NeuralBlitz Inorganic Chemistry
What I Do
I provide comprehensive inorganic chemistry tools including coordination chemistry, organometallic compounds, crystal field theory, transition metal complexes, and spectroscopic analysis for chemistry applications.
When to Use Me
- Coordination compound analysis
- Crystal field splitting calculations
- Organometallic reaction mechanisms
- Transition metal spectroscopy
- Ligand field theory
- Inorganic synthesis planning
Core Concepts
- Coordination Chemistry: Ligands, coordination numbers
- Crystal Field Theory: d-orbital splitting, CFSE
- Ligand Field Theory: Molecular orbital approach
- Organometallic Chemistry: Metal-carbon bonds
- Spectroscopy: UV-Vis, IR, NMR, EPR
- Redox Chemistry: Oxidation states, potentials
- Solid State: Defects, non-stoichiometry
- Bioinorganic: Metalloenzymes, metals in biology
Code Examples
Coordination Chemistry
from itertools import permutations
LIGAND_TYPES = {
'monodentate': 1,
'bidentate': 2,
'tridentate': 3,
'tetradentate': 4,
'hexadentate': 6
}
GEOMETRIES = {
2: 'linear',
3: 'trigonal planar',
4: 'tetrahedral/square planar',
5: 'trigonal bipyramidal/square pyramidal',
6: 'octahedral',
8: 'square antiprismatic'
}
def coordination_number(metal, ligands):
return sum(LIGAND_TYPES.get(ligand, 1) for ligand in ligands)
def effective_atomic_number(metal_z, oxidation_state, ligands):
metal_e = metal_z - oxidation_state
ligand_e = sum(18 if l in ['CO', 'CN-', 'NO+'] else 2 for l in ligands)
return metal_e + ligand_e
def igeometry(coordination_number, metal_electron_config):
if coordination_number == 4:
d_count = metal_electron_config.get('d_electrons', 0)
if d_count < 8:
return 'tetrahedral'
else:
return 'square planar'
return GEOMETRIES.get(coordination_number, 'unknown')
def ionization_isomerism(metal, ligands, counter_ions):
return len(list(permutations(counter_ions)))
def hydrate_isomerism(metal, ligands, water_positions):
return water_positions
coordination_number = coordination_number('Fe', ['H2O', 'H2O', 'CN-', 'CN-', 'CN-', 'CN-'])
print(f"Coordination number: {coordination_number}")
EAN = effective_atomic_number(26, 2, ['CO', 'CO', 'CO', 'CO'])
print(f"Effective atomic number: {EAN}")
Crystal Field Theory
import numpy as np
def cfse_oh(d_electrons, spin_state, delta_oct):
high_spin = {
0: 0, 1: 0, 2: 0, 3: -0.4*delta_oct, 4: -0.8*delta_oct,
5: -1.2*delta_oct, 6: -1.6*delta_oct + P, 7: -2.0*delta_oct + P,
8: -2.4*delta_oct + 2*P, 9: -1.8*delta_oct + 2*P, 10: -2.4*delta_oct + 2*P
}
low_spin = {
0: 0, 1: -0.4*delta_oct, 2: -0.8*delta_oct, 3: -1.2*delta_oct,
4: -1.6*delta_oct, 5: -2.0*delta_oct, 6: -2.4*delta_oct, 7: -2.8*delta_oct,
8: -3.2*delta_oct, 9: -3.6*delta_oct + 2.5*P, 10: -4.0*delta_oct + 2.5*P
}
if spin_state == 'high':
return high_spin.get(d_electrons, 0)
return low_spin.get(d_electrons, 0)
def tanabe_sugano_diagram(d_electron):
diagrams = {
'd1': 'Ground state: 2T2g',
'd2': 'Ground state: 3T1g',
'd3': 'Ground state: 4A2g',
'd5_high': 'Ground state: 6A 'd61g',
_low': 'Ground state: 1A1g'
}
return diagrams.get(f'd{d_electron}', 'Consult diagram')
def magnetic_moment(spin_only, spin_quantum):
return np.sqrt(spin_quantum * (spin_quantum + 2))
def orbital_contribution_L(L):
return np.sqrt(L * (L + 1))
def racah_parameter(A, B, C):
return A - B, B, C
delta_oct = 15000 # cm^-1
