SEARCH · Search NASA

Results for “Complex reflection coefficients”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 73 records · Page 4

All sources NASA NTRS DOE OSTI

DOE OSTI2017

Structural Emergence in Particle Dispersions

Particle dispersions, such as pigment-based inks, comprise weakly bound, milled nanoparticles. The properties of these pigments depend on both their chemical composition and a rather complex structural hierarchy which emerges from these dispersions. The emergence of structure under semidilute conditions is related to the structure of the dilute particles, the particle spacing (mesh size), processing history, and the interaction potential. Kinetic simulations could predict such emergence using these input parameters. In this paper, organic pigments are studied as an example of the importance of emergent structure to predict properties such as brilliance and opacity. Organic pigments are used to impart color to commercial inks, plastics, coatings, and cosmetics. In many cases, dilute pigments are mass fractal structures consisting of aggregated nanoparticles held together by weak van der Waals forces. In water, surfactant is added to create a pigment dispersion (an ink). The final properties of a pigment emerge from a complex interplay between aggregation and dispersion of aggregates as a function of concentration. Samples of the organic pigment yellow 14, PY14, were milled to four primary particle sizes to study the effect on structural emergence. The interaction between surfactant-stabilized PY14 aggregates in an aqueous medium was quantified by the second virial coefficient, A 2 , which reflects long-range interactions. The degree of aggregation is associated with short-range attractive interactions between primary particles. In this series of pigments, the degree of aggregation increases dramatically with reduction in primary particle size. Concurrently, the second-order virial coefficient, A 2 , increases reflecting stronger long-range repulsive interactions with particle size. Structural emergence can be understood through the percolation concentration and the filler mesh size. A 2 is translated into a repulsive interaction potential for use in dissipative particle dynamics simulations to enable predictive modeling. Furthermore, this description of the interactions between dispersed pigment aggregates allows for a more scientific and predictive approach to understand structural emergence.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Spread Spectrum Time Domain Reflectometry (SSTDR) Digital Twin Simulation of Photovoltaic Systems for Fault Detection and Location

Utilizing spread spectrum time domain reflectometry (SSTDR) to detect, locate, and characterize faults in photovoltaic (PV) systems is examined in this paper. We present a method to obtain the model parameters that are needed to produce digital twin SSTDR responses for PV systems. The digital twin SSTDR responses could be used to predict faults within the PV systems. Here, the model parameters are the reflection and transmission coefficients at each impedance discontinuity in the PV system along with the propagation coefficients across each PV cable segment. We obtain model parameter by applying inverse modeling techniques to experimental SSTDR data associated with PV systems. Our model parameters can be used in any digital twin simulation method for modeling reflectometry in frequency-dependent and complex loads. For validation, we used the model parameters in a graph network simulation engine and adapted it to be used for SSTDR digital twin simulations in PV systems. We produced simulations for 0 to 10 PV modules connected in series. We also simulated SSTDR responses for open circuit disconnections in a PV setup containing 10 PV modules in series. Results show that all but one simulated disconnect locations match experimental disconnection locations of the same setup with an error of less than 5%.

14 SOLAR ENERGY↗

DOE OSTI2015

The role of nanoparticle rigidity on the diffusion of linear polystyrene in a polymer nanocomposite

The impact of the inclusion of a nanoparticle in a polymer matrix on the dynamics of the polymer chains is an area of recent interest. In this article, we describe the role of nanoparticle rigidity or softness on the impact of the presence of that nanoparticle on the diffusive behavior of linear polymer chains. The neutron reflectivity results clearly show that the inclusion of 10 nm soft nanoparticles in a polymer matrix (R g ~ 20 nm) increases the diffusion coefficient of the linear polymer chain. Surprisingly, thermal analysis shows that these nanocomposites exhibit an increase in their glass transition temperature, which is incommensurate with an increase in free volume. Therefore, it appears that this effect is more complex than a simple plasticizing effect. Results from small-angle neutron scattering of the nanoparticles in solution show a structure that consists of a gel like core with a corona of free chain ends and loops. Furthermore, the increase in linear polymer diffusion may be related to an increase in constraint release mechanisms in the reptation of the polymer chain, in a similar manner to that which has been reported for the diffusion of linear polymer chains in the presence of star polymers.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μ ECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

