Grass to Gas: Using computational chemistry to optimize producing fuels from biomass

Grass to Gas: Using computational chemistry to optimize producing fuels from biomass


Hi folks: Scott Auerbach here. I’m a
professor of chemistry at UMass Amherst and I’m a very proud user of the
Massachusetts Green High Performance Computing Center. And the reason why I’m
so proud is that there are really two problems that I’m, you know, trying to
solve with that. The first is related to energy. The fact is after all these years we’re
still getting about eighty percent of our energy from non-sustainable fossil
fuels. And the problem with that is they will run out, but even more importantly,
is their impact with carbon and climate change. And so there’s a lot of potential
for example in using biofuels and fuel cells, and those are the two areas that I
work in. Now with biofuels – I’m going to focus on that – the potential is that, that
could be a so-called carbon neutral kind of fuel, meaning that we grow crops, we
refine them into fuels, we use them, but then that carbon that goes into the
atmosphere gets sequestered by the next crop. The problem is, and now here’s the
second problem, is that the refining process is not so efficient, so it’s not
really affordable yet. So we need new kinds of catalysts, new kinds of
materials to really, you know, facilitate that. And that’s where computing comes in. There are so many possible catalysts that we could use we couldn’t possibly do experiments on
them all. So we do computing actually for two reasons: One reason is it helps us to
round down the list of the really good possible leads, but it’s also the most
powerful possible microscope, so we can actually see the dance of the atoms. So
at the end of the day, the goal is for our work at the Massachusetts Green
High Performance Computing Center to actually give us an affordable, really
sustainable process for making biofuels.

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