Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “diffusivity”

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.

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

At least 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Correlating the Viscosity and Rate of Water Diffusion in Semisolid Gel-Forming Aerosol Particles

Aerosol particles are known to exist in highly viscous amorphous states at a low relative humidity and temperature. The slow diffusion of molecules in viscous particles impacts the uptake and loss of volatile and semivolatile species and the rate of heterogeneous chemistry. Recent work has demonstrated that in particles containing organic molecules and salts, the formation of two-phase gel states is possible, leading to observations of rigid particles that resist coalescence. The way that molecules diffuse and transport in gel systems is not well-characterized. In this work, we use an electrodynamic balance to levitate sample particles containing a range of organic compounds in mixtures with calcium chloride and measure the rate of water diffusion. Particles of the pure organics have been shown to form viscous amorphous states, while in mixtures with divalent salts, coalescence measurements have revealed the apparent solidification of particles, consistent with the formation of a gel state facilitated by ion–molecule interactions. We report in several cases that water transport can actually be increased in the rigid gel state relative to the pure compound that forms a viscous state under similar conditions. These measurements reveal the limitations of using viscosity as a metric for predicting molecular diffusion and that the gel structure that forms is a much stronger controlling factor in the rate of diffusion. In conclusion, this underscores the need for diffusion measurements as well as a deeper understanding of noncovalent molecular assembly that leads to supramolecular structures in aerosol particles.

organic-inorganic mixtures↗
DOE OSTI2025

Experimental Characterization of Hydrogen Diffusion in Shale Rocks for Geologic Storage Applications

As global energy systems undergo a transition to cleaner alternatives, geologic hydrogen storage has emerged as a promising solution for large-scale energy storage. A critical factor in determining the feasibility of this approach is the effectiveness of caprock formations, such as shale, in preventing hydrogen migration. This study investigates the diffusion behavior of hydrogen through shale to assess its suitability as a caprock for geologic hydrogen storage. Using a novel double-seal core holder design and a through-diffusion apparatus, hydrogen diffusion was measured through shale rock from the Eagle Ford and Wolfcamp Formations under dry conditions. These measurements were complemented by microstructural and mineralogical analyses using low-pressure nitrogen adsorption and X-ray diffraction. The effective diffusion coefficient of hydrogen in these shale caprocks ranged from 2.51 × 10 –8 to 9.85 × 10 –8 m 2 /s. Notably, we observed that the diffusion behavior was more related to the pore network structure and could not be attributed to differences in the total pore volume between shale types alone. Here, to further understand the role of pore network complexity, a fractal pore model was developed to correlate tortuosity with the fractal dimension of the pore structure (a measure of pore network complexity). The proposed model closely matched tortuosity values obtained from diffusion experiments, outperforming existing theoretical tortuosity–porosity correlations. These findings provide key quantitative parameters needed to assess the feasibility of geologic hydrogen storage as well as insights that can be applied to hydrogen storage in a range of geologic formations.

08 HYDROGEN↗
DOE OSTI2025

Defect-Mediated Diffusion Pathways in Spodumene Accelerate Lithium Transport

Lithium extraction from naturally occurring α-spodumene is hindered by poor lithium diffusivity, necessitating high-temperature phase transformation to a low-density β polymorph. Although β spodumene exhibits up to 5 orders of magnitude higher lithium-ion diffusivity, both phases have diffusion activation energies between 0.8 and 1 eV, indicating that polymorph density is not the controlling factor over diffusivity. We show that aluminum vacancies facilitate lithium-ion diffusion in α-spodumene by reducing the migration barrier from 2.4 to 0.9 eV. Bond valence site energy and nudged elastic band calculations show a new lithium local minimum site which promotes a one-dimensional percolation network by reducing the lithium intersite distance from 4.5 Å to 2.9 Å. However, aluminum vacancies are energetically unfavorable to percolate through the whole structure, resulting in very low net lithium diffusivity and highlighting the critical role of nonstoichiometric defects in facilitating lithium transport in rigid aluminosilicate structures.

