One-dimensional Reaction Research Articles

Opposite effects of a reaction-driven viscosity decrease on miscible viscous fingering depending on the injection flow rate

When a less-viscous solution of a reactant $A$ displaces a more-viscous solution of another reactant $B$ , a fast bimolecular $A + B \rightarrow C$ reaction decreasing locally the viscosity can influence the viscous fingering (VF) instability taking place between the two miscible solutions. We show both experimentally and numerically that, for monotonic viscosity profiles, this decrease in viscosity has opposite effects on the fingering pattern depending on the injection flow rate. For high flow rates, the reaction enhances the shielding effect, creating VF patterns with a lower surface density, i.e. thinner fingers covering a smaller area. In contrast, for lower flow rates, the reaction stabilises the VF dynamics, i.e. delays the instability and gives a less-deformed displacement, reaching in some cases an almost-stable displacement. Nonlinear simulations of reactive VF show that these opposite effects at low or high flow rates can only be reproduced if the diffusivity of $A$ is larger than that of $B$ , which favours a larger production of the product $C$ and, hence, a larger viscosity decrease. The analysis of one-dimensional viscosity profiles reconstructed on the basis of a one-dimensional reaction–diffusion–advection model confirms that the VF stabilisation at low Péclet number and in the presence of differential diffusion of reactants originates from an optimum reaction-driven decrease in the gradient of the monotonic viscosity profile.

Accuracy of Reaction Coordinate Based Rate Theories for Modelling Chemical Reactions: Insights From the Thermal Isomerization in Retinal.

Modern potential energy surfaces have shifted attention to molecular simulations of chemical reactions. While various methods can estimate rate constants for conformational transitions in molecular dynamics simulations, their applicability to studying chemical reactions remains uncertain due to the high and sharp energy barriers and complex reaction coordinates involved. This study focuses on the thermal cis-trans isomerization in retinal, employing molecular simulations and comparing rate constant estimates based on one-dimensional rate theories with those based on sampling transitions and grid-based models for low-dimensional collective variable spaces. Even though each individual method to estimate the rate passes its quality tests, the rate constant estimates exhibit considerable disparities. Rate constant estimates based on one-dimensional reaction coordinates prove challenging to converge, even if the reaction coordinate is optimized. However, consistent estimates of the rate constant are achieved by sampling transitions and by multi-dimensional grid-based models.

Exploring Facile Oxygen Evolution Reaction One-Dimensional Catalyst Synthesis Using K2PtCl6

Proton Exchange Membrane (PEM) water electrolysers are one of the most promising technologies for easy production of zero-carbon green hydrogen.The global demands for decarbonised means of production have pushed for greater development of this technology. However, PEM water electrolysers reach an efficiency limit most notably due to the sluggish kinetics of the Oxygen Evolution Reaction (OER) happening at their anodes. This is heavily due to the four electron transfer steps creating a consequential energetic barrier.As such, there is significant interest in improving OER catalysts to achieve optimal performance. This entails minimising rare metal usage to meet current sustainability ambitions. Modifying catalytic structures and creating nanoshapes to reduce the metal loading is a popular method to achieve this goal.In our work, we provide a novel facile method to synthesise nanocatalysts with evidence of predicted better performance than commercial OER catalysts.Previous work conducted by the group focused on using a K2PtCl6 precursor to develop one-dimensional Hydrogen Evolution Reaction (HER) catalysts.Inspired by this, K2IrCl6 is used as a precursor and via a facile wet chemistry method iridium nanomaterials are obtained. The main process involves adding formic acid as the reducing agent and water solvent and letting the reaction occur in a stainless-steel autoclave at the desired temperature.The advantages of this reaction include the accessibility of the reagents and the facile extraction method that does not require centrifuging. Other attempted wet methods presented in literature were subject to consequent product losses due to strong electrostatic interactions between the iridium particles and the plastic centrifuging vessels.The focus of the experimental work is to investigate the effects of temperature, time of reaction, and quantity of reducing agent on the performance of the resulting catalysts. This was done by altering the parameters of an existing internal protocol.Electrochemical performance is measured via Linear Sweep Voltammetry, and specifically looking at the overpotential at 10 mA.cm2. Voltammetry tests were performed in a 0.1M perchloric acid electrolyte, purged with nitrogen for 40 minutes before each measurement. Iridium black was used as a reference commercial catalyst, giving an overpotential of 358 mV at 10 mA.cm-2.Current results indicate that higher performance is achieved by doubling the amount of formic acid compared to the initial protocol (1.08 mL vs 0.54 mL). A difference of up to 50 mV in overpotential was observed, suggesting that further investigation on the optimal quantity of reducing agent to be added might be of interest. Improved results were also obtained by letting the reaction run for 24 hours compared to the initial setting of 14 hours.TEM images will be obtained for each sample in order to make two correlations: one between the synthesis conditions and the structure of the catalyst, and the main one between the structure and the performance of each material. The goal of this comparison will be to determine if one-dimensionality leads to the best OER performances in our catalyst. Furthermore, the catalysts will be put through single-cell tests to observe whether the performance trends observed with Linear Sweep Voltammetry hold when the catalysts are put in practical application settings. Further work will include combining the optimal catalyst with a support to further reduce iridium loading. Figure 1

