Journal of Power Sources Advances最新文献

To alleviate water flooding in cathode electrodes of polymer electrolyte fuel cells (PEFCs), it is essential to design the optimum channel/electrode structure for rapid water removal. This study presented a novel hybrid structure with the channel hydrophilization and electrode perforation for accelerating the through-plane water discharge and demonstrated the effect of its structure on the water transports in the cathode channel and gas diffusion layer (GDL) of a working PEFC with optical and X-ray imaging. The results revealed that the hydrophilization of the channel walls encourages the through-plane water suction form the GDL to the channel. Furthermore, the electrode perforation promotes the in-plane water discharge from the fine porous media to the large penetration grooves and holes. The synergistic effect of these two water transports in the hybrid structure effectively alleviates the flooding in the porous layers and enhances the oxygen diffusibility, resulting in significant improvement of the cell performance.

Understanding ionomer distribution properties that facilitate proton conduction and oxygen transfer to Pt particles in the cathode catalyst layer (CCL) of the polymer electrolyte fuel cell (PEFC) is essential for optimized design of CCL with high cell performance. In this study, the model structure of Ketjen black (KB) as porous carbon was numerically simulated. After validating the model, the relationship between the weight ratio of ionomer/carbon (I/C) and ionomer coverage was investigated. Moreover, relative proton conductivity of simulated KB was compared with the reference data of Vulcan XC-72 (VB) as non-porous carbon. Under the same I/C ratio conditions, ionomer coverage significantly differed depending on the carbon support. Moreover, under the same carbon volume ratio conditions, simulated KB exhibited lower relative proton conductivity than VB because simulated KB had the lower ionomer volume ratio than that of simulated VB. The relationship between ionomer content and ionomer properties differ depending on the carbon support. The results of our study can contribute to designing an optimal catalyst layer.

The reduction of capital and operational expenditure in polymer electrolyte water electrolysis (PEWE) is of crucial importance for materializing the hydrogen economy. Optimizing the components and design of PEWE cells is a major contribution to this goal. In this study, we have analyzed the impact of reducing the anodic porous transport layer (PTL) thickness by over one order of magnitude from 2 mm to 0.16 mm while keeping other parameters in the PTL constant for a systematic comparison. PTL morphology and its impact on cell performance have been correlated by X-ray tomographic microscopy (XTM) and overpotential breakdown analysis. We found that varying PTL thicknesses in this range can contribute to up to 120 mV overpotential at 4 A/cm² which can be attributed to water transport limitations below the flow field land in thin PTLs. Furthermore, the results indicate that there is an optimal thickness in dependency of the flow field design. For the investigated class of materials, this is corresponding to roughly half of the flow field land size. Subsequently, a guideline was deduced for the optimal relation of PTL thickness and flow field characteristics.

Porphyrins constitute a class of attractive materials for harvesting sunlight and promote chemical reactions following their natural activity for the photosynthetic process in plants. In this work, we employ an in-silico design strategy to propose novel porphyrin-based materials as photocatalysts for hydrogen evolution reaction (HER). More specifically, a set of meso-substituted porphyrins with donor-acceptor architecture are evaluated within the density functional theory (DFT) framework, according to these screening criteria: i) broad absorption spectrum in the ultraviolet–visible (UV–Vis) and near infrared (NIR) range, ii) suitable redox potentials to drive the uphill reaction that lead to molecular hydrogen formation, iii) low exciton binding free energy (E_b), and iv) low hydrogen binding free energy (ΔG_H), a quantity that should present low HER overpotentials, ideally ΔG_H = 0. The outcomes indicate that the Se-containing compound, where the donor ligands are attached to the porphyrin core by the spacer, outstands as the most promising candidate that is presented in this work. It displays a broad absorption in the visible and NIR regions to up to 1000 nm, suitable catalytic power, low E_b (in special in high dielectric constant environment, such as water) and the lowest ΔG_H = +0.082 eV. This is comparable, in absolute values, to the value exhibited by platinum (ΔG_H = −0.10 eV), one of the most efficient catalysts for HER.

The negative and positive electrodes of lithium-ion batteries exhibit different structural characteristics. In this study, considering the characteristics of each electrode layer of a lithium-ion battery, the correlation equation of the effective ion conductivity was formulated using a machine learning model. In general, the tortuosity depends on the porous structure, and therefore, the morphology of the packed particles. The graphite particles that constitute the negative electrode have a flat shape, in terms of the aspect ratio. Therefore, the tortuosity of a structure likely depends on the aspect ratio. In contrast, because the positive electrode represents a secondary aggregate, the tortuosity depends on the particle morphology. In this scenario, the parameters representing the particle shape are unclear. Considering these aspects, the tortuosity for the negative electrode in terms of the particle aspect ratio was predicted through nonlinear regression based on a support vector machine. The tortuosity for the positive electrode was predicted using the cross-sectional image of the electrode, with the particle shape considered as a feature. This clarified the correlation between the tortuosity and other structural properties or images. The obtained findings can be applied in various fields pertaining to porous materials and facilitate the optimization of structural designs.

