**5. Some case studies**

the –log of the series. The free NIH ImageJ software [27] is for instance useful to handle the

By taking the average absorbance from a given region of an image for all images, the absorption spectrum for that region is obtained. In principle, the technique gives access to a single pixel spectra analysis, practically the dimension of the selected region is limited by the lens resolution,

Given that the different oxygen reduction states present in discharged metal/oxygen electrodes (superoxide, peroxide, and carbonate) are characterized by distinct absorption peak energies (around 529, 531, and 533 eV, respectively) oxygen‐state‐resolved maps can be obtained. A full quantitative approach consists in measuring a full set of pure reference samples (superoxide, peroxide, and carbonate in our case) and fit with them the obtained measured spectra. If measured reference spectra are not available some sophisticated linear algebra technique such as principle component analysis and factor analysis can also be used for the interpretation of XANES spectra. The number of principal components determined in this way was used as the basis for multivariate curve resolution‐alternating least squares (MCR‐ALS) analysis [28]. However, precise and accurate calculations of all spectral features are still difficult and not always reliable. Presently, quantitative analyses of XANES spectra using *ab initio* calculations are very rare, and a full description of absorption spectra data analysis and interpretation is beyond the scope of this paragraph. Here, we will describe a simple, qualitative/semiquantitative approach based on the construction of absorbance image differences *D* obtained from single absorbance images at specific energies. Let us consider two chemical species A and B in

*μ*(*E* ) *t* = (*μ<sup>m</sup>*,*<sup>A</sup>*(*E* ) *ρ<sup>A</sup>* + *μ<sup>m</sup>*,*<sup>B</sup>*(*E* ) *ρ<sup>B</sup>* ) *t* (16)

where *μ*(*E* ) *t* represents the intensity map in the absorbance image at a generic energy *E*. Now if we choose the two values of the energy position of the absorption edge peak maximum and minimum, respectively, of the species A for instance (*E*MAX,A and *E*min,A in **Figure 9**), we can

*DA* = (*μ<sup>m</sup>*,*<sup>A</sup>*(*E*MAX,*<sup>A</sup>* ) *ρ<sup>A</sup>* + *μ<sup>m</sup>*,*<sup>B</sup>*(*E*MAX,*<sup>A</sup>* ) *ρ<sup>B</sup>* − *μ<sup>m</sup>*,*<sup>A</sup>*(*E*min,*<sup>A</sup>* ) *ρ<sup>A</sup>* − *μ<sup>m</sup>*,*<sup>B</sup>*(*E*min,*<sup>A</sup>* ) *ρ<sup>B</sup>* ) *t* (17)

and assuming *μ<sup>m</sup>*,*<sup>B</sup>*(*E*MAX,*<sup>A</sup>* ) ≈ *μ<sup>m</sup>*,*<sup>B</sup>*(*E*min,*<sup>A</sup>* ) , (18)

*DA* ≈ (*μ<sup>m</sup>*,*<sup>A</sup>*(*E*MAX,*<sup>A</sup>* ) −*μ<sup>m</sup>*,*<sup>A</sup>*(*E*min,*<sup>A</sup>* ) )*ρ<sup>A</sup> t* (19)

portional to the concentration map of the chemical specie A. Doing the same for the specie B,

*t*, that is the absorbance image difference *D*A is pro-

(*E*MAX,*<sup>A</sup>* ) *ρ<sup>A</sup>*

.

the effectiveness of the series alignment, and single pixel spectra noise to 5–6 pixels2

a thickness *t*, then the corresponding absorbance will be (from Eq. 12):

write for the corresponding absorbance difference *D*<sup>A</sup>

(*E*MAX,*<sup>A</sup>* ) ⇒ *DA* ≈ *μ<sup>m</sup>*,*<sup>A</sup>*

If *μ<sup>m</sup>*,*<sup>A</sup>*

we have:

(*E*min,*<sup>A</sup>* ) ≪ *μ<sup>m</sup>*,*<sup>A</sup>*

aligned TXM images and doing calculations with them.

108 X-ray Characterization of Nanostructured Energy Materials by Synchrotron Radiation

*4.5.2. Spatial distribution of the discharge products*

Thanks to the measurement and analysis procedure described above, synchrotron‐based energy‐dependent transmission soft X‐ray microscopy (TXM) provides a unique access to the chemical state, spatial distribution, and morphology of oxygen‐containing materials in particular when they present different oxidation states, regardless of their crystalline state. Given its lateral resolution, this technique is also able to reveal minor components that are not evident in the integrated spectra, but are well localized. In this part we will illustrate the potential of this technique with some examples of energy‐dependent TXM measurements on cathodes discharged in Li/O2 cells. In fact, this allows detecting different oxygen‐bearing components among the discharge products of electrodes, and associating it to its characteristic shape. In this way it is possible to quantify and localize the distribution of the oxygen discharge products, revealing lithium superoxide, peroxide, hydroxide, and carbonates.

#### **5.1. Spatial distribution of products**

The discharge products of Li/O2 batteries are reported in the literature with different morphology and composition. When the product is well crystalline electron diffraction coupled to TEM may help to assign a given composition to a certain morphology. In our case, we can attribute a composition to most oxygen‐bearing objects, regardless of the crystallinity. To illustrate this, **Figure 10** shows a typical example. The overall spectrum of this sample presents features of superoxide (529 eV), peroxide (531 eV), and a component typical for fully oxidized oxygen, such as carbonate or hydroxide (533 eV). However, spectra at different points differ significantly, which implies that heterogeneity is larger than the spatial resolution, and that the spectral (and hence chemical) difference between the various independent components is remarkable. By inspecting differential images we can localize the spots where each component has maximum concentration. We can distinctly observe oblate particles with an essentially peroxide composition, probably corresponding to platelets reported in the literature [29]. Centering at the 533 eV peak some needle‐like particles, but also a more diffuse background can be imaged. Local spectra strongly suggest a hydroxide‐like composition. The weaker superoxide component cannot be localized at specific points. A very characteristic spectrum, that is not evident in the overall spectrum, is instead found in a few spots, and corresponds to ice, probably deposited during the transfer process. Even if this component is external to the cathode material, it shows that statistically less relevant components that do not appear in the total spectrum can be detected if spatially well localized. Thus, the fluctuating local spectra not only greatly facilitate the attribution of the components that appear mixed in the integrated spectrum, but even allow lowering the detection limit.

**Figure 10.** Left: Combination of differential images for different spectral components. Each component is represented with a respective color: superoxide (yellow), peroxide (magenta), hydroxide (green), and ice (cyan). Right: Absorption spectra in the O K region at selected points and for the overall image.
