Publications

2024

1. BEaTmap: Simplified Rigorous BET Analysis of Isothermal Adsorption Data

   Ellsworth Bell, David Morris, Mohammad Amin Sadeghi, and 1 more author

   Journal of Open Research Software, 2024

   BEaTmap is an open-source software designed to improve the analysis of isothermal adsorption data. The specific surface area of a porous material is commonly obtained through applying the classic BET theory to the adsorption of inert gases and vapors in the relative pressure range of 5% to 35% and presented as a single value. However, this cookie-cutter approach can yield thermodynamically inconsistent results that are incorrect or misleading. BEaTmap provides a conceptual tool for analyzing the entire set of isothermal adsorption data with BET theory and presents the range of all possible values that are thermodynamically and mathematically consistent. The analysis is presented as a heatmap indicating the results for all valid relative pressure ranges, offering the user a more comprehensive specific surface area answer. The code is written in Python and is available as both a web app and Python package. BEaTmap, documentation, and examples are freely available on GitHub.

2. Rapid prediction of particle-scale state-of-lithiation in Li-ion battery microstructures using convolutional neural networks

   Sam Ly, Mohammad Amin Sadeghi, Niloofar Misaghian, and 2 more authors

   Applied Energy, 2024

   A machine learning (ML) model was developed to study the discharge behaviour of a Li(x)Ni(0.33)Mn(0.33)Co(0.33)O2 half-cell with particle-scale resolution. The ML model could predict the state-of-lithiation of the particles as a function of time and C-rate. Although direct numerical simulation has been well established in this area as the prevalent method of modeling batteries, computational expense increases going from 1D-homogenized model to particle-resolved 3D models. The present ML model was trained on a total of sixty different electrodes with various lengths for a total of 4 different C-rates: 0.25, 1, 2, and 3C. The ML model used convolutional layers, resulting in an image-to-image regression network. To evaluate model performance, the root mean squared error was compared between the state of lithiation (SoL) predicted by the ML model and ground truth results from pore-scale direct numerical simulation (DNS) on unseen electrode configurations. It was shown that the ML model can predict the SoL at better than 99% accuracy in terms of relative error, but almost an order of magnitude faster than the DNS approach. The present work was limited to 2D cases but demonstrates that ML is a viable path forward for studying real 3D microstructures.

3. Predicting PEMFC performance from a volumetric image of catalyst layer structure using pore network modeling

   Mohammad Amin Sadeghi, Zohaib Atiq Khan, Mehrez Agnaou, and 7 more authors

   Applied Energy, 2024

   A pore-scale model of a PEMFC cathode catalyst layer was developed using the pore network approach and used to predict polarization behavior. A volumetric image of a PEMFC catalyst layer was obtained using FIB-SEM with 4 nm resolution in all 3 directions. The original image only differentiated between solid and void, so a simple but effective algorithm was developed to insert tightly packed, but non-overlapping carbon spheres into the solid phase, which were then decorated with catalyst sites. The resultant image was a 4-phase image containing void, ionomer, carbon, and catalyst, each in proportion to the known Pt loading, carbon-to-ionomer ratio, and porosity. A multiphase pore network model was extracted from this image, and multiphysics simulations were conducted to predict the polarization behavior of an operating cell. It was shown that not only can beginning of life polarization performance be predicted with minimal fitting parameters, but degraded performance 30 k cycles was also well captured with no additional fitting. This latter result was accomplished by deleting catalyst sites from the network in proportion to the experimentally observed distribution of electrochemical surface area loss, obtained from TEM image of catalyst loading. The model included partitioning of oxygen into the ionomer phase, explicitly incorporating the oxygen transport resistance which dominates cell performance at higher current density. Although Knudsen diffusion is present at the scales present (< 100 nm), it represented a negligible fraction of the total transport resistance, which was dominated by the low solubility and slow diffusivity in the ionomer phase. This work showed that the performance of a typical PEMFC is highly dependent on the structural details of the catalyst layer, to the extent that polarization curves can be well predicted by direct inspection of an image of the catalyst layer. This work paves the way for a deeper understanding of the structure-performance relationship in these complex materials and the search for optimized catalyst layer designs.