Barrier-crossing transition-path times for non-Markovian systems.

By simulation and asymptotic theory, we investigate the transition-path time of a one-dimensional finite-mass reaction coordinate crossing a double-well potential in the presence of non-Markovian friction. First, we consider single-exponential memory kernels and demonstrate that memory accelerates transition paths compared to the Markovian case, especially in the low-mass/high-friction limit. Then, we generalize to multi-exponential kernels and construct an asymptotic formula for the transition-path time that compares well with simulation data.

Influence of CO2 injection rate and memory water on depressurization-assisted replacement in natural gas hydrates and the implications for effective CO2 sequestration and CH4 exploitation

This study was conducted to investigate the influence of CO2 injection rate and memory water on guest exchange and hydrate reformation behavior in CH4 hydrate-bearing sediment, using a one-dimensional (1-D) reactor to optimize depressurization-assisted replacement. Increasing the CO2 injection rate facilitated the rapid sweeping of CH4 into the pore space of the hydrate-bearing sediment, inducing immediate hydrate reformation and preventing CH4 re-enclathration in the middle region of the 1-D reactor. Despite dissociating 50 % of the initial CH4 hydrate for depressurization-assisted replacement, the replacement efficiency converged at around 70 % due to drastically reformed gas hydrates hindering mass transfer. Introducing time intervals between depressurization and replacement to reduce the residual memory effect of water led to delayed hydrate reformation, altering the longitudinal distribution of replacement efficiency in the 1-D reactor. An increase in replacement efficiency toward the outlet was seen, suggesting that improved CO2 propagation in the hydrate-bearing sediment resulted in a higher CO2 concentration in the vapor phase at the point of hydrate reformation, in turn enhancing CO2 storage efficiency in the newly formed hydrates. These experimental results highlight the importance of CO2 injection rates and time intervals in determining the hydrate reformation behaviors of hydrate-bearing sediments and optimizing the efficiency of depressurization-assisted replacement.

Simulating Crystallization in a Colloidal System Using State Predictive Information Bottleneck Based Enhanced Sampling.

We investigate crystal nucleation in supersaturated colloid suspensions using enhanced molecular dynamics simulations augmented with machine learning techniques. The simulations reveal that crystallization in the model colloidal system studied here, with particles interacting through a repulsive screened Coulomb Yukawa potential, proceeds from vapor to dense liquid droplet to crystalline phases across multiple high barriers. Employing a one-dimensional reaction coordinate derived from the State Predictive Information Bottleneck framework, our simulations capture back-and-forth phase transitions across multiple barriers effectively in biased metadynamics simulations. We obtain relative free energy differences between different phases and also quantify the roles of different molecular level features in driving the phase changes.

Solution of Fishers equation using homotopy perturbation Aboodh transform method

In this study, a nonlinear one-dimensional reaction Diffusion Equation called Fisher’s equation is solved using Aboodh Transform coupled with HPM. This method is applied to the general one-dimensional Fishers equation. This technique is demonstrated with three unique examples with different initial conditions solved with the values of β=1 and a=0. This simplicity of Aboodh transform makes the rapid rate of convergence from approximate to exact solution answers makes it easy and dependable.

