Effective Electronic Conductivity Research Articles

Theoretical Studies on γ-Photon/Neutron Shielding Characteristics of RE3+Co-Doped Borate, Phosphate, and Silicate Glass Systems

The Phy-X program software was used to investigate the γ- and neutron shielding properties of trivalent rare earth co-doped borate-, phosphate- and silicate glasses (G1-G10) concerning elemental composition and density. The attenuation coefficients (μ/ρ and μ), half/tenth value layer (HVL and TVL), and mean free path (MFP) were calculated, and each revealed the influence of rare earth including the Pb, Ba, and Bi elements. Energy and compositional dependent effective atomic number (Zeff), effective electron density (Neff), and effective electron conductivity (Ceff) of all the glasses were studied. On the high-density glasses G6, G9, and G10, the HVL and TVL values are projected to be lower than on the other examined glasses. The neutron radiation shielding abilities of the studied glasses were investigated by determining removal cross-section (RCS). The results were compared to those of commercially available materials such as concrete, graphite, water, and Hematite-serpentine concrete. The γ-ray exposure buildup and energy absorption factor values (EBF/EABF) of glass samples were determined in the energy range of 0.015 to 15 MeV.

Digital-Twin Modeling and Simulations on All-Solid-State Batteries

The use of digital twin technology for investigating all-solid-state batteries (ASSBs) has gained significant attention. Digital twin modeling is crucial in obtaining key design parameters such as effective ionic or electronic conductivity, specific contact area, which are difficult to obtain through experiments. This work presents two methods, bottom-up and top-down, for creating 3D microstructures of composite electrodes and discusses recent progress in upgrading both methods to uncover veiled parameters in ASSBs. Furthermore, the digital-twin electrode structures are coupled with electrochemical, thermal, and mechanical changes. This work is a valuable guideline for researchers working on ASSBs and related fields.

Morphology and Transport Characterization of Catalyst Layers for CO2 Reduction

Gas diffusion electrodes (GDEs) help to reduce transport limitations in devices for electrochemical CO2 reduction. Homogenized modeling of such devices requires input of morphological characteristics and effective transport properties of the porous structure, which can be obtained by pore-scale methods. Due to the small pore sizes and layer thicknesses, such characterization of the catalyst layer (CL) is difficult. In this work, CL structures digitalized by FIB-SEM nano-tomography were analyzed to obtain a set of morphological descriptors and were considered as geometrical domains in direct pore-scale simulations to calculate tensors of effective diffusion coefficient, permeability, tortuosity, and effective ionic and electronic conductivity. The CL properties vary significantly depending on the diffusion and flow direction, and samples of similar composition and synthesis technique exhibit different transport behaviors. The reported results provide a quantitative morphological analysis and a reliable set of effective properties to be used in homogenized device modeling, a net improvement over the most commonly used empirical or analytical formulations. The data set was obtained directly from the pore-scale structure of real CLs and contributes to making device-scale homogenized modeling of GDEs more accurate and reliable.

Machine Learning-Assisted Multi-Objective Optimization of Battery Manufacturing from Synthetic Data Generated by Physics-Based Simulations