2023

1. Utilizing pore network modeling for performance analysis of multi-layer electrodes in vanadium redox flow batteries

   Niloofar Misaghian, Mohammad Amin Sadeghi, Kyu-min Lee, and 2 more authors

   Journal of The Electrochemical Society, 2023

   Vanadium redox flow batteries (VRFBs) are promising energy storage devices. The microstructure of the porous electrode affects the performance of VRFBs. Therefore, identifying optimized electrode structures is an active research area. However, designing optimal microstructures requires studying varieties of structural parameters and design cases using a modeling tool with low computational cost. In this study, a pore network modeling (PNM) framework was developed to study the effects of multi-layer electrodes on VRFB electrode performance. In contrast to previous experimental works that were focused on multi-layer structure of the same material, this study explored the effect of using different microstructures in each layer. Using an image generation algorithm, fibrous materials were generated from which pore networks were extracted. The developed PNM included a modification by adding throat nodes in the geometry to accommodate a velocity dependent mass transfer coefficient. The results showed that putting a highly permeable layer near the membrane provides an alternative preferential path for fluid to distribute and supply those regions with reactive species, resulting in 57% increase in limiting current density in contrast to the opposite order. However, selection of the desired structures must be based on a trade-off between the current/power density and pressure drop.

2. Bottom-up design of porous electrodes by combining a genetic algorithm and a pore network model

   Rik Gorp, Maxime Heijden, Mohammad Amin Sadeghi, and 2 more authors

   Chemical Engineering Journal, 2023

   The microstructure of porous electrodes determines multiple performance-defining properties, such as the available reactive surface area, mass transfer rates, and hydraulic resistance. Thus, optimizing the electrode architecture is a powerful approach to enhance the performance and cost-competitiveness of electrochemical technologies. To expand our current arsenal of electrode materials, we need to build predictive frameworks that can screen a large geometrical design space while being physically representative. Here, we present a novel approach for the optimization of porous electrode microstructures from the bottom-up that couples a genetic algorithm with a previously validated electrochemical pore network model. In this first demonstration, we focus on optimizing redox flow battery electrodes. The genetic algorithm manipulates the pore and throat size distributions of an artificially generated microstructure with fixed pore positions by selecting the best-performing networks, based on the hydraulic and electrochemical performance computed by the model. For the studied VO2+/VO2+ electrolyte, we find an increase in the fitness of 75% compared to the initial configuration by minimizing the pumping power and maximizing the electrochemical power of the system. The algorithm generates structures with improved fluid distribution through the formation of a bimodal pore size distribution containing preferential longitudinal flow pathways, resulting in a decrease of 73% for the required pumping power. Furthermore, the optimization yielded an 47% increase in surface area resulting in an electrochemical performance improvement of 42%. Our results show the potential of using genetic algorithms combined with pore network models to optimize porous electrode microstructures for a wide range of electrolyte composition and operation conditions.

2022

1. Porous electrodes in redox flow batteries

   Kiana Amini, Mohammad Amin Sadeghi, Mark Pritzker, and 1 more author

   In Encyclopedia of Energy Storage, 2022

   Porous electrodes are central to redox flow batteries. The increased surface area provided by the porous material dramatically enhances the volumetric current density that can be obtained at a given voltage. However, this creates challenges in delivering the reactants to the electrode due to transport resistances in the porous electrode. This article provides an overview the various materials and structures typically used as electrodes, and how the structural properties of an electrode impact battery performance via the effective transport properties. A 2D pore-scale model is developed as a means of illustrating the various trade-offs that must be considered, such as pressure drop, flow rate, reactive surface area, and pumping cost.

2. Extending pore network models to include electrical double layer effects in micropores for studying capacitive deionization

   Michael McKague, Hamed Fathiannasab, Mehrez Agnaou, and 2 more authors

   Desalination, 2022

   Capacitive Deionization (CDI) is an electro-driven water desalination device. It consists of oppositely charged porous electrodes on either side of a dielectric spacer. The electrodes may be either capacitive or pseudocapacitive electrodes. Capacitive electrodes work by storing ions in the Electrical Double Layers (EDLs) of micropores. To model the uptake of ions into EDLs, transport equations are coupled with appropriate EDL structure model. Previous research has shown that pore structure effects CDI performance. A Pore Network Model (PNM) is a unique tool capable of measuring the effects of pore structure. To the best of the author’s knowledge, there is no pore network model of CDI or any PNM in general which applies EDL theory. It is in this work that discretized PDEs are derived for modelling the capacitive storage of ions in a pore network using EDL theory. The EDL theories studied were: Helmholtz, Gouy-Chapman-Stern and modified Donnan. Results from the PNM framework were compared to finite element solver results, and it was found that the error in concentration and potential never exceeded 10% or 15% respectively. Lastly, the new pore network framework was used to accurately predict salt content in effluent of a stop flow CDI cell found in literature.

3. Prediction of diffusional conductance in extracted pore network models using convolutional neural networks

   Niloo Misaghian, Mehrez Agnaou, Mohammad Amin Sadeghi, and 4 more authors

   Computers & Geosciences, 2022

   Pore network modeling (PNM) based on networks extracted from tomograms is a well-established tool for simulating pore-scale transport behavior in porous media. A key element of this approach is the accurate determination of pore-to-pore conductance values, which is a complex task that greatly affects the accuracy of flow and diffusive mass transport studies. Classic methods of conductance estimation based on analytical solutions and shape factors only apply to simple pore geometries, whereas real porous media contain irregular-shaped pores. Although direct numerical simulations (DNS) can accurately estimate conductance considering pores’ real morphology, it has a high computational cost that becomes infeasible for large tomograms. The present work remedies this problem using a deep learning (DL) approach, with a specific focus on diffusional transport which has received less attention than hydraulic conductance. A convolutional neural network (CNN) model was trained to estimate diffusive conductance of PNM elements from volumetric images of porous media. The developed framework estimates the diffusive conductance by analyzing individual pore-to-pore 3D images isolated from the tomogram to fully capture the topology and shapes. A key outcome of the present work is that only images of the pore regions are used as input data, avoiding excessive preprocessing time for data preparation. The results of the diffusive conductance prediction show good agreement with the test data obtained by DNS method, with 0.94 R^2 prediction accuracy and a speedup of 500x in prediction runtime.

4. Investigating the role of the triple-phase boundary in zinc-air cathodes using pore network modeling

   Niloofar Misaghian, Mohammad Amin Sadeghi, Edward Roberts, and 1 more author

   Journal of The Electrochemical Society, 2022

   Zinc-air flow batteries are a promising energy storage technology. Their performance depends on their porous cathodes where the oxygen reduction reaction (ORR) occurs. A key feature of the cathode is the invasion of electrolyte, creating the so-called triple phase boundary between air, electrolyte and catalyst, which is shown in this work to be an overly simplified picture. In this study a mathematical framework based on pore network modeling (PNM) was developed to better understand the interplay between electrode structure, transport of species and electrolyte invasion. The results suggest that increasing electrolyte volume provides highly branched invasion pattern and enhances performance up to a saturation of 0.7, whereas further invasion reduces air-liquid interfacial area and lowers the performance. Interestingly, at lower saturations (<0.3) the liquid structure is so excessively branched that hydroxide ions are unable to diffuse to the anode at a sufficient rate, resulting in supersaturation, which is a degradation problem. The pore size distribution of the catalyst layer also affects the performance with wider pore size distributions generally performing better. This work represents the first 3D PNM of a zinc-air cathode that includes all the key physics and transport mechanisms, enabling prediction of the structure-performance relationship of porous cathodes.

