The most geekish discovery EVER :-)

There is a not-very-documented feature in MacOS 10.9 that I just stumbled upon.

If you save a text file in SMILES format, Mac will show the correct image, scalable, in preview.
SMILES screen shot!
This is a very simple text file. It’s the shorthand for the chemical structure of DDT, and its sole content is


which is the Simplified Molecular-Input Line Entry System, or SMILES, abbreviation.

It doesn’t make that the icon of the file, just next to it you can see the BBEdit document for a really large molecule (which also renders) that I copied from the Wikipedia entry on SMILES.

Now, I’m nowhere near geek enough to dig into the UNIX guts and find the function call. But it’s pretty cool.

Learning from your mistakes (nerdy)

A challenging experiment today for the third week of O-chem lab. The goal was to separate an acid from a neutral substance and get both pure. For rookies, this was a tough task as they had never used a sep funnel before.

For pre-lab, they had to read the lab manual and the Mohrig techniques book, watch a video from the MIT lab techniques site, then run through an on-line exercise I did in the course web-site. Most of them did that.
Procedure was partially outlined in the lab manual but they had to come up with the idea of re-precipitating the acid from its conjugate base drawing on their experience in the previous lab, where they confirmed the presence of an organic acid by re-precipitating. Ether solution, extraction with 5% NaOH, re precipitate the acid and heat to boiling to digest it, then evaporate off ether to recover the neutral. ID both by melting point from a list of 8 possibles.

But there are always surprises in O-chem lab.

  1. Adding the clear NaOH to a clear ether solution makes a precipitate? Well, the sodium salt of one of the acids might not be all that soluble.
  2. You’re not getting a precipitate? Try adding the HCl to the aqueous base layer instead of the ether.
  3. You’ve boiled off almost all the ether and you still have no solid? Give it a sec… Wham, the whole liquid layer crystallizes because
    • you had it supercooled, or
    • because the compound was so amazingly soluble you had to get every last bit of Et2O out of there
    • Or maybe because you heated your ether flask over boiling water instead of 50-60 degrees, so that liquid you thought was ether was really melted biphenyl.
    • Or maybe it’s water from the bath that you slopped in there.

And then there’s the beautifully educational mistakes.

  • A 50 mL Erlenmeyer tips over when heated in a 600 mL beaker’s water bath. Ether boils off instantly, leaving a skin of product on the water. “OK, let’s pretend you’ve spent weeks making that compound, so every molecule is precious. How can you get it back?”
  • A flask of boiling water with crystals of naphthalene subliming onto the lip (why was that even in your water layer?)
  • A sep funnel with fuzzy white crystals growing all around the leaky stopcock.

Making mistakes isn’t bad. Not noticing you’ve made a mistake, that’s bad. Not learning from your mistake, that’s bad. Making the same mistake over and over, not so good.

If there wasn’t room for mistakes, there wouldn’t be as much room for learning. And man, was there a lot of room for learning today.

But then you get the big payoff:


Models and reality

Yesterday, the topic for O-Chem was resonance structures and quantum*. One of the points I tried to get across to the class was about the appropriate selection and use of models.
Models are what we use to represent nature – perhaps the entire world, if you’re feeling solipsistic- and we try to build our models to get usefully close to “reality” as tested by experiment.
I want my students to realize that there are different levels of modeling, and that they should use the one that is simplest without being too simple. And most important, if a model is too simple, they should recognize that the appropriate choice is to pick a better model.

If a model’s explanation agrees with experiment, great. Maybe the model is a good representation of reality. Or maybe it’s just coincidence, and you might soon find that other experiments don’t fit your model. Then, if you’re a good scientist, you either revise your model to accommodate the new results, or you might have to chuck the model entirely and come up with a new one. Thomas S. Kuhn’s “The Structure of Scientific Revolutions“, which every science major should read in college, goes through that process with several examples.

Many cases of scientific error come about because a researcher is overly attached to his or her pet theory, a model of how nature works that makes some predictions. The model works well enough during early work to show some promising results. But as the experiments become more detailed, sometimes the predictions of the model don’t match the data. If the researcher is overly invested (psychologically, professionally, or financially), s/he may, instead of discarding or further refining the model, revise the experiment until it shows something closer to the “expected” results. Generally this begins as an innocent assumption: “I don’t know why the mice died. I’ll try again and this time make sure to watch them closer.” In bad cases, the behavior evolves to changing it he parameters of the experiment so that it can no longer produce results inconsistent with the model, at the expense of failing to actually test the theory in the first place. And, ultimately, sometimes this does degenerate into outright fraud.

Submitted for your consideration today: a lot of the problems in the world come from people using models in inappropriate situations.

  • A model of human behavior that works fairly well with Americans or Western Europeans might be disastrous when attempting to work with other cultures.
  • A model of the relationship between humanity and the natural world that was suitable for a low-population, high-resource country (17th-19th century America) no longer works well when resources are scarcer and demand for them is great.
  • A model of economic behavior that applies at one set of conditions fails in a different set of conditions.
  • A model of interpersonal generosity and mutual support that works well in small tribes/towns might not apply as well in a huge city. (And vice versa.)

