Most practical scientists and technologists who regularly work with solvents and polymers have heard of Hansen Solubility Parameters (HSP – note that the abbreviation is usually a plural but sometimes can be singular). They generally know that they are a “good thing” and if pushed they might say that HSP encode the principle that “like dissolves like”. If you go to the website of any reputable provider of solvents you will find the HSP of their products as part of their information pack for their customers. So it would be fair to say that HSP are an accepted part of the technical infrastructure on which we all depend.
Yet if you probe a little deeper, you find two conflicting criticisms of HSP.
The first is that they are too trivial. They can be seen as a cheap trick that’s sometimes useful, but not something that can take on “real” problems. They can also be seen as “mere correlations” with nothing of substance to them.
The second criticism is that they are too hard. The poor scientist has to do a lot of work with, say, 48 solvents then attempt some tricky number crunching in order to determine the HSP of the system in which they are interested.
The result of these criticisms (sometimes overt, usually un-stated) is that remarkably few of us use HSP as a routine part of our working lives.
The reason that I’m writing this introduction is that despite having known Charles for many years and reaped the benefit of his hard work, I’d never really thrown myself the challenge of personally using HSP. This all changed when I read the 2nd edition of his magisterial Handbook (Hansen, C. M., Hansen Solubility Parameters: A User’s Handbook, CRC Press, Boca Raton FL, 2007 – referred to as the Handbook in the rest of this book) where he revealed a few more details of how his Sphere program computed HSP. I realised that I would be able to write a version for myself. As soon as I got it working, I tried it out on a pressing technical problem in my business and found that from very little work I got a remarkable amount of insight and, from that, a new product for the marketplace.
That first version of Sphere now looks rather crude. With Charles’ help and encouragement I added capabilities that now make it a formidable practical tool that both Charles and I use routinely.
We believe that providing you with a copy of HSPiP, videos (created from within the software) to help you understand it, a set of the worked examples in this book, and the book itself you will become a convert. You will find that HSP are neither too trivial nor too hard.
To produce the examples in this book we have had to rely on the generosity of a number of research teams who have allowed us to use their HSP data. We warmly thank them for their help and acknowledge their individual contributions in the appropriate chapters.
A word of advice about using HSPiP. It’s really rather powerful. It had to be. Without that power we couldn’t have done all the things we’ve done for the book. But power tends to come with complexity. I’ve tried to keep things simple. Which means that some of the power is hidden in neat short-cuts and tricks. To get to know those, it really will be helpful if you read the Help file. I know that no-one (including myself) reads Help files. But if you find yourself wishing that HSPiP would do such-and-such, there’s a chance that by looking in the Help you’ll find that it does.
Because chemical nomenclature is somewhat ambiguous, the chemicals in the main database Sphere Solvent Data.hsd are provided with CAS Numbers and Smiles nomenclature. Any database is bound to have some inaccuracies and uncertainties. We’ve done our best to minimize them. We warmly thank our co-author Dr Hiroshi Yamamoto for the huge amount of work he put in to providing the CAS and Smiles data and for his eagle eyes that detected a number of errors.
Now to the guarantee mentioned at the start of this section. I am taking personal responsibility for some of the more exploratory ideas in the book and software. I am therefore offering a personal guarantee that when you show that the ideas are wrong I will (a) upgrade the relevant section(s) of the book/software and (b) make it clear that I was wrong and (c) acknowledge you (if you wish) as the source of the correction. Science thrives on its falsifiability and I positively welcome the chance for myself and the HSP community to learn from the refutation of ideas which seem to me to be reasonable on the basis of the evidence to hand at the time of writing.
Given the litigious environment in which we all live, I have to follow the Guarantee with a disclaimer:
Disclaimer: The theories, examples, formulae, calculations and datasets used in this eBook and software are based on extensive theoretical research and experimentation over many years by the HSP community. But they should only be used as a guide to any particular issue. Hansen-Solubility.com cannot be held responsible for problems resulting from use of the eBook, software and datasets.
Go ahead. Look for the examples that are closest to your technical area, see how valuable the insights from HSP can be, then use HSPiP to become a convert in the way that I have.
You can email me at email@example.com
Finally, I must thank my company, MacDermid, for giving me the academic freedom to write this book with Dr Hansen. They are not responsible for any flaws in the book.
Note for the 2nd Edition
HSPiP users have not been shy in offering detailed critiques of the 1st edition, suggestions for improvements, errors in the data etc. This is exactly what I’d hoped for. In addition to the big changes for the 2nd Edition (e.g. Y-MB automated HSP and the IGC modeller) there have been many small changes that make it easier for users to get the most out of HSP. The 2nd Edition Update also added some important extra outputs from Y-MB including Environmental outputs for intelligent consideration of VOC issues.
