Getting Started With NeXLCore

NeXLCore is a Julia language library that provides the core data, algorithms and data structures for the NeXL collection of microanalysis libraries.

It can be installed from the Julia package repo.

using Pkg 
Pkg.add("NeXLCore")

or

> ]add NeXLCore

Primarily NeXLCore provides:

• Definitions of data types relevant to X-ray microanalysis
  • Element : Borrowed from the third-party PeriodicTable library
  • Material : Combinations of Elements
  • SubShell : Representing K, L1, L2, L3, M1,... sub-shells
  • AtomicSubShell : Representing a SubShell in a specific Element
  • Transition : Representing a non-forbidden X-ray transition between SubShells - like "K-L3"
  • CharXRay : Representing a Transition in a specific Element
  • KRatio : A ratio of X-ray intensities
• Algorithms to work on these data structures, including (but not limited to)
  • energy(xx) where xx may be an AtomicSubShell or a CharXRay (Always in eV!!!)
  • mac(xx, yy) where xx may be an Element or Material and yy may be a CharXRay or a Float64
• NeXLCore defines two useful macros. Learn them, love them, use them...
  • n"???" which creates Elements, SubShells, AtomicSubShells, Transitions and CharXRays from AbstractStrings
  • mat"???" which creates Materials from AbstractStrings like mat"AlNaSi3O8"
• Throughout NeXL, units are always electron-volt (energy), centimeter (length), second (time), gram (mass) and angles are in radians even when it seems a little odd.
  • A foolish consistency? I think not...
• NeXL uses Gadfly for plotting.
  • Many things you'd want to plot can be plotted using using Gadfly; plot(x)
  • However, to minimize overhead, plotting support is not loaded (thanks to Requires) until Gadfly is explicitly loaded by the user.
  • Plots can be readily embedded into Jupyter notebooks and Weave documents.
• NeXL uses DataFrames for tabular data.

Let's get this party started...

Load the library

using NeXLCore

Element

Constructing Element objects

julia> e1, e2, e3 = n"Ca", elements[20], parse(Element, "Pu")
(Element(Calcium), Element(Calcium), Element(Plutonium))

julia> es = [ n"Ca", n"21", n"Ti", n"Vanadium" ]
4-element Vector{Element}:
 Element(Calcium)
 Element(Scandium)
 Element(Titanium)
 Element(Vanadium)

Note the use of n"??". We'll see a lot of this.

Elements come with lots of useful information...

julia> e3
Plutonium (Pu), number 94:
        category: actinide
     atomic mass: 244.0 u
         density: 19.816 g/cm³
      molar heat: 35.5 J/mol⋅K
   melting point: 912.5 K
   boiling point: 3505.0 K
           phase: Solid
          shells: [2, 8, 18, 32, 24, 8, 2]
e⁻-configuration: 1s² 2s² 2p⁶ 3s² 3p⁶ 4s² 3d¹⁰ 4p⁶ 5s² 4d¹⁰ 5p⁶ 6s² 4f¹⁴ 5d¹⁰ 6p⁶ 7s² 5f⁶
      appearance: silvery white, tarnishing to dark gray in air
         summary: Plutonium is a transuranic radioactive chemical element with symbol Pu and atomic number 94. It is an actinide metal of silvery-gray appearance that tarnishes when exposed to air, and forms a dull coating when oxidized. The element normally exhibits six allotropes and four oxidation states.
   discovered by: Glenn T. Seaborg
          source: https://en.wikipedia.org/wiki/Plutonium

To help you to iterate over all elements for which there is a complete set of atomic and X-ray data there is the function

julia> eachelement()
99-element Vector{Element}:
 Element(Hydrogen)
 Element(Helium)
 Element(Lithium)
 Element(Beryllium)
 Element(Boron)
 Element(Carbon)
 Element(Nitrogen)
 Element(Oxygen)
 Element(Fluorine)
 Element(Neon)
 ⋮
 Element(Protactinium)
 Element(Uranium)
 Element(Neptunium)
 Element(Plutonium)
 Element(Americium)
 Element(Curium)
 Element(Berkelium)
 Element(Californium)
 Element(Einsteinium)

