Polymer Molecular-Mass Averages
Number-average, weight-average and dispersity
Lesson 2397 of 4,500 · Biomolecules and Polymers
Learning objectives
- Calculate number-average and weight-average molar mass
- Interpret dispersity as a measure of distribution breadth
Introduction
A polymer sample rarely consists of chains all the same length. Reporting “the molecular weight” without saying which average can conceal a broad distribution. Number-average molar mass gives each chain equal counting weight; weight-average emphasizes heavier chains. Their ratio, dispersity, summarizes part of the spread.
Core explanation
Suppose a sample contains Nᵢ chains of molar mass Mᵢ in each class i. The number-average molar mass is Mn=(ΣNᵢMᵢ)/(ΣNᵢ). It is the total mass contribution divided by the total number of molecules, expressed in g mol⁻¹. Each chain counts once in the denominator, so many short chains can noticeably lower Mn even if they contribute modest total mass.
The weight-average molar mass is Mw=(ΣNᵢMᵢ²)/(ΣNᵢMᵢ). Because Mᵢ is squared in the numerator, heavier chains contribute disproportionately. For a distribution of positive chain masses, Mw is at least Mn, with equality only for a perfectly uniform chain mass. Their ratio Đ=Mw/Mn is dimensionless and at least one in the ideal mathematical definition.
Consider equal numbers of chains at 10,000 and 30,000 g mol⁻¹. Mn=(10,000+30,000)/2=20,000 g mol⁻¹. Mw=(10,000²+30,000²)/(10,000+30,000)=25,000 g mol⁻¹. Dispersity is 25,000/20,000=1.25. The two averages differ even though there are only two chain sizes.
Number-average can be estimated by methods sensitive to molecule counts, such as certain end-group analyses or osmotic measurements under appropriate conditions. Light scattering and size-exclusion chromatography can provide other averages or distributions, with calibration and model assumptions. A measured result should state the method because branching and interactions can affect apparent size and inferred molar mass.
Dispersity does not uniquely specify the full distribution. Two very different distributions can share the same Mn and Mw. A small amount of extremely long chains can strongly affect Mw and rheology while leaving much of the number distribution short. For processing, high-mass tails and branching can matter alongside the reported averages.
The word “molecular weight” is common, but molar mass with units g mol⁻¹ is the precise quantity used in these formulas. When comparing samples, ensure both averages refer to the same chemical species and measurement conditions. A copolymer's repeat-unit composition may vary among chains as well as chain length, complicating simple DP estimates.
For a chain with molar mass M and repeat-unit molar mass M₀, DP≈M/M₀ only when end-group contributions are small or accounted for. A sample-average DP should specify whether it is number- or weight-averaged. That choice can matter substantially for broad distributions.
Step-by-step reasoning
1. List chain classes and counts Nᵢ. 2. Calculate total number ΣNᵢ and total mass sum ΣNᵢMᵢ. 3. Compute Mn. 4. Compute ΣNᵢMᵢ² and divide by total mass sum for Mw. 5. Divide Mw by Mn and check that Đ≥1.
Visual explanation
Draw a histogram with many short chains and a few long ones. Mark Mn nearer the number-rich region and Mw shifted toward heavy chains. A second panel with identical-length chains shows both marks coinciding and Đ=1.
Real-world analogy
Average classroom height counts each student once, resembling a number average. If taller students were weighted by how much material their clothes use, the average would lean toward them, resembling a mass-weighted average. The analogy only illustrates weighting, not polymer measurement chemistry.
Real-world example
Two polyethylene batches may share Mn but have different high-mass tails. The batch with more very long chains can show different melt viscosity and processing behavior. A manufacturer therefore may report a distribution or multiple averages rather than only a single nominal molar mass.
Why?
Why is Mw usually larger than Mn? The weight-average formula gives greater influence to high-mass chains. If all chains have identical mass, that preference changes nothing; when masses differ, heavier chains pull Mw upward relative to the equal-chain-count average.
Common misconception
“Dispersity 1.2 tells the exact percentage of each chain length.” It is one ratio of two averages. Many distinct distributions can produce the same value, so a full chromatogram or distribution estimate is needed for more detail.
Worked example
A sample has two 10 kg mol⁻¹ chains and one 40 kg mol⁻¹ chain. Mn=(2×10+1×40)/3=20 kg mol⁻¹. Mw=(2×10²+1×40²)/(2×10+40)=1800/60=30 kg mol⁻¹. Therefore Đ=30/20=1.5. Units cancel in Đ, but both averages retain molar-mass units.
Quick check
1. Which average is more influenced by very long chains? Answer: Mw, the weight-average molar mass. 2. What is Đ for perfectly identical chain masses? Answer: One.
Exam focus
Write each summation formula and keep count Nᵢ separate from mass Mᵢ. Check units and the inequality Mw≥Mn. State that dispersity summarizes spread but cannot reconstruct the full distribution.
Advanced insight
Size-exclusion chromatography separates chains by hydrodynamic volume, not directly by molar mass. Calibration against linear standards can misestimate branched polymers because a branched chain may occupy a different volume at the same mass. Absolute detectors or suitable models can reduce this ambiguity.
Summary
Mn counts chains equally, Mw weights heavier chains more strongly, and dispersity Đ=Mw/Mn summarizes their difference. Because polymer samples contain length distributions, reporting the named average and measurement method is essential for meaningful comparisons.
Practice questions
1. Two chains have masses 5 and 15 arbitrary mass units. Find Mn. Answer: (5+15)/2=10 units when one chain of each mass is present. 2. For the same two chains, find Mw. Answer: (5²+15²)/(5+15)=250/20=12.5 units. 3. Can Đ be below one for an exact positive chain-mass distribution? Answer: No. Mathematically Mw≥Mn, so Đ≥1; a lower measured value would indicate error or inconsistent estimates.