Chemical structure↗
DOE OSTI2020

Long-Range Exciton Diffusion in Two-Dimensional Assemblies of Cesium Lead Bromide Perovskite Nanocrystals

Förster resonant energy transfer (FRET)-mediated exciton diffusion through artificial nanoscale building block assemblies could be used as an optoelectronic design element to transport energy. However, so far, nanocrystal (NC) systems supported only diffusion lengths of 30 nm, which are too small to be useful in devices. In this work, we demonstrate a FRET-mediated exciton diffusion length of 200 nm with 0.5 cm 2 /s diffusivity through an ordered, two-dimensional assembly of cesium lead bromide perovskite nanocrystals (CsPbBr 3 PNCs). Exciton diffusion was directly measured via steady-state and time-resolved photoluminescence (PL) microscopy, with physical modeling providing deeper insight into the transport process. This exceptionally efficient exciton transport is facilitated by PNCs' high PL quantum yield, large absorption cross section, and high polarizability, together with minimal energetic and geometric disorder of the assembly. This FRET-mediated exciton diffusion length matches perovskites' optical absorption depth, thus enabling the design of device architectures with improved performances and providing insight into the high conversion efficiencies of PNC-based optoelectronic devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Diffusion of Hydrophilic to Hydrophobic Forms of Nile Red in Aqueous C 12 EO 10 Gels by Variable Area Fluorescence Correlation Spectroscopy

In this work, solute diffusion within lyotropic liquid crystal gels prepared from a series of water and decaethylene glycol monododecyl ether (C 12 EO 10 ) mixtures was explored by variable area fluorescence correlation spectroscopy. Aqueous C 12 EO 10 gels were prepared in concentrations ranging from 55:45 to 70:30 wt% of surfactant and water. Small angle X-ray scattering revealed that these gels comprise hexagonal mesophases of cylindrical micelles. Micelle spacing was found to decrease with increasing C 12 EO 10 concentration. Three different Nile red (NR) dyes were employed as model solutes and were separately doped into the gels at nanomolar levels. These include a hydrophilic form of NR incorporating an anionic sulfonate group (NRSO 3 - ), a hydrophobic form incorporating a fourteen-carbon alkane tail (NRC 14 ), and commercial NR as an intermediate case. FCS data acquired from the gels revealed that NRSO 3 - diffused primarily in 3D. Its diffusion coefficient exhibited a monotonic decrease with increasing gel concentration and micelle packing density, consistent with confinement of its motions as a result of its exclusion from the micelle cores. NRC 14 exhibited the smallest diffusion coefficient, most likely due to its larger size and enhanced interactions with the micelle cores. NR yielded an intermediate diffusion coefficient and the most anomalous behavior of the three dyes, attributable to its facile partitioning between core and corona regions, and greater participation by 1D diffusion. The results of these studies afford an improved understanding of molecular mass transport through soft-matter nanomaterials like those being developed for use in drug delivery and membrane based chemical separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Multiscale modeling of solute diffusion in triblock copolymer membranes

We develop a multiscale simulation model for diffusion of solutes through porous triblock copolymer membranes. The approach combines two techniques: self-consistent field theory (SCFT) to predict the structure of the self-assembled, solvated membrane and on-lattice kinetic Monte Carlo (kMC) simulations to model diffusion of solutes. Solvation is simulated in SCFT by constraining the glassy membrane matrix while relaxing the brush-like membrane pore coating against the solvent. The kMC simulations capture the resulting solute spatial distribution and concentration-dependent local diffusivity in the polymer-coated pores; we parameterize the latter using particle-based simulations. We apply our approach to simulate solute diffusion through nonequilibrium morphologies of a model triblock copolymer, and we correlate diffusivity with structural descriptors of the morphologies. We also compare the model’s predictions to alternative approaches based on simple lattice random walks and find our multiscale model to be more robust and systematic to parameterize. Furthermore, our multiscale modeling approach is general and can be readily extended in the future to other chemistries, morphologies, and models for the local solute diffusivity and interactions with the membrane.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Se diffusion in CdTe thin films for photovoltaics