In-orbit satellite REIMEI, developed by the Japan Aerospace Exploration Agency, has been relying on off-the-shelf Li-ion batteries since its launch in 2005. The performance and durability of Li-ion batteries is impacted by various degradation mechanisms, one of which is the growth of the solid-electrolyte interphase (SEI). In this article, we analyse the REIMEI battery and parameterize a full-cell model with electrochemical cycling data, computer tomography images, and capacity fading experiments using image processing and surrogate optimization. We integrate a recent model for SEI growth into a full-cell model and simulate the degradation of batteries during cycling. To validate our model, we use experimental and in-flight data of the satellite batteries. Our combination of SEI growth model and microstructure-resolved 3D simulation shows, for the first time, experimentally observed inhomogeneities in the SEI thickness throughout the negative electrode for the degraded cells.

Government investment constitutes a large portion of overall investment in research and development of lithium-ion batteries (LIBs) and other future battery technologies with the goal of electrifying the transportation sector and so removing a major source of global greenhouse gas emissions. Poor investments, however, can result in taxpayer funding losses and political backlash, making clear communication and informed decision-making critical. This manuscript presents the Battery Component Readiness Level scale, an overhauled version of the Technology Readiness Level (TRL) scale currently utilized by the EU for innovation programs that has been customized for use in battery technology development. It retains the structure of the EU TRL scale while adding in-depth description of technology-specific development as well as discussion of aspects such as manufacturability and cost that are necessary to understand technological promise and risk. Its use by the EU and other parties involved in battery development can thus improve communication between all involved sectors, from government to academia to industry, and can aid in better-informed decision-making regarding investments. This can ultimately contribute to a more efficient electrification of the transportation sector and any other sectors where batteries display transformative potential.

The catalyst layer (CL) being the site of electrochemical reactions, is the core subunit of the membrane electrode assembly (MEA) in polymer electrolyte fuel cells (PEFCs). Thus, the porous structure of the CL has a significant influence on oxygen transfer resistance and affects the charge/discharge performance. In this study, the three-dimensional (3D) porous structure of the catalyst layer is reconstructed based on the deep convolutional generative adversarial network (DCGAN) deep learning method, utilizing focused ion beam scanning electron microscopy (FIB-SEM) microstructure graphs as training data. Each set of spatial-continuous microstructure graphs, generated by DCGAN with interpolation in latent space, is applied to build a unique 3D microstructure of the CL without the use of real FIB-SEM data. Meanwhile, distinct interpolation conditions in the DCGAN are discussed to optimize the ultimate structure by approaching the structural information to real data, including that of porosity, particle size distribution, and tortuosity. Moreover, the comparison of real and generated structural data reveal that the data generated by DCGAN shows an adjacency relationship with real data, indicating its potential applicability in the field of electrochemical simulation with reduced situational costs.

The paper reports the performance of the electrochemical capacitor operating with a nanoparticle-modified electrolyte. 7 mol L⁻¹ KSCN aqueous solution, known as the electrolyte exhibiting redox activity originating from pseudohalide anion (SCN⁻), has been enriched by gold nanoparticles at nanomolar concentration. The cycle life, specific energy of the device and power retention have been improved. The influence of nanoparticles concentration on the electrochemical capacitor performance has also been verified. All the nanoparticle-modified electrolytes display very high conductivity (∼370 mS cm⁻¹); it is confirmed that the high energy density is retained at the whole range of applied current densities: 13.7 Wh kg⁻¹ (at 1 A g⁻¹) and 12.1 Wh kg⁻¹ (at 20 A g⁻¹).

The inherent properties of non-aqueous electrolytes are highly associated with the identity of salt anions. To build highly conductive and chemically/electrochemically robust electrolytes for lithium-ion batteries (LIBs) and rechargeable lithium metal batteries (RLMBs), various kinds of weakly coordinating anions have been proposed as counterparts of lithium salts and ionic liquids. Among them, bis(fluorosulfonyl)imide anion ([N(SO₂F)₂]⁻, FSI⁻) has aroused special attention in battery field due to the unique physical, chemical, and electrochemical properties of the FSI-based electrolytes. Herein, an overview on the synthetic methodologies of the FSI-based salts (e.g., alkali metal salts, ionic liquids) is provided, and their applications in LIBs and RLMBs are also updated. Future directions on developing FSI-based and/or FSI-derived electrolytes are presented. The present work is anticipated to inspire the design and screening of new anions for battery use, particularly, those stemming from sulfonimide anions.