Binding models describe the adsorption and desorption kinetics governing how solutes interact with the stationary phase in a chromatography column. Efflux implements ten binding models of varying complexity to cover ion exchange, hydrophobic interaction, colloidal, dual-modulator, and pH-dependent chromatography.
Salt vs Non-salt Components
Most binding models distinguish between salt (modulator) components and non-salt (protein/solute) components:
Salt components: Modulator species (e.g. NaCl) that influence adsorption/desorption rates but do not bind themselves.
Non-salt components: Proteins or solutes that undergo adsorption/desorption kinetics.
The total salt concentration at a given point is:
csalt=s∈salt∑cp,s
Linear
Linear (Henry) adsorption kinetics. The simplest binding model: each component binds independently, with no competition, no capacity limit and no salt modulation. Operates on all components.
dtdqi=ka,icp,i−kd,iqi
Symbol
Description
Unit
Typical Range
ka,i
Adsorption rate constant
1/s
10−8 – 108
kd,i
Desorption rate constant
1/s
10−8 – 108
At equilibrium the bound concentration is proportional to the pore concentration, qi=Hicp,i with Henry coefficient Hi=ka,i/kd,i. Retention depends only on this ratio, so elution is independent of load. Suitable for weakly retained solutes, tracers, and dilute (non-saturating) conditions. Note that ka,i has units of 1/s here, unlike the 1/(M·s) of Langmuir.
Langmuir
Multi-component competitive Langmuir kinetics. Operates on all components with no salt modulation.
q0 tracks remaining binding capacity after accounting for sites blocked by bound molecules (νj sites per molecule). q^0 further reduces available capacity via steric shielding (σj). Adsorption scales as a power law in available capacity; desorption scales as a power law in salt concentration. Higher salt drives elution.
MPM Langmuir
Mobile phase modulated Langmuir with exponential adsorption modulation and power-law desorption modulation.
The saturation sum is over non-salt components only. γi can be positive (salt promotes adsorption) or negative (salt inhibits). βi controls the strength of salt-driven desorption.
HIC 1
Non-competitive cooperative adsorption model with salt-dependent anomalous desorption kinetics, developed by Wang et al. (2016). Operates on non-salt components only.
Adsorption has per-component saturation (not competitive across components). ni=1 gives simple Langmuir-like adsorption; ni>1 gives cooperative behavior. The desorption exponent depends on salt through β, giving anomalous kinetics. Decreasing salt concentration (typical HIC gradient) reduces β, promoting elution.
HIC 2
The most complex binding model, developed by Jäpel et al. (2025). Multi-factor salt and concentration modulation with competitive saturation and nonlinear desorption. Operates on non-salt components only.
Competitive Langmuir kinetics whose binding affinity is modulated by two independent mobile-phase modulators rather than a single lumped salt. Intended for hydrophobic interaction separations run with dual-gradient or mixed-modulator mobile phases, where two species (for example a kosmotropic salt plus a second salt or buffer system) jointly drive retention. You select the two modulating components I1 and I2 in the model settings. Operates on non-salt components only.
I1 and I2 are the local mobile-phase concentrations of the two selected modulator components. The exponential factor is a log-linear (bilinear) modulation of the effective binding affinity: a1,i and a2,i set each modulator's individual sensitivity, while the cross term a12,i captures synergy or antagonism between the two. Positive coefficients strengthen adsorption as the modulator concentration rises (salting-out, typical of HIC); negative coefficients weaken it. Desorption is simple first-order in qi, and the saturation sum runs over non-salt components only. Setting a1,i=a2,i=a12,i=0 reduces the model to plain competitive Langmuir.
Colloidal 1
Models lateral interactions between adsorbed molecules on the surface, based on the colloidal energetics framework of Oberholzer & Lenhoff (1999). As surface coverage increases, the energy barrier for further adsorption rises. Operates on non-salt components only.
dtdqi=ka,icp,ie−ϕi−kd,iqi
where the lateral interaction energy is:
ϕi=exp(m1,iθm2,i)−1
θ=j∈non-salt∑qmax,jqj
The coefficients m1 and m2 are precomputed from physical parameters using the Oberholzer correlations:
θ is the total fractional surface coverage. ϕi is the lateral interaction energy, which increases with coverage. Higher coverage raises the energy barrier for further adsorption (e−ϕi decreases). Desorption follows simple first-order kinetics with no lateral interaction effect.
SMA-pH
pH-modulated Steric Mass Action where the characteristic charge ν is a polynomial function of local pH. Operates on non-salt components only.
Identical to SMA except νi varies with local pH instead of being constant. As pH changes, the protein's effective charge changes, altering both adsorption cooperativity and salt-driven desorption. Typical use: pH gradient elution on IEX columns where raising pH weakens cation-exchange binding (decreasing ν). Setting vph,i=0 and vph2,i=0 reduces to standard SMA with constant νi=vi.
Langmuir-pH
Langmuir kinetics with independent salt and pH modulation of adsorption and desorption. Extends MPM Langmuir with exponential pH terms. Operates on non-salt components only.
Adsorption is modulated by both salt (γi) and pH (γpH,i) inside a single exponential. Desorption is modulated by salt (power law csaltβi) and pH (exponential eδpH,i⋅pH) as independent multiplicative factors. Setting γpH,i=0 and δpH,i=0 recovers MPM Langmuir. Suitable for mixed-mode chromatography where both salt and pH gradients are used.
References
Wang, G., et al. (2016). Hydrophobic interaction chromatography model. J. Chromatogr. A, 1465, 71-78.
Jäpel, R., et al. (2025). Unified HIC isotherm. J. Chromatogr. A, 1756, 466095.
Oberholzer, M. R., & Lenhoff, A. M. (1999). Protein adsorption isotherms through colloidal energetics. Langmuir, 15, 3905-3914.
Xu, X., & Lenhoff, A. M. (2009). Lattice Boltzmann simulations. J. Chromatogr. A, 1216, 6177-6195.