`pyEQL` Overview¶

pyEQL is an open-source python library for solution chemistry calculations and ion properties developed by the Kingsbury Lab at Princeton University.

Documentation | How to Install | GitHub

Installation¶

Uncomment and run the code cell below, if you do not already have pyEQL

[1]:

# pip install pyEQL

Main feature: The `Solution` class¶

[2]:

from pyEQL import Solution

[3]:

s1 = Solution({"Mg+2": "0.2 mol/L", "Cl-1": "0.4 mol/L"}, temperature="20 degC")

Bulk Properties¶

[4]:

s1.density

[4]:

1.01387468274498 kg/l

[5]:

s1.conductivity

[5]:

2.9723368076814505 S/m

[6]:

s1.volume

[6]:

1 l

[7]:

s1.pressure

[7]:

1 atm

[8]:

s1.temperature

[8]:

293.15 K

[9]:

s1.osmotic_pressure

[9]:

1286751.9671270065 Pa

Composition¶

[10]:

s1.components

[10]:

{'H2O(aq)': 55.22159416581102, 'Cl[-1]': 0.4, 'Mg[+2]': 0.2, 'H[+1]': 1e-07, 'OH[-1]': 1e-07}

[11]:

s1.solvent

[11]:

'H2O(aq)'

[12]:

s1.cations

[12]:

{'Mg[+2]': 0.2, 'H[+1]': 1e-07}

[13]:

s1.anions

[13]:

{'Cl[-1]': 0.4, 'OH[-1]': 1e-07}

[14]:

s1.neutrals

[14]:

{'H2O(aq)': 55.22159416581102}

Species Concentrations¶

[15]:

s1.get_amount("Mg+2", "M")

[15]:

0.20000000000000007 M

[16]:

s1.get_amount("Cl-", "%")

[16]:

1.3987132967563214

[17]:

s1.get_amount("Mg+2", "eq/L")

[17]:

0.4 mol/l

[18]:

s1.get_amount("Mg+2", "ug/kg")

[18]:

4886249.788899191 µg/kg

Transport¶

[19]:

s1.get_transport_number("Na+")

[19]:

0.0

[20]:

s1.get_transport_number("Mg+2")

[20]:

0.3099794809803665

[21]:

s1.get_transport_number("Cl-")

[21]:

0.6900190851732657

Speciation¶

[22]:

s1.equilibrate()

[23]:

s1.components

[23]:

{'H2O(aq)': 55.22159416581102, 'Cl[-1]': 0.3832398866053062, 'Mg[+2]': 0.18323989223683687, 'MgCl[+1]': 0.0167601067972765, 'H[+1]': 1.2403217124548544e-07, 'OH[-1]': 9.844304910095208e-08, 'HCl(aq)': 5.431779796106619e-09, 'MgOH[+1]': 7.932688947003906e-15, 'O2(aq)': 3.1477611183050287e-26, 'HClO(aq)': 8.450945207723896e-29, 'ClO[-1]': 3.3676720804550185e-29, 'H2(aq)': 5.442721694626408e-35, 'ClO2[-1]': 0.0, 'ClO3[-1]': 0.0, 'ClO4[-1]': 0.0, 'HClO2(aq)': 0.0}

Saving `Solution` to a file¶

[24]:

from monty.serialization import dumpfn

dumpfn(s1, "test_solution.json")

[25]:

s1.as_dict()

[25]:

{'@module': 'pyEQL.solution',
 '@class': 'Solution',
 '@version': '0.14.0.post1.dev19+gbc296a6.d20240313',
 'solutes': {'H2O(aq)': '55.22159416581102 mol',
  'Cl[-1]': '0.3832398866053062 mol',
  'Mg[+2]': '0.18323989223683687 mol',
  'MgCl[+1]': '0.0167601067972765 mol',
  'H[+1]': '1.2403217124548544e-07 mol',
  'OH[-1]': '9.844304910095208e-08 mol',
  'HCl(aq)': '5.431779796106619e-09 mol',
  'MgOH[+1]': '7.932688947003906e-15 mol',
  'O2(aq)': '3.1477611183050287e-26 mol',
  'HClO(aq)': '8.450945207723896e-29 mol',
  'ClO[-1]': '3.3676720804550185e-29 mol',
  'H2(aq)': '5.442721694626408e-35 mol',
  'ClO2[-1]': '0.0 mol',
  'ClO3[-1]': '0.0 mol',
  'ClO4[-1]': '0.0 mol',
  'HClO2(aq)': '0.0 mol'},
 'volume': '1 l',
 'temperature': '293.15 K',
 'pressure': '1 atm',
 'pH': 6.906465653690637,
 'pE': 8.5,
 'balance_charge': None,
 'solvent': 'H2O(aq)',
 'engine': 'native',
 'database': {'@module': 'maggma.stores.mongolike',
  '@class': 'JSONStore',
  '@version': '0.57.8',
  'paths': ['/home/ryan/miniconda3/envs/skagit2/code/pyEQL/src/pyEQL/database/pyeql_db.json'],
  'read_only': True,
  'serialization_option': None,
  'serialization_default': None,
  'key': 'formula'},
 'default_diffusion_coeff': 1.6106e-09,
 'log_level': 'ERROR'}

Units-Aware Calculations¶

[26]:

s1.volume.to("mL")

[26]:

1000.0000000000001 ml

[27]:

s1.volume.to("gal")

[27]:

0.26417205235814856 gal

[28]:

s1.osmotic_pressure.to("bar").magnitude

[28]:

12.634083700417097

Contribution Opportunities¶

Benchmarking

Compiling additional validation data for activity, conductivity, etc.
Quantifying error associated with different models
Refactoring unit tests suite to separate benchmarking

Documentation

Writing tutorials
Writing expanded docs
Cleaning up / updating docstrings

New Features

Better viscosity model
Expanded unit testing (increase test coverage to 90%)
Additional properties
Additional mixing rules / models for mixed electrolytes

Database

Expand database doverage to include additional species
More viscosity coefficients
Add ‘sho’ parameter
More diffusion coefficients

Software Engineering

Additional refactoring (e.g., mypy linting for robustness)
Bugfixes

pyEQL Overview¶