Note for the 3rd Edition
I’m now retired from MacDermid and this has given me more time to focus on HSPiP. This was vital because creating the 3rd Edition has been a major project. We, of course, took on issues raised by the HSPiP community. But with Hiroshi Yamamoto as a key member of the HSPiP team we set ourselves some tough challenges. As far as we are aware, the severest critics of HSP are ourselves and we spent long hours checking large datasets for the quality of the predictions by the software. We also found ourselves developing new predictive techniques for GC, solubility, azeotropes, adhesion etc. The whole HSPiP package now offers a formidable array of practical predictive tools, backed up by as much validation as we could find. We know it is not perfect. But, as ever, we hope that you, the user community, will keep us alert to opportunities to improve all aspects of the package.
We must comment on the Polymer dataset. This has not changed much from the previous editions simply because little new data has come in. But we’ve added a grading system and a document explaining it, so you can more readily choose between the different values on offer. At the heart of the problem is the definition of “polymer” and “soluble”. A “PET” might be one of many things. One data set might be interested in real solubility of low molecular weight amorphous PET and another might be interested in the swelling of high molecular weight crystalline PET. Inevitably there will be differences in the results. This isn’t a weakness of HSP. It’s up to you as a scientist to know what sort of polymer and what sort of solubility is important for you. If there isn’t good data in the HSPiP set or in the literature then believe me, because I’ve often done it myself, it’s very easy to measure the value of your polymer for your purposes. We’ve added a whole chapter to explain in detail how to do this.
The old Sphere Solvent Data.hsd from the previous editions has now been supplanted by an integrated dataset which contains much more useful data on over 10,000 chemicals spanning a wide range of interests. It’s divided into two parts. The first ~1200 entries are the “official” set. The data provided with it is, wherever possible real data. The other ~8,800 entries contain predicted data. But the predictions are based on the real data available for many of those entries. When you use the program you can include the full dataset by selecting the “10,000” option. You can still load the old dataset if you wish.
The new version of the eBook comes with an improved reader. A number of users have asked why they can’t copy or print the text and we always have to give the same reply. We very much want the eBook to remain an integral part of the whole HSPiP package, and if we allowed it to be copied/printed then pirated editions of the eBook would quickly appear. We know this is an inconvenience for our honest users, but we really don’t have much choice in the matter.
Some of the screen shots in the book are from the earlier edition. It didn’t seem necessary to update them all as you will quickly be able to accommodate any minor differences. For example in the Optimizer the eBook says that the user can click the 2 button to find a good blend of two solvents. It would be obvious to a user of the 3rd Edition that clicking the 3 button helpfully finds the optimum blend of three solvents.
Looking at the opening of this Introduction I’m pleased with one thing. HSPiP has already started to change views of HSP. Increasingly they are being seen as having that very rare combination – ease of use with power of prediction. The feedback from the HSPiP community has confirmed this many times over. The software has now been cited in prestigious publications and the idea that HSPiP are “mere correlations” now starts to look rather quaint. The fact that HSP have addressed deep issues in areas as diverse as DNA sequencing and graphene dispersions indicates that there is a lot more that HSP can do.
In addition to the official Hansen website, www.hansen-solubility.com we encourage you to visit Dr Yamamoto’s spirited Pirika site, http://www.pirika.com/. Hiroshi enjoys pushing the boundaries of HSP with data-driven speculations. If you disagree with his speculations, he will be happy to respond to your views.
Finally, two Thank You paragraphs.
The first is to the HSPiP community. The interaction with you, the challenges, the queries, the requests, the feedback have all been much appreciated. With such a large community I can’t always guarantee to give an instant and satisfactory response, but I can guarantee to try my best.
The second, and he doesn’t know that I’ve added this bit to the text, is to Charles. His constant wisdom and encouragement, his astonishing collection of historical data and papers (and ability to find just the right bit of information), his razor-sharp mind for piercing through the inadequacies of my own understanding have always been much appreciated by me. He’s never doubted, over 40 years, that HSP could continue to provide key insights into real-world technical problems. HSPiP and the HSPiP community have more than proved him correct in his views.
Note for the 3.1 release
Another meeting of the HSPiP team led to the usual lively debate about improvements to HSPiP. Because the HSPiP user community is, we’re delighted to say, very demanding there were many ideas for improvements, as well as our own roadmap. We reached a few key decisions:
1. To “liberate” the eBook. It’s now included as a straightforward PDF file which you can read, print etc. as you wish. The Book icon simply opens the file in Acrobat Reader.
