High-Resolution Mapping of Discharge Product in Li─O2 Batteries
Abstract
Lithium-oxygen (Li─O2) batteries have the highest theoretical specific energy of any chemical battery (∼3500 Wh kg-1). However, their practical capacity is usually lower than theoretically expected, particularly at higher rates of discharge. Air electrode surface passivation and slow O2 mass transport through the porous electrode structure are the primary limitations on Li─O2 discharge capacity. To determine which of these factors is most significant, it is necessary to spatially determine the relative utilisation of different portions of the electrode. Here, we demonstrate how cross-sectional scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS) can be used to semi-quantitatively map the discharge product distribution within Li─O2 air electrodes. The distribution shows increased discharge product accumulation at the O2-side of the electrode, confirming that pore blockage and oxygen starvation are key factors leading to under-utilisation of the air electrode in Li─O2 cells. Simulated Li─O2 battery results align with the characterised discharge product distribution, lending validation to the model. Lab-based X-ray nano-computed tomography corroborates the SEM-EDS data to ensure that sample preparation had minimal effect on the measured distribution. Collectively, these techniques can be used to determine the cause of performance changes due to different discharge conditions, alternative electrolyte compositions, or electrode structure.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.