How to use the polymer dispersity index calculator
Find how spread out the chain lengths of a polymer sample are from its weight and number average molar masses. Enter weight average molar mass, number average molar mass in the fields above; the dispersity, distribution type are computed instantly and plotted on the chart so you can see how the answer changes.
Common searches this calculator answers include “polymer dispersity”, “PDI”, “molecular weight distribution”, “calculate polymer dispersity index”, “find dispersity”.
Formula
Variables
- Mw — Weight average molar mass (g/mol)
- Mn — Number average molar mass (g/mol)
Worked example
With weight average molar mass = 120000 g/mol, number average molar mass = 80000 g/mol, the calculator gives dispersity ≈ 1.5; distribution type ≈ Typical distribution text.
Frequently asked questions
What is the polymer dispersity index calculator used for?
Find how spread out the chain lengths of a polymer sample are from its weight and number average molar masses. It helps with questions such as “polymer dispersity”, “PDI”, “molecular weight distribution”.
What is the formula for the polymer dispersity index calculation?
Dispersity = Mw / Mn. Distribution type (text) = [Dispersity]<1.1?"Very narrow distribution":([Dispersity]<2?"Typical distribution":"Broad distribution"). Here Mw is weight average molar mass (g/mol), Mn is number average molar mass (g/mol).
What inputs do I need for the polymer dispersity index calculator?
You need weight average molar mass in g/mol, number average molar mass in g/mol. Each field is pre-filled with a typical example so you can see how the result responds as you edit the numbers.
How do I use the polymer dispersity index calculator?
Type values into the input fields. Results and the chart update instantly. Use Reset to restore the example values, Copy results to paste them elsewhere, or Share to copy a link that preserves your inputs.
Is the polymer dispersity index result exact?
Calculations use standard formulas and double-precision arithmetic, then display about seven significant figures. Real-world measurements carry uncertainty, so treat results as estimates unless your inputs are exact.