5. Assessing the versatility and robustness of pore network modeling to simulate redox flow battery electrode performance

   Maxime Heijden, Rik Gorp, Mohammad Amin Sadeghi, and 2 more authors

   Journal of The Electrochemical Society, 2022

   Porous electrodes are core components that determine the performance of redox flow batteries. Thus, optimizing their microstructure is a powerful approach to reduce system costs. Here we present a pore network modeling framework that is microstructure and chemistry agnostic, iteratively solves transport equations in both half-cells, and utilizes a network-in-series approach to simulate the local transport phenomena within porous electrodes at a low computational cost. In this study, we critically assess the versatility and robustness of pore network models to enable the modeling of different electrode geometries and redox chemistries. To do so, the proposed model was validated with two commonly used carbon fiber-based electrodes (a paper and a cloth), by extracting topologically equivalent networks from X-ray tomograms, and evaluated for two model redox chemistries (an aqueous iron-based and a non-aqueous TEMPO-based electrolyte). We find that the modeling framework successfully captures the experimental performance of the non-aqueous electrolyte but is less accurate for the aqueous electrolyte which was attributed to incomplete wetting of the electrode surface in the conducted experiments. Furthermore, the validation reveals that care must be taken when extracting networks from the tomogram of the woven cloth electrode, which features a multiscale microstructure with threaded fiber bundles. Employing this pore network model, we elucidate structure-performance relationships by leveraging the performance profiles and the simulated local distributions of physical properties and finally, we deploy simulations to identify efficient operation envelopes.

2021

1. Pore network modeling of galvanostatic discharge behaviour of lithium-ion battery cathodes

   Zohaib Atiq Khan, Mehrez Agnaou, Mohammad Amin Sadeghi, and 2 more authors

   Journal of The Electrochemical Society, 2021

   The performance of Lithium-Ion batteries (LIB’s) strongly depends on 3D microstructure and continued research is needed for the development and optimization of electrode designs to further reduce cost and improve performance and durability. In this work, a pore network modelling approach is presented to understand the structure-performance relationship of porous cathodes of LIB’s. It was demonstrated that pore network models can efficiently predict the rate-dependent capacity of an electrode using only a 3-phase tomogram as input. The developed modelling framework was used to perform structural analysis on two Li(Ni0.5Mn0.3Co0.2)O2 (NMC532) cathodes of different thickness and calendaring pressure and revealed important insights of microstructural heterogeneities inside porous structures, including spatial distribution of concentration, potential and state of lithiation in electrolyte, active material and carbon binder domain. The computational performance of the pore network model was analyzed, and excellent performance was demonstrated, taking hours instead of weeks for a similar direct numerical simulation. The novel modelling framework reported in this study will enable the study of local heterogeneities in other types of cathode material to help screen next-generation electrode designs, augmenting and informing time-consuming cell fabrication and laboratory testing.

2. Simulation of methane steam reforming in a catalytic micro-reactor using a combined analytical approach and response surface methodology

   Mostafa Pourali, Javad Abolfazli Esfahani, Mohammad Amin Sadeghi, and 2 more authors

   International Journal of Hydrogen Energy, 2021

   In this study, a steady-state analytical model for heat and mass transfer in a 2D micro-reactor coated with a Nickel-based catalyst is developed to investigate microscale hydrogen production. Appropriate correlations for each species’ net rate of production or consumption, mass diffusivity, and the heat of reactions are developed using a detailed reaction mechanism of methane steam reforming. The energy and species conservation equations are then solved for the reactive mixture coupled with the wall energy equation. Finally, the response surface methodology (RSM) is employed to study the effects of channel height, inlet velocity and temperature, wall thickness and conductivity, and external heat flux on CH4 conversion. It is found that the inlet gas temperature, among different parameters, has the most influence on the overall performance of the microchannel hydrogen production. Also, the maximum necessary heat of reforming reaction increases by 84% and 26% if the CH4 conversion changes from 50% to 60% and 60% to 70%, respectively. The developed analytical simulation can be a useful tool for designing experiments in micro-scale hydrogen production.

