytic After Treatment System (CATS), that was originated in our research group, was used to solve the governing equations. Kinetic parameters, including the pore diffusivity and reaction rate constant were obtained by optimization techniques using data from thin-bed experiments performed at Oak Ridge National Laboratory. The performance of a deep bed predicted using the optimized parameters showed promising agreement with the experimental data.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Flux Upcycling of Degraded Layered Cathodes to LiNi x Mn y Co z O 2 (NMCs) with Gradient Transition Metal Distribution

The rising demand for lithium-ion batteries (LIBs) has intensified the need for efficient recycling methods to address both supply chain constraints and environmental impacts. Direct upcycling, distinguished by its ability to achieve both the structural and compositional integrity of cathode materials, has gained prominence as a sustainable alternative to conventional pyrometallurgical and hydrometallurgical processes. However, the current direct upcycling methods are typically limited by incorporating Li and/or Ni, significantly constraining the adaptability across diverse LiNi x Mn y Co z O 2 (NMCs). Here, in this study, a versatile molten salt approach is reported that expands the scope of direct upcycling by enabling simultaneous incorporation of Li, Ni, and Mn. This methodology facilitates flexible conversion among diverse NMC compositions, including non-stoichiometric Co/Mn systems such as upcycling degraded LiCoO 2 (D-LCO), LiNi 1/3 Mn 1/3 Co 1/3 O 2 (D-NMC111), LiNi 0.8 Mn 0.1 Co 0.1 O 2 (D-NMC811) to surface Mn enriched NMC111, LiNi 0.5 Mn 0.3 Co 0.2 O 2 (NMC532), and NMC811, respectively. The gradient transition metal distribution in upcycled products, characterized by Mn-enriched outer layers and Co/Ni-enriched cores enhances the interfacial stability of NMC cathodes, addressing critical challenges in long-term performance and structural integrity. These results highlight the potential of flux methods for advancing the upcycling of spent cathodes and producing high-performance materials for next-generation LIBs applications.

lithium -ion batteries↗
DOE OSTI2023

Molecular interactions in short-chain perfluoroalkyl carboxylic acids and aqueous solutions

The presence of short-chain per- and polyfluoroalkyl substances in water poses a major health and environmental challenge. Here, we have performed high-energy small- and wide-angle X-ray scattering measurements on CF 3 [CF 2 ] n COOH (where n = 1, 2, 3 represents the chain length) and their aqueous solutions at 10% mole concentrations to characterize their molecular interactions at the atomic and nanometer length scales. The experimental wide-angle structure factors have been modelled using Empirical Potential Structural Refinement. The oxygen–oxygen partial X-ray pair distribution functions show that the coordination number between the hydroxyl oxygen on the acid and surrounding oxygen water molecules increases significantly with acid chain length, rising from 3.2 for n = 1 to 4.1 for n = 3. The small-angle scattering is dominated by a sharp, high-intensity peak at Q 1 ~ 0.2 Å -1 and a smaller peak at Q 2 = 1.2 Å -1 for n = 3, both of which decrease with decreasing chain length. The Q 2 peak is attributed to groups of adjacent non-bonded acid molecules, and Q 1 has contributions from both correlations between acid molecules and water–water interactions. In all cases, the models show nanoscale aggregation occurs in the form of denser channels of winding hydrogen-bonded chains, approximately 20 water molecules in length, surrounding clusters of acid molecules.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