Recent efforts in the ongoing energy transition showed that Lithium-Ion Batteries (LIBs) represent a suitable technology for portable devices and electric vehicles due to their high performances and relatively good cell durability. However, the optimization of the electrode manufacturing process establishes a critical step to ensure high-quality LIBs. Nowadays, such optimization is currently based on trial-and-error and costly approaches. Indeed, LIB electrode manufacturing is a complex process involving multiple interlinked steps, including numerous process parameters that unravel sparse conditions to explore. In that sense, the digitalization of the manufacturing process highlights deep insights into its impact on the electrode and cell properties to accelerate and support autonomous productions and the prototyping activities at the experimental level. [1] We have shown in previous works that 3D-resolved physics-based models constitute meaningful tools to simulate the manufacturing process chain and bring to the community a series of unique computational 3D-resolved models for unlocking the relationships between fabrication parameters and electrode properties. [2, 3] Nevertheless, the physics-based models can still have a huge computational cost for high-throughput screening and their integration in optimization loops. In this work, we reported an innovative computational approach for digital-based manufacturing process optimization. Supported by a deterministic Machine Learning (ML)-based pipeline, we raised a multi-objective optimization of LIB electrode properties and inverse design of its fabrication process using synthetic data, as Figure 1A illustrates. In the end, this gave us the possibility to manufacture the real electrode to validate its physical relevance. To do so, we first focused on the setting of a smart design of experiments based on low-discrepancy sequences, exploring our manufacturing parameters space, to then evaluate various synthetic electrode properties related to the kinetic, ionic, and electronic transport properties of 3D-generated microstructures. The latter are generated using the ARTISTIC physics-based models' chain. [2, 3] Those synthetic data constituted a relevant database, to train and validate an accurate deterministic regression approach for fitting the electrode properties as a function of the manufacturing parameters. These so-derived regression models represented a meaningful tool to bypass the manufacturing physical modeling chain for focusing on unexplored manufacturing conditions. [4] Indeed, we took the benefits of combining these models to raise a multi-objective optimization loop using the Bayesian optimization (BO) framework to pinpoint the optimal manufacturing conditions to produce a high-performance 3D microstructure (Figure 1B). [5] This objective function granted equal weights for each electrode property when optimizing to guarantee a good balance of the optimal properties with minimal tortuosity factor, maximal effective electronic conductivity, maximal active surface area between active material and pores, and maximal density. The good results were carried out by combining BO and regression functions to considerably accelerate the search for the best manufacturing parameters to adopt. In the end, we have manufactured the electrode cell using the aforementioned optimal condition predicted by the optimization loop, to experimentally validate the physical relevance of this BO prediction. As a perspective in our future research, we aim to extend this study to more manufacturing steps (e.g. electrolyte infiltration, formation, and electrochemical performance) and additional fabrication parameters (e.g. calendering speed, particle size) and show that our approach can be transferred to accelerate the manufacturing of battery technologies and the manufacturing of composite materials in general.[1] Meyer, C, Kosfeld, M., Haselrieder, W., Kwade, A. Process modeling of the electrode calendering of lithium-ion batteries regarding the variation of cathode active materials and mass loadings. Journal of Energy Storage, 18, 2018[2] Lombardo, T., Caro, F., Ngandjong, A. C., Hoock, J-B., Duquesnoy, M., Delepine, J-C., Ponchelet, A., Doison, S., Franco, A. A. The ARTISTIC online calculator: exploring the impact of lithium-ion battery electrode manufacturing parameters interactively through your browser. Batteries & Supercaps, 5(3), 2022.[3] Duquesnoy, M., Lombardo, T., Caro, F., Haudiquez, F., Ngandjong, A. C., Xu, J., Oularbi, H., Franco, A. A. Functional data-driven framework for fast forecasting of electrode slurry rheology simulated by molecular dynamics. npj Computational Materials, 8(161), 2022.[4] ARTISTIC Project. https://www.erc-artistic.eu/. Accessed on November 2022.[5] Gunantara, N. A review of multi-objective optimization: methods and its applications. Cogent Engineering, 5(1), 2018. Figure 1

Ultralight FeSiAl micro-flake flying with propylene to favor fast growth of carbon nanotube arrays at 99 % high-efficient conversion