2020

1. Modeling transport of charged species in pore networks: Solution of the Nernst–Planck equations coupled with fluid flow and charge conservation equations

   Mehrez Agnaou, Mohammad Amin Sadeghi, Thomas G Tranter, and 1 more author

   Computers & Geosciences, 2020

   A pore network modeling (PNM) framework1 for the simulation of transport of charged species, such as ions, in porous media is presented. It includes the Nernst–Planck (NP) equations for each charged species in the electrolytic solution in addition to a charge conservation equation which relates the species concentration to each other. Moreover, momentum and mass conservation equations are adopted and there solution allows for the calculation of the advective contribution to the transport in the NP equations. The proposed framework is developed by first deriving the numerical model equations (NMEs) corresponding to the partial differential equations (PDEs) based on several different time and space discretization schemes, which are compared to assess solutions accuracy. The derivation also considers various charge conservation scenarios, which also have pros and cons in terms of speed and accuracy. Ion transport problems in arbitrary pore networks were considered and solved using both PNM and finite element method (FEM) solvers. Comparisons showed an average deviation, in terms of ions concentration, between PNM and FEM below 5% with the PNM simulations being over times faster than the FEM ones for a medium including about pores. The improved accuracy is achieved by utilizing more accurate discretization schemes for both the advective and migrative terms, adopted from the CFD literature. The NMEs were implemented within the open-source package OpenPNM based on the iterative Gummel algorithm with relaxation. This work presents a comprehensive approach to modeling charged species transport suitable for a wide range of applications from electrochemical devices to nanoparticle movement in the subsurface.

2. Dispersion modeling in pore networks: A comparison of common pore-scale models and alternative approaches

   Mohammad Amin Sadeghi, Mehrez Agnaou, Jake Barralet, and 1 more author

   Journal of contaminant hydrology, 2020

   Mass transfer in porous media resulting from dispersion occurs in a wide variety of applications such as water treatment, flow batteries, flow in aquifers, enhanced oil recovery, and packed-bed reactors. The underlying mechanisms of dispersion are the molecular diffusion superimposed on the advective transport induced by the fluid flow. Modeling dispersion in pore networks can be performed at a much lower computational cost compared to that in direct numerical simulations (DNS) such as finite element or the lattice Boltzmann methods, so it can be regarded as a suitable alternative provided its accuracy is sufficient. The most common approach to model dispersion in network models is based on the first-order upwind scheme, despite its known limitations in terms of accuracy for certain flow and transport regimes. In this study, three alternative pore-scale models for dispersion, which are more accurate than the existing ones, were derived and tested in pore network simulations. These models were adopted from the CFD literature and are based on a spatial discretization of the advection-diffusion equation using the hybrid and power-law finite difference schemes and the exact solution of the one-dimensional advection-diffusion equation. Finally, considering dispersion problems over arbitrary porous structures, consisting of stick-and-ball geometries, and different flow and mass transfer arrangements, the developed models were validated. Validation was carried-out through comparisons between results obtained with DNS, using a finite element solver, and those from pore network simulations. It is shown that under a wide range of dispersion regimes (up to the onset of the dispersion power-law regime), the relative error (with respect to DNS results) introduced by the power-law and exact solution-based models is consistently below 1%, whereas the use of the upwind scheme leads to >10% of relative error, depending on the dispersion regime. All the dispersion models developed in this study were implemented as part of the open-source network modeling package, OpenPNM.