An adaptive least-squares global sensitivity method and application to a plasma-coupled combustion prediction with parametric correlation

We introduce an efficient non-intrusive surrogate-based methodology for global sensitivity analysis and uncertainty quantification. Modified covariance-based sensitivity indices (mCov-SI) are defined for outputs that reflect correlated effects. The overall approach is applied to simulations of a complex plasma-coupled combustion system with disparate uncertain parameters in sub-models for chemical kinetics and a laser-induced breakdown ignition seed. The surrogate is based on an Analysis of Variance (ANOVA) expansion, such as widely used in statistics, with orthogonal polynomials representing the ANOVA subspaces and a polynomial dimensional decomposition (PDD) representing its multi-dimensional components. The coefficients of the PDD expansion are obtained using a least-squares regression, which both avoids the direct computation of high-dimensional integrals and affords an attractive flexibility in choosing sampling points. This facilitates importance sampling using a Bayesian calibrated posterior distribution, which is fast and thus particularly advantageous in common practical cases, such as our large-scale demonstration, for which the asymptotic convergence properties of polynomial expansions cannot be realized due to computation expense. Effort, instead, is focused on efficient finite-resolution sampling. Standard covariance-based sensitivity indices (Cov-SI) are employed to account for correlation of the uncertain parameters. Magnitude of Cov-SI is unfortunately unbounded, which can produce extremely large indices that limit their utility. Alternatively, mCov-SI are then proposed in order to bound this magnitude $\in$ [0, 1]. The polynomial expansion is coupled with an adaptive ANOVA strategy to provide an accurate surrogate as the union of several low-dimensional spaces, avoiding the typical computational cost of a high-dimensional expansion. It is also adaptively simplified according to the relative contribution of the different polynomials to the total variance. Finally, the approach is demonstrated for a laser-induced turbulent combustion simulation model, which includes parameters with correlated effects.

42 ENGINEERING↗

DOE OSTI2014

A Multi-Stage Wear Model for Grid-to-Rod Fretting of Nuclear Fuel Rods

The wear of fuel rod cladding against the supporting structures in the cores of pressurized water nuclear reactors (PWRs) is an important and potentially costly tribological issue. Grid-to-rod fretting (GTRF), as it is known, involves not only time-varying contact conditions, but also elevated temperatures, flowing hot water, aqueous tribo-corrosion, and the embrittling effects of neutron fluences. The multi-stage, closed-form analytical model described in this paper relies on published out-of-reactor wear and corrosion data and a set of simplifying assumptions to portray the conversion of frictional work into wear depth. The cladding material of interest is a zirconium-based alloy called Zircaloy-4, and the grid support is made of a harder and more wear-resistant material. Focus is on the wear of the cladding. The model involves an incubation stage, a surface oxide wear stage, and a base alloy wear stage. The wear coefficient, which is a measure of the efficiency of conversion of frictional work into wear damage, can change to reflect the evolving metallurgical condition of the alloy. Wear coefficients for Zircaloy-4 and for a polyphase zirconia layer were back-calculated for a range of times required to wear to a critical depth. Inputs for the model, like the friction coefficient, are taken from the tribology literature in lieu of in-reactor tribological data. Concepts of classical fretting were used as a basis, but are modified to enable the model to accommodate the complexities of the PWR environment. Factors like grid spring relaxation, pre-oxidation of the cladding, multiple oxide phases, gap formation, impact, and hydrogen embrittlement are part of the problem definition but uncertainties in their relative roles limits the ability to validate the model. Sample calculations of wear depth versus time in the cladding illustrate how GTRF wear might occur in a discontinuous fashion during months-long reactor operating cycles. A means to account for grid/rod gaps and repetitive impact effects on GTRF wear is proposed

fretting↗

DOE OSTI2015

Oxidation of a model alkane aerosol by OH radical: the emergent nature of reactive uptake

