Applying chemometrics to study battery materials: Towards the comprehensive analysis of complex operando datasets

Marcus Fehse, Antonella Iadecola, Moulay Tahar Sougrati, Paolo Conti, Marco Giorgetti, Lorenzo Stievano

Research output: Contribution to journalReview articleScientificpeer-review

18 Citations (Scopus)


In the last decade, a rapidly growing number of operando spectroscopy analyses have helped unravelling the electrochemical mechanism of lithium and post-lithium battery materials. The corresponding experiments usually produce large datasets containing many tens or hundreds of spectra. This considerable amount of data is calling for a suitable strategy for their treatment in a reliable way and within reasonable time frame. To this end, an alternative and innovating approach allowing one to extract all meaningful information from such data is the use of chemometric tools such as Principal Component Analysis (PCA) and multivariate curve resolution (MCR). PCA is generally used to discover the minimal particular structures in multivariate spectral data sets. In the case of operando spectroscopy data, it can be used to determine the number of independent components contributing to a complete series of collected spectra during electrochemical cycling. The number of principal components determined by PCA can then be used as the basis for MCR analysis, which allows the stepwise reconstruction of the “real” spectral components without needing any pre-existing model or any presumptive information about the system. In this paper, we will show how such approach can be effectively applied to different techniques, such as Mössbauer spectroscopy, X-ray absorption spectroscopy or transmission soft X-ray microscopy, for the comprehension of the electrochemical mechanisms in battery studies.

Original languageEnglish
Pages (from-to)328-337
Number of pages10
JournalEnergy Storage Materials
Publication statusPublished - 2019


  • Batteries
  • Chemometrics
  • Electrode materials
  • Energy storage
  • Multivariate curve resolution - alternating least square
  • Principal Component Analysis

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