Quenching Analysis

Fit fluorescence quenching data with layered occupancy and quenching models.

1. Data Input

Upload CSV or Excel

Upload a file with two columns: first column = [H]₀ / mM, second column = Fluorescence / a.u.

A header row is allowed and will be ignored automatically.

[H]₀ / mM

Fluorescence / a.u.

[I]₀ / mM

F₀ / a.u.

2. Model Presets

Binding model

Quenching mode

3. Set Parameters

K_d

Fix

Dissociation Constant Host-Dye (range: 0.1 nM – 100 mM)

Stoichiometry (n)

Fix

For 1:1 Exact and 1:1 Hill Approximation models, n is fixed to 1. For Hill Empirical, this control becomes the Hill exponent h.

α (Fractional Dimness)

Fix

α = relative brightness of the bound state (0–2; α > 1 means bound state is brighter).

k_q·τ₀ / mM⁻¹

Fix

k_q·τ₀ = dynamic quenching parameter (0.0001–100; log slider).

Weighted Fitting

Optional weighting for heteroscedastic fluorescence noise.

4. Fit and Update

Quenching Model:

\[ \frac{F_0}{F} = \frac{1 + k_q\tau_0[H]_{\mathrm{free}}}{1-\theta(1-\alpha)} \]

Binding / Occupancy Model:

\[ \theta = \frac{([I]_0+[H]_0+K_d)-\sqrt{([I]_0+[H]_0+K_d)^2 - 4[I]_0[H]_0}}{2[I]_0} \]

Free host term:

\[ [H]_{\mathrm{free}} = [H]_0 - \theta[I]_0 \]

5. Fit Report

Model output

Fit summary, parameter estimates, and quality notes will appear here after you update the model.

6. Final Figure

Preset and layout

Journal theme: Style:

Journal theme and Column now both trigger the matching preset values. Width stays tied to the active journal/column preset even when stats block or residual analysis is changed.

Manual sizing and output

Width: mm Height: mm DPI:

Axis/frame scale: Font scale:

Stats block: Residual: Log X-axis

Fitting Strategy:
1. Start from the two-layer preset that best matches your expected binding model and quenching behavior.
2. Use simpler constrained presets first, then unlock more parameters only if residuals remain structured.
3. For independent sites, consider fixing n initially. For Hill fits, treat h as empirical.
4. Ensure residuals scatter randomly around zero before interpreting the fitted mechanism.

Hongbin Wu AG Cordes / TU Dortmund hongbin.wu@tu-dortmund.de