The efficiency of catalyst-gas contact and the selectivity of catalyzing carbon source cracking determine the yield of highly oriented carbon nanotube arrays (CNTAs). Herein, the flying FeSiAl (FSA) micro sheets were in full contact with propylene (C3H6), effectively catalyzing propylene gas cracking with an astonishing 99 % carbon conversion rate. The propylene with more carbon atoms and high safety grew carbon nanotube arrays faster. The growth kinetics of the CNTAs were studied, and the apparent activation energy of the CNTAs was 49.04 kJ/mol. This indicated that there were two diffusion modes: the surface diffusion of the catalyst and the step-edge diffusion of the graphene-catalyst interface. The well-dispersed linear CNTAs (1D) and a point-like carbon black (0D) conductive agent form a 1D/0D conductive network, which was used as a conductive additive for lithium/fluorocarbon (CFx) primary cells. Effective electron conduction improved the rate performance of the battery, which was increased from 1229.22 Wh/kg at 2 C to 1358.35 Wh/kg at 4 C. The high catalytic efficiency of propylene molecules with high carbon content and FeSiAl (FSA) alloy provides a new method for large-scale and efficient production of CNTAs. High-orientation CNTAs have potential application prospects in the field of energy storage.

The Preparation of UiO‐66‐NH2/CNT Electrocatalyst and its High Catalytic Performance for Electrochemical Synthetic Ammonia in Neutral Electrolyte

AbstractThe electrochemical synthesis of ammonia is a sustainable green ammonia synthesis technology. The core reaction involved in this process‐the nitrogen reduction reaction (NRR) is a slow kinetic reaction, which requires efficient catalysts to improve the ammonia yield rate. A series of UiO‐66‐NH2/CNT‐X (X=0.05, 0.10, 0.15, 0.20 g) electrocatalysts with different carbon nanotube (CNT) content were synthesized by hydrothermal method. UiO‐66‐NH2/CNT‐0.15 with appropriate CNT content has the highest NH3 yield of 1.85×10−10 mol cm−2 s−1 (22.644 μg h−1 mgcat−1) at −0.3 V (vs. RHE), and the Faradaic efficiency (FE) is 37.78 %. The main reason is that the conductivity of CNT improves the charge transfer efficiency and ensures the effective electron conduction path of the catalyst. Besides, CNT in UiO‐66‐NH2/CNT‐X composite material inhibits the aggregation of UiO‐66‐NH2 particles, exposes more active sites and reduces the particle size of UiO‐66‐NH2. So, the synergistic effect between UiO‐66‐NH2 particles and CNT improves the ammonia yield.

3D microscale modeling of NMC cathodes using multi-resolution FIB-SEM tomography

The electrochemical performance of the lithium-ion battery (LIB) is intimately linked to the electrodes’ microstructure dictated by the arrangement of the different constituents. The conductive binder domain (CBD) is of critical importance to battery performance; however, the impact of CBD content on electrode performance is not uniquely identified without altering its morphology. Herein, we use realistic digital cathode microstructures reconstructed from focused ion beam scanning electron microscopy (FIB-SEM) tomography with multiple magnification levels for two NMC111 cathodes to assess the sensitivity of the effective electronic conductivity to the intrinsic conductivity of the CBD and active material (AM), deconvolute the impact of CBD content from morphology on effective transport properties, and highlight the impact of CBD nanoporosity on the pore phase tortuosity. The effective electronic conductivity is more sensitive to changes in the bulk conductivity of the conductive additives where increasing their conductivity by one order of magnitude results in a 10-fold increase in the effective conductivity. Additionally, while the CBD content alone with fixed morphology brings about a nonlinear change in the effective conductivity, the CBD morphology is critical even though it becomes less significant at high CBD contents. Finally, the CBD nanoporosity can lower the tortuosity by ∼17%.

Machine learning-assisted multi-objective optimization of battery manufacturing from synthetic data generated by physics-based simulations

