Pirika logo
JAVA, HTML5 & Chemistry Site

Top page of Pirika

Official HP HSPiP(Hansen Solubility Parameters(HSP) in Practice)
HSPiP How to buy

Hansen Solubility Parameter (HSP)
  Basic HSP
  Bio, Medical, Cosmetic
  Properties Estimation
  Analytical Chemistry
  Formulating for Cosmetics
  DIY:Do It Yourself

  Properties Estimations
  Polymer Science
  Chemical Engineering
  Molecular Orbital
  Other Chemistry
  DIY:Do It Yourself

Other Writing

Ad Space for you


Ad Space for you




last update

HSP Application note #41

Quantitative solubility parameter


HSPiP Team Senior Developer, Dr. Hiroshi Yamamoto


HSPiP ver. 4 have the Power Tool, "Y-Fit". This power Tool is improved the Quatntitative analysis so much.

Sphere program is the main program in HSPiP.

Hansen Sphere

To determine if the parameters of two molecules (usually a solvent and a polymer) are within range a value called interaction radius (R0) is given to the substance being dissolved. This value determines the radius of the sphere in Hansen space and its center is the three Hansen parameters.

From version 3.1.X, Double Spheres function is available.

Pirika provide JAVA 3D Demo Applet to browse the Sphere(s).
The HTML5 Sphere Viewer examples are available here.
Now we have Power Tool "Sphere Viewer", GUI HTML5 software on HSPiP ver. 4.


HSPiP(Hansen Solubility Parameters in Practice)

The first edition of HSPiP that appeared in November, 2008, greatly enhanced the usefulness of the Hansen solubility parameters (HSP).

The HSP values of over 1200++ chemicals and 500 polymers are provided in convenient electronic format and have been revised and updated using the latest data sources in the second edition (March, 2009).

A third edition of the HSPiP appeared in March, 2010. There are now 10,000 compounds in the HSP file which also includes data on density, melting point, boiling point, critical parameters, Antoine constants and much more. The user is able to carry out many different sorts of optimisations of solubility, evaporation, diffusion, adhesion, create their own datasets (automatically if required) and explore the huge range of applications for HSP in coatings, paints, nanoparticles, cosmetics, pharma, organic photovoltaics and much more.

The 3rd Edition v3.1 was released on 12 December 2010. Current users can upgrade free (now v3.1.09) by downloading the latest .msi installer from http://hansen-solubility.com

The 4th Edition v4.0.x was released on 2 Jan. 2013. The Current users can upgrade with free charge.

2013.1.28 The Visual How to manual of HSPiP. You can understand what HSPiP can do.
Please check the Functional Group List whether your targets are available with HSPiP.
How to purchase HSPiP
2013..1.2 The HSPiP ver. 4 include Power Tools for HSPiP power user.

It determine HSP of solute.

Hansen Solubility Parameters (HSP)

Hansen Solubility Parameters(HSP) were developed by Charles M. Hansen as a way of predicting if one material will dissolve in another and form a solution. They are based on the idea that "like dissolves like" where one molecule is defined as being 'like' another if it bonds to itself in a similar way.
Specifically, each molecule is given three Hansen parameters, each generally measured in MPa0.5:
dD:The energy from dispersion bonds between molecules
dP:The energy from dipolar intermolecular force between molecules
dH:The energy from hydrogen bonds between molecules.
These three parameters can be treated as Vector for a point in three dimensions also known as the Hansen space. The nearer two molecules HSP Vector are in this three dimensional space, the more likely they are to dissolve into each other.

What can perhaps be surprising is that one can assign HSP to so many different things. Gases like carbon dioxide, solids like carbon-60, sugar, and biological materials like human skin, depot fat, DNA, and even some proteins all have HSP. The list can be continued with drugs, polymers, plasticizers, and in fact any organic material and even many inorganic materials like salts. The only requirement for an experimental confirmation is that the material must behave differently in a sufficient number of test solvents upon contact.

Pirika JAVA Demo Applet calculate HSP. HSPLight is available here.
Please refer to e-Book of HSPiP if you want know more about HSP.
About the Power Tools that handle HSP more effectively.