A deconvolution analysis strategy on the thermoreconstitution process of Co/Fe layered double hydroxides: Complementary correlations between physicochemical variations and thermokinetic analyses

A variety of physicochemical analyses were systematically conducted on the complex thermoreconstitution process of Co/Fe layered double hydroxides, and deconvolution analysis strategy was utilized in elucidating the reconstitution process. The thermal analysis of Co/Fe layered double hydroxides indicated that approximately 27.8 % weight loss, three weight loss peaks and two pronounced endothermic phenomena were detected via thermogravimetry, differential thermogravimetry, and differential scanning calorimetry, respectively. In the thermoreconstitution process of Co/Fe layered double hydroxides, scanning electron microscopy images indicated that the layered structure gradually collapsed, fractured into pieces, and partially melted. Meanwhile, significant X-ray diffraction peak broadening phenomena were detected and further confirmed by the d-spacing values in high-resolution transmission electron microscope images, which were mainly caused by the lattice expansion effect and the heterogeneous microstrain effect. Three reactions were deconvolved from the differential thermogravimetry curves, and the thermokinetic models of Reaction 2 indicated that volatile components in the form of H2O and CO2 escaped from the solid-state residuals via a one-dimensional diffusion reaction model. The major products in solid-state residuals were cubic Cobalt(III) oxide and Iron(II, III) oxide. The thermokinetic models played a complementary role in elucidating the physicochemical variations that occurred throughout the thermoreconstitution process of Co/Fe layered double hydroxides.

Thermal isomerization rates in retinal analogues using Ab-Initio molecular dynamics.

For a detailed understanding of chemical processes in nature and industry, we need accurate models of chemical reactions in complex environments. While Eyring transition state theory is commonly used for modeling chemical reactions, it is most accurate for small molecules in the gas phase. A wide range of alternative rate theories exist that can better capture reactions involving complex molecules and environmental effects. However, they require that the chemical reaction is sampled by molecular dynamics simulations. This is a formidable challenge since the accessible simulation timescales are many orders of magnitude smaller than typical timescales of chemical reactions. To overcome these limitations, rare event methods involving enhanced molecular dynamics sampling are employed. In this work, thermal isomerization of retinal is studied using tight-binding density functional theory. Results from transition state theory are compared to those obtained from enhanced sampling. Rates obtained from dynamical reweighting using infrequent metadynamics simulations were in close agreement with those from transition state theory. Meanwhile, rates obtained from application of Kramers' rate equation to a sampled free energy profile along a torsional dihedral reaction coordinate were found to be up to three orders of magnitude higher. This discrepancy raises concerns about applying rate methods to one-dimensional reaction coordinates in chemical reactions.

Log-periodic oscillations as real-time signatures of hierarchical dynamics in proteins.

The time-dependent relaxation of a dynamical system may exhibit a power-law behavior that is superimposed by log-periodic oscillations. D. Sornette [Phys. Rep. 297, 239 (1998)] showed that this behavior can be explained by a discrete scale invariance of the system, which is associated with discrete and equidistant timescales on a logarithmic scale. Examples include such diverse fields as financial crashes, random diffusion, and quantum topological materials. Recent time-resolved experiments and molecular dynamics simulations suggest that discrete scale invariance may also apply to hierarchical dynamics in proteins, where several fast local conformational changes are a prerequisite for a slow global transition to occur. Employing entropy-based timescale analysis and Markov state modeling to a simple one-dimensional hierarchical model and biomolecular simulation data, it is found that hierarchical systems quite generally give rise to logarithmically spaced discrete timescales. By introducing a one-dimensional reaction coordinate that collectively accounts for the hierarchically coupled degrees of freedom, the free energy landscape exhibits a characteristic staircase shape with two metastable end states, which causes the log-periodic time evolution of the system. The period of the log-oscillations reflects the effective roughness of the energy landscape and can, in simple cases, be interpreted in terms of the barriers of the staircase landscape.

Quantitative Analysis of the Complex Time Evolution of a Camphor Boat

The motion of a camphor boat on the water’s surface is a long-studied example of the direct transformation of chemical energy into a mechanical one. Recent experimental papers have reported a complex character of boat motion depending on the location of the camphor source. If the source is close to the stern, the boat moves at a constant speed. When it is shifted towards the boat center, oscillations of speed are observed. When the source is close to the boat center, pulses of speed followed by oscillations appear. Here, we focus on numerical simulations of camphor boat motion. We discuss approximations that allow us to reduce the numerical complexity of the problem and formulate a model in which the equation for boat velocity is coupled with a one-dimensional reaction–diffusion equation for camphor surface concentration. We scanned the phase space of model parameters and found the values that give qualitative agreement with the experiments. The model predicts all types of boat motion (continuous, oscillating, and pulsating) observed in experiments. Moreover, the model with selected parameter values shows that for specific locations of the camphor source, a spike in speed is followed by transient oscillations, which are an inherent part of speed relaxation.