As you can see, each element comes with many different properties which can be accessed by the field names. PeriodicTable uses Unitful to provide physical units for quantities.

julia> fieldnames(Element)
(:name, :appearance, :atomic_mass, :boil, :category, :color, :cpk_hex, :density, :discovered_by, :el_config, :melt, :molar_heat, :named_by, :number, :period, :phase, :source, :spectral_img, :summary, :symbol, :xpos, :ypos, :shells)

julia> e1.name, name(e1)
("Calcium", "Calcium")

julia> e1.symbol, symbol(e1)
("Ca", "Ca")

julia> e1.atomic_mass, a(e1)
(40.0784 u, 40.0784)

julia> e1.number, z(e1)
(20, 20)

julia> e1.boil
1757.0 K

julia> e1.density, density(e1)
(1.55 g cm^-3, 1.55)

julia> e1.el_config
"1s² 2s² 2p⁶ 3s² 3p⁶ 4s²"

Material

The Material structure carries composition information as mass fractions of the elements. This object also carries name, atomic weight, and other properties like density. A simple way to create Material objects is the mat"??" macro. To get the mass fraction's out index the object with an element. All the Elements in a Material are accessed via keys(...).

julia> albite = mat"AlNaSi3O8"
AlNaSi3O8[O=0.4881,Na=0.0877,Al=0.1029,Si=0.3213]

julia> albite[n"Al"], albite[n"Na"], albite[n"Tc"]
(0.10289723395373596, 0.08767415772881798, 0.0)

julia> keys(albite) # keys(...) for consistency with other Julia objects
KeySet for a Dict{Element, Float64} with 4 entries. Keys:
  Element(Aluminium)
  Element(Sodium)
  Element(Silicon)
  Element(Oxygen)

julia> collect(keys(albite)) # Now maybe this is a little more clear
4-element Vector{Element}:
 Element(Aluminium)
 Element(Sodium)
 Element(Silicon)
 Element(Oxygen)

julia> a(n"Na", albite)
22.989769282

You can enter mass fractions in directly using the mat"??" syntax.

julia> mat = mat"0.8*Fe+0.15*Ni+0.05*Cr"
0⋅8⋅Fe+0⋅15⋅Ni+0⋅05⋅Cr[Cr=0.0500,Fe=0.8000,Ni=0.1500]

There are more sophisticated ways to create materials with additional properties. For example, I could have created a richer definition of albite.

julia> albite = parse(Material, "AlNaSi3O8", name="Albite", density=2.60, atomicweights=Dict(n"Na"=>23.0))
Albite[O=0.4881,Na=0.0877,Al=0.1029,Si=0.3213,2.60 g/cm³]

julia> all(e->a(e)==a(e,albite), keys(albite)) # Not all are default
false

julia> a(n"Na", albite),  a(n"O", albite)
(23.0, 15.999)

julia> ss = parse(Material, "0.8*Fe+0.15*Ni+0.05*Cr", name="Stainless", density=7.5)
Stainless[Cr=0.0500,Fe=0.8000,Ni=0.1500,7.50 g/cm³]

julia> ss[n"Fe"], density(ss), name(ss)
(0.8, 7.5, "Stainless")

julia> all(e->a(e)==a(e,ss), keys(ss)) # The atomic weights are the default values (from PeriodicTable)
true

Alternatively, I could have built albite in terms of atom fractions. Note that the mass fractions are different because the assumed atomic weight of sodium is different.

julia> albite2 = atomicfraction("Albite", n"Al"=>1, n"Na"=>1, n"Si"=>3, n"O"=>8, properties=Dict{Symbol,Any}(:Density=>2.6), atomicweights=Dict(n"Na"=>22.0))
Albite[O=0.4900,Na=0.0842,Al=0.1033,Si=0.3225,2.60 g/cm³]

julia> using DataFrames

julia> asa(DataFrame, albite2)
4×7 DataFrame
 Row │ Material  Element  Z      A        C(z)       Norm[C(z)]  A(z)
     │ String    String   Int64  Float64  Float64    Float64     Float64
─────┼─────────────────────────────────────────────────────────────────────
   1 │ Albite    O            8  15.999   0.489962    0.489962   0.615385
   2 │ Albite    Na          11  22.0     0.0842174   0.0842174  0.0769231
   3 │ Albite    Al          13  26.9815  0.103287    0.103287   0.0769231
   4 │ Albite    Si          14  28.085   0.322534    0.322534   0.230769