This program has been used for many years and make great success. And we start to add new function into sphere program. We will soon release new ver. 3.1.X.
We will explain new functions.

The first function is trying to solve quantity problem.
Classic Sphere set score 1 for good solvents, and set 0 for bad solvents.
So, the result of Classic Sphere is qualitative result.
I will explain this with data.

Abietic Acid, we have quantitative solubility data.
With using Y-MB estimation, HSP of this molecule is [17.5, 3.1, 6.3].

Name dD dP dH Vol g/100CC
HCFC-141b 15.7 4 1 95 1.375
HCFC-225cb 13.1 2.9 1 130.1 1.092
carbon tetrachloride 17.8 0 0.6 97.1 5.0688
chloroform 17.8 3.1 5.7 80.5 172.724
dichloromethane 17 7.3 7.1 64.4 123.798
CFC-113 14.7 1.6 0 119.2 1.2424
tetrachloroethylene 18.3 5.7 0 102.8 115.02
trichloroethylene 18 3.1 5.3 90.1 178.364
1,1,2-trichloroethane 18.2 5.3 6.8 92.9 64.845
1,2-dichloroethane 18 7.4 4.1 79.4 43.75
Acetone 15.5 10.4 7 73.8 18.249
methyl ethyl ketone 16 9 5.1 90.2 28.175
diethyl ether 15.49 2.9 4.6 104.7 57.753
toluene 18 1.4 2 106.6 78.897
1,1,1-trichloroethane 16.8 4.3 2 99.3 94.9696

For example, if we assign 60g/100CC as score 1 and calculate Classic SPhere, the HSP become [18.2, 4.8, 4.4] and Radius become 6.15. So if the HSP distance is shorter than 6.15, then we expect that solvent is good solvent. I show the result below figure.

  HSP distance is same, but solubility amount is very different.  

So, if the HSP distance is shorter than 6.15, every solvents are good. But this does not mean larger solubility amount.
It is qualitative result.
Then we have problem for searching larger solubility solvents.
Which direction is better if we want to search for more effective solvents.
This problem is also happen when we search liquid-liquid extraction solvents.
We want to know the solvent that partition coefficient is largest, not relative quality.

To solve this problem, I developed new algorithm to search sphere center that have good correlation with real solubility data. Now I am testing this function with developer version.

Obtained result is [17.6, 4.1, 5.9] and this result is much near to Y-MB result.

If you have Smiles structure and HSPiP software, Y-MB function will calculate HSP immediately.

Smiles(Simplified Molecular Input Line Entry Syntax)

SMILES is a string obtained by printing the symbol nodes encountered in a depth-first tree traversal of a chemical graph.
"Organic subset" of B, C, N, O, P, S, F, Cl, Br, and I, brackets can be omitted.
Branches are described with parentheses, as in CCC(=O)O for propionic acid
Double and triple bonds are represented by the symbols '=' and '#'
Ring closure labels are used to indicate connectivity between non-adjacent atoms in the SMILES

Pirika JAVA Demo Applet getting Smiles. Draw2Smiles is available here.
Now we have Power Tool "Draw 2 Smiles", GUI HTML5 software on HSPiP ver. 4.


Y-MB Properties Estimation

Y-MB break Smiles into correspponding Functional Groups and Estimate various Properties. These estimation schemes are come from Pirika technologies.

Pirika JAVA Demo Applet calculate Properties. PirikaLight is available here.
Now we have Power Tool "Y-Predict", GUI HTML5 software on HSPiP ver. 4.


This program search the most reliable line(you can choose log fitting) that come from real solubility with HSP distance. With this result, we can easily find out Chloroform or Trichloroethylene is the good solvent and if we want to search more effective solvents, this help a lot.

Drag=Rotate, Drag+Shift=Larger/Smaller, Drag+Alt or Command(Window key)=Translate.

