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Hansen Solubility Parameters in Practice (HSPiP) e-Book Contents
(How to buy HSPiP)

It’s a common problem. You have used some ink or paint and you need to clean it up. In Denmark, as in most countries, painters usually used white spirit. It was cheap and effective. But when Denmark (a leading country in health & environment issues) discouraged the use of white spirit because it was harmful to the painters it became necessary to replace it with something safer.

That was the start of a process that saw many formulas having to be replaced by something even safer (to humans or to the environment). What was the best way to do this?

One of us (Hansen) became deeply involved in this technical challenge and looks with some pride at the fact that the formulae developed many years ago using HSP principles are the backbone of many of the “environmentally friendlier” solvents in use around the world today.

Trial and error can be made to work, but it is slow and inefficient. It is also highly unlikely to come up with an optimal solution. It is hard to imagine that anyone would have found the 74:26 mixture in the earlier chapter through trial and error.

With HSP the process is clear. Find the HSP of the main polymer in the ink/paint. Find the radius of the sphere around the central point. Then choose a solvent blend that is well inside the sphere. Because the whole process is based on numbers it is a lot more scientific and, more importantly, a lot quicker to come up with an optimum solution.

It is simplistic to define “optimal” simply to mean “best dissolving” but of course it should include other factors such as cost, evaporation rate, odour etc. as discussed in the previous chapter. The point is that if you can’t put a number to “best dissolving” you can’t include it in any other numerical optimisation.

So, let’s take our ink and test it with a bunch of solvents. As we can do this in the fume cupboard in the lab and can wear appropriate personal protective equipment we can use a range of unusual solvents to give us the best possible information.

How many should we use? The original Hansen work used 88 solvents because it was trying to work out a self-consistent set of values for those solvents. What a set of ~30-40 achieves is probably all you need if you really want an authoritative answer and if the ink is genuinely unknown. If you have a reasonable idea of the ink’s properties you might be able to skip hydrocarbons if the ink is obviously highly hydrophilic or alcohols if it is obviously highly hydrophobic. And there is lots of good data from people using 16-24 solvents.

The point here is to be flexible. If you are really interested in the deep science of a polymer, then ~40 is a good idea. If you know that you won’t do a test with 40 solvents (life’s too short) then persuade yourself that 16 isn’t too hard and then throw in another few just to make sure.

The really important thing is to make sure that your chosen solvents span a large range in D, P, H space. Let’s look at an absurd example.

Figure 01 Using file Absurd

The program gives a good fit to the data. But there’s obviously something wrong with the chosen solvents. For this absurd example we simply took the lowest 16 D values from a large list of solvents and assigned the solvents randomly as Inside or Outside. It simply makes no sense to use this range of solvents, unless you are certain that your polymer has a very low D value.

Now look at this plot, from Hansen’s Polymer B (a polymethylmethacrylate) using the 88 original solvents:

Figure 02 Using file Polymer88B

Solvents span a wide part of D, P, H space but you can see that there are clusters which over-represent some areas and voids where there are too few. The voids, unfortunately, are nature’s fault – there just are some areas where there are few solvents. The clusters show that 88 is too many solvents for practical work – you get little extra information for a lot of extra work.

You see the same thing in the 3 2D plots:

Figure 03 Using file Polymer88B

So now let’s go to the “life’s too short” approach and just use 16 solvents.

Figure 04 Using file Chapter 3

This is a repeat of the second figure in the previous chapter.. You can see that in the P v H plot the solvents cover a good span, though a few more high-P solvents might be helpful to be sure about the boundary in P-space and we are definitely short of high D solvents – notice all the empty space in the H v D and P v D plots.

So, is 16 enough? The good news is that you can make your own judgement after you’ve tested the 16. If it’s really important for you to characterise this polymer then choose, say, 3 extra solvents and do the tests. You don’t have to re-do the original 16 tests, just 3 more targeted tests.

Here are the data with the 3 extra solvents:

Figure 05 Using file Chapter4

We can now compare the HSP and Radii of the two data sets






Original 16





Revised 19





Table 01

The centre has shifted slightly and the Radius is smaller. That difference of ~0.6 in the Radius could make all the difference if you were considering using a marginal solvent.

Now that you know what the HSP of your polymer are, you will want to find a solvent with which to dissolve it for efficient cleaning.

The program lets you sort the solvents by their RED number – with low numbers being a smaller distance from the polymer.

Reminder: RED is Relative Energy Difference and is the ratio of the distance from the centre of the sphere for a given solvent to the radius of the sphere.

Here are 16 of the 19 solvents used in the test:

Figure 06 “Inside”=1 and “Outside”=0 solvents

The first 4 solvents are quite good matches (low RED number) and considered generally not safe to use, the fifth, acetone, is controversial: it’s “good” because in the USA it is “low VOC” but in most respects it’s “bad” because most of it evaporates before you can use it.

But remember, for defining the Sphere for this polymer we used solvents that were technically useful (such as the tetrabromoethane) but which we would never want to use in practice. It’s perfectly possible that none of your test solvents would be of interest for the real world. The vital thing is to get a good idea of the polymer’s HSP and radius. After that you need a rational way to find the best solvent for your application.

That is the topic of the next chapter.


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