The optimization of the electrodes manufacturing process is critical to ensure high-quality Lithium-Ion Battery (LIB) cells, in particular for automotive applications. LIB electrode manufacturing is a complex process involving multiple steps and parameters. We have shown in our previous works that 3D-resolved physics-based models constitute very useful tools to provide insights into the impact of the manufacturing process parameters on the textural and performance properties of the electrodes. However, their high-throughput application for electrode properties optimization and inverse design of manufacturing parameters is limited due to the high computational cost associated with these models. In this work, we tackle this issue by proposing a generalizable and innovative approach, supported by a deterministic machine learning (ML)-assisted pipeline for multi-objective optimization of LIB electrode properties and inverse design of its manufacturing process. Firstly, the pipeline generates a synthetic dataset from physics-based simulations with low discrepancy sequences, that allows to sufficiently represent the manufacturing parameters space. Secondly, the generated dataset is used to train deterministic ML models to implement a fast multi-objective optimization, to identify an optimal electrode and the manufacturing parameters to adopt in order to fabricate it. Lastly, this electrode was successfully fabricated experimentally, proving that our modeling pipeline prediction is physical-relevant. Here, we demonstrate our pipeline for the simultaneous minimization of the electrode tortuosity factor and maximization of the effective electronic conductivity, the active surface area, and the density, all being parameters that affect the Li+ (de-)intercalation kinetics, ionic, and electronic transport properties of the electrode.

(Digital Presentation) Three-Dimensional Pore-Scale Modelling of NMC Cathodes Using Multi-Resolution FIB-SEM Images

Lithium-ion battery (LIB) cathodes are porous electrodes made of active material (AM) that stores lithium, a composite of carbon additives and polymeric binder (CBD) that facilitates electron transport and ensures the mechanical integrity of the electrode, and electrolyte-filled pore space that facilitates lithium-ion transport [1]. The volume fraction and morphology of the different constituents at the microscale necessarily determine transport properties and influence the measurable performance [2]. In this work, the impact of NMC electrode microstructure on the effective transport properties is studied using FIB-SEM-based three-dimensional (3D) particle-resolved microscale simulations. The effect of AM and CBD bulk electronic conductivity on the effective electronic conductivity is first studied and used to highlight that the impact of the AM bulk conductivity is negligible compared to that of the CBD. Next, the impact of CBD volume fraction, in isolation from its morphology, is studied using morphological operations by eroding and dilating the CBD phase in the FIB-SEM images and analyzing its impact on the effective conductivity using multiple 3D reconstructions. Increasing the CBD volume fraction results in a nonlinear increase in the effective electronic conductivity. To study the effect of CBD morphology, two stochastic CBD reconstruction techniques are proposed. The first method places new CBD voxels preferentially next to existing CBD voxels, and the second method deposits the CBD randomly in the pore space. The effective electronic conductivity for microstructures containing stochastic CBD morphologies is calculated and compared to that evaluated for microstructures with eroded and dilated CBD. The CBD generated stochastically results in a predicted higher effective electronic conductivity primarily due to a lower CBD tortuosity when compared to the CBD generated with morphological operations. Finally, the impact of CBD porosity on electrode tortuosity is studied by estimating the pore-phase tortuosity considering a solid and a porous CBD. The diffusivity of the porous CBD is estimated using multi-resolution FIB-SEM images. Results show that not accounting for the CBD porosity increases the electrode tortuosity by a factor of up to three at low electrode porosities.

(Digital Presentation) Conceptual Design of Oxide-Based Solid-State Li-Battery for Urban Air Mobility