There are many methods for transforming representation of the composition.

julia> ss = parse(Material,"0.78*Fe+0.15*Ni+0.04*Cr",name="Stainless")
Stainless[Cr=0.0400,Fe=0.7800,Ni=0.1500]

julia> analyticaltotal(ss)
0.9700000000000001

julia> atomicfraction(ss)
Dict{Element, Float64} with 3 entries:
  Element(Iron)     => 0.807719
  Element(Chromium) => 0.0444878
  Element(Nickel)   => 0.147793

julia> normalizedmassfraction(ss)
Dict{Element, Float64} with 3 entries:
  Element(Iron)     => 0.804124
  Element(Chromium) => 0.0412371
  Element(Nickel)   => 0.154639

julia> asnormalized(ss)
N[Stainless,1.0][Cr=0.0412,Fe=0.8041,Ni=0.1546]

julia> compare(ss, asnormalized(ss))
3×11 DataFrame
 Row │ Material 1  Material 2        Elm     C₁(z)      C₂(z)    ΔC          Δ ⋯
     │ String      String            String  Float64    Float64  Float64     F ⋯
─────┼──────────────────────────────────────────────────────────────────────────
   1 │ Stainless   N[Stainless,1.0]  Fe      0.804124      0.78  0.0241237     ⋯
   2 │ Stainless   N[Stainless,1.0]  Cr      0.0412371     0.04  0.00123711
   3 │ Stainless   N[Stainless,1.0]  Ni      0.154639      0.15  0.00463918
                                                               5 columns omitted

It is also possible to define materials using NeXLUncertainties.UncertainValues. However, it is better to use the full uncertainty calculation to perform transforms since this handles correlated quantities correctly.

julia> ss=material("Stainless",n"Fe"=>uv(0.79,0.01),n"Ni"=>uv(0.15,0.003),n"Cr"=>uv(0.04,0.002))
Stainless[Cr=0.0400,Fe=0.7900,Ni=0.1500]

julia> ss[n"Fe"]
0.790 ± 0.010

julia> atomicfraction(ss)[n"Fe"]
0.810 ± 0.010

SubShell

SubShell objects are not often used directly but are occasionally returned by other methods so I'll just mention them in passing. SubShell represent the notion of a sub-shell independent of which element it is associated with. There are properties of sub-shells that don't depend on the element like the angular momentum quantum numbers.

julia> ss = n"L3"
L3

julia> shell(ss) # Shells are identified by a Char
L

julia> NeXLCore.n(ss), NeXLCore.l(ss), NeXLCore.j(ss)
(2, 1, 3//2)

julia> allsubshells
49-element Vector{SubShell}:
 K
 L1
 L2
 L3
 M1
 M2
 M3
 M4
 M5
 N1
 ⋮
 Q5
 Q6
 Q7
 Q8
 Q9
 Q10
 Q11
 Q12
 Q13

julia> ksubshells, lsubshells, msubshells, nsubshells
((K,), (L1, L2, L3), (M1, M2, M3, M4, M5), (N1, N2, N3, N4, N5, N6, N7))

There is one gotcha with SubShells and the n"??" notation. What is n"K"? Potassium or the K-subshell? The answer for NeXL is potassium. The K-subshell is n"K1" like the first L-subshell is n"L1". (This is rarely ever an issue)

julia> n"K1", n"K"
(K, Element(Potassium))

AtomicSubShell

AtomicSubShell joins an Element to a SubShell. You'll only be permitted to create AtomicSubShell objects for sub-shells which exist for the ground state of the element. (X-ray microanalysis only deals with ground state atoms. Astronomers and plasma physicists not so much...)