Incorporating reduced axis system embedding into ab initio tunnelling-rotation Hamiltonians with curvilinear vibrational Møller–Plesset perturbation theory

Large-amplitude vibrational motion influences the rovibrational structure of molecules that tunnel between multiple wells. Reaction path (RP) Hamiltonians, and curvilinear coordinates more generally, are useful for modelling pure vibrational motion in these systems and provide a practical framework for calculating accurate ab initio anharmonic vibrational energies and tunnelling splittings with perturbation theory. These computational tools also offer the means to address rotation-vibration coupling associated with large-amplitude motion in rotating molecules. In this paper, we incorporate the reduced axis system (RAS) frame embedding with RP Hamiltonians and second-order vibrational Møller-Plesset perturbation theory (VMP2). Because the RP-RAS Hamiltonian eliminates rotation-vibration momentum coupling everywhere along a one-dimensional reaction path, it is well suited for rovibrational VMP2 methods, the convergence of which relies critically on approximate vibration-vibration and vibration-rotation separability. The accuracy of this combined RP-RAS-VMP2 scheme is demonstrated by comparisons with numerically exact variational calculations and VMP2 parameters based on traditional Eckart embeddings for reduced-dimension models of torsional tunnelling in hydrogen peroxide and inversion tunnelling in cyclopropyl radical. The favourable computational scaling of VMP2 makes it a promising strategy for calculating accurate tunnelling-rotation parameters for medium-sized and larger molecules in full dimensionality.

Simulation of Total Harmonic Distortion of Solid Oxide Fuel Cells for Carbon Deposition Detection

Identifying the degradation mechanisms at the early stage of operation is important for the long-term operation of solid oxide fuel cells (SOFCs). Compared to conventional methods, total harmonic distortion analysis (THDA) can significantly reduce the test time for identifying performance degradation during SOFC operation. In this study, a one-dimensional transient elementary reaction kinetic model of an SOFC fueled with syngas is developed. The model incorporates the coupling effect of elementary chemical and electrochemical reactions, the electrode microstructure, the charge and mass transport processes, and the detailed evolution reaction of surface adsorbed carbon. A THDA simulation calculation method was developed and applied to determine the failure mode of anode carbon deposition. The amplitude, duration, and harmonic number of the perturbation signal are determined to improve fault detection for THDs. The results show that the use of THD can not only detect carbon accumulation behavior at the early stage of SOFC operation but also distinguish the specific degradation mechanism caused by carbon deposition: the hindered SOFC charge transfer reaction can be detected in the frequency range of 100–4000 Hz, and the hindered gas diffusion process inside the anode can be detected in the frequency range of 0.01–10 Hz.

CO2-Free Hydrogen Production by Methane Pyrolysis Utilizing a Portion of the Produced Hydrogen for Combustion

Air pollutants such as carbon dioxide and nitrogen oxides emitted by the combustion of fossil fuels have become the subject of increasing concern. Hydrogen has accordingly emerged as a promising low-emission alternative energy source. Among the various methods for hydrogen production, methane pyrolysis, which produces hydrogen without emitting carbon dioxide, has gained substantial attention. This study evaluated the self-sustainability of a new hydrogen production system based on methane pyrolysis, in which a portion of the hydrogen produced is used as combustion fuel rather than relying on catalysts and electrical heating. Coupled heat transfer and one-dimensional reaction simulations employing two plug-flow reactors of a counterflow double-pipe heat exchanger were conducted to investigate the feasibility and efficiency of the proposed system, as well as the influence of flow conditions on hydrogen production. The results confirmed system viability, informed the estimation of hydrogen production rates, and provided methane conversion rate data emphasizing the critical role of low-flow conditions and residence time in system efficiency. Additionally, the production of carbon constituted a significant aspect of system efficiency. These findings indicate that the proposed system can produce environmentally friendly hydrogen, contributing to its potential utilization as a sustainable energy source.