In metropolitan areas, traffic congestion and pollution are serious challenges due to the continuous rapid growth in the number of vehicles and population. These concerns have prompted a global trend toward the electrification of transportation powertrains. Electric vertical take-off and landing (eVTOL) aircraft is an efficient and safe alternative mode of transportation with a significant impact on urban mobility, which attracts considerable attention [1]. It could avoid traffic on the ground and allow people to commute between two locations in less time while requiring no additional run-up space. Nevertheless, one of the main challenges with such aircraft is to provide electrical storage systems, such as batteries, which are safe, efficient, reliable, and capable of producing the energy and power demand to meet mission requirements [2].High flammability and risk of thermal runaway in current Li-ion batteries motivate to develop next-generation chemistries such as all-solid-state batteries (ASSB) as alternative electrical storage systems for eVTOL. However, ASSB currently suffer from low solid electrolyte conductivity, as well as non-utilised active material, and significant reaction overpotentials due to a small effective active area [3]. Electrode composition and structure have a crucial impact on three performance-relevant microstructure parameters, i.e. effective active area, effective ionic conductivity, and effective electronic conductivity. However, considering these restrictions experimentally by producing and evaluating a wide range of design parameters to achieve a well-performing design for ASSB is both time- and resource-intensive. To address these issues, we conduct model-based structure evaluation. Here, we extend the P2D model with a microstructure surrogate model based on Laue's work [4] for Li-ASSB (see Fig. 1-a), because electrode microstructure is important for the effective parameters. Further, this link between the P2D model and microstructure modelling could provide a better understanding of the electrode structure and especially the effects of percolation, which cannot be properly covered by classical Bruggeman approaches. This model is then incorporated into a global optimisation algorithm to determine the optimal design of solid-state cathode with respect to eVTOL power demand. Finally, we compare the performance of the battery at the identified optimal electrode design, electrode reference design, and electrode design with that of the Bruggeman approach. Based on our prior research, we identified that hybrid electrolytes with 12.7 vol% LLZTO had the best performance among other types of hybrid electrolytes at a relatively high C-rate, i.e. 1C. Therefore, we employed this electrolyte for the modelling of Li-ASSB for eVTOL application [5].Fig. 1-b shows an exemplary result from a simpler model without percolation, i.e. Bruggemann approach, and a microstructure surrogate model. As can be observed, taking into account a more realistic structure in the electrode, the ASSB could only meet 32% of the mission requirements. However, optimisation of solid-state cathode structure significantly increases flight time in comparison to the reference electrode.In summary, we could show that future electrification of transportation powertrains will necessitate optimisation of the solid-state electrode structure and composition in order to meet the high demands for energy and power density. Also, our work highlights the urgent need to consider microstructure physical effects at an early stage of design for a realistic sizing of batteries. References Jain et.al., Proc. 4th Int. Conf. Electron. Commun. Aerosp. Technol. (2020) 1173–1178Donateo et.al., Aerospace 7, no. 5 (2020): 56Bielefeld et.al., The Journal of Physical Chemistry C 123, no. 3 (2018): 1626-1634Laue et.al., Energy Technology, 8(2) (2020): 1801049Toghyani, et.al., J. Electrochem. Soc (2022). Figure 1

Formation of carnation-like ZIF-9 nanostructure to achieve superior electrocatalytic oxygen evolution

Rational design and controllable synthesis of metal-organic frameworks nanosheets is critical for electrochemical catalysis. Herein, a carnation-like ZIF-9 nanostructure made of nanosheets is grown on nickel foam (ZIF-9/NF) by a simple one-step solvothermal method, the morphology evolution and the electrocatalytic oxygen evolution properties have been investigated by controlling the solvothermal time. The binder-free ZIF-9-d/NF (60 h, solvothermal time is 60 h) electrode delivers efficient electrocatalytic oxygen evolution reaction activity with low overpotentials of 312 and 337 mV at 50 and 100 mA cm−2, respectively. Furthermore, ZIF-9-d/NF (60 h) exhibits excellent stability without obvious attenuation for at least 30 h at 200 mA cm−2. The excellent performances can be attributed to the combination of the highly exposed active sites in the ZIF-9-d nanosheets, as well as the effective electron conduction and mass transfer. This work provides much possibilities for ZIF-9 nanosheets as binder-free electrode for electrocatalyst.

Reliable Lab-Scale Evaluation of Solid Oxide Fuel Cells Electrode Performance – the Effect of Electrode Thickness and Current Collecting Spacing

