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L-edge sum rule analysis on 3d transition metal sites: from d10 to d0 and towards application to extremely dilute metallo-enzymes.

Hongxin WangStephan FriedrichLei LiZiliang MaoPinghua GeMahalingam BalasubramanianDaulat S Patil
Published in: Physical chemistry chemical physics : PCCP (2018)
According to L-edge sum rules, the number of 3d vacancies at a transition metal site is directly proportional to the integrated intensity of the L-edge X-ray absorption spectrum (XAS) for the corresponding metal complex. In this study, the numbers of 3d holes are characterized quantitatively or semi-quantitatively for a series of manganese (Mn) and nickel (Ni) complexes, including the electron configurations 3d10→ 3d0. In addition, extremely dilute (<0.1% wt/wt) Ni enzymes were examined by two different approaches: (1) by using a high resolution superconducting tunnel junction X-ray detector to obtain XAS spectra with a very high signal-to-noise ratio, especially in the non-variant edge jump region; and (2) by adding an inert tracer to the sample that provides a prominent spectral feature to replace the weak edge jump for intensity normalization. In this publication, we present for the first time: (1) L-edge sum rule analysis for a series of Mn and Ni complexes that include electron configurations from an open shell 3d0 to a closed shell 3d10; (2) a systematic analysis on the uncertainties, especially on that from the edge jump, which was missing in all previous reports; (3) a clearly-resolved edge jump between pre-L3 and post-L2 regions from an extremely dilute sample; (4) an evaluation of an alternative normalization standard for L-edge sum rule analysis. XAS from two copper (Cu) proteins measured using a conventional semiconductor X-ray detector are also repeated as bridges between Ni complexes and dilute Ni enzymes. The differences between measuring 1% Cu enzymes and measuring <0.1% Ni enzymes are compared and discussed. This study extends L-edge sum rule analysis to virtually any 3d metal complex and any dilute biological samples that contain 3d metals.
Keyphrases
  • transition metal
  • high resolution
  • metal organic framework
  • machine learning
  • mass spectrometry
  • deep learning
  • climate change
  • neural network
  • human health
  • liquid chromatography
  • health risk assessment