julia> ass = n"Fe L3"
Fe L3

julia> shell(ass), ass.subshell
(L, L3)

julia> jumpratio(ass)
6.3305

julia> has(n"C",n"L3"), has(n"C",n"L2")  # Carbon Kα1 is K-L2!!!
(true, true)

julia> n"C L2" # works while n"C L3" throws an exception
C L2

julia> energy(ass), energy(n"Ca K")
(713.0, 4041.0)

julia> kk=n"K K"
K K

julia> element(kk), shell(kk), kk.subshell # This works as you'd expect. (Relevant to the earlier gotcha notice...)
(Element(Potassium), K, K)

Transition

Transitions are the analog to SubShell. They represent the non-element related information associated with optical (in the broad sense) transitions. You can only create Transitions for transitions with a non-negligible transition rate in some element.

julia> trs = n"K-L3", n"L3-M5", n"M5-N7"
(K-L3, L3-M5, M5-N7)

julia> alltransitions
(K-L2, K-L3, K-M2, K-M3, K-M4, K-M5, K-N2, K-N3, K-N4, K-N5, K-O2, K-O3, K-O4, K-O5, K-P2, K-P3, L1-L2, L1-L3, L1-M2, L1-M3, L1-M4, L1-M5, L1-N2, L1-N3, L1-N4, L1-N5, L1-O2, L1-O3, L1-O4, L1-O5, L1-P2, L1-P3, L2-L3, L2-M1, L2-M3, L2-M4, L2-N1, L2-N3, L2-N4, L2-N6, L2-O1, L2-O3, L2-O4, L2-O6, L2-P1, L2-P3, L2-Q1, L3-M1, L3-M2, L3-M3, L3-M4, L3-M5, L3-N1, L3-N2, L3-N3, L3-N4, L3-N5, L3-N6, L3-N7, L3-O1, L3-O2, L3-O3, L3-O4, L3-O5, L3-O6, L3-O7, L3-P1, L3-P2, L3-P3, L3-Q1, M1-M2, M1-M3, M1-N2, M1-N3, M1-O2, M1-O3, M1-P2, M1-P3, M2-M4, M2-N1, M2-N4, M2-O1, M2-O4, M2-P1, M2-Q1, M3-M4, M3-M5, M3-N1, M3-N4, M3-N5, M3-O1, M3-O4, M3-O5, M3-P1, M3-Q1, M4-N2, M4-N3, M4-N6, M4-O2, M4-O3, M4-O6, M4-P2, M4-P3, M5-N3, M5-N6, M5-N7, M5-O3, M5-O6, M5-O7, M5-P3)

julia> ktransitions
(K-L2, K-L3, K-M2, K-M3, K-M4, K-M5, K-N2, K-N3, K-N4, K-N5, K-O2, K-O3, K-O4, K-O5, K-P2, K-P3)

julia> kalpha, kbeta
((K-L2, K-L3), (K-M3, K-N3, K-N2, K-M2, K-N5, K-N4, K-M5, K-M4))

julia> ltransitions
(L1-L2, L1-L3, L1-M2, L1-M3, L1-M4, L1-M5, L1-N2, L1-N3, L1-N4, L1-N5, L1-O2, L1-O3, L1-O4, L1-O5, L1-P2, L1-P3, L2-L3, L2-M1, L2-M3, L2-M4, L2-N1, L2-N3, L2-N4, L2-N6, L2-O1, L2-O3, L2-O4, L2-O6, L2-P1, L2-P3, L2-Q1, L3-M1, L3-M2, L3-M3, L3-M4, L3-M5, L3-N1, L3-N2, L3-N3, L3-N4, L3-N5, L3-N6, L3-N7, L3-O1, L3-O2, L3-O3, L3-O4, L3-O5, L3-O6, L3-O7, L3-P1, L3-P2, L3-P3, L3-Q1)

julia> mtransitions
(M1-M2, M1-M3, M1-N2, M1-N3, M1-O2, M1-O3, M1-P2, M1-P3, M2-M4, M2-N1, M2-N4, M2-O1, M2-O4, M2-P1, M2-Q1, M3-M4, M3-M5, M3-N1, M3-N4, M3-N5, M3-O1, M3-O4, M3-O5, M3-P1, M3-Q1, M4-N2, M4-N3, M4-N6, M4-O2, M4-O3, M4-O6, M4-P2, M4-P3, M5-N3, M5-N6, M5-N7, M5-O3, M5-O6, M5-O7, M5-P3)

julia> shell.( trs )
(K, L, M)

julia> inner.( trs )
(K, L3, M5)

julia> outer.( trs )
(L3, M5, N7)

The lists of transitions will suddenly seem useful in just a minute...