2. To update a few HSP values in the dataset. This was prompted by Hiroshi’s careful analysis of anomalies in values of different series and also some fresh experimental data from an HSP user on dimethyl and diethyl succinate. These data have caused us to add specific entries for many of these important esters (glutarate, adipate…) and, most significantly, to change the value for DBE which is a mixture of such esters. The previous value had been worked out many years ago with a major DBE manufacturer and seemed to be correct based on the data at the time. But the data on dimethyl succinate was compelling so we have had no choice but to update the value. The changed molecules are: 1,2,3-Trichloro Propene; 4-Ethyl-1,3-Dioxolane-2-one; N-Ethyl Formamide; Propionamide; N-Acetylmorpholine; DBE. We’ve also added some important new solvents: Dimethyl 2-Methyl Glutarate (a variation on the DBE esters); some potentially interesting bio-derived solvents with interesting properties: Glycerol carbonate and its acetate and ether; Dimethyl Isosorbide, of great interest to the cosmetics community.
3. To make sure that users knew what changes have been made to each update (major or minor) so in future all releases will come with a Version Information document.
4. To add “advanced” options to the sphere fitting. There is now a “data” method where you can enter actual solubilities, swellabilities etc. And, with many reservations about it, a double sphere method which acknowledges that some materials may have two domains (e.g. a diblock co-polymer) and therefore two spheres.
5. To change the standard format of files from .ssd (Sphere Solvent Data) to .hsd (Hansen Solubility Data). You will still be able to read all your old .ssd files but if you re-save them then they are saved in the .hsd format. The reason for the change is simple. There is so much possible information to be stored in these files that we had to “liberate” the format from its rigid structure. Now each file comes with a header row and the program does its best to identify the key components and create a table with the standard elements in their usual place and any other elements (which could be user-specific if you wish) are tacked on to the end.
6. To assign Donor/Acceptor values to the δH value to capture the different modes of hydrogen bonding that are possible. This was our most difficult decision. As you will read in the 4th Dimension chapter this change hasn’t caused the entire 3-parameter HSP to collapse. Indeed, there are many good reasons why this change makes very little difference in most cases. Read the new chapter to find out why.
Finally, Hiroshi and I persuaded Charles to write a short history of HSP. This followed his comment during our meeting that “I never called them Hansen Solubility Parameters until Beerbower started to use the term in his publications”. As his original version (the Main Track) was a rather too short and modest, we asked him to add some more personal details (the Side Track). His description of how he devised the first 88 HSP values using rods, wires and magnets was fascinating and we begged to see a photo. But it seemed that no image existed. However, after almost giving up the search through his archives, Charles found a picture which we’ve scanned in as best we can. For those who are used to doing optimization at the push of an Excel button it’s sobering to imagine how much hard work went in to devising the whole basis of HSP with such apparatus.
Note for the 4th Edition
The world is changing to “apps” that can be used on all platforms from phones through tablets to PCs and Macs. In the long term, HSPiP will have to be liberated from its reliance on the PC platform. For now that is impossible – apps and the variety of browsers place crippling limitations on what can be done, making any attempt at an app-based HSPiP futile. But to help us on the journey we have started the tradition of Power Tools – extra tools that aren’t absolutely required for day-to-day use in HSPiP but which will appeal to users with specific needs. The flexibility to create new Power Tools and, importantly, to allow users to upgrade quickly to the newest versions means that the HSPiP community can adapt quickly. And one of the key Power Tools is a Sphere Viewer which lets you send .hsd files to colleagues who do not have HSPiP, allowing them to see Sphere fits for themselves. The limitations on Power Tools are almost all to do with the different philosophies of different browsers with, inevitably, Internet Explorer being the one with the most problems and the least functionality for users.
The addition of Power Tools has not distracted us from HSPiP itself. As promised, an improved Y-MB engine offers better predictions of HSP and other properties. The downside of that good news is that you will find that some molecules of interest to you will have changed predicted values. As always with predictions, as a scientist you have to choose which values to prefer. As we’ve often said, precise prediction is impossible – we can only strive to reduce errors across a broad range of molecules. There are refinements to polymer predictions, the introduction of a prediction of EACN (Effective Alkane Carbon Number) which are increasingly important in terms of surfactant theory, especially HLD-NAC for which I’ve written separate (and free) apps. We would have loved to have had a breakthrough in appling HSP theory to surfactants, but it’s a long road ahead.
Between 3.1 and this 4th Edition was 3.2 which continued the tradition of a steady stream of improvements based on user feedback. The three of us continue to greatly appreciated the ideas, challenges and bug reports from users. Together we have made the whole a much better package, which is why we continue to offer users free upgrades as a “thank you” for their contributions.