An accurate description of the evolution of organic aerosol in the Earth’s atmosphere is essential for climate models. However, the complexity of multiphase chemical and physical transformations has been challenging to describe at the level required to predict aerosol lifetimes and changes in chemical composition. In this work a model is presented that reproduces experimental data for the early stages of oxidative aging of squalane aerosol by hydroxyl radical (OH), a process governed by reactive uptake of gas phase species onto the particle surface. Simulations coupling free radical reactions and Fickian diffusion are used to elucidate how the measured uptake coefficient reflects the elementary steps of sticking of OH to the aerosol as a result of a gas-surface collision, followed by very rapid abstraction of hydrogen and subsequent free radical reactions. It is found that the uptake coefficient is not equivalent to a sticking coefficient or an accommodation coefficient: it is an intrinsically emergent process that depends upon particle size, viscosity, and OH concentration. An expression is derived to examine how these factors control reactive uptake over a broad range of atmospheric and laboratory conditions, and is shown to be consistent with simulation results. Well-mixed, liquid behavior is found to depend on the reaction conditions in addition to the nature of the organic species in the aerosol particle.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Electronic and optical properties of Fe, Pd, and Ti studied by reflection electron energy loss spectroscopy

We have studied the electronic and optical properties of Fe, Pd, and Ti by reflection electron energy-loss spectroscopy (REELS). REELS spectra recorded for primary energies in the range from 300 eV to 10 keV were corrected for multiple inelastically scattered electrons to determine the effective inelastic-scattering cross section. The dielectric functions and optical properties were determined by comparing the experimental inelastic-electron scattering cross section with a simulated cross section calculated within the semi-classical dielectric response model in which the only input is Im(−1/ε) by using the QUEELS-ε(k,ω)-REELS software package. The complex dielectric functions ε(k,ω), in the 0–100 eV energy range, for Fe, Pd, and Ti were determined from the derived Im(−1/ε) by Kramers-Kronig transformation and then the refractive index n and extinction coefficient k. The validity of the applied model was previously tested and found to give consistent results when applied to REELS spectra at energies between 300 and 1000 eV taken at widely different experimental geometries. In the present paper, we provide, for the first time, a further test on its validity and find that the model also gives consistent results when applied to REELS spectra in the full range of primary electron energies from 300 eV to 10000 eV. This gives confidence in the validity of the applied method.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2017

Dynamics and unsteady morphologies at ice interfaces driven by D 2 O–H 2 O exchange

The growth dynamics of D 2 O ice in liquid H 2 O in a microfluidic device were investigated between the melting points of D 2 O ice (3.8 °C) and H 2 O ice (0 °C). As the temperature was decreased at rates between 0.002 °C/s and 0.1 °C/s, the ice front advanced but retreated immediately upon cessation of cooling, regardless of the temperature. This is a consequence of the competition between diffusion of H 2 O into the D 2 O ice, which favors melting of the interface, and the driving force for growth supplied by cooling. Raman microscopy tracked H/D exchange across the solid H 2 O–solid D 2 O interface, with diffusion coefficients consistent with transport of intact H 2 O molecules at the D 2 O ice interface. At fixed temperatures below 3 °C, the D 2 O ice front melted continuously, but at temperatures near 0 °C a scalloped interface morphology appeared with convex and concave sections that cycled between growth and retreat. Finally, this behavior, not observed for D 2 O ice in contact with D 2 O liquid or H 2 O ice in contact with H 2 O liquid, reflects a complex set of cooperative phenomena, including H/D exchange across the solid–liquid interface, latent heat exchange, local thermal gradients, and the Gibbs–Thomson effect on the melting points of the convex and concave features.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Optical Classification of Lower Amazon Waters Based on In Situ Data and Sentinel-3 Ocean and Land Color Instrument Imagery