Manipulating CdSeTe bandgrading to enhance photocurrent and carrier lifetime is an essential part of high-performance CdTe photovoltaics (PVs). Here, we examine Se diffusion kinetics in single-crystal and polycrystalline CdTe during deposition, thermal annealing, and CdCl 2 treatments. Se distributions are determined by dynamic secondary-ion-mass spectroscopy and Auger electron spectroscopy depth profiling and coupled with electron backscatter diffraction images of the crystalline structure. Effective bulk and grain boundary diffusion coefficients are determined by analytical models and discussed in the context of processing and film morphology. Se is found to diffuse in CdTe at much higher rates during CdCl 2 treatments than with thermal processing alone. GB diffusion also occurs at a significantly faster rate than bulk diffusion. As a result of these two effects, the near interface bulk and GB Se diffusion during CdCl 2 treatments dominates the bandgrading profiles in CdTe PVs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Maximum CO 2 diffusion inside leaves is limited by the scaling of cell size and genome size

Maintaining high rates of photosynthesis in leaves requires efficient movement of CO 2 from the atmosphere to the mesophyll cells inside the leaf where CO 2 is converted into sugar. CO 2 diffusion inside the leaf depends directly on the structure of the mesophyll cells and their surrounding airspace, which have been difficult to characterize because of their inherently three-dimensional organization. Yet faster CO 2 diffusion inside the leaf was probably critical in elevating rates of photosynthesis that occurred among angiosperm lineages. Here we characterize the three-dimensional surface area of the leaf mesophyll across vascular plants. We show that genome size determines the sizes and packing densities of cells in all leaf tissues and that smaller cells enable more mesophyll surface area to be packed into the leaf volume, facilitating higher CO 2 diffusion. Measurements and modelling revealed that the spongy mesophyll layer better facilitates gaseous phase diffusion while the palisade mesophyll layer better facilitates liquid-phase diffusion. Our results demonstrate that genome downsizing among the angiosperms was critical to restructuring the entire pathway of CO 2 diffusion into and through the leaf, maintaining high rates of CO 2 supply to the leaf mesophyll despite declining atmospheric CO 2 levels during the Cretaceous.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Explainable machine learning for hydrogen diffusion in metals and random binary alloys

Hydrogen diffusion in metals and alloys plays an important role in the discovery of new materials for fuel cell and energy storage technology. While analytic models use hand-selected features that have clear physical ties to hydrogen diffusion, they often lack accuracy when making quantitative predictions. Machine learning models are capable of making accurate predictions, but their inner workings are obscured, rendering it unclear which physical features are truly important. To develop interpretable machine learning models to predict the activation energies of hydrogen diffusion in metals and random binary alloys, we create a database for physical and chemical properties of the species and use it to fit six machine learning models. Our models achieve root-mean-squared errors between 98–119 meV on the testing data and accurately predict that elemental Ru has a large activation energy, while elemental Cr and Fe have small activation energies. By analyzing the feature importances of these fitted models, we identify relevant physical properties for predicting hydrogen diffusivity. While metrics for measuring the individual feature importances for machine learning models exist, correlations between the features lead to disagreement between models and limit the conclusions that can be drawn. Instead grouped feature importance, formed by combining the features via their correlations, agree across the six models and reveal that the two groups containing the packing factor and electronic specific heat are particularly significant for predicting hydrogen diffusion in metals and random binary alloys. In conclusion, this framework allows us to interpret machine learning models and enables rapid screening of new materials with the desired rates of hydrogen diffusion.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Optical information transfer through random unknown diffusers using electronic encoding and diffractive decoding

Free-space optical information transfer through diffusive media is critical in many applications, such as biomedical devices and optical communication, but remains challenging due to random, unknown perturbations in the optical path. We demonstrate an optical diffractive decoder with electronic encoding to accurately transfer the optical information of interest, corresponding to, e.g., any arbitrary input object or message, through unknown random phase diffusers along the optical path. This hybrid electronic-optical model, trained using supervised learning, comprises a convolutional neural network-based electronic encoder and successive passive diffractive layers that are jointly optimized. After their joint training using deep learning, our hybrid model can transfer optical information through unknown phase diffusers, demonstrating generalization to new random diffusers never seen before. The resulting electronic-encoder and optical-decoder model was experimentally validated using a 3D-printed diffractive network that axially spans <70λ, where λ = 0.75 mm is the illumination wavelength in the terahertz spectrum, carrying the desired optical information through random unknown diffusers. The presented framework can be physically scaled to operate at different parts of the electromagnetic spectrum, without retraining its components, and would offer low-power and compact solutions for optical information transfer in free space through unknown random diffusive media.