Understanding and optimizing electrochemically active zone for oxygen reduction reaction in solidoxide fuel cells (SOFCs) cathodes are indispensable to maximize device’s performance. For mixed ionicand electronic conductors, the range of this zone depends on the oxygen surface exchange and solid-stateoxygen/electron transport properties of materials [1]. This aspect is also critical to realize validcharacterization of the electrode’s electrochemical activities. For instance, when a material’s electronicconductivity is low or the electrical contact points are spaced beyond a critical distance, the electrodepolarization resistance measured by electrochemical impedance spectroscopy will be controlled by the sheetresistance not the oxygen exchange resistance [2]. The situation becomes more complicated as the electricalconductivity of porous layer, which is an advantageous structure with large surface area and facile gas phasetransport, considerably deviates from the material’s intrinsic property. It is expected that porosity,connectivity, and impurity contents can have significant impact on porous electrode’s electronic conduction[3].In this context, we have studied effective in-plane electronic conductivity of lanthanum strontiumcobalt ferrite (LSCF) porous layers. The in-plane conductivity was measured by four-line probeconfiguration [4], modified from the widely used four-point probe technique to be applied for a porouslayer. We have confirmed that the in-plane electrical conductivity of screen-printed LSCF porous layers isone or two orders of magnitude lower than the material’s bulk property in ambient air, depending ontemperatures. This electronic conductivity deviation was taken into account to rationally designcombination of electrode thickness and current collector spacing and, in turn, to guarantee a validassessment of electrode performance. In addition, we found that electrical conductivity of LSCF porouslayers decreases by 20~30 %, within relatively short period of time (250~400 hours) upon repeated thermalcycles (500 ℃ to 700 ℃). Furthermore, we found that in-plane conductivity of thin (<10 µm) electrodes isstrongly dependent on the measurement time and the local atmospheric environment relative to thickerelectrodes. Special attention is required to use a thin electrode to evaluate electrode performance.This work was supported by the US Department of Energy (DOE), Office of Fossil Energy, (Officeof Clean Coal & Carbon Management), “Solid Oxide Fuel Cell Manufacturing in Support of Office ofFossil Energy” program through Argonne National Laboratory under FWP No. 27327.1.[1] S.B. Adler et al. J. Electrochem. Soc. 143 (1996) 3554-3564; S.B. Alder Solid State Ionics 111 (1998)125-134.[2] B.A. Boukamp et al. Solid State Ionics 192 (2011) 404-408; B.A. Boukamp et al. Solid State Ionics 283(2015) 81-90.[3] L. Holzer et al. J. Mater. Sci. 48 (2013) 2934-2952.[4] S.W. Peterson et al. J. Electrochem. Soc. 161 (2014) A2175-A2181.

Machine-Learning Based Transport Property Analytics in Porous Electrodes

The electrochemical performance of lithium-ion batteries strongly relies upon the composition and microstructure of the porous electrodes. An electrochemically-coupled set of transport processes underlie the composite electrodes depending on the microstructural arrangement of its constituent material phases. However, the intrinsically stochastic, anisotropic and heterogeneous nature of these porous electrodes makes the characterization of their effective transport properties quite complex. In this work, we examine the use of machine learning as a viable route for fast and accurate abstraction of effective electrode properties, e.g. the tortuosity and effective electronic conductivity of exemplar electrode microstructures based on three-dimensional X-ray tomography images. The proposed computational framework can be generally extended to characterize various modes of transport resistances in porous media.

Ion‐Conducting, Electron‐Blocking Layer for High‐Performance Solid Electrolytes

Lithium metal batteries bring greater promise for energy density, often relying on solid‐state electrolytes to meet critical benchmarks. However, Li dendrite formation is a prevailing problem that limits the cycle life and Coulombic efficiency of solid‐state Li metal batteries. For the first time, a thin (&lt;100 nm) layer of electronically insulating, ionically conducting lithium phosphorus oxynitride (LiPON) is applied using atomic layer deposition between a Li anode and garnet Li7La3Zr2O12 (LLZO). The performance of a conformal LiPON layer as an electron barrier in symmetric Li‐LLZO cells is observed through potential step chronoamperometry, galvanostatic cycling, electron microscopy, and various spectroscopic techniques. The LiPON‐coated LLZO achieves 100 times lower electronic conductance than LLZO alone. Cycling carried out at 0.1 mA cm−2 for 100 cycles demonstrates that suppression of electron pathways into the bulk solid electrolyte improves the cycle life of a lithium metal cell. These findings suggest an electronic conductivity effect in solid‐state electrolytes. A strategy is demonstrated to design thin‐film (LiPON)‐modulated bulk solid‐state electrolytes (LLZO) capable of maintaining high ionic conductivity and electrochemical stability while reducing the effective electronic conductivity, which results in significantly decreased dendrite formation, improved cycle life, and greater interfacial integrity between the electrolyte and a Li anode.