Optical water types (OWTs) were identified from an in situ dataset of concomitant biogeochemical and optical parameters acquired in the Amazon River and its tributaries, in the Lower Amazon region, at different hydrological conditions from 2014 to 2017. A seasonal bio-optical characterization was performed. The k-means classification was applied to the in situ normalized reflectance spectra (rn(λ)), allowing the identification of four OWTs. An optical index method was also applied to the rn(λ) defining the thresholds of the OWTs. Next, level-3 Sentinel-3 Ocean and Land Color Instrument images representative of the seasonal discharge conditions were classified using the identified in situ OWTs as reference. The differences between Amazon River and clearwater tributary OWTs were dependent on the hydrological dynamics of the Amazon River, also showing a strong seasonal variability. Each OWT was associated with a specific bio-optical and biogeochemical environment assessed from the corresponding absorption coefficient values of colored dissolved organic matter (aCDOM) and particulate matter (ap), chlorophyll-a and suspended particulate matter (SPM) concentrations, and aCDOM/ap ratio. The rising water season presented a unique OWT with high SPM concentration and high relative contribution of ap to total absorption compared to the other OWTs. This bio-optical characterization of Lower Amazon River waters represents a first step for developing remote sensing inversion models adjusted to the optical complexity of this region.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Detection and Signal Processing for Near-Field Nanoscale Fourier Transform Infrared Spectroscopy

Researchers from a broad spectrum of scientific and engineering disciplines are increasingly using scattering-type near-field infrared spectroscopic techniques to characterize materials non-destructively with nanoscale spatial resolution. However, a sub-optimal understanding of a technique's implementation can complicate data interpretation and act as a barrier to entering the field. Here the key detection and processing steps involved in producing scattering-type near-field nanoscale Fourier transform infrared spectra (nano-FTIR) are outlined. The self-contained mathematical and experimental work derives and explains: i) how normalized complex-valued nano-FTIR spectra are generated, ii) why the real and imaginary components of spectra qualitatively relate to dispersion and absorption respectively, iii) a new and generally valid equation for spectra which can be used as a springboard for additional modeling of the scattering processes, and iv) an algebraic expression that can be used to extract an approximation to the sample's local extinction coefficient from nano-FTIR. The algebraic model for weak oscillators is validated with nano-FTIR and attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectra on samples of polystyrene and Kapton and further provides a pedagogical pathway to cementing some of the technique's key qualitative attributes.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Comparison of Zirconium Redistribution in BISON EBR-II Models Using FIPD and IMIS Databases with Experimental Post Irradiation Examination

Metallic fuels have seen increased interest for future sodium fast reactors due to their material properties: high thermal conductivities and advantageous neutronic properties allow for greater fission densities. One drawback to typical metallic fuels is zirconium redistribution, which impacts this advantageous material and its neutronic properties. Unfortunately, the processes behind zirconium migration behavior are understood using first principles, so before these fuels are implemented in future fast reactors, characterization and fuel qualification regimes must be completed. These activities can be supported through the use of robust modeling using the most accurate empirical models currently available to fuel researchers around the world. The tool that allows researchers to model this complex coupled thermo-mechanical behavior and nuclear properties is BISON. Additionally, BISON model parameters need to be compared against PIE measurements. The current work utilizes two fuel pins from EBR-II experiment X441 to optimize various model parameters, including porosity correction factor, thermal conductivity, phase transition temperature, and diffusion coefficient multipliers, before implementing the final model for seven fuel pins with differing characteristics. To properly evaluate the BISON simulations, the results are compared to PIE metallography data for each fuel pin, to ensure the zirconium redistribution is properly reflected in the simulation results. Six out of seven analyzed fuel pins demonstrate good agreement between the metallography images and BISON results, showing alignment of the Zr-rich, Zr-depleted, and moderately Zr-enriched zones at various axial heights along the fuel pins. Further work is needed to refine the model parameters for general pin use.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Equation of State of CO 2 Shock Compressed to 1 TPa

Equation-of-state (pressure, density, temperature, internal energy) and reflectivity measurements on shock-compressed CO 2 at and above the insulating-to-conducting transition reveal new insight into the chemistry of simple molecular systems in the warm-dense-matter regime. CO 2 samples were precompressed in diamond-anvil cells to tune the initial densities from 1.35 g/cm 3 (liquid) to 1.74 g/cm 3 (solid) at room temperature and were then shock compressed up to 1 TPa and 93 000 K. Lastly, variation in initial density was leveraged to infer thermodynamic derivatives including specific heat and Gruneisen coefficient, exposing a complex bonded and moderately ionized state at the most extreme conditions studied.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Molecular diversity of dissolved organic matter reflects macroecological patterns in river networks

