Elucidating mechanistic background of the origin and rates of peroxide formation in low temperature proton exchange fuel cells

Degradation of electrode-membrane assembly of the low-temperature hydrogen fuel cells represents one of the main obstacles in wider adoption of these clean and efficient electrochemical sources of electrical energy. Chemical degradation of proton exchange membrane is initiated by hydrogen peroxide formation, which forms in the fuel cell as a by-product to water in oxygen reduction reaction and decomposes to reactive radical species, damaging to the membrane chemical structure. Depending on the operating conditions of the fuel cell, the source of hydrogen peroxide can be either cathode, anode, or, as we argue in the paper, also the Pt particles in the membrane, which originate from the cathode catalyst dissolution, diffusion into the membrane and redeposition of Pt ions inside the membrane. In the paper we propose a mathematical model of intertwined physical processes in membrane and catalyst layer, aimed at unifying the description of hydrogen peroxide formation throughout entire membrane-electrode assembly at any fuel cell operating conditions. The model results, compared to experimental data, indicate that Pt particles inside the membrane can indeed be an important source of hydrogen peroxide in aged fuel cells. For a fresh fuel cell, numerical simulation using proposed model show that hydrogen peroxide can be formed at either cathode or anode, depending on the fuel cell operating condition, but with anode production being more prominent in standard fuel cell operating conditions

Genetic predisposition of suicidal behavior: variants in GRIN2B, GABRG2, and ODC1 genes in suicide attempt and completed suicide In two Balkan populations

Introduction: Suicidal behavior ranges between suicidal ideation and completed suicide. Completed suicide accounts for over 700,000 deaths worldwide, while attempted suicide is 20 times more frequent. Genetic background is an important factor contributing to suicidal behavior, and candidate genes linked to several neurotransmitter systems have been investigated. Alternations in glutamate, γ-aminobutyric acid (GABA) and polyamine systems have been detected in suicidal behavior. Our aim was to differentiate genetic predispositions underlying two different types of suicidal behavior, attempted and completed suicide, in two Balkan populations. Methods: The study sample included 173 suicide attempters with comorbid psychiatric disorders (major depressive disorder, bipolar affective disorder, or schizophrenia), 216 non-suicidal psychiatric patients and 172 healthy controls from Serbia, and 333 suicide completers and 356 non-suicidal autopsy controls from Slovenia. Variants in the genes GRIN2B (rs2268115 and rs220557), GABRG2 (rs424740), and ODC1 (rs1049500 and rs2302614) were genotyped by TaqMan assays and analyzed using PLINK. Results: The CA genotype of rs220557 in the GRIN2B gene increases the risk for completed suicide (OR=1.51, p=0.021), and particularly violent suicide (OR=1.49, p=0.037), compared to controls. In the ODC1 gene, the CA genotype of rs2302614 decreases the risk for completed suicide compared to suicide attempt (OR=0.32, p=0.012). Marginally, the AC haplotype for variants rs1049500-rs2302614 in the ODC1 gene decreases the risk for completed suicide compared to suicide attempt (OR=0.50, p=0.052). Conclusion: Specific genetic variants of the glutamate and the polyamine systems are differently distributed among diverse suicidal phenotypes, thus providing further information on the implication of these systems in suicidality

Theoretical analysis of particle size re-distribution due to Ostwald ripening in the fuel cell catalyst layer

The limited durability of hydrogen fuel cells is one of the main obstacles in their wider adoption as a clean alternative technology for small scale electricity production. The Ostwald ripening of catalyst material is recognized as one of the main unavoidable degradation processes deteriorating the fuel cell performance and shortening its lifetime. The paper systematically studies how the modeling approach towards the electrochemically driven Ostwald ripening in the fuel cell catalyst differs from the classical diffusion driven models and highlights how these differences affect the resulting evolution of particle size distribution. At moderately low electric potential, root-law growth of mean particle size is observed with linear relation between mean particle size and standard deviation of particle size distribution, similar to Lifshitz-Slyozov-Wagner theory, but with broader and less skewed distribution. In case of high electric potential, rapid particle growth regime is observed and qualitatively described by redeposition of platinum from a highly oversaturated solution, revealing the deficiencies of the existing platinum degradation models at describing the Ostwald ripening in the fuel cells at high electric potentials. Several improvements to the established models of platinum degradation in fuel cell catalysts are proposed, aimed at better description of the diffusion processes involved in particle growth due to Ostwald ripening