2019

1. PoreSpy: A Python toolkit for quantitative analysis of porous media images

   Jeff T. Gostick, Zohaib A. Khan, Thomas G. Tranter, and 4 more authors

   Journal of Open Source Software, 2019
2. Mass transfer in fibrous media with varying anisotropy for flow battery electrodes: Direct numerical simulations with 3D X-ray computed tomography

   Matthew DR Kok, Rhodri Jervis, Tom G Tranter, and 4 more authors

   Chemical Engineering Science, 2019

   A numerical method for calculating the mass transfer coefficient in fibrous media is presented. First, pressure driven flow was modelled using the Lattice Boltzmann Method. The advection-diffusion equation was solved for convective-reacting porous media flow, and the method is contrasted with experimental methods such as the limiting current diffusion technique, for its ability to determine and simulate mass transfer systems that are operating at low Reynolds number flows. A series of simulations were performed on three materials; specifically, commercially available carbon felts, electrospun carbon fibers and electrospun carbon fibers with anisotropy introduced to the microstructure. Simulations were performed in each principal direction (x,y,z) for each material in order to determine the effects of anisotropy on the mass transfer coefficient. In addition, the simulations spanned multiple Reynolds and Péclet numbers, to fully represent highly advective and highly diffusive systems. The resulting mass transfer coefficients were compared with values predicted by common correlations and a good agreement was found at high Reynolds numbers, but less so at lower Reynolds number typical of cell operation, reinforcing the utility of the numerical approach. Dimensionless mass transfer correlations were determined for each material and each direction in terms of the Sherwood number. These correlations were analyzed with respect to each materials’ permeability tensor. It was found that as the permeability of the system increases, the expected mass transfer coefficient decreases. Two general mass transfer correlations are presented, one correlation for isotropic fibrous media and the other for through-plane flow in planar fibrous materials such as electrospun media and carbon paper.

3. Role of electrode microstructure in performance of electrochemical energy storage devices

   Mohammad Amin Sadeghi

   PhD Thesis, McGill University, 2019

   Renewable energy is being increasingly deployed at both small and large scales. At large scale, the intermittent nature of renewable energy sources prohibits a full integration with the current power grid. Among others, redox flow battery is a promising energy storage technology that can be used to mitigate this issue. At smaller scale, specifically the transportation sector, the industry is already undergoing a change toward electric vehicles. Fuel cell and plug-in electric vehicles have been commercially deployed in the past few years and are projected to replace the traditional heat engine vehicles. Both the redox flow battery and fuel cell technologies are still expensive. Maximizing the energy and power density of these devices is one way to reduce their cost. This thesis is directed toward developing the required numerical framework through which the multiphysics occurring within the microstructure of electrochemical devices could be better understood, and eventually the impact of their internal structure on their overall performance could be predicted. Reactive transport was studied in two different systems of diffusion-dominated and combined advection-diffusion, which are relevant in the context of fuel cells and redox flow batteries. The pore network approach was employed for modeling transport in these systems. This approach enabled the study of meaningfully large domains that otherwise would be infeasible using typical pore-scale modeling approaches, usually referred as direct numerical simulation (DNS). As for diffusion-dominated systems, reactive transport in a hierarchically porous particle was considered, and the effect of the internal structure was studied. It was found that using a hierarchy of porosity, a 350% increase in power density could be achieved, merely by manipulating the internal structure. It was also found that using smaller-sized templates for creating macropores within a nanoporous particle leads to much larger performance gains when increasing the volume fraction of macropores. For energy storage devices in which mass transfer is a result of both diffusion and advection, it was found that the most common approach for modeling dispersion in pore networks is too crude an approximation. Therefore, in a comprehensive study, this discrepancy was demonstrated via comparison with DNS as ground truth and was followed by introducing a novel approach adapted from the CFD literature. The developed pore-scale model was shown to agree remarkably well with the results obtained from DNS with a maximum relative error of 0.5%. Finally, the developed pore-scale model was used in a multiphysics pore-network study of redox flow batteries, demonstrated in the context of a hydrogen bromine system. The effect of porosity at constant fiber diameter and fiber alignment on the overall performance of the battery was studied, both of which can be manipulated during electrode manufacture. It was shown that despite the decrease in available reactive surface area, the battery generally performed better at higher porosities. Furthermore, it was shown that aligning the fibers along the flow direction, while initially helping the electrode performance, leads to diminishing returns beyond slight alignment. This phenomenon was shown to correlate with the diminishing return in permeability of the electrode as alignment increased. This observation, along with the trend in performance against porosity shows the significance of permeability as the dominant factor in performance of such systems. This thesis highlights pore-network modeling as a practical way to model transport in energy storage devices at the pore-scale with minimal computing requirements. Developing such a modeling framework paves the way for better understanding how the internal structure of these devices affects their performance, serving as a guideline for making better prototypes with higher energy densities. The power of this framework was demonstrated in the context of two promising energy storage devices, but we expect that the different studies presented in this thesis be the cornerstone for many future studies, through which a more complete understanding of multiphysics at pore-scale will be achieved.

