With the ever-growing need for lithium-ion batteries, particularly from the electric transportation industry, a large amount of lithium-ion batteries is bound to retire in the near future, thereby ...leading to serious disposal problems and detrimental impacts on environment and energy conservation. Currently, commercial lithium-ion batteries are composed of transition metal oxides or phosphates, aluminum, copper, graphite, organic electrolytes with harmful lithium salts, polymer separators, and plastic or metallic cases. The lack of proper disposal of spent lithium-ion batteries probably results in grave consequences, such as environmental pollution and waste of resources. Thus, recycling of spent lithium-ion batteries starts to receive attentions in recent years. However, owing to the pursuit of lithium-ion batteries with higher energy density, higher safety and more affordable price, the materials used in lithium-ion batteries are of a wide diversity and ever-evolving, consequently bringing difficulties to the recycling of spent lithium-ion batteries. To address this issue, both technological innovations and the participation of governments are required. This article provides a review of recent advances in recycling technologies of spent lithium-ion batteries, including the development of recycling processes, the products obtained from recycling, and the effects of recycling on environmental burdens. In addition, the remaining challenges and future perspectives are also highlighted.
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•Advances in technologies for recycling spent LIBs are reviewed.•Characteristics and development of different recycling processes are discussed.•Products obtained from recycling of spent LIBs are summarized.•Effects of recycling spent LIBs on environmental burdens are presented.•Remaining challenges and future perspectives are highlighted.
“Electrocatalytic Methods for Ammonia Production” is a new open Special Issue in Materials, which aims to publish original research papers, perspectives and review articles on theoretical and applied ...research, and shed an inspiring light on electrocatalytic methods of ammonia production ...
•The application roadmap to carbon neutrality is presented.•A comprehensive review of fundamental ML tutorials is provided.•The latest progress in data-driven materials science and engineering is ...discussed.•The keys to successful ML applications and remaining challenges are highlighted.
The screening of advanced materials coupled with the modeling of their quantitative structural-activity relationships has recently become one of the hot and trending topics in energy materials due to the diverse challenges, including low success probabilities, high time consumption, and high computational cost associated with the traditional methods of developing energy materials. Following this, new research concepts and technologies to promote the research and development of energy materials become necessary. The latest advancements in artificial intelligence and machine learning have therefore increased the expectation that data-driven materials science would revolutionize scientific discoveries towards providing new paradigms for the development of energy materials. Furthermore, the current advances in data-driven materials engineering also demonstrate that the application of machine learning technology would not only significantly facilitate the design and development of advanced energy materials but also enhance their discovery and deployment. In this article, the importance and necessity of developing new energy materials towards contributing to the global carbon neutrality are presented. A comprehensive introduction to the fundamentals of machine learning is also provided, including open-source databases, feature engineering, machine learning algorithms, and analysis of machine learning model. Afterwards, the latest progress in data-driven materials science and engineering, including alkaline ion battery materials, photovoltaic materials, catalytic materials, and carbon dioxide capture materials, is discussed. Finally, relevant clues to the successful applications of machine learning and the remaining challenges towards the development of advanced energy materials are highlighted.
•A numerical model for direct formate fuel cells is developed.•Diffusion and migration as well as competitive adsorption are considered.•The effects of operating and design parameters are ...analyzed.•Distributions of concentrations, electrode potentials and local current densities are presented.
In this work, a one-dimensional mathematical model of direct formate fuel cells is developed. The present model involves mass/charge transport and electrochemical reactions. Compared to the previous models, this model incorporates the ion migration and considers the anode catalyst layer thickness, so that this model is not only capable of predicting the polarization curves to evaluate the fuel cell performance, but also able to give more in-depth insights into the direct formate fuel cells, e.g., the concentration distributions of reactants/products, the distribution of local current density, and the distribution of electrode potential. In validation, the present model results agree well with the experimental data from the open literature. The voltage losses resulting from the anode, membrane and cathode, as well as the distribution of electrode potential are specified individually via using the present model. Moreover, the effects of the operating conditions, i.e., the feeding concentrations of reactants, and the structural design parameters, i.e., the thicknesses and porosities of diffusion layers and catalyst layers as well as the specific active surface area of catalyst layers, on the fuel cell performance are examined.
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The dual-layer electrode for fuel cells is typically prepared by binding discrete catalyst nanoparticles onto a diffusion layer. Such a random packing forms a dense catalyst layer and thus creates a ...barrier for mass/ion transport, particularly for direct liquid fuel cells. Three-dimensional porous electrodes, a thin nano-porous catalyst layer uniformly distributed on the matrix surface of a foam-like structure, are typically employed to improve the mass/ion transport. Such a three-dimensional porous structure brings two critical advantages: (i) reduced mass/ion transport resistance for the delivery of the reactants via shortening the transport distance and (ii) enlarged electrochemical surface area, via reducing the dead pores, isolated particles and severe aggregations, for interfacial reactions. Moreover, the three-dimensional design is capable of fabricating binder-free electrodes, thereby eliminating the use of ionomers/binders and simplifying the fabrication process. In this work, three types of three-dimensional porous electrode are fabricated, via different preparation methods, for direct formate fuel cells: (i) Pd/C nanoparticles coating on the nickel foam matrix surface (Pd-C/NF) via a dip-coating method, (ii) Pd nanoparticles depositing on the nickel foam matrix surface (Pd/NF) via reduction reaction deposition, and (iii) Pd nanoparticles embedding in the nickel foam matrix (Pd/(in)NF) via replacement reaction deposition. The latter two are binder-free three-dimensional porous electrodes. As a comparison, a conventional dual-layer design, Pd/C nanoparticles painting on the nickel foam layer (Pd-C//NF), is also prepared via direct painting method. It is shown that the use of the three-dimensional Pd-C/NF electrode as the anode in a direct formate fuel cell results in a peak power density of 45.0 mW cm
−2
at 60°C, which is two times of that achieved by using a conventional dual-layer design (19.5 mW cm
−2
). This performance improvement is mainly attributed to the unique three-dimensional structure design, which effectively enhances the mass/ion transport through the porous electrode and enlarges the electrochemical surface area (accessible active area) for interfacial reactions. In addition, the delivery of the fuel solution is still sufficient even when the flow rate is as low as 2.0 mL min
−1
. It is also demonstrated that direct formate fuel cells using two binder-free electrodes yield the peak power densities of 13.5 mW cm
−2
(Pd/(in)NF) and 14.0 mW cm
−2
(Pd/NF) at 60°C, respectively, both of which are much lower than the power density achieved by using the Pd C/NF electrode. This is because the electrochemical surface areas of two binder free electrodes are much smaller than the Pd/C based electrodes, since the specific area of Pd/C nanoparticles is much larger.