d6_cfse = cfse_oh(6, 'low', delta_oct)
print(f"CFSE for low-spin d6: {d6_cfse:.0f} cm^-1")
spin_only = 2 # S = 2
mu_so = magnetic_moment(True, spin_only)
print(f"Spin-only magnetic moment: {mu_so:.1f} BM")
Ligand Field Theory
def ligand_field_splitting(ligand_series):
spectrochemical_series = ['I-', 'Br-', 'Cl-', 'F-', 'OH-', 'H2O', 'NH3', 'en', 'NO2-', 'CN-', 'CO']
return ligand_series in spectrochemical_series
def nephelauxetic_effect(beta):
return beta # B_free / B_complex
def mixing_coefficient(d_electrons, ligands):
return 0.1 * d_electrons * len(ligands)
def molecular_orbital_diagram(metal, ligands, symmetry):
return {'sigma': [], 'pi': [], 'delta': []}
def backbonding_strength(metal_d_electrons, pi_acceptor_ligands):
return metal_d_electrons * len(pi_acceptor_ligands) / 2
def covalency_parameter(h):
return 1 - h # h = (beta_free - beta) / beta_free
def charge_transfer_energy(metal_oxidation, ligand_donation, pi_backbonding):
return metal_oxidation - ligand_donation + pi_backbonding
Organometallic Chemistry
def electron_counting_ionic(metal_ox, metal_group, ligands):
return metal_group - metal_ox + sum(ligand_hapticity(lig) for lig in ligands)
def electron_counting_covalent(metal_group, ligands):
return metal_group + sum(ligand_hapticity(lig) for lig in ligands)
def ligand_hapticity(eta_n):
return n
def effective_atomic_number_rule(electron_count):
return 18 # Noble gas configuration
def stability_18_electron_rule(total_electrons):
if total_electrons == 18:
return 'Stable 18-electron complex'
elif total_electrons < 18:
return f' electron-deficient: {18 - total_electrons} electrons needed'
return f' electron-rich: {total_electrons - 18} electrons extra'
def catalytic_cycle_step(oxidative_addition, rate_constant):
if oxidative_addition:
return 'OA - increase oxidation state by 2'
return 'RE - reductive elimination'
electron_count = electron_counting_covalent(8, ['CO', 'CO', 'CO', 'CO', 'H'])
print(f"Electron count: {electron_count}")
stability = stability_18_electron_rule(18)
print(stability)
Inorganic Spectroscopy
def d_d_transition_energy(CFSE, pairing_energy):
return CFSE + pairing_energy
def selection_rules(delta_l, delta_s, parity):
return delta_l == 1 and delta_s == 0 and parity == 'odd'
def extinction_coepsilon(epsilon_max, bandwidth):
return epsilon_max * bandwidth
def ir_stretching_frequency(bond_order, reduced_mass):
return 1/(2*np.pi) * np.sqrt(k / reduced_mass)
def epr_g_value(h_nu, beta_e, D):
return (h_nu - D) / (beta_e * B)
def nmr_chemical_shift(reference, sample):
return (nu_sample - nu_reference) / nu_reference * 1e6
def mossbauer_isomer_shift(electron_density):
return electron_density
nu_ref = 100.0
nu_sample = 100.5
shift = nmr_chemical_shift(nu_ref, nu_sample)
print(f"Chemical shift: {shift:.1f} ppm")
Best Practices
- Oxidation States: Assign carefully
- Spectroscopic Assignment: Consider all transitions
- Magnetic Properties: Measure experimentally
- Kinetics: Consider substitution mechanisms
- Bonding: Use appropriate model
Common Patterns
# Walsh diagrams
def walsh_diagram_correlations():
pass
# Covalent bond classification
def covalent_classification():
pass
Core Competencies
- Coordination chemistry
- Crystal field theory
- Organometallic chemistry
- Inorganic spectroscopy
- Structure-property relationships