CollisionDB: Fit Function: li_eex

Fit Function: `li_eex_schweinzer`

Fit Function	$σ_{e}^{e x c} (E) = \frac{5.984 \times 10^{- 16}}{E} [\frac{E - I}{E}]^{A_{6}} \times [\sum_{j = 1}^{4} \frac{A_{j}}{(E / I)^{j - 1}} + A_{5} \ln (\frac{E}{I})]$
Comments	Python code requires NumPy imported as `np`.

Python

Arguments

name	description	units	type(s)
`E`	impact energy	eV	`float`, `np.ndarray`
`A1`	fit coefficient	cm² eV	`float`
`A2`	fit coefficient	cm² eV	`float`
`A3`	fit coefficient	cm² eV	`float`
`A4`	fit coefficient	cm² eV	`float`
`A5`	fit coefficient	cm² eV	`float`
`A6`	fit coefficient		`float`
`I`	threshold energy	eV	`float`

Return values

name	description	units	type(s)
`sigma`	cross section	cm²	`float`, `np.ndarray`

Code

def li_ein_schweinzer(E, A1, A2, A3, A4, A5, A6, I):
    """
    This function calculates electron impact excitation cross sections (in cm2) of 
    Li.
    param E: requested electron-impact energy in eV
    type E: float, np.ndarray
    param Ai: fit coefficient 
    type Ai: float
    param I: threshold energy in eV
    type I: float
    """
    sigma = 5.984e-16/E * ((E-I)/E)**A6 * (A1/((E/I)**0) + A2/((E/I)**1) +  A3/((E/I)**2)
                    + A4/((E/I)**3) + A5*np.log(E/I))
    return sigma

Fit Function: li_eex_schweinzer

Python

Fit Function: `li_eex_schweinzer`