Benchmarking of Coatings for Cathode Active Materials in Solid-State Batteries Using Surface Analysis and Reference Electrodes.

Fast and reliable evaluation of degradation and performance of cathode active materials (CAMs) for solid-state batteries (SSBs) is crucial to help better understand these systems and enable the synthesis of well-performing CAMs. However, there is a lack of well-thought-out procedures to reliably evaluate CAMs in SSBs. Current approaches often rely on X-ray photoelectron spectroscopy (XPS) for the evaluation of degradation. Unfortunately, XPS sensitivity is not very high, and minor but relevant degradation products may not be detected and distinguished. Furthermore, degradation caused by the current collector (CC) itself is usually not distinguished from CAM-induced degradation. This study uses a modified CC, which allows us to separate electrochemical degradation caused by the CC from degradation at the CAM itself. Using this CC, we present an approach using time-of-flight secondary ions mass spectrometry (ToF-SIMS) that offers high sensitivity and reliability. Principal component analysis (PCA) is applied to differentiate secondary ions as well as identify those mass fragments that correlate with degradation products. This approach also enables distinguishing between different pathways of degradation. To evaluate the kinetic performance of the samples, three-electrode rate tests are performed. Electrochemical characterization evaluates the kinetic performance of the samples under investigation. The samples are finally rated with a score that allows a reliable comparison between the different materials and offers a complete picture of the materials' characteristics in terms of electrochemical performance and degradation.

Identification of Lithium Compounds on Surfaces of Lithium Metal Anode with Machine-Learning-Assisted Analysis of ToF-SIMS Spectra.

Detailed knowledge about contamination and passivation compounds on the surface of lithium metal anodes (LMAs) is essential to enable their use in all-solid-state batteries (ASSBs). Time-of-flight secondary ion mass spectrometry (ToF-SIMS), a highly surface-sensitive technique, can be used to reliably characterize the surface status of LMAs. However, as ToF-SIMS data are usually highly complex, manual data analysis can be difficult and time-consuming. In this study, machine learning techniques, especially logistic regression (LR), are used to identify the characteristic secondary ions of 5 different pure lithium compounds. Furthermore, these models are applied to the mixture and LMA samples to enable identification of their compositions based on the measured ToF-SIMS spectra. This machine-learning-based analysis approach shows good performance in identifying characteristic ions of the analyzed compounds that fit well with their chemical nature. Moreover, satisfying accuracy in identifying the compositions of unseen new samples is achieved. In addition, the scope and limitations of such a strategy in practical applications are discussed. This work presents a robust analytical method that can assist researchers in simplifying the analysis of the studied lithium compound samples, offering the potential for broader applications in other material systems.

Artificial Intelligence Applied to Battery Research: Hype or Reality?

This is a critical review of artificial intelligence/machine learning (AI/ML) methods applied to battery research. It aims at providing a comprehensive, authoritative, and critical, yet easily understandable, review of general interest to the battery community. It addresses the concepts, approaches, tools, outcomes, and challenges of using AI/ML as an accelerator for the design and optimization of the next generation of batteries─a current hot topic. It intends to create both accessibility of these tools to the chemistry and electrochemical energy sciences communities and completeness in terms of the different battery R&D aspects covered.

Electrochemical assessments of droplet contents in microfluidic channels. Application to the titration of heterogeneous droplets.

Series of aqueous droplets containing redox species were generated on-demand in a microfluidic channel and detected downstream by an electrochemical cell. Depending on the cell geometry, amperometric detections were performed to simultaneously determine the velocity, volume and content of circulating droplets in oil. Volumes and velocities were estimated from specific transition times on the chronoamperometric responses, while charge were evaluated from current integration. The results showed that the total charge within droplets was controlled by the geometry of the electrochemical cell and droplet velocity, leading to accurate determinations of droplet content under specific operating conditions. An active merging of droplets with titrating solutions was tested for analytical purposes. The results demonstrated that even if the mixing was not complete during detection, the assessment of droplet content was still valid. The performance of electrochemical detection was thus evidenced to determine the content of heterogeneous droplets. This property is pertinent since the design of sophisticated circuits is no longer required to fully homogenize the droplet content before characterization, opening broader perspectives in droplet-based microfluidics.