4. Exploring the impact of electrode microstructure on redox flow battery performance using a multiphysics pore network model

   Mohammad Amin Sadeghi, Mehrez Aganou, Matthew Kok, and 4 more authors

   Journal of The Electrochemical Society, 2019

   The redox flow battery is a promising energy storage technology for managing the inherent uncertainty of renewable energy sources. At present, however, they are too expensive and thus economically unattractive. Optimizing flow batteries is thus an active area of research, with the aim of reducing cost by maximizing performance. This work addresses microstructural electrode optimizations by providing a modeling framework based on pore-networks to study the multiphysics involved in a flow battery, with a specific focus on pore-scale structure and its impact on transport processes. The proposed pore network approach was extremely cheap in computation cost (compared to direct numerical simulation) and therefore was used for parametric sweeps to search for optimum electrode structures in a reasonable time. It was found that that increasing porosity generally helps performance by increasing the permeability and flow rate at a given pressure drop, despite reducing reactive surface area per unit volume. As a more nuanced structural study, it was found that aligning fibers in the direction of flow helps performance by increasing permeability but showed diminishing returns beyond slight alignment. The proposed model was demonstrated in the context of a hydrogen bromine flow battery but could be applied to any system of interest.

5. Dye removal using hairy nanocellulose: experimental and theoretical investigations

   Mandana Tavakolian, Hannah Wiebe, Mohammad Amin Sadeghi, and 1 more author

   ACS Applied Materials & Interfaces, 2019

   Adsorption is a common technique for the treatment of dye-contaminated wastewater. Achieving a high dye removal capacity is a common challenge with sustainable, low-cost adsorbents. Recently, a class of easily functionalized, biorenewable cellulose nanoparticles called hairy nanocellulose has been developed. Electrosterically stabilized nanocrystalline cellulose (ENCC), which can be synthesized from wood pulp through a two-step oxidation by periodate and chlorite, is a form of hairy nanocellulose with a high negative charge density, and thus has the potential for a high adsorption capacity. In this work, the adsorption of methylene blue, a cationic dye, by ENCC was shown to occur up to charge stoichiometry (1400 mg dye/g adsorbent), at which point aggregation of ENCC–dye complexes is observed. A model is developed to show that the adsorption can be described by an ion-exchange mechanism and is influenced by the presence of other ions. Equilibrium dye removal is reduced at both high ionic strengths and low pH. To facilitate handling, composite hydrogel beads of sodium alginate and ENCC (ALG–ENCC beads) are developed, and their methylene blue removal capacity is shown to maintain a high removal capacity (1250 mg/g). ALG–ENCC beads provide a facile way to employ these nanoparticles on a larger scale, providing a potential means for the removal of dyes and other contaminants at larger wastewater volumes.

6. Selective exposure of platinum catalyst embedded in protective oxide layer on conductive titanium carbide support

   Zishuai Zhang, Mohammad Amin Sadeghi, Nicolas Brodusch, and 5 more authors

   Materials Today Energy, 2019

   Despite high conductivity and large surface area, poor corrosion resistance limits the use of carbon black as a fuel cell catalyst support. Here, the formation of two titanium carbide-based core-shell nanostructures with an ultrathin layer of cobalt oxide were studied as alternative to carbon black. Titanium carbide was selected due to its electrical conductivity and high corrosion resistance. Two different nanostructures on TiC supports were prepared with Pt either on or below a cobalt oxide layer. For the sub-oxide Pt catalyst, the oxide could be removed selectively where it covered the Pt to make it partially exposed and catalytically available, yet sufficient Pt was anchored to the remaining oxide to provide stability. This anchoring prevented Pt nanoparticle detachment and aggregation as determined by ∼100% catalytic activity remained at 0.1 M KOH, and ∼92% catalytic activity remained at 0.1 M HClO4 after 16.7 h, room temperature. A core-shell model has computationally been investigated to confirm the function of the cobalt shell as diffusion barrier to protect carbide from oxidation.