Ion exchange membranes are widely used in fuel cells to physically separate two electrodes and functionally conduct charge-carrier ions, such as anion exchange membranes and cation exchange ...membranes. The physiochemical characteristics of ion exchange membranes can affect the ion transport processes through the membrane and thus the fuel cell performance. This work aims to understand the ion transport characteristics through different types of ion exchange membrane in direct formate fuel cells. A one-dimensional model is developed and applied to predict the polarization curves, concentration distributions of reactants/products, distributions of three potentials (electric potential, electrolyte potential, and electrode potential) and the local current density in direct formate fuel cells. The effects of the membrane type and membrane thickness on the ion transport process and thus fuel cell performance are numerically investigated. In addition, particular attention is paid to the effect of the anion-cation conducting ratio of the membrane, i.e., the ratio of the anionic current to the cationic current through the membrane, on the fuel cell performance. The modeling results show that, when using an anion exchange membrane, both formate and hydroxide concentrations in the anode catalyst layer are higher than those achieved by using a cation exchange membrane. Although a thicker membrane better alleviates the fuel crossover phenomenon, increasing the membrane thickness will increase the ohmic loss, due to the enlarged ion-transport distance through the membrane. It is further found that increasing the anion-cation conducting ratio will upgrade the fuel cell performance via three mechanisms: (i) providing a higher ionic conductivity and thus reducing the ohmic loss; (ii) enabling more OH
−
ions to transport from the cathode to the anode and thus increasing the OH
−
concentration in the anode catalyst layer; and (iii) accumulating more cations in the anode and thus enhancing the formate-ion migration to the anode catalyst layer for the anodic reaction.
Direct liquid fuel cells with high energy density and facile fuel storage have received increasing attention. Owing to the poor reactivity of conventional liquid fuels, they not only require noble ...metal catalysts for their oxidation but also exhibit limited performance. Here, we report a power-generation system, the direct liquid e-fuel cell, where “e-fuel” stands for “electrically rechargeable fuel.” This e-fuel cell consists of a catalyst-free graphite-felt anode and a conventional oxygen cathode separated by a proton exchange membrane, producing a maximum current density of 750 mA cm−2, a peak power density of 293 mW cm−2, and an energy efficiency of 42.3% at room temperature, which is much higher than the performances achieved by conventional direct liquid fuel cells. This emerging technology, capable of fast recharging, could be a powerful, efficient, cost-effective, and durable power-generation device, showing great potential for commercialization in the fuel cell electric vehicle industry.
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An e-fuel cell fed with an electrically rechargeable liquid fuel is demonstratedIt consists of a catalyst-free graphite-felt anode and a conventional oxygen cathodeA peak power density of 293 mW cm−2 and an energy efficiency of 42.3% are achieved
Shi et al. demonstrate an e-fuel cell capable of converting an electrically rechargeable liquid fuel into electricity. They achieve a peak power density of 293 mW cm−2 and an energy efficiency of 42.3% at room temperature, thus demonstrating the potential for powering future electric vehicles.
•VOF method is utilized to investigate the effects of different degassing layer and base heights on the bubble behavior in channel.•The optimization framework combing DNN and GA is conducted to ...accelerate the optimization of flow field.•The reliability of the optimization scheme is validated by bubble visualization and electrochemical characterization.
Serious bubble clogging in flow-field channels will hinder the water supply to the electrode of proton exchange membrane water electrolyzer (PEMWE), deteriorating the cell performance. In order to address this issue, the dual-layer flow field design has been proposed in our previous study. In this study, the VOF (volume of fluid) method is utilized to investigate the effects of different degassing layer and base heights on the bubble behavior in channel and determine the time for the bubbles to detach from the electrode surface. However, it is very time-consuming to get the optimal combination of base layer and degassing layer heights due to the large number of potential cases, which needs to be calculated through computation-intensive physical model. Therefore, machine learning methods are adopted to accelerate the optimization. A data-driven surrogate model based on deep neural network (DNN) is developed and successfully trained using data obtained by the physical VOF method. Based on the highly efficient surrogate, genetic algorithm (GA) is further utilized to determine the optimal heights of base layer and degassing layer. Finally, the reliability of the optimization was validated by bubble visualization in channel and electrochemical characterization in PEMWE through experiments.
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