HSP Science-based Formulation for Plastics

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Some of the familiar tasks while formulating plastics include:

• Finding a new solvent blend for working with a polymer
• Ensuring that all the components in a polymer blend have the right level of “happiness” together (especially new, green, plasticizers)
• Finding the best way to match the properties of the polymer to those of an another in order to optimize for adhesion

Although in these and other plastics issues, we can do a lot with trial and error but using a science-based approach - Hansen Solubility Parameters is a much more effective way to arrive at speedy solutions to our formulation problems.

Learn from Professor Steven Abbott what Hansen Solubility Parameters are and how they should become a go-to tool for science-based formulation.

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Better Plastics via Science-based Formulation using Hansen Solubility Parameters

So you need to find a new solvent blend for your polymer, or you need to identify a plasticizer that won’t exude to the surface, or you need to get maximum compatibility between your polymer and another one for maximum adhesion. How do you use the minimum effort to get the maximum balance of desired properties?

If you try to use ideas such as “hydrophobic/hydrophilic” or “polar/non-polar” you are likely to be disappointed in the results. The words are vague and don’t begin to describe the subtleties of the sorts of molecular interactions that affect plastics and their ingredients.

Focusing Specific Areas of Plastics

Formulating scientifically requires numbers and we especially need to know whether how “like” or “unlike” any components in our formulation might be. A good measure would be a “distance” between components, and for this, we need three numbers that describe each chemical, polymer, particle or additive used in our plastic formulation.

Why must we use three? It turns out that two is too small and four is much too complicated. Though, it is worth noting that the molecular size is an extra parameter that is routinely used.

We start by describing:

1. What those three numbers are
2. How you can predict or measure them
3. How you can use them in three specific areas of plastics

The three specific areas of plastics include:

• Determining the key polymer HSP properties
• Ensuring compatibility between the ingredients added to a polymer
• Ensuring compatibility between polymers to obtain, for example, good adhesion

The 3 Hansen Solubility Parameters (HSP)

The three HSP describe three key, familiar features of any molecule:

• The Dispersive aspect
• The Polar aspect
• The Hydrogen-bonding aspect.

Formulators have no problem with the Polar and Hydrogen-bonding aspects. The Dispersive part is less familiar but still intuitive – it’s the general (van der Waals) interactions between all molecules. Molecules with a broad electron cloud (e.g. aromatics); self-interact more strongly than those with a tight cloud (e.g. alkanes).

These three factors are the Hansen Solubility Parameters.

The table below lists the HSP of some common solvents:

Solvent	δD	δP	δH
Acetonitrile	15.3	18	6.1
Acetone	15.5	10.4	7
Benzene	18.4	0	2
Diethyl Ether	14.5	2.9	4.6
Dimethyl Sulfoxide	18.4	16.4	10.2
Hexane	14.9	0	0
Ethyl Acetate	15.8	5.3	7.2
Ethanol	15.8	8.8	19.4
Methylene Dichloride	17	7.3	7.1
N-Methyl-2-Pyrrolidone	18	12.3	7.2
Tetrahydrofuran	16.8	5.7	8
Water	15.5	16	42.3

Looking at the list above, there is no problem making sense of the broad trend of the numbers.

• Acetonitrile, for example, is very polar, so its δP value is high, whereas its δH value is not so big as its ability to hydrogen-bond is modest. Ethanol is medium in terms of δP value, with a large δH value, as we would expect from such a hydrogen-bonded solvent. Each of them has a relatively small δD value.
• Benzene and DMSO both have higher δD values. Thanks to the large electron clouds around them!
• Hexane is a simple, relatively low δD solvent; acetone and ethyl acetate are just middle-of-the-road in their three parameters.

Determining How "Like" Molecules are

Now we are set up to find how “like” two molecules are. We simply calculate the “distance”, D, in 3D space between any pair using the famous HSP formula (including a factor of 4 for the δD values)

D² = 4(δD1-δD2)²+ (δP1-δP2)²+ (δH1-δH2)²

• D values of less than, say, 4 represent a reasonable match and values greater than, say, 8 represent a poor match. So if you have a target molecule and a list of potentially compatible molecules, you just need to calculate D between the target and each molecule.
• After sorting them from low (good) to high (bad), then find which of the low D molecules meet your other requirements, such as cost or greenness.

Typical examples would be if the target is a polymer and the molecules are solvents, or they might be plasticizers. Equally, the target could be an API and the molecules could be polymers, in order to identify which would be best for controlled release. HSP also work very well when the target is a pigment or nanoparticle and the aim is to create a coating or a stable polymer blend (e.g. nanoclays for performance enhancement).

What if no molecule matches all your plastics formulation requirements?

Thanks to another piece of HSP magic, if there is no single molecule that meets all your requirements, you can create a rational blend between two molecules which have high D values but which you like to use for other reasons. Suppose each molecule has a reasonable match of δD and δP with your target but one has a low δH and the other has a high δH.

The HSP of a blend is simply the weighted average of the components. So, in this case, the δH can be tuned to be a close match to your target. Therefore, a blend of unusable molecules (D too large) becomes usable. This ability to create great blends from poor starting materials is the secret to HSP’s success over the past 50 years.

Given that to do these calculations you need the HSP values, where can you find them?


 » Continue reading to learn how to determine HSP of a particlefor a "happy" formulation