Search for: All records

Lithium-ion battery formation is a pivotal step that dictates performance, longevity, and manufacturing safety. At the core of this process is the formation and evolution of the solid electrolyte interphase (SEI), whose nanoscale thickness, high environmental sensitivity, and dynamic behavior have long hindered direct characterization. Here, we exploit the SEI-induced refractive index matching effect and utilize operando optical microscopy to directly visualize SEI growth in real time. Our observations reveal pronounced lateral heterogeneity and asynchronicity during the first lithiation of graphite anodes. Surprisingly, high formation current promotes synchronized SEI growth, leading to a more uniform SEI coverage. Building on this mechanistic insight, we designed a pulsed high-current formation protocol for 2 Ah LiFePO₄(LFP)/graphite pouch cells, achieving nearly an order-of-magnitude reduction in formation time while simultaneously enhancing the cycling performance. These findings challenge the prevailing belief that low currents are essential for developing a uniform SEI and pave a path toward safer and more efficient large-scale battery production. 

An electrode in cylindrical or pouch cell batteries contains millions of active particles embedded in a conductive network. Battery performance, such as voltage, capacity, and cyclic efficiency, is a collective response of the particle network. We use optical microscopy to measure the local, heterogeneous state of charge of individual particles upon charging and discharging. The optical reflectivity is proportional to Li composition in the ternary oxide LiNi_xMn_yCo_zO₂(NMC) cathode. Through clustering analysis, we determine the scale of heterogeneity where a representative volume in the composite electrode contains 100 to 1,000 particles. The heterogeneous activity in the particle network can be described by Weibull defect population at the particle interface with the conductive matrix. 

The application of Large Language Models (LLMs) in Software Engineering (SE) continues to grow rapidly across both industry and academia. As these models become integral to critical SE processes, ensuring their reliability and trustworthiness becomes essential. Achieving this requires a balanced approach to trust: excessive trust can introduce security vulnerabilities, while insufficient trust may hinder innovation. However, the conceptual landscape of trust in LLMs for SE(LLM4SE) remains unclear. Key concepts such as trust, distrust, and trustworthiness lack precise definitions, factors that shape trust formation remain underexplored, and metrics for trust in LLMs remain undeveloped. To clarify the current research landscape and identify future directions, we conducted a comprehensive review of\(88\)articles: a systematic review of\(18\)studies on LLMs in SE, supplemented by an analysis of\(70\)articles from the broader trust literature. Furthermore, we surveyed\(25\)domain experts to gather practitioners’ perspectives on trust and identify gaps between their experiences and the existing literature. Our findings provide a structured overview of trust-related concepts in LLM4SE, outlining key areas for future research. This study contributes to building more trustworthy LLM-assisted software engineering processes, ultimately supporting safer and more effective adoption of LLMs in SE. 

Operandoimaging techniques have become increasingly valuable in both battery research and manufacturing. However, the reliability of these methods can be compromised by instabilities in the imaging setup andoperandocells, particularly when utilizing high-resolution imaging systems. The acquired imaging data often include features arising from both undesirable system vibrations and drift, as well as the scientifically relevant deformations occurring in the battery sample during cell operation. For meaningful analysis, it is crucial to distinguish and separately evaluate these two factors. To address these challenges, we employ a suite of advanced image-processing techniques. These include fast Fourier transform analysis in the frequency domain, power spectrum-based assessments for image quality, as well as rigid and non-rigid image-registration methods. These techniques allow us to identify and exclude blurred images, correct for displacements caused by motor vibrations and sample holder drift and, thus, prevent unwanted image artifacts from affecting subsequent analyses and interpretations. Additionally, we apply optical flow analysis to track the dynamic deformation of battery electrode materials during electrochemical cycling. This enables us to observe and quantify the evolving mechanical responses of the electrodes, offering deeper insights into battery degradation. Together, these methods ensure more accurate image analysis and enhance our understanding of the chemomechanical interplay in battery performance and longevity. 

In lithium-ion battery manufacturing, following electrode preparation and cell assembly, electrolyte filling and wetting is a critical and throughput-determining step that often takes tens of hours due to the slow electrolyte infiltration of porous electrodes. This prolonged wetting process significantly limits production efficiency and increases manufacturing costs, highlighting the need for more effective electrolyte wetting strategies. In this study, we investigated the electrolyte wetting behavior of 2 Ah LiFePO₄(LFP)–graphite (Gr) pouch cells using ultrasonic transmission imaging and electrochemical impedance spectroscopy. Although elevated temperature can moderately accelerate electrolyte wetting, the improvement remains insufficient for practical production. Inspired by the sponge-like absorption behavior in the densely packed, highly tortuous, and irregular porous structures, we developed a pulsed pressurizing strategy that applies intermittent mechanical pressure to promote electrolyte penetration, successfully reducing the impregnation time to within 1 h. Electrochemical cycling tests further confirm that applying pressure during wetting does not compromise battery performance. This work offers a practical and scalable solution to significantly shorten electrolyte wetting time and accelerate the overall production process of lithium-ion batteries. 

The quality of lithium-ion battery (LIB) electrodes is critical to ensuring optimal performance, safety, and lifespan. Defects in graphite electrodes, such as cracks, agglomeration, and coating inhomogeneity, can severely impact battery performance by disrupting ion transport, promoting inactive lithium formation, and posing safety risks. In this study, we investigate coating defects in graphite electrodes and their influence on electrochemical performance. Among the observed defects, surface cracks were found to cause the most severe capacity degradation, likely due to local thickness variations and lithium accumulation at exposed copper sites. To enable comprehensive defect detection, we propose a multimodal characterization approach that integrates optical imaging, infrared thermography, and X-ray radiography. This strategy combines surface, thermal, and volumetric information, allowing for reliable, in-line assessment of coating uniformity and defect severity, and offering practical guidance for improving electrode manufacturing quality control.