Plasma enhanced atomic layer deposition of a (nitrogen doped) Ti phosphate coating for improved energy storage in Li-ion batteries

The use of Ti phosphate as a functional coating for Li ion battery electrodes has been investigated, as well as the effect of nitrogen doping on its electrochemical properties. First, previous knowledge on PE-ALD of Ti phosphate (using an exposure sequence of trimethylphosphate plasma–oxygen plasma–titaniumisopropoxide) was used to study an altered process using a nitrogen plasma, i.e. TMP* - N2* - TTIP. This enabled the deposition of a nitrogen doped (6 at.%) Ti phosphate with a growth per cycle of 0.4 nm/cycle. Next, a dual-source precursor (diethylphosphoramidate plasma, or DEPA*) was introduced instead of TMP*, allowing for a higher growth rate (0.6 nm/cycle) and a higher nitrogen level (8.6 at.%). The ionic transparency of the phosphate slightly decreased due to nitrogen incorporation, but the effective transversal electronic conductivity showed to be three times higher after nitrogen doping. A 2 nm coating of (un)doped Ti phosphate significantly improved the rate capability of a lithium nickel manganese cobalt oxide (NMC) electrode, increasing the amount of energy that can be stored at high (dis)charging speeds with a factor 10 (at 5C). In addition, the undoped titanium phosphate coating offered increased stability, retaining 84% of the capacity after 100 cycles at 1C with respect to 79% for the uncoated electrode.

Performance degradation prediction of direct internal reforming solid oxide fuel cell due to Ni-particle coarsening in composite anode

Long-term stability and durability of solid oxide fuel cell (SOFC) are the major challenges for fuel flexibility and commercialization. In this study, a fully coupled multi-field model is developed by coupling a modified Ni-particle coarsening model and a microstructure model for predicting the performance degradation of methane direct reforming (DIR) SOFC due to Ni-particle coarsening. The effect of Ni-particle coarsening on percolation probability of Ni-particle, effective TPB area, effective electron-conductivity, maximum power density, and the degradation rate of DIR-SOFC are quantitatively investigated under different operating conditions. The results indicate that to enhance the electrical performance and to reduce the degradation rate, the optima of the operating conditions and anode microstructure for the long-term DIR-SOFC are as follows: 750 °C for the cell operating temperature, 1.0 for anode inlet steam to carbon ratio (S/C), 0.3 A/cm2 for the operating current density, and corresponding to Ni-particle with an initial diameter of 0.6 μm, the optimal YSZ-particle diameter is 1.0 μm.

ZnO‐Based Conversion/Alloying Negative Electrodes for Lithium‐Ion Batteries: Impact of Mixing Intimacy

Conversion/alloying materials, such as transition metal (TM)‐doped ZnO, are showing superior performance over pure ZnO due to the presence of the TM, enabling the reversible formation of Li2O due to the enhanced electronic conductivity within the single particle once being reduced to the metallic state upon lithiation. Herein, the impact of introducing Co as representative TM at the atomic level in ZnO compared with mixtures of nano‐ and microsized CoO and ZnO is investigated. While even rather simple mixtures provide higher capacities than pure ZnO, an intimate mixing of nanoparticulate CoO and ZnO leads to a further increase due to the more homogeneous dispersion of Co. Nonetheless, the “atomic mixing” via doping still provides the highest capacities—for both nano‐ and microparticles, thus highlighting the importance of the very fine distribution of Co (and generally the TM) for realizing effective electron conduction pathways to enable the reversible formation of Li2O.