CharXRay

Finally! What we came here for... CharXRay represent a specific Transition in a specific Element. Again you can only create CharXRay objects for characteristic X-rays with non-negligible transition rates. (i.e. Ones that you might see in a X-ray spectrum or wavescan.)

First, let's create some characteristic X-rays using n"??" notation or characteristic(...)

julia> feka1, fela = n"Fe K-L3", n"Fe L3-M5"
(Fe K-L3, Fe L3-M5)

julia> feka = characteristic(n"Fe",kalpha) # Filters kalpha to produce only those CharXRay that exist for Fe
2-element Vector{CharXRay}:
 Fe K-L2
 Fe K-L3

julia> fekb = characteristic(n"Fe",kbeta)
4-element Vector{CharXRay}:
 Fe K-M2
 Fe K-M3
 Fe K-M4
 Fe K-M5

Some properties of characteristic X-rays:

julia> inner.(feka)
2-element Vector{AtomicSubShell}:
 Fe K
 Fe K

julia> outer.(feka)
2-element Vector{AtomicSubShell}:
 Fe L2
 Fe L3

julia> transition.(feka)
2-element Vector{Transition}:
 K-L2
 K-L3

julia> all(s->s==Shell(1), shell.(feka))
true

julia> all(e->e==n"Fe", element.(feka))
true

Let's extract some energy-related properties from these objects. Of course, it is in eV.

julia> energy.(feka) # The x-ray energy
2-element Vector{Float64}:
 6391.0
 6404.0

julia> edgeenergy.(feka) # ionization edge energy
2-element Vector{Float64}:
 7117.0
 7117.0

Often we want to know the relative line-weights of the transitions.

julia> weight.(NormalizeByShell, characteristic(n"Fe", ltransitions)) # sum(...)=1
14-element Vector{Float64}:
 0.018496071140949553
 0.027458978447654655
 5.6159001496276406e-5
 7.626531067395561e-5
 0.015900322795429925
 0.0005602847601257496
 0.19820901939432387
 0.00504988682492424
 0.07653042038194807
 0.0005808975914771919
 0.0005656442630357776
 0.014616501978985185
 0.6272246604554809
 0.009782467973760324

julia> weight.(NormalizeBySubShell, characteristic(n"Fe", ltransitions)) # sum(...)=3
14-element Vector{Float64}:
 0.36281109751121987
 0.5386236910104718
 0.0011015911872705015
 0.001495988032095743
 0.07236645722658287
 0.00255
 0.9021002094400838
 0.022983333333333335
 0.10493673137495092
 0.0007965132585051979
 0.0007755982495291314
 0.02004180735131569
 0.8600358573423819
 0.013413492423317293

julia> brightest(characteristic(n"Fe", ltransitions))
Fe L3-M5

Some other X-ray related properties...

julia> λ.(feka)  # this is \lambda (wavelength in cm)
2-element Vector{Float64}:
 1.9399811990799606e-8
 1.9360430735977558e-8

julia> ν.(feka)  # this is \nu (frequency in 1/s)
2-element Vector{Float64}:
 1.545336924616471e18
 1.5484803106311816e18

julia> ω.(feka)  # this is \omega (angular frequency in radian/s)
2-element Vector{Float64}:
 9.7096382593923e18
 9.729388736214723e18

julia> wavenumber.(feka) # In 1/cm
2-element Vector{Float64}:
 5.1546891303598806e7
 5.165174337478434e7

Finally, mass absorption coefficients. MACs quantify the degree to which X-rays are absorbed as they travel through material. MACs are available for Element or for Material. Here we are accepting the default (FFAST) algorithm for the MACs except in the last line.