General concept for autoignitive reaction wave covering from subsonic to supersonic regimes

We consider a one-dimensional (1D) autoignitive reaction wave in a reactive flow system comprising unburned premixed gas entering from the inlet boundary and burned gas exiting from the outlet boundary. In such a 1D system at given initial temperature, it is generally accepted that steady-state solutions can only exist if the inlet velocity matches either the velocity of deflagration wave, as determined by the burning rate eigenvalue in the subsonic regime, or the velocity of detonation wave as dictated by the Chapman–Jouguet condition in the supersonic regime. Based on our recently published theory that ignition is equivalent to deflagration wave with unity Lewis number, we believe that it is possible to redefine deflagration wave from ignition. Thus, we have developed the general concept of “autoignitive reaction wave” and shown theoretically that there are two distinct regions that can maintain steady-state solutions in both the subsonic and supersonic regimes. Based on this theory, we selected inlet velocities that are predicted to yield either steady-state or flashback solutions and conducted numerical simulations. This novel approach revealed that steady-state solutions are possible not only at the velocity of the deflagration wave in the subsonic regime and the velocity of the detonation wave in the supersonic regime, but also across a broad range of inlet velocities. Furthermore, we identify a highly stable autoignitive reaction wave that emerges when the inlet velocity surpasses the velocity of detonation wave, devoid of the typical shock wave commonly seen in detonation waves. This “supersonic autoignitive reaction wave” lacks the instability-inducing detonation cell structure, suggesting the potential for the development of novel combustor concepts.

On committor functions in milestoning.

As an optimal one-dimensional reaction coordinate, the committor function not only describes the probability of a trajectory initiated at a phase space point first reaching the product state before reaching the reactant state but also preserves the kinetics when utilized to run a reduced dynamics model. However, calculating the committor function in high-dimensional systems poses significant challenges. In this paper, within the framework of milestoning, exact expressions for committor functions at two levels of coarse graining are given, including committor functions of phase space point to point (CFPP) and milestone to milestone (CFMM). When combined with transition kernels obtained from trajectory analysis, these expressions can be utilized to accurately and efficiently compute the committor functions. Furthermore, based on the calculated committor functions, an adaptive algorithm is developed to gradually refine the transition state region. Finally, two model examples are employed to assess the accuracy of these different formulations of committor functions.

Integration of Machine Learning Models into Three-Dimensional Numerical Simulation of Polymer Electrolyte Membrane Fuel Cells

Numerical simulations are powerful tools for understanding phenomena in Polymer Electrolyte Membrane Fuel Cells (PEMFCs) and developing PEMFCs with high performance and durability. We have been developing a three-dimensional (3D) PEMFC simulator P-Stack [1]. P-Stack can perform 3D cell/stack simulations for long-time operation about 10 ~ 100 hours at most. However, fuel cell degradation occurs on longer time scales. To simulate such very long time scales within acceptable computational time, the computational speed need to be improved innovatively. As one of the approaches, we have integrated machine learning models into the simulation.In the power generation calculation of P-Stack, an in-plane current density distribution is determined by calculating a one-dimensional (1D) electrochemical reaction model at each point. In the present study, the 1D electrochemical reaction model is replaced with a machine learning model. The learning data is the result of “pseudo-1D” power generation calculation of 500 cases generated by P-Stack. Here, “pseudo-1D” means the power generation calculation for sampling is performed for small cell geometry, guaranteeing that in-plane distribution is almost homogeneous. It also suitable for sampling calculation because computational cost is very low. In the data generation by P-Stack, the input parameters of P-Stack were determined by Latin hypercube sampling. The input parameters for the machine learning model are the potential, the concentration of each gas species in the anode and cathode catalyst layers, and the temperature. The prediction parameters are current density, cell resistance, overvoltages, molar flux of each gas species and heat flux.As shown in Figure 1, the machine learning model well predicts the target parameters, and the coefficient of determination was about 0.9 for various prediction targets of the verification data. As a result, machine learning could be used to predict power generation. We have implemented this machine learning model into P-Stack. We will also discuss the stability and accuracy of computation coupled with machine learning model and P-Stack, and how much the calculation can be accelerated by our approach.Reference[1] T. Tsukamoto, T. Aoki, H. Kanesaka, T. Taniguchi, T. Takayama, H. Motegi, R. Takayama, S. Tanaka, K. Komiyama, M. Yoneda, J. Power Sources 488 (2021) 229412 Figure 1

Ratcheting synthesis.