36 MATERIALS SCIENCE↗
NASA NTRS2002

Diffusion NMR methods applied to xenon gas for materials study

We report initial NMR studies of (i) xenon gas diffusion in model heterogeneous porous media and (ii) continuous flow laser-polarized xenon gas. Both areas utilize the pulsed gradient spin-echo (PGSE) techniques in the gas phase, with the aim of obtaining more sophisticated information than just translational self-diffusion coefficients--a brief overview of this area is provided in the Introduction. The heterogeneous or multiple-length scale model porous media consisted of random packs of mixed glass beads of two different sizes. We focus on observing the approach of the time-dependent gas diffusion coefficient, D(t) (an indicator of mean squared displacement), to the long-time asymptote, with the aim of understanding the long-length scale structural information that may be derived from a heterogeneous porous system. We find that D(t) of imbibed xenon gas at short diffusion times is similar for the mixed bead pack and a pack of the smaller sized beads alone, hence reflecting the pore surface area to volume ratio of the smaller bead sample. The approach of D(t) to the long-time limit follows that of a pack of the larger sized beads alone, although the limiting D(t) for the mixed bead pack is lower, reflecting the lower porosity of the sample compared to that of a pack of mono-sized glass beads. The Pade approximation is used to interpolate D(t) data between the short- and long-time limits. Initial studies of continuous flow laser-polarized xenon gas demonstrate velocity-sensitive imaging of much higher flows than can generally be obtained with liquids (20-200 mm s-1). Gas velocity imaging is, however, found to be limited to a resolution of about 1 mm s-1 owing to the high diffusivity of gases compared with liquids. We also present the first gas-phase NMR scattering, or diffusive-diffraction, data, namely flow-enhanced structural features in the echo attenuation data from laser-polarized xenon flowing through a 2 mm glass bead pack. c2002 John Wiley & Sons, Ltd.

NASA Discipline Life Sciences Technologies↗
DOE OSTI2024

Direct Visualization of Defect‐Controlled Diffusion in van der Waals Gaps

Abstract Diffusion processes govern fundamental phenomena such as phase transformations, doping, and intercalation in van der Waals (vdW) bonded materials. Here, the diffusion dynamics of W atoms by visualizing the motion of individual atoms at three different vdW interfaces: hexagonal boron nitride (BN)/vacuum, BN/BN, and BN/WSe 2 , by recording scanning transmission electron microscopy movies is quantified. Supported by density functional theory (DFT) calculations, it is inferred that in all cases diffusion is governed by intermittent trapping at electron beam‐generated defect sites. This leads to diffusion properties that depend strongly on the number of defects. These results suggest that diffusion and intercalation processes in vdW materials are highly tunable and sensitive to crystal quality. The demonstration of imaging, with high spatial and temporal resolution, of layers and individual atoms inside vdW heterostructures offers possibilities for direct visualization of diffusion and atomic interactions, as well as for experiments exploring atomic structures, their in situ modification, and electrical property measurements of active devices combined with atomic resolution imaging.

Chemistry↗
DOE OSTI2022

Passive and active peer effects in the spatial diffusion of residential solar panels: A case study of the Las Vegas Valley

This research analyzes the role of peer influences on the adoption of residential rooftop solar photovoltaic panels (PV) within the context of the Diffusion of Innovation Theory. PV literature indicates that adopters are influenced by word of mouth (WOM) information exchange with peers, i.e., active peer effects, while other studies suggest that living near households with visible rooftop PV installations influences adoption, i.e., passive peer effects. We bridge the gap in this literature by conducting a mixed methods analysis. We develop and administer a survey to Las Vegas Valley (LVV) residents to identify current and potential PV adopters' perceptions of peer-effects and consumer intention variables. We conduct a spatial analysis of Google's Project Sunroof data to identify LVV neighborhoods in the later stages of the PV diffusion process, i.e., those with the highest PV adoption rates. Key results show that current PV adopters living in early diffusion areas report significantly higher active and passive peer effects compared to adopters in later diffusion areas. Potential adopters in later diffusion areas report higher passive peer effects than those in early diffusion areas. Overall, because LVV has a low PV adoption rate (<3%), short term strategies aimed at increasing PV adoption should emphasize WOM active peer effects. Here, we caution against long-term green marketing strategies focusing solely on peer-effects as the PV market matures.