julia> mac( n"Ni", n"Fe K-L3") # In cm²/g
83.47985630233804

julia> Dict(map(cxr->(cxr=>( mac(n"Ni",cxr), weight(NormalizeToUnity, cxr))), characteristic(n"Ni", ltransitions)))
Dict{CharXRay, Tuple{Float64, Float64}} with 14 entries:
  Ni L3-M5 => (1698.0, 1.0)
  Ni L1-M3 => (11180.9, 0.0365307)
  Ni L2-M3 => (1909.69, 0.000899998)
  Ni L1-M2 => (11241.6, 0.0238633)
  Ni L1-M4 => (9484.41, 8.56925e-5)
  Ni L2-N1 => (9646.64, 0.00699209)
  Ni L2-M1 => (2149.93, 0.0220208)
  Ni L3-M3 => (1997.62, 0.000923545)
  Ni L3-N1 => (1688.03, 0.0132159)
  Ni L2-M4 => (9714.16, 0.299355)
  Ni L3-M1 => (2254.4, 0.0900353)
  Ni L3-M2 => (2008.36, 0.00095055)
  Ni L3-M4 => (1698.0, 0.0354701)
  Ni L1-M5 => (9484.41, 0.000112495)

julia> mac( mat"0.8*Fe+0.15*Ni+0.05*Cr", n"C K-L2") # Carbon K-L3 in stainless steel (interpreted as mass fractions of elements)
12267.458123099908

julia> mac( mat"AlNaSi3O8", n"O K-L3") # O K-L3 in Albite (interpreted as a chemical formular)
3843.7861561329455

julia> mac( mat"AlNaSi3O8", n"O K-L3", DefaultAlgorithm), mac( mat"AlNaSi3O8", n"O K-L3", DTSA) # Compare and contrast...
(3843.7861561329455, 4121.0562998626965)

KRatio

k-ratios are the core quantity for X-ray microanalysis. We measure intensities but k-ratios make the intensities meaningful.

julia> kr = KRatio(
             [n"Fe K-L3", n"Fe K-L2" ],
             Dict(:BeamEnergy=>20.0e3, :TakeOffAngle=>deg2rad(40.0)), # Unknown properties
             Dict(:BeamEnergy=>20.0e3, :TakeOffAngle=>deg2rad(40.0)), # Standard properties
             mat"Fe2O3", # Standard composition
             uv(0.343563,0.0123105)) # The k-ratio value
k[Fe K-L3 + 1 other, Fe2O3] = 0.344 ± 0.012

Combine k-ratios together in Vector.

julia> props =  ( Dict(:BeamEnergy=>20.0e3, :TakeOffAngle=>deg2rad(40.0)),
                  Dict(:BeamEnergy=>20.0e3, :TakeOffAngle=>deg2rad(40.0)))
(Dict(:BeamEnergy => 20000.0, :TakeOffAngle => 0.6981317007977318), Dict(:BeamEnergy => 20000.0, :TakeOffAngle => 0.6981317007977318))

julia> krs = [
         KRatio(characteristic(n"O", ktransitions), props..., mat"SiO2", uv(0.984390, 0.00233)),
         KRatio(characteristic(n"Na", ktransitions), props..., mat"NaCl", uv(0.155406, 0.00093)),
         KRatio(characteristic(n"Al", ktransitions), props..., mat"Al", uv(0.068536, 0.000733)),
         KRatio(characteristic(n"Si", ktransitions), props..., mat"Si", uv(0.219054, 0.00023)),
         KRatio(characteristic(n"Th", mtransitions), props..., mat"Th", uv(-0.00023, 0.00046)),
       ]
5-element Vector{KRatio}:
 k[O K-L3 + 1 other, SiO2] = 0.9844 ± 0.0023
 k[Na K-L3 + 1 other, NaCl] = 0.1554 ± 0.0009
 k[Al K-L3 + 3 others, Al] = 0.0685 ± 0.0007
 k[Si K-L3 + 3 others, Si] = 0.2190 ± 0.0002
 k[Th M5-N7 + 31 others, Th] = -0.0002 ± 0.0005

julia> nonnegk.(krs)
5-element Vector{UncertainValue}:
 0.9844 ± 0.0023
 0.1554 ± 0.0009
 0.0685 ± 0.0007
 0.2191 ± 0.0002
 0.0000 ± 0.0005

julia> elms(krs)
Set{Element} with 5 elements:
  Element(Aluminium)
  Element(Sodium)
  Element(Thorium)
  Element(Oxygen)
  Element(Silicon)

KRatio objects match well with individual spectra or individual point acqusitions in WDS. For hyper-spectra, them KRatios object type might be more appropriate. KRatios assumes that all the properties are in common for all the entries in the object so it maintains only one copy.