Deciphering dissolved organic matter (DOM) molecular complexity is crucial for understanding ecosystem function. Using the continental-scale Worldwide Hydrobiogeochemistry Observation Network for Dynamic Rivers Systems (WHONDRS) Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) dataset, we reveal fundamental scaling patterns of DOM chemodiversity with watershed characteristics. Analysis of 54 river sites shows local and regional watershed features significantly influence DOM chemodiversity (2500–8718 unique formulae), exhibiting consistent scaling patterns across compound classes and a novel latitudinal gradient (decreasing diversity with increasing latitude). Scaling relationships for DOM composition vary by compound class. Crucially, the scaling parameters (B, baseline chemodiversity; Z, sensitivity) are linearly interrelated. This B–Z relationship is most robust for potentially bio-labile carbohydrates (coefficient of determination R 2 ≈ 0.85), diminishing for recalcitrant, plant-derived molecules (such as lignin), and indicates (potential) biolability-dependent coupling between baseline diversity and environmental responsiveness. These quantitative scaling relationships, with scaling exponents ranging from − 2.1 to 2.2 across compound classes, enable prediction of DOM composition across watersheds, offering a framework to understand ecosystem responses to environmental change. This research bridges biogeochemistry and ecology, providing tools to anticipate molecular transformations across scales.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2015

The benefits of using time-frequency analysis with synthetic aperture focusing technique

Improvements in detection and resolution are always desired and needed. There are various instruments available for the inspection of concrete structures that can be used with confidence for detecting different defects. However, more often than not that confidence is heavily dependent on the experience of the operator rather than the clear, objective discernibility of the output of the instrument. The challenge of objective discernment is amplified when the concrete structures contain multiple layers of reinforcement, are of significant thickness, or both, such as concrete structures in nuclear power plants. We seek to improve and extend the usefulness of results produced using the synthetic aperture focusing technique (SAFT) on data collected from thick, complex concrete structures. A secondary goal is to improve existing SAFT results, with regards to repeatedly and objectively identifying defects and/or internal structure of concrete structures. Towards these goals, we are applying the time-frequency technique of wavelet packet decomposition and reconstruction using a mother wavelet that possesses the exact reconstruction property. However, instead of analyzing the coefficients of each decomposition node, we select and reconstruct specific nodes based on the frequency band it contains to produce a frequency band specific time-series representation. SAFT is then applied to these frequency specific reconstructions allowing SAFT to be used to visualize the reflectivity of a frequency band and that band's interaction with the contents of the concrete structure. We apply our technique to data sets collected using a commercial, ultrasonic linear array (MIRA) from two 1.5m × 2m × 25cm concrete test specimens. One specimen contains multiple layers of rebar. The other contains honeycomb, crack, and rebar bonding defect analogs. This approach opens up a multitude of possibilities for improved detection, readability, and overall improved objectivity. We will focus on improved defect/reinforcement isolation in thick and multilayered reinforcement environments. Additionally, the ability to empirically explore the possibility of a frequency-band-defect-type relationship or sensitivity becomes available.

36 MATERIALS SCIENCE↗

DOE OSTI2015

The Benefits of Using Time-Frequency Analysis with Synthetic Aperture Focusing Technique

