Manuca

Manuca is a small command-line Python program to calculate various forms of the mean (or effective) atomic number and other physical properties of compounds.

The name stems from it's intended use as a Mean Atomic Number Calculator.

The tool can help to create complicated mixtures of materials for simulations (e.g. get stoichiometries of compounds, for DTSA-II, Casino, or SRIM) and to calculate the effective atomic numbers proposed/used in various publications.

Quickstart and Examples

You can explore Manuca without installing anything by opening the Binder link above in a new tab.

Enter a stoichiometry, e.g. H2O and confirm with Enter.

Quit by entering q.

Chemparse can handle (single-level) parentheses, here for the Fe-based superconductor Ba(Fe0.92Co0.08)2As2:

The multi compound mode is meant to handle more complex compounds, e.g. 73% of H2O and 27% of SiO2.

A more complicated random mixture of superconductors and BaZrO3: 42% YBa2Cu3O6.9, 42% Ba(Fe0.92Co0.08)2As2, and 16% BaZrO3:

To finish, let's put in the periodic system (let's call it an ultra-high-entropy alloy):

... and in the end:

The periodic-table-elements string can be generated from mendeleev:

from mendeleev.fetch import fetch_table
df = fetch_table('elements') #pandas data frame
print(''.join(df['symbol'].to_list()))

Requirements and Installation

Manuca works by utilizing chemparse to evaluate the stoichiometric formula from a user input. Then, mendeleev is used to retrieve the element-specific data. NumPy is used for calculations.

• Clone or download the repository (or just manuca.py).

• Optional: Create a fresh environment:

    conda create -n manuca python=3.9

    conda activate manuca

• Install the required Python packages:

    pip install numpy chemparse mendeleev

• Run Manuca (from the folder where the manuca.py is located):

    python manuca.py

Documentation

Output

Manuca version 0.14 calculates the following outputs:

• Composition in atomic % (at.%) and weight % (wt.%)

• Mean/effective atomic numbers are calculated from the atomic numbers (), the weight fractions (), and atomic fractions (). Formulas were taken from

  Howell, P. G. T., Davy, K. M. W. & Boyde, A. Mean atomic number and backscattered electron coefficient calculations for some materials with low mean atomic number. Scanning 20, 35–40 (1998).

  • Atomic-percent average:

    $\overline{Z}=\sum_i a_i Z_i$

  • Müller (1954),

    $\overline{Z}=\sum_i c_i Z_i$

  • Sandick & Allen (1954)

    $\overline{Z}=\sum_i a_i Z^2_i/\sum_i a_i Z_i$

  • Joyet (1953) / Hohn & Niedrig (1972) / Büchner (1973):

    $\overline{Z}=\sqrt{\sum_i a_i Z^2_i}$

  • Everhart (1960):

    $\overline{Z}=\sum_i c_i Z^2_i/\sum_i c_i Z_i$

  • Donovan (2003, with x = 0.8 and x = 0.7):

    $\overline{Z}=\sum_i a_i Z^{1.8}_i/\sum_i a_i Z_i^{0.8}$
    $\overline{Z}=\sum_i a_i Z^{1.7}_i/\sum_i a_i Z_i^{0.7}$

  • Langmore (effective $Z$ for elastic scattering) (see also Zhang et al., and Basha et al.)

    $\overline{Z}_\text{eff,el}=\left(\sum_i a_i Z^{3/2}_i\right)^{2/3}$

    Langmore et al. Collection of scattered electrons in dark field electron -microscopy. 1. Elastic-scattering Optik, 38 (1973), pp. 335-350

  • Egerton (effective $Z$, and $A$ in g/mol for inelastic scattering for EELS/EFTEM):

    $\overline{Z}_\text{eff,inel}=\sum_i a_i Z^{1.3}_i/\sum_i a_i Z^{0.3}_i$

    $\overline{A}_\text{eff,inel}=\sum_i a_i A^{1.3}_i/\sum_i a_i A^{0.3}_i$

    Eq. (5.4) from Egerton, R. F. Electron Energy-Loss Spectroscopy in the Electron Microscope. (Springer US, 2011). doi:10.1007/978-1-4419-9583-4.

• Total and average atomic mass/molecular weight in g/mol.

• Backscatter electron coefficient/yield (at 20 keV):

  $\eta_i = -0.0254 + 0.016 \cdot Z_i - 1.86 \cdot 10^{-4}\cdot Z_i^2 + 8.3\cdot10^{-7}\cdot Z_i^3$
  $\eta = \sum_i c_i \eta_i$

  Goldstein et al., Scanning Electron Microscopy and X-Ray Microanalysis, 2018, p. 17.

Citing

If you find this project useful, you can share/cite it via the following Zenodo DOI: