< List of probability distributions

The **Flory-Schulz distribution**, based on Paul Flory and G. V. Shulz’s [1] work on chain polymerization models, was developed to describe relative ratios of molecule lengths after a polymerization reaction — the process where a chemical reaction links monomer units to form long chains. The distribution describes the relative ratios of different length polymers that are found in an ideal step-growth polymerization — such as ethylene oligomerization [2].

While different types of polymer weight distributions can generate polymer chains, including the log-normal distribution, the Flory-Schulz distribution is thought to be the most probable one [3].

## Flory-Schulz distribution properties

The probability mass function (PMF) for the Flory-Schulz distribution is [1]

** a^{2}k(1 − a)^{k − 1}**, 0 <

*a*< 1,

where *k* is the number of monomers in the chain.

The cumulative distribution function (CDF)

**1 – (1 – a)^{k} (1 + ak)**

The exponential distribution is a good approximate for the Flory-Schultz distribution when *k *is large [4] or when *a *tends to 1 [5]. The empirically determined constant *a *is related to the fraction of unreacted monomer remaining [6].

Other properties include:

- Mean = (2/
*a*) = 1 - Mode = 1 / (log(1 –
*a*)) - Variance = (2 – 2
*a*) /*a*^{2}.

## Other applications

The Flory-Schulz distribution isn’t limited to applications in chemistry. For example, when applied to disease survival / cure rate contexts, *M* can be considered as a random variable with support on set {0, 1, 2, …} [7, 8]. In that context, the PMF can be shifted to [9]

*P*(*M *= *m*)= *η*^{2}(m + 1)(1 – *η*)^{m }, *m *= 0, 1, 2, …, 0 < η < 1

(note the use of *η *= *a *and *m* = *k*)

with a probability generating function (PGF) of

The distribution has also been used to investigate how proteins, which can co-aggregate into amyloid fibrils, are related to pathologies such as Alzheimer’s disease [5].

## References

[1] Flory, P.J. Molecular Size Distribution in Linear Condensation Polymers. *J. Am. Chem. Soc.* **1936**, *58*, 1877–1885. [Google Scholar]

[2] Weissner, T. (2022). Toward Enhancing the Synthesis of Renewable Polymers: Feedstock Conversions and Functionalizable Copolymers. Dissertation.

[3] Pan, J. (Ed.) (2014) Modelling Degradation of Bioresorbable Polymeric Medical Devices. Elsevier Science.

[4] Polymers: Molecular Weight and its Distribution

[5] Prigent, S. eta al. Size distribution of amyloid fibrils. Mathematical

models and experimental data. Retrieved April 28, 2023 from: https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=1fd6b3146d0d4051f8afcb0d85a32e66083cf64e

[6] IUPAC, *Compendium of Chemical Terminology*, 2nd ed. (the “Gold Book”) (1997). Online corrected version: (2006–) “most probable distribution“. doi:10.1351/goldbook.M04035

[7] Gallardo, D.I.; Gómez, H.W.; Bolfarine, H. A new cure rate model based on the Yule-Simon distribution with application to a melanoma data set. *J. Appl. Stat.* **2017**, *44*, 1153–1164. [Google Scholar]

[8] Gallardo, D.I.; Gómez, Y.M.; Castro, M.D. A flexible cure rate model based on the polylogarithm distribution. *J. Stat. Comput. Simul.* **2018**, *88*, 2137–2149. [Google Scholar]

[9] Azimi, R. et al. A New Cure Rate Model Based on Flory–Schulz Distribution: Application to the Cancer Data. *Mathematics* 2022, *10*(24), 4643; https://doi.org/10.3390/math10244643