Monte Carlo

NeXLCore also includes a rudimentary Monte Carlo simulator of electron trajectories. While it is currently limited to modeling electron trajectories, it can be extended to handle quite complex sample geometries because it is based on the GeometryBasics package that defines both simple and meshed shapes. Currently, basic blocks and spheres have been implemented.

julia> # Build a alumina coated silica particle on a carbon substrate
       mat = parse(Material, "SiO2", density=2.648)
SiO2[O=0.5326,Si=0.4674,2.65 g/cm³]

julia> sample = coated_particle(mat, 1.0e-4, parse(Material, "Al2O3", density=3.99), 0.1e-4, parse(Material, "C", density=2.0))
Region[Chamber, GeometryBasics.Rect3{Float64}([-100.0, -100.0, -100.0], [200.0, 200.0, 200.0]), H[H=1.0000,0.00 g/cm³], 2 children]

Now let's run a MC simulation to compute the path length of an electron in a material.

using Gadfly # for plot(...)

# Each call to the trajectory function runs a single electron trajecory while calling the `do`
# clause at each elastic scatter point.  The arguments to the do clause are a representation
# of the electron and the Region in which the last step occured.
function mc_path_length(e0, mat)
  len=0.0
  trajectory(gun(Electron, e0, 1.0e-6), bulk(mat)) do electron, region
    len += region.material == mat ? NeXLCore.pathlength(electron) : 0.0
  end
  return len
end
# Let's look at the path-length as a function of incident energy.
# The downward spikes are ???                (Backscattered e⁻)
plot(e0->mc_path_length(e0,mat), 1.0e3, 20.0e3)

Or a second example...

# Let's look at the number of scatter events as a function of incident energy.
function mc_n_scatters(e0, mat)
  cx=0
  trajectory(gun(Electron, e0, 1.0e-6), bulk(mat)) do electron, region
    cx += 1
  end
  return cx
end

plot(e0->mc_n_scatters(e0, mat), 1.0e3, 20.0e3)

There is more but this should get you started. As always, the code is the ultimate resource and you have it in your hands. Please report any bugs you find at NeXLCore.

Appendix: Plotting with Gadfly

There are a number of helpful plotting methods to take an overhead look at various NeXLCore attributes.

Plot the X-ray energy for all transitions in all elements

using Gadfly
display(plot(collect(ktransitions), mode = :Energy))
display(plot(collect(ltransitions), mode = :Energy))
display(plot(collect(mtransitions), mode = :Energy))

Plot the X-ray line weight for all transitions

display(plot(collect(ktransitions), mode = :Weight))
display(plot(collect(ltransitions), mode = :Weight))
display(plot(collect(mtransitions), mode = :Weight))

Plot the edge energy for all subshells in all elements.

display(plot(collect(ksubshells), :EdgeEnergy))
display(plot(collect(lsubshells), :EdgeEnergy))
display(plot(collect(msubshells), :EdgeEnergy))

Plot the fluorescence yield for all subshells in all elements.

display(plot(collect(ksubshells), :FluorescenceYield))
display(plot(collect(lsubshells), :FluorescenceYield))
display(plot(collect(msubshells), :FluorescenceYield))

Finally, to compare MAC algorithms...

display(NeXLCore.compareMACs(n"C"))
display(NeXLCore.compareMACs(n"U"))

Or MAC algorithms one at a time...

display(plot(DefaultAlgorithm, n"Ag"))
display(plot(DTSA, n"Au"))

Or many elements at once...

plot(DefaultAlgorithm, collect(keys(albite)),xmax=5.0e3)

Or a Material MAC...

plot(DefaultAlgorithm, [keys(albite)..., albite], xmax=5.0e3)