`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¶