14 SOLAR ENERGY↗
DOE OSTI2020

Fission gas diffusion and release for Cr 2 O 3 -doped UO 2 : From the atomic to the engineering scale

Here, the anticipated benefits of large grains in Cr 2 O 3 -doped UO 2 pellets include improved mechanical and fission gas retention properties. To support the assessment of fission gas release (FGR) from doped pellets, the impact of doping on fission gas diffusivity for in-reactor conditions must be understood. In this work, we tackle this issue by informing the fission gas model within the BISON fuel performance code using material models developed at the atomic scale. The investigation of intra-granular fission gas diffusivity in Cr 2 O 3 -doped UO 2 is carried out by adapting a cluster dynamics model that, accounting for UO 2 thermochemistry, is capable of describing Xe diffusion under irradiation in undoped UO 2 as the starting point. Using a thermodynamic analysis, it is shown that in stoichiometric UO 2 with additions of Cr 2 O 3 the oxygen potential is defined by the Cr-Cr 2 O 3 two-phase equilibrium. Using the cluster dynamics model, the predicted Xe diffusivity in doped UO 2 was significantly increased in both the intrinsic and irradiation-enhanced regimes compared to undoped UO 2 as a result of higher concentrations of uranium and oxygen vacancies, respectively. This is a consequence of the more oxidizing conditions at high temperature, and more reducing conditions at low temperature, as a result of doping. Arrhenius functions have been fitted to the cluster dynamics results to enable implementation of the new diffusivities in the BISON fission gas behavior model. BISON simulations were carried out, showing the competing effects of the enlarged grains and the new fission gas diffusivity model, which act to suppress and enhance fission gas release, respectively. The new physics-informed model was validated against in-reactor experimental measurements under normal operation. Additionally, benchmarking was carried out for power ramp conditions. The predicted fission gas release agreed well with the experimental data, showing noticeable improvements over the standard UO 2 model.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Diffusion of cesium in oxidized and unoxidized IG-110 nuclear graphite

Time-release diffusion measurements of cesium have been conducted over the temperature range 1073 K – 1973 K on oxidized and unoxidized IG-110 graphite. Four cesium concentrations were tested to investigate the concentration dependence of the diffusion coefficient. Two levels of oxidation were tested and compared to unoxidized concentration-matched sets to explore the effects of graphite oxidation. Here, the results demonstrate that cesium diffusion coefficient in unoxidized IG-110 graphite is independent of concentration within the range 34 – 163 µg Cs/g graphite . Above this, the effective cesium diffusion coefficient changes with concentration. The diffusion coefficient was increased by a factor of 2–12 in the oxidized set with 7.8% mass loss. These results can be used to aid predictive modeling of cesium diffusion in HTGR cores.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Pattern formation in a coupled membrane-bulk reaction-diffusion model for intracellular polarization and oscillations

Reaction-diffusion systems have been widely used to study spatio-temporal phenomena in cell biology, such as cell polarization. Coupled bulk-surface models naturally include compartmentalization of cytosolic and membrane-bound polarity molecules. Here we study the distribution of the polarity protein Cdc42 in a mass-conserved membrane-bulk model, and explore the effects of diffusion and spatial dimensionality on spatio-temporal pattern formation. We first analyze a one-dimensional (1-D) model for Cdc42 oscillations in fission yeast, consisting of two diffusion equations in the bulk domain coupled to nonlinear ODEs for binding kinetics at each end of the cell. In 1-D, our analysis reveals the existence of symmetric and asymmetric steady states, as well as anti-phase relaxation oscillations typical of slow-fast systems. We then extend our analysis to a two-dimensional (2-D) model with circular bulk geometry, for which species can either diffuse inside the cell or become bound to the membrane and undergo a nonlinear reaction-diffusion process. We also consider a nonlocal system of PDEs approximating the dynamics of the 2-D membrane-bulk model in the limit of fast bulk diffusion. In all three model variants we find that mass conservation selects perturbations of spatial modes that simply redistribute mass. In 1-D, only anti-phase oscillations between the two ends of the cell can occur, and in-phase oscillations are excluded. In higher dimensions, no radially symmetric oscillations are observed. Instead, the only instabilities are symmetry-breaking, either corresponding to stationary Turing instabilities, leading to the formation of stationary patterns, or to oscillatory Turing instabilities, leading to traveling and standing waves. Codimension-two Bogdanov—Takens bifurcations occur when the two distinct instabilities coincide, causing traveling waves to slow down and to eventually become stationary patterns. Our work clarifies the effect of geometry and dimensionality on behaviors observed in ma.ss-conserved cell polarity models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements

Physics-informed neural networks (PINNs) were recently proposed in [18] as an alternative way to solve partial differential equations (PDEs). A neural network (NN) represents the solution, while a PDE-induced NN is coupled to the solution NN, and all differential operators are treated using automatic differentiation. Here, we first employ the standard PINN and a stochastic version, sPINN, to solve forward and inverse problems governed by a non-linear advection–diffusion–reaction (ADR) equation, assuming we have some sparse measurements of the concentration field at random or pre-selected locations. Subsequently, we attempt to optimise the hyper-parameters of sPINN by using the Bayesian optimisation method (meta-learning) and compare the results with the empirically selected hyper-parameters of sPINN. In particular, for the first part in solving the inverse deterministic ADR, we assume that we only have a few high-fidelity measurements, whereas the rest of the data is of lower fidelity. Hence, the PINN is trained using a composite multi-fidelity network, first introduced in [12], that learns the correlations between the multi-fidelity data and predicts the unknown values of diffusivity, transport velocity and two reaction constants as well as the concentration field. For the stochastic ADR, we employ a Karhunen–Loève (KL) expansion to represent the stochastic diffusivity, and arbitrary polynomial chaos (aPC) to represent the stochastic solution. Correspondingly, we design multiple NNs to represent the mean of the solution and learn each aPC mode separately, whereas we employ a separate NN to represent the mean of diffusivity and another NN to learn all modes of the KL expansion. For the inverse problem, in addition to stochastic diffusivity and concentration fields, we also aim to obtain the (unknown) deterministic values of transport velocity and reaction constants. The available data correspond to 7spatial points for the diffusivity and 20 space–time points for the solution, both sampled 2000 times. We obtain good accuracy for the deterministic parameters of the order of 1–2% and excellent accuracy for the mean and variance of the stochastic fields, better than three digits of accuracy. In the second part, we consider the previous stochastic inverse problem, and we use Bayesian optimisation to find five hyper-parameters of sPINN, namely the width, depth and learning rate of two NNs for learning the modes. Here, we obtain much deeper and wider optimal NNs compared to the manual tuning, leading to even better accuracy, i.e., errors less than 1% for the deterministic values, and about an order of magnitude less for the stochastic fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Point Defects Control Guest Molecule Diffusion in the 1D Pores of Zn(tbip)

Molecular diffusion plays a critical role in metal-organic frameworks (MOFs) within the application of kinetic chemical separations. We carefully study in this work the unexpected role of point defects for short-chain alkanes diffusing in Zn(tbip), an MOF with rigid one-dimensional (1D) channels. Inside a defect-free Zn(tbip), guest molecules are expected to follow single-file diffusion along 1D channels. It has been found previously by Heinke et al. that these parallel 1D channels are connected for molecular diffusion. Our density functional theory (DFT) calculations suggest that linker vacancy defects could arise under experimentally relevant conditions by removing a pair of linkers. Further climbing-image nudged elastic band (cNEB) DFT calculations indicate that hopping of short-chain alkanes between adjacent 1D channels over defect windows can occur at moderate temperatures. In addition to providing insights into connected adjacent 1D channels in Zn(tbip), Heinke et al. also inferred that most 1D pores are blocked from a microkinetic model. Additionally, we explored the influence of hydrolyzed linker created by the formation of linker removal inside 1D pores. Our DFT calculations show that the linkers can effectively block the pores and the linker diffusion in 1D channels is slow. Our results, for the first time, offer a mechanistic explanation of the unexpected molecular diffusion behavior in this MOF with 1D channels.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