All of those are instances of models where the model has validity in some instances, but fails when it is applied to other conditions. A more dangerous occurrence is when the model is knowingly false but is promulgated in order to serve an agenda. Climate-change denialism might fit that. The Laffer Curve in tax theory is self-evident at the extremes but not supported by data in the areas where policy is made.

And when people respond with hostility to challenges to their models, the results can range from argument to warfare.

What models do we cling to that need to be adjusted or replaced?

*For the non-chemists out there, apologies for forthcoming confusion. For the chemists, sorry for the oversimplification and probably the inaccuracies. The following is an overview of the models under consideration.

The concept of “resonance structures” is a pre-quantum way of explaining how two seemingly different arrangements (called Lewis structures) of electrons in a molecule can coexist. In the simplest model of bonding, atoms form molecules by sharing their electrons two at a time. But sometimes there can be more than one reasonable way of distributing those electron pairs. Experiment often shows that the molecule is best described as an average of these possible distributions.

So in O-chem, resonance structures are a pretty useful tool for describing molecules and their behavior. For example, we can explain why a particular reaction gives two products but not a third, using this concept. So even though it is a pretty simple model, it allows us to make reasonably good predictions most of the time.

General chem courses typically then add in the “extended π system” model, claiming that a resonance hybrid (average structure) can be viewed as one big bond extending over all atoms involved. Better in some ways because at least it uses orbitals.

Now introduce quantum and the LCAO-MO model. (Viewed from a safe distance, it’s fairly harmless.) LCAO-MO tells us that we can replace the resonance structure model with a set of larger molecular orbitals that are built by using the atomic orbitals available from the bonding atoms. For the simple resonance structures in the acetate ion, there are three contributing p atomic orbitals and thus three molecular orbitals can be generated. There are enough electrons available to full two of them, and the resulting picture agrees with the resonance model and explains why there is only a charge on the oxygen atoms, not the carbonyl carbon.

Back to the lab!

Today was the first day back in lab. We started classes last week, but the “lab” was just the safety & orientation class, not a real experiment. Then Labor Day off, so today, Tuesday, was the first real lab. The theme was recrystallization and melting points, so I showed that clip “89% Pure Junk” from The French Connection. They used authentic 1971 lab equipment and even had a correct melting point for the heroin (discounting slightly for the 11% impurities…)

The lab is in two parts, a recrystallization (benzoin acid adulterated with iron oxide, copper sulfate, phenylacetic acid, and phenoxyacetic acid) and a melting-point mix-and-match where the students are given an unknown, which was whatever we had on the shelf with mp = 130-135, and have to find at least one person with the same substance and one person with a different substance, and prove it by mixed melting points. That’s a good “get-to-know-you” lab because they are forced to interact with peers.

And the recrystallization worked better than expected. The Fe2O3 gave a pink tint to the solid that required hot filtration, the copper sulfate showed how water-soluble impurities remain in solution, and the two organic contaminants knocked the melting point down by a good ten degrees. The purified benzoic acid was white, and right back at 122 where it belonged after 20-30 minutes in the drying oven.

You forget just how little technique people know at the beginning of the year. Three flasks boiled dry and two broke, one of which dumped choking vapors all over. Note: even though the solvent is only water, they have to do the recrystallization in the hood anyway! But I didn’t see any pink or blue product turned in and yields were roughly 50% which is not bad for a beginner.

Today, beginning to work on how to use ChemDoodle Web Tools in teaching. This may require more than the D2L web site can handle, so I may wind up adding pages about O-chem to test out.
The ultimate goal is to convert class notes into an E-book with interactive or at least multimedia figures.

Proof of… What, exactly?

The recent best seller “Proof of Heaven” relies on the visions of a neurosurgeon with a severe infection in his brain. But the visions and sense of numinous feelings he had are apparently fairly common effects from recreational abuse of the hallucinogen DMT. I had seen this commentary and Sam Harris’s more harsh debunking about it a while ago, but was reminded (and given a topic for today’s post) by this item in the NYT.

So, is the reportedly similar experience of many people who’ve had extreme medical emergencies really “proof of heaven”, or is it a result of highly stressed brain cells dumping a lot of neurotransmitters? Or to put it another way, if there is a real “heaven” that people almost-but-not-quite visit, does that mean there’s some validity to the psychedelic drug experience?
Finally, is there any way to counter the idea that the visions and ecstatic states that led to the meme of angels, floating, awesome numinousness, etc. might have arisen internally from a meditative, trance-like, or even epileptic state?

Sorry, but this neurosurgeon probably wasn’t as cortically damaged as he claimed, or he wouldn’t have been able to write a book, let alone return to work. Dude, he was trippin’.