Improvements in detection and resolution are always desired and needed. There are various instruments available for the inspection of concrete structures that can be used with confidence for detecting different defects. However, more often than not that confidence is heavily dependent on the experience of the operator rather than the clear, objective discernibility of the output of the instrument. The challenge of objective discernment is amplified when the concrete structures contain multiple layers of reinforcement, are of significant thickness, or both, such as concrete structures in nuclear power plants. We seek to improve and extend the usefulness of results produced using the synthetic aperture focusing technique (SAFT) on data collected from thick, complex concrete structures. A secondary goal is to improve existing SAFT results, with regards to repeatedly and objectively identifying defects and/or internal structure of concrete structures. Towards these goals, we are applying the time-frequency technique of wavelet packet decomposition and reconstruction using a mother wavelet that possesses the exact reconstruction property. However, instead of analyzing the coefficients of each decomposition node, we select and reconstruct specific nodes based on the frequency band it contains to produce a frequency band specific time-series representation. SAFT is then applied to these frequency specific reconstructions allowing SAFT to be used to visualize the reflectivity of a frequency band and that band s interaction with the contents of the concrete structure. We apply our technique to data sets collected using a commercial, ultrasonic linear array (MIRA) from two 1.5m x 2m x 25cm concrete test specimens. One specimen contains multiple layers of rebar. The other contains honeycomb, crack, and rebar bonding defect analogs. This approach opens up a multitude of possibilities for improved detection, readability, and overall improved objectivity. We will focus on improved defect/reinforcement isolation in thick and multilayered reinforcement environments. Additionally, the ability to empirically explore the possibility of a frequency-band-defect-type relationship or sensitivity becomes available.

concrete NDE↗

DOE OSTI2021

Single-Molecule Kinetics of Styrene Hydrogenation on Silica-Supported Vanadium: The Role of Disorder for Single-Atom Catalysts

We report a theoretical approach for the study of supported atom catalysis is developed based on recent advances in the study of single-molecule kinetics. This view is particularly useful in exhibiting the role of disorder in single-atom and single-site catalysts on amorphous supports. The distribution of passage times (or waiting times) through a complex catalytic network originating from a set of coupled active sites is described by a probability distribution function (PDF), f(t), that reflects the local environment of the reaction center. An efficient algorithm is developed based on the linear algebra of the Markov transition matrix that produces f(t) or its moments. The kinetics of the hydrogenation reaction of styrene on an organovanadium(III) catalyst supported on amorphous silica is studied. A kinetic model consisting of three intertwined catalytic cycles emanating from three chemically distinct active sites is proposed to describe the chemistry. Density functional theory (DFT) calculations are employed to determine the free energy barriers of the reactions, which are used to construct the rate coefficient matrix. The disorder induced by the amorphous support material is divided into a low-dimensional short-range component reflecting the covalent structures near the reaction center and a weaker long-range component modeling the bulk randomness. The results are computed and analyzed for a wide range of concentration values and disorder scenarios. The unusual structure in the f(t) PDF is found to occur for certain cases that reveal the contribution of multiple catalytic pathways acting in concert.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Autocorrelation and Multifractal Detrended Fluctuation Analyses Reveal Superdiffusive Mass Transport in Solvent-Filled Nanoporous Media

Fluorescence fluctuation spectroscopy experiments were conducted to better understand the complex mass transport dynamics of organic molecules in liquid-filled nanoporous media. Anodic aluminum oxide (AAO) membranes incorporating 10 and 20 nm diameter cylindrical pores were employed as model materials. Nile red (NR) dye was used as a fluorescent tracer. The dye was dissolved separately in ethanol and toluene at a concentration of 20 nM and used to fill the membrane nanopores. Confocal fluorescence microscopy was employed to capture photon intensity time series data reflecting apparent diffusion of the dye within the pores. Autocorrelation of these data revealed that NR diffusion within the membranes occurred over a broad range of time scales. The autocorrelation decays were fit to a model for one-dimensional diffusion incorporating both fast and slow components having apparent diffusion coefficients, D f and D s , differing by a factor of ∼100. The fast mechanism was attributed to hindered bulk-like diffusion in the central pore cavity, while slow diffusion likely involved absorption of the dye to the pore surfaces. Unfortunately, important evidence of diffusion anomalies is lost in the broad autocorrelation decays obtained. The method of multifractal detrended fluctuation analysis (MF-DFA) was applied to the same data as a means to overcome this limitation. MF-DFA revealed that time series acquired from within the nanopores were multifractal and exhibited evidence of anomalous superdiffusion, likely resulting from the participation of a desorption-mediated diffusion mechanism. Monte Carlo simulations of time series modeling desorption-mediated diffusion in cylindrical nanopores provided support for this assignment. Here, the new knowledge gained affords an improved understanding of hydrocarbon dynamics within nanoporous oil and gas shales.

Diffusion↗