If you are using HTML5 enable browser such as Chrome, Safari or FireFox (IE9 is out of support), you will see the Canvas. If you pick solvent, solvent name will appear. The Green large sphere is obtained by Classic Sphere, rhe Cyan large sphere is obtained by quantitative method. There is no significant difference. But "Real" solubility have difference showed by charts.

I show you other example.
I have the real solubility data for oleic acid.
I assign score as 1 for more solubility than 30g/100CC.

Hcode name dD dP dH ScoreC ScoreAmount
122 carbon tetrachloride 17.8 0 0.6 1 107.712
156 chloroform 17.8 3.1 5.7 1 136.988
534 nitromethane 15.8 18.8 5.1 0 0.6774
456 methyl alcohol 14.7 12.3 22.3 0 24.9956
10 Acetonitrile 15.3 18 6.1 0 0.8646
367 1,2-dichloroethane 18 7.4 4.1 1 32.625
7 Acetone 15.5 10.4 7 0 21.646
570 isopropyl alcohol 15.8 6.1 16.4 1 43.175
481 methyl ethyl ketone 16 9 5.1 0 26.9675
328 ethyl acetate 15.8 5.3 7.2 1 39.688
92 butanol 16 5.7 15.8 1 45.765
255 diethyl ether 15.49 2.9 4.6 1 42.78
148 chlorobenzene 19 4.3 2 1 94.01
181 cyclohexane 16.8 0 0.2 1 62.32
102 butyl acetate 15.8 3.7 6.3 1 42.336
417 hexane 14.9 0 0 0 29.2596
698 o-xylene 17.8 1 3.1 1 77.44
532 nitroethane 16 15.5 4.5 0 2.2946
404 furfural 18.6 14.9 7 0 1.5015

If you calculate with Classic Sphere, HSP become [16.9, 0.6, 9.4] radius 9.23.
If I use real solubility Sphere, HSP become [18.7, 1.1, 4.4].
You can see the difference very easily with these 2 figures.

If you want to search more effective solvents, you can find the direction with this result.
There are 3 exceptions, they are alcohol. I will explain the reason in this article.

Drag=Rotate, Drag+Shift=Larger/Smaller, Drag+Alt or Command(Window key)=Translate.

If you are using HTML5 enable browser such as Chrome, Safari or FireFox (IE9 is out of support), you will see the Canvas. If you pick solvent, solvent name will appear. The Green large sphere is obtained by Classic Sphere, rhe Cyan large sphere is obtained by quantitative method. This case, the difference is large. You can easily find that the all solvents are distributing from the Cyan Sphere center, and it contribute to quantitative relationship between distance and solubility.

The 3rd example is Capsaicin real solubility data to acetone-water mixture.

HSP of Solvents Mixture

[dDm, dPm, dHm]=[(a*dD1+b*dD2), (a*dP1+b*dP2),(a*dH1+b*dH2)]/(a+b)

Volume base ratio.

Pirika Java demo applet design solvents mixture. GSD is available here.


Name dD dP dH Score(mg)
1 18.1 17.1 16.9 0.896
2 17.835788 16.419146 15.893962 2.598
3 17.579272 15.758124 14.917228 6.743
4 17.323692 15.099514 13.944058 9.748
5 17.05961 14.418995 12.938515 13.241
6 16.804316 13.761122 11.966434 16.49
7 16.541924 13.084958 10.967326 17.378
8 16.288164 12.431038 10.001086 17.94
9 16.026344 11.756348 9.004156 17.687
10 15.756152 11.060084 7.975348 17.19
11 15.5 10.4 7 11.4

The result become [17.9, 13.1, 7.8]. You can find out the most effective solvents.
If you are interested in these result, you can apply, for example, which fraction % of Ethanol - water mixture (I mean which liquor is the best for extract capsaicin)

Y-MB result is [18, 10.5, 12.8]. And real solubility and HSP distance is very hard to understand.

Now I am developing more effective code with using the reasonable initial guess. And maybe implement in HSPiP version 3.1.X.

Or I may make Thriller Seeker Version independently.

And with this method there is NOT concept of radius.
Because there is no boundary of 0, 1.

You can use this function from HSPiP V3.1.X