Transient Momentum Balance—A Method for Improving the Performance of Mean-Value Engine Plant Models

energie

Hybrid methodology for efficient on the fly (re)parametrization of proton exchange membrane fuel cells electrochemical model for diagnostics and control applications

Successful parametrization and re-parametrization of the models used in PEMFC observer applications is instrumental to assure ideal control of the system. To increase ease of parametrisation and accuracy of parameter determination, this paper presents framework of twin analytical ansatzes for modelling PEMFC response in time and frequency domains sharing same set of calibration parameters. Owing to thermodynamically consistent modelling basis of the twin models, which are based on electrochemical model with state-of-the-art extrapolation capabilities and replication of the experimental data with one set of calibration parameters, they are valid in all current density regions. Furthermore, unique sharing of the calibration parameters enables unprecedented enrichment of the dataset that can be used to determine values of model\u27s calibration parameters with higher certainty or enhances identification of individual calibration parameters that are otherwise harder to be uniquely determined. Additionally, proposed hybrid methodology also enables a significant reduction in the measurement time and enable re-parametrization on the fly

Methodology for evaluation of contributions of Ostwald ripening and particle agglomeration to growth of catalyst particles in PEM fuel cells

The degradation of the catalyst layer represents one of the main limiting factors in a wider adoption of fuel cells. The identification of the contributions of different mechanisms of catalyst degradation, namely the Ostwald ripening and particle agglomeration, is an important step in the development of mitigation strategies for increasing fuel cell reliability and prolonging its life time. In this paper, the degradation phenomena in high temperature polymer electrolyte membrane fuel cell (HT-PEMFC) are analyzed using a physically-based model of fuel cell operation and catalyst degradation, describing carbon corrosion, platinum dissolution and consequent growth of catalyst particles. The model results indicate significantly different time dependence of catalyst particle growth resulting from different mechanisms: linear growth in the case of particle agglomeration and root-like time dependence for the Ostwald ripening. Based on these results, a new analytic method is proposed, performed by the fitting of a test root-function to the time profile of the particle size growth and using best-fit parameters to identify the prevailing growth mechanism. Using this method on a particle growth time trace deduced from in situ cyclic voltammetry measurement during HT-PEMFC degradation, we are able to identify the agglomeration as the main mechanism of catalyst particle grow

Surrogate model for improved simulations of small-scale sludge incineration plants

Although various modelling approaches exist for the simulation of solid fuel combustion, no specific model hasbeen developed for the accurate description of gas-phase combustion in small-scale combustion devices. This isparticularly limiting in scenarios when volatile-rich, complex and incompletely described solid fuels such assewage sludge are used. To address this issue, an accurate description of combustion from the fuel bed onwardsis required as well as an improved description of emitted volatiles. This paper introduces an innovative surro-gate-based combustion model that combines data on sludge devolatilisation and measured combustion char-acteristics to offer a new surrogate composition. The composition includes heavy hydrocarbon species to ac-curately describe combustion evolution. A sensitivity analysis revealed that H2contributes significantly tocombustion evolution, while the most robust surrogate composition is obtained when ethanol is used as a leadingrepresentative of heavier hydrocarbons. The model can be used to produce suitable surrogates for the mainsludge combustion interval, offering the required improvement in fuel descriptions and accuracy of simulationsin the vicinity of the fuel bed. Hence, this model is particularly suitable for the optimisation of temperature, heatrelease rate, and concentration field in combustion chambers with limited volumes

