#solo2010: Green Chain Reaction; details and offers of help

Typed into Arcturus

As I have blogged already (http://wwmm.ch.cam.ac.uk/blogs/murrayrust/?p=2526) we are planning an exciting communal event for Science Online where we investigate whether chemical reactions are getting greener. I’ve already had two offers of help today and I’ll post details later. To keep you up to speed this is what we are hoping to analyse. EVEN IF YOU ARE NOT A CHEMIST TAKE THE TIME TO HAVE A LOOK. It’s not as difficult as it looks.

The title compound was synthesized as described in the literature. To glycine (1.00 mol) and potassium hydroxide (1.00 mmol) in 10 ml of methanol and 5 ml of waterw as added 2-hydroxy-1-naphthaldehyde (1.00 mmol in 10 ml of methanol) dropwise. The yellow solution was stirred for 2.0 h at 333 K. The resultant mixture was added dropwise to Cu(II) nitrate hexahydrate (1.00 mmol) and pyridine (1.00 mmol) in an aqueous methanolic solution (20 ml, 1:1 <i>v</i>/<i>v</i>), and heated with stirring for 2.0 h at 333 K. The brown solution was filtered and left for several days, brown crystals had formed that were filtered off, washed with water, and dried under vacuum.

Let’s do a Jabberwocky on it. You’ll remember :

 The Jabberwock, with eyes of flame,
Came whiffling through the tulgey wood,
  And burbled as it came!

One, two! One, two! And through and through
  The vorpal blade went snicker-snack!
He left it dead, and with its head
  He went galumphing back.

“And, has thou slain the Jabberwock?

And Alice says perceptively:

“…Somebody killed something: that’s clear, at any rate–,”

And that’s the point. I think we could all say of the chemistry

“something was added to something and heated: that’s clear at any rate”.

Let’s rewrite:

To SOMETHING1 (1.00 mol) and SOMETHING2 (1.00 mmol) in 10 ml of LIQUID1 and 5 ml of water was added SOMETHING3 (1.00 mmol in 10 ml of LIQUID1) dropwise. The yellow solution was stirred for 2.0 h at 60degrees C. The resultant mixture was added dropwise to SOMETHING4 (1.00 mmol) and LIQUID2 (1.00 mmol) in an aqueous methanolic solution (20 ml, 1:1 volume/volume), and heated with stirring for 2.0 h at at 60 degrees C. The brown solution was filtered and left for several days, brown crystals had formed that were filtered off, washed with water, and dried under vacuum.

[NB ml = millilitre – and 1 litre = 1.7 pints. mmol is millimole which is a very small burrowing animal a chemical measure of how much molecular stuff there is] That’s really not very different from making cocktails or cakes! You don’t have to understand why it works.

The good news is that the machine knows (or can work out) all the SOMETHINGs (they are in open Databases such as Pubchem (http://pubchem.ncbi.nlm.nih.gov/ ). You’ll see that the whole recipe is just a number of ACTIONs. We (Lezan Hawizy and others) have catalogued the actions and they are commonly:

  • ADD SOMETHING to SOMETHING
  • WAIT for TIME
  • HEAT/COOL at TEMPERATURE
  • STIR (vigorously/gently)
  • DRY (under vacuum/over drying agent)
  • FILTER (solution/solid)

There are a few others. There’s also:

  • OBSERVATION (solid, bubbling, colour, explosion, etc.)

So it’s quite feasible for machines or humans to abstract this into a formal account.

 

We are now doing this with Mat Todd from Sydney and over the next 24 hours intending to come up with a description of a chemical reaction as a set of events. We’ll publish this shortly.

Meanwhile we are dusting off our natural language-processing tools and we’d be delighted if anyone out there would like to take part. At this stage it probably needs someone who has run Java programs – we’ll get some distribution bugs. But maybe we can also build a server.

… more later

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3 Responses to #solo2010: Green Chain Reaction; details and offers of help

  1. Dan Hagon says:

    Hi Peter, sounds a really fun project. I’m happy to help out with some Java coding. Also I have a cloud-hosted virtual machine I’m not really making much use of right now which you’re welcome to use.

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  3. Chris Rusbridge says:

    I read this immediately after reading http://blog.okfn.org/2010/08/09/cataloguing-bibliographic-data-with-natural-language-and-rdf/ and wondered at the serendipitous similarity! The blog post referred to by Wiiliam Waites starts “In the grand tradition of W3C IRC bots, I’ve started some speculative work on a robot that tries to understand natural language descriptions of works and their authors and generates RDF. It is written in Python and uses ORDF, the NLTK and FuXi.”

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