As I was told by experts you should use what you really measure. You do not measure the size by number as you get by counting methods or TEM. The number based is only derived mathematically and give you an idea about an average by numbers. Depending on polydispersity this is naturally lower as the volume based, because small particles have considerably smaller volumes. This however, also means that uncertainty/error is much larger for the smaller particles for the number based. Therefore you should avoid to give the size based on numbers. What you get by the method is intensity weighted distribution (based on intensity of scattering). This is good for comparison of different samples measured by the same method. However, intensity of scattering of the smaller particles is considerably lower. For this Mie correction is applied to calculate volume based results. For your sample therefore you should also get smaller average by volume based than by intensity based. Be aware that DLS is not good at investigation of bi- and multimodal samples.

You need to use techniques other than DLS to thoroughly quantify your grainsize. DLS gives you information on the effective size of particles in your system, which can include hydration layers of water/solvent as well as clumping of the particles. Unless you look at them with e.g. TEM/SEM/XRD to get another handle on the grainsize, you will not be able to tell which measurement means the most to you.

As I was told by experts you should use what you really measure. You do not measure the size by number as you get by counting methods or TEM. The number based is only derived mathematically and give you an idea about an average by numbers. Depending on polydispersity this is naturally lower as the volume based, because small particles have considerably smaller volumes. This however, also means that uncertainty/error is much larger for the smaller particles for the number based. Therefore you should avoid to give the size based on numbers. What you get by the method is intensity weighted distribution (based on intensity of scattering). This is good for comparison of different samples measured by the same method. However, intensity of scattering of the smaller particles is considerably lower. For this Mie correction is applied to calculate volume based results. For your sample therefore you should also get smaller average by volume based than by intensity based. Be aware that DLS is not good at investigation of bi- and multimodal samples.

Thank you very much to all....

@ Dear titus sir, Can you send me some notes on mie correction?

Dear Bhavin,

the original article is

Mie, Gustav (1908). "Beiträge zur Optik trüber Medien, speziell kolloidaler Metallösungen". Annalen der Physik 330 (3): 377–445.

You will find numerous resources including Wikipedia when searching on the web.

The number distribution particle size will not count the big particle. The intensity-based particle size is usually reported.

Thank you very much for your kind help.

You are welcome. So in my view, the first thing you need to check is if your sameple is monodispersed or polydispersed. If it is monodispersed, it should be fine to use the number-based particle size data, which can give you a smaller particle size than intensity-based size.

Yes thanks a lot again..that is why i was confused. The PDI value of my sample is 0.214. So it must be mono-disperse... There might be some less amount of aggregation which gives higher particle size in intensity distribution.

Isn't it???

Yes, I have seen correlation function and correlogram (graph) but i could not able to interpret. Do you have any articles related to its interpretation.

This discussion on LinkedIn maybe helpful

http://www.linkedin.com/groupAnswers?viewQuestionAndAnswers=&discussionID=275227394&gid=1624317&goback=.nmp_*1_*1_*1_*1_*1_*1_*1_*1_*1_*1#commentID_null

Especially the answers of Wolfgang give a good background.

Thank you very much sir.

Dear Azeez,

you have to be a member of the group to read it. Myself quitted the group because it was not managed properly, regarding my posts were blocked for unknown reason.