Study of the electrophysical properties of nanostructured porous germanium as a promising material for electrodes of electrochemical capacitors

Electrochemical capacitors (ECC) are a fast charging devices, with high power density, capacity and increased life time. Nanostructured semiconductors are now considered as the promising materials for electrodes of such devices due to its conductive properties and effective surface. One of such materials is the porous germanium which can be used as an electrode in electrochemical capacitors. In this article the novel approach based on the method of ion implantation was developed to grow these structures. This method allows to obtain a structures up to 1 μm thick. The object of this work was the investigation of the electrophysical characteristics of samples of nanostructured porous germanium (Ge) depending on the implantation dose and surface morphology. The scientific novelty of this research lies in the search the structures with the highest effective surface area and electronic conductivity, capable of multiplying the energy capacity and specific power of ECC. Methods: The samples of amorphous Ge were grown on dielectric single-crystal substrates of Al2O3. The thickness of samples was 600 and 1000 nm. The magnetron sputtering and ion implantation methods were used to growth these structures. The irradiation with Ge+ ions produced with an energy of 40 keV and the range of implantation doses varied from 2·1016 to 12•1016 ion / cm2. The study of electrical properties was carried out on the Hall installation HL55PC at the NPP KVANT in Moscow. The following parameters were measured: the sheet concentration of carriers in the near-surface layer, electrical resistance, mobility of the charge carriers, Hall coefficient. As a result, the dependences of carriers concentration and their mobility as the function of the implantation dose and thickness of the samples of nanostructured porous germanium were determined, and the results were analyzed. Results: It was found that ion implantation of single-crystal germanium leads to an increase in the carrier concentration in the near-surface layer. To sum up, the most suitable material as an electrode for ECC is the porous germanium with the maximum dose of ion implantation and the largest thickness. The maximum sheet carrier concentration that was obtained in the study for Ge is 1017 cm-2.

On the reaction rate distribution in porous electrodes

Reaction rate distribution across porous electrodes in Li-ion battery applications largely determines the overall battery performance. In the present work, expressions for the reaction rate distribution across porous electrodes are analytically derived and analyzed for small current and short time applications. The dependency on the effective ionic and electronic conductivities is systematically investigated and discussed. It is found that in the case of equal effective electronic and ionic conductivities, the reaction rate distribution is symmetric around the electrode mid-point. Small conductivities induce the charge-transfer reaction to preferentially occur at the interface of the current collector and separator, while high conductivities make the reaction rate distribution uniform across the electrode thickness. In the case of unequal conductivities, a decrease in the effective electronic conductivity shifts the reaction rate distribution towards the electrode/current collector interface. In contrast, a decrease in the effective ionic conductivity shifts the reaction rate distribution towards the electrode/separator interface. It is also found that the reaction rate distribution shows saturating behavior when the effective electronic or ionic conductivity grows infinitely. A further increase in the effective ionic or electronic conductivity does not lead to any further reaction rate distribution changes.

Ce-Doped Ordered Mesoporous Cobalt Ferrite Phosphides as Robust Catalysts for Water Oxidation.

Controllable synthesis and rational design of ordered nanostructures are crucial for their renewable energy applications. In this work, a mesoporous CoP/Fe2 P doped with 5 % Ce by a simple nanocasting method is designed as a superior electrocatalyst for the oxygen evolution reaction (OER). The well-designed composite delivers an efficient electrocatalytic activity with a low overpotential of 250 mV at 10 mA cm-2 and excellent long-term stability with no degradation after 10 h of electrochemical OER test, superior to that of the state-of-the-art RuO2 electrocatalyst in alkaline electrolyte. A comprehensive analysis demonstrates that the outstanding OER performance is due to the desirable combination of the highly exposed active centers in the Ce-doped bimetallic phosphides, efficient mass transfer, and effective electron conduction owing to the hierarchically mesoporous hybridization. Furthermore, the synergistic effect between Ce and CoP/Fe2 P accelerates the migration rate of electrons/ions and increases the electrochemical active area. This excellent OER performance observed by Ce doping of CoP/Fe2 P makes them possible candidates toward OER in alkaline electrolytes.