7. Tailoring carbon nanotube microsphere architectures with controlled porosity

   Zishuai Zhang, Mohammad Amin Sadeghi, Rhodri Jervis, and 4 more authors

   Advanced Functional Materials, 2019

   Nanomaterials are at the core of fuel cell electrodes, providing high-area catalytic, proton, and electron conducting surfaces, traditionally on carbon black supports. Other carbons, e.g., carbon nanotubes (CNTs) and graphene are less prone to oxidation; however, their handling is not trivial due to health risks associated with their size. Assembling them into microscale structures without jeopardizing their performance is ideal, but there are mass transfer limitations as thickness increases. In this work, a soluble acicular calcium carbonate (aragonite) is used as a porogen to create connected porosity in microspheres. Increasing macroporosity has a considerable positive impact on the mass transfer process. The experimental manipulation of porosity of the microspheres is combined with pore network modeling to better understand how pore distribution throughout the whole microsphere can optimize platinum utilization decorated onto the CNTs. Oxygen reduction reaction (ORR) activity is compared with the prepared composite materials and a commercial Pt/C catalyst for 4 weeks. The composite materials exhibit a highly interconnected network resulting in a 3.4 times higher ORR activity (at 0.9 V vs reversible hydrogen electrode) than that of the nanoporous spheres with no macroporosity.

2017

1. Pore network modeling of reaction-diffusion in hierarchical porous particles: The effects of microstructure

   Mohammad Amin Sadeghi, Mahmoudreza Aghighi, Jake Barralet, and 1 more author

   Chemical Engineering Journal, 2017

   A general framework based on pore network modeling is presented for simulation of reactive transport in a porous catalyst with a hierarchy of porosity. The proposed framework is demonstrated in the context of steady state reactive transport inside a nanoporous catalyst particle interlaced with macropores that result from the use of pore-formers. A comprehensive parametric study was performed to examine the influence of structural features namely macroporosity, pore size ratio, and the particle size, as well as transport properties namely pore Damköhler number, on the net reaction rate inside the particle. The results showed that depending on the Damköhler number, increasing the macroporosity does not necessarily improve the catalytic activity of the particle. It was also shown that particles with lower pore size ratios are more kinetically active. The key finding of this work was to demonstrate and quantify how microstructure influences the reactivity of hierarchical porous catalyst particles.

2015

1. A comprehensive study on CO2 solubility in brine: Thermodynamic-based and neural network modeling

   Mohammad Amin Sadeghi, Hossein Salami, Vahid Taghikhani, and 1 more author

   Fluid Phase Equilibria, 2015

   Phase equilibrium data are required to estimate the capacity of a geological formation to sequester CO2. In this paper, a comprehensive study, including both thermodynamic and neural network modeling, is performed on CO2 solubility in brine. Brine is approximated by a NaCl solution. The Redlich–Kwong equation of state and Pitzer expansion are used to develop the thermodynamic model. The equation of state constants are adjusted by genetic algorithm optimization. A novel approach based on a neural network model is utilized as well. The temperature range in which the presented model is valid is 283–383 K, and for pressure is 0–600 bar, covering the temperature and pressure conditions for geological sequestration. A two-layer network consisting 5 neurons in its hidden layer, was chosen as the optimum topology. The regression coefficient for the neural network model was calculated R2 = 0.975. In addition, the neural network model showed lower mean absolute percentage error (3.41%) compared to the thermodynamic model (3.55%).