Synthetic chemistry has traditionally relied on reactions between reactants of high chemical potential and transformations that proceed energetically downhill to either a global or local minimum (thermodynamic or kinetic control). Catalysts can be used to manipulate kinetic control, lowering activation energies to influence reaction outcomes. However, such chemistry is still constrained by the shape of one-dimensional reaction coordinates. Coupling synthesis to an orthogonal energy input can allow ratcheting of chemical reaction outcomes, reminiscent of the ways that molecular machines ratchet random thermal motion to bias conformational dynamics. This fundamentally distinct approach to synthesis allows multi-dimensional potential energy surfaces to be navigated, enabling reaction outcomes that cannot be achieved under conventional kinetic or thermodynamic control. In this Review, we discuss how ratcheted synthesis is ubiquitous throughout biology and consider how chemists might harness ratchet mechanisms to accelerate catalysis, drive chemical reactions uphill and programme complex reaction sequences.

Investigating the impact of modeling assumptions and closure models on the steady-state prediction of solar-driven methane steam reforming in a porous reactor

In this work, the effect of different modeling assumptions and closure models — frequently considered in the literature of solar thermochemical reactors and closely-related areas — is investigated. The application of different modeling assumptions and closure models may strongly affect the results accuracy and compromise a meaningful comparison across literature works. The following is herein investigated: (i) the role of upstream and downstream fluid regions to the two-phase reactor region; (ii) relevance of species and gas-phase thermal diffusion mechanisms; (iii) reaction heat accounted for in gas- or solid-phase energy balance equations; (iv) local thermal equilibrium (LTE) vs. local thermal non-equilibrium (LTNE) models; (v) model dimension (one-dimensional vs. two-dimensional axisymmetric models); (vi) local volumetric convection heat transfer correlations; and (vii) effective solid thermal conductivity correlations. The relevance of this work extends well beyond the current application (methane steam reforming in a volumetric solar reactor), since similar models and assumptions have also been widely applied for predicting the performance of volumetric solar absorbers and dry reforming solar reactors. The results show that an upstream fluid region should be considered while applying inlet first-type boundary conditions and diffusion transport in the corresponding governing equations to ensure full-conservation and simultaneously to account for developing profiles upstream the reactor inlet section. For the operating conditions considered, species diffusion and gas-phase heat conduction are particularly relevant at the reactor centerline but with a negligible integral effect. A significant difference in the reactor performance is observed while accounting for the reaction heat from surface reactions in the solid- or gas-phase energy balances — for the lowest inlet gas velocity herein considered, the thermochemical efficiency (methane conversion) is approximately equal to 70.8% (76.3%) and 77.3% (84.7%) assigning the reaction heat to the gas- and solid-phase energy balances, respectively. LTE model results are strikingly different from the results obtained with the LTNE model considering the reaction heat accounted for in the gas-phase energy balance but not significantly different from the results computed with the LTNE model with the reaction heat assigned to the solid-phase energy balance. One-dimensional reactor modeling provided with an average concentrated solar heat flux value results in a similar average performance as that given by a two-dimensional model but fails to predict the high temperatures observed at reactor centerline — for the lowest inlet gas velocity, the difference between the maximum solid temperatures predicted by one- and two-dimensional models is about 340K.

NH3-Fed Patterned Electrode Solid Oxide Fuel Cell: Experimental Performance Characterization and Elementary Reaction Modeling

Ammonia-fueled solid oxide fuel cells (SOFCs) have attracted the focus of researchers due to the feature of no carbon emission. Understanding the reaction mechanism is vital for the design and optimization of NH3-fed SOFCs. However, the catalytic decomposition reactions involved in porous electrodes led to the difficulty to distinguish the anode reaction mechanism. In the present study, we utilized a patterned anode to get a designed triple phase boundary (TPB) and avoid the effects of porous electrodes. In the performance test, we found that the current density of the NH3-fed SOFC was approximately 20% of that of the H2-fed SOFC under a similar working position at 750℃. The difference may mainly resulted from the limit of catalytic decomposition of NH3 caused by the narrow reactive area in the patterned electrode. A one-dimensional elementary reaction model was further developed to validate this hypothesis and to provide additional evidence for the reaction mechanisms of the NH3-fed SOFC.