The deep-DRT: A deep neural network approach to deconvolve the distribution of relaxation times from multidimensional electrochemical impedance spectroscopy data

Data-Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects

Battery characterization and prognosis are essential for analyzing underlying electrochemical mechanisms and ensuring safe operation, especially with the assistance of superior data-driven artificial intelligence systems. This review provides a unique perspective on recent progress in data-driven battery characterization and prognosis methods. First, recent informative image characterization and impedance spectrum as well as high-throughput screening approaches on revealing battery electrochemical mechanisms at multiple scales are summarized. Thereafter, battery prognosis tasks and strategies are described, with the comparison of various physics-informed modeling strategies. Considering unlocking mechanisms from tremendous battery data, the dominant role of physics-informed interpretable learning in accelerating energy device development is presented. Finally, challenges and prospects on data-driven characterization and prognosis are discussed toward accelerating energy device development with much-enhanced electrochemical transparency and generalization. This review is hoped to supply new ideas and inspirations to the next-generation battery development.

Unveiling the spatiotemporal dynamics of membrane fouling: A focused review on dynamic fouling characterization techniques and future perspectives

Membrane technology has emerged as a crucial method for obtaining clean water from unconventional sources in the face of water scarcity. It finds wide applications in wastewater treatment, advanced treatment, and desalination of seawater and brackish water. However, membrane fouling poses a huge challenge that limits the development of membrane-based water treatment technologies. Characterizing the dynamics of membrane fouling is crucial for understanding its development, mechanisms, and effective mitigation. Instrumental techniques that enable in situ or real-time characterization of the dynamics of membrane fouling provide insights into the temporal and spatial evolution of fouling, which play a crucial role in understanding the fouling mechanism and the formulation of membrane control strategies. This review consolidates existing knowledge about the principal advanced instrumental analysis technologies employed to characterize the dynamics of membrane fouling, in terms of membrane structure, morphology, and intermolecular forces. Working principles, applications, and limitations of each technique are discussed, enabling researchers to select appropriate methods for their specific studies. Furthermore, prospects for the future development of dynamic characterization techniques for membrane fouling are discussed, underscoring the need for continued research and innovation in this field to overcome the challenges posed by membrane fouling.

Polarization of disk electrodes in high-conductivity electrolyte solutions

We investigate the polarization of disk electrodes immersed in an electrolyte solution and subjected to a small external AC voltage over a wide range of frequencies. A mathematical model is developed based on the Debye-Falkenhagen approximation to the coupled Poisson-Nernst-Planck equations. Analytical techniques are used for predicting the spatial distribution of the electric potential and the complex impedance of the system. Scales for impedance and frequency are identified, which lead to a self-similar behavior for a range of frequencies. Experiments are conducted with gold electrodes of sizes in the range 100-350 μm immersed in a high-conductivity KCl solution over five orders of magnitude in frequency. A collapse of data on impedance magnitude and phase angle onto universal curves is observed with scalings motivated by the mathematical model. A direct comparison with the approximate analytical formula for impedance is made without any fitting parameters, and a good agreement is found for the range of frequencies where the analytical model is valid.

Novel Perovskite Structured Nd0.5Ba0.5Co1/3Ni1/3Mn1/3O3-δ as Highly Efficient Catalyst for Oxygen Electrode in Solid Oxide Electrochemical Cells

Developing catalytic materials with highly efficient oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) is essential for lower-temperature solid oxide fuel cell (SOFC) and electrolysis cell (SOEC) technologies. In this work, a novel triple perovskite material, Nd0.5Ba0.5Co1/3Ni1/3Mn1/3O3-δ, has been developed and employed as a catalyst for both ORR and OER in SOFC and SOEC operations at relatively lower temperatures, showing a low polarization resistance of 0.327 Ω cm2, high-power output of SOFC up to 773 mW cm-2 at 650 °C, and a high current density of 1.57 A cm-2 from SOEC operation at 1.5 V at 600 °C. The relaxation time distribution reveals that Nd0.5Ba0.5Co1/3Ni1/3Mn1/3O3-δ could maintain a slow polarization process at the relatively low operating temperature, offering a significant antipolarization advantage over other perovskite electrode materials. The Nd0.5Ba0.5Co1/3Ni1/3Mn1/3O3-δ electrode provides a low energy barrier of about 0.36 eV in oxygen ion mobility, which is beneficent for oxygen reduction/evolution reaction processes.

Cross-linked solid-liquid interfaces enable a fast proton transport in the aluminate heterostructure electrolyte

Having a highly-conductive protonic electrolyte is an essential requirement of developing solid ceramic fuel cell (SCFC) operated below 600 °C. Proton transport in solid electrolyte structure occurs via a bulk conduction mechanism in conventional SCFC, which may not be so efficient; therefore we have developed a fast proton conducting NaAlO₂/LiAlO₂ (NAO-LAO) heterostructure electrolyte, achieving the ionic conductivity of 0.23 S cm^-1 thanks to its rich cross-linked solid-liquid interfaces; the SCFC employing this new developed electrolyte showed a maximum power density of 844 mW cm^-2 at 550 °C, and the fuel cell could still operate at even lower temperatures down to 370 °C, although the output reduced to 90 mW cm^-2. The proton-hydration liquid layer promoted the formation of cross-linked solid-liquid interfaces in the NAO-LAO electrolyte, which promoted the construction of solid-liquid hybrid proton transportation channels and effectively reduced polarization loss, leading to high proton conduction at even lower temperatures. This work provides an efficient design approach for developing enabling electrolytes with high proton conductivity for SCFCs to be operated at relatively lower temperatures (300-600 °C) than traditional solid oxide fuel cells which operate above 750 °C.

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook

In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providing powerful resources towards automation, exploration, and development. In this review, we evaluate the state-of-the-art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences). We start from the traditional imaging techniques to reach the newest higher-dimensionality ones, also covering the recent advances in spectroscopy and tomography. Additionally, the present review provides a practical guide for microscopists, and in general for material scientists, but not necessarily advanced machine learning practitioners, to straightforwardly apply the offered set of tools to their own research. To conclude, we explore the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research (e.g., high-energy physics, astronomy, Earth sciences, and even robotics, videogames, or marketing and finances), in order to narrow down the incoming future of electron microscopy, its challenges and outlook.