Multi-scale modelling of Lithium-ion batteries

Multi-scale and multi-domain mathematical models capable of modelling main electrochemical reactions, side reactions and heat generation can reduce the time and cost of lithium-ion battery development and deployment, since these processes decisively influence performance, durability and safety of batteries. Experimental evidences clearly indicate the importance of the interplay between electric and thermal boundary conditions, cell design and applied materials, side reactions as well as safety implications of batteries, which are not yet captured to a sufficient level by simulations models. As an answer to this challenge, the paper presents an advanced multi-scale battery modelling framework that can be seamlessly integrated into multi-domain models. The key hypothesis is that nanoscopic transport phenomena and resulting heat generation decisively influence the entire chain of mechanisms that can lead to the outbreak of the thermal runaway. This is confirmed by developing a multi-scale battery modelling framework that is based on the continuous modelling approach featuring more consistent virtual representation of the electrode topology and incorporating the coupled chain of models for heat generations and side reactions. As a result, the battery modelling framework intuitively yet insightfully elucidates the entire chain of phenomena from electric and thermal boundary conditions, over cell design and properties of applied materials to solid electrolyte interphase growth, its decomposition and subsequent side reactions at the anode, cathode and the electrolyte that lead to the thermal runaway. One of key results comprises multi-level main and side reaction driven heat transfer cross-talk between the anode and the cathode. Therefore, the presented advanced multi-scale battery modelling framework represents a contribution to the advanced virtual development of batteries thereby contributing to tailoring battery design to a specific application

Theoretical analysis of particle size re-distribution due to Ostwald ripening in the fuel cell catalyst layer

The limited durability of hydrogen fuel cells is one of the main obstacles in their wider adoption as a clean alternative technology for small scale electricity production. The Ostwald ripening of catalyst material is recognized as one of the main unavoidable degradation processes deteriorating the fuel cell performance and shortening its lifetime. The paper systematically studies how the modeling approach towards the electrochemically driven Ostwald ripening in the fuel cell catalyst differs from the classical diffusion driven models and highlights how these differences affect the resulting evolution of particle size distribution. At moderately low electric potential, root-law growth of mean particle size is observed with linear relation between mean particle size and standard deviation of particle size distribution, similar to Lifshitz-Slyozov-Wagner theory, but with broader and less skewed distribution. In case of high electric potential, rapid particle growth regime is observed and qualitatively described by redeposition of platinum from a highly oversaturated solution, revealing the deficiencies of the existing platinum degradation models at describing the Ostwald ripening in the fuel cells at high electric potentials. Several improvements to the established models of platinum degradation in fuel cell catalysts are proposed, aimed at better description of the diffusion processes involved in particle growth due to Ostwald ripening

Closed-form formulation of the thermodynamically consistent electrochemical model considering electrochemical co-oxidation of CO and H [sub] 2 for simulating solid oxide fuel cells

Achieving efficient solid oxide fuel cell operation and simultaneous prevention of degradation effects calls for the development of precise on-line monitoring and control tools based on predictive, computationally fast models. The originality of the proposed modelling approach originates from the hypothesis that the innovative derivation procedure enables the development of a thermodynamically consistent multi-species electrochemical model that considers the electrochemical co-oxidation of carbon monoxide and hydrogen in a closed-form. The latter is achieved by coupling the equations for anodic reaction rates with the equation for anodic potential. Furthermore, the newly derived model is capable of accommodating the diffusive transport of gaseous species through the gas diffusion layer, yielding a computationally efficient quasi-one-dimensional model. This resolves a persistent knowledge gap, as the proposed modelling approach enables the modelling of multi-species fuels in a closed form, resulting in very high computational efficiency, and thus enable the model’s real-time capability. Multiple validation steps against polarisation curves with different fuel mixtures confirm the capability of the newly developed model to replicate experimental data. Furthermore, the presented results confirm the capability of the model to accurately simulate outside the calibrated variation space under different operating conditions and reformate mixtures. These functionalities position the proposed model as a beyond state-of-the-art tool for model supported development and control applications