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NARRATOR:
We live in a built world.
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Engineering and technology,
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built upon
innovations and inventions,
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stretching back
thousands of years.
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Some of our creations,
like machines,
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boost our bodies' abilities.
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Others help us reach
outside our comfort zones.
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We have left an indelible mark
on the planet.
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And now the time has come
to use our skills
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to make a better world.
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WORKER:
...two, three, lower.
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NARRATOR:
Like inventing a new way
to fly,
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electrically.
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Or a device that can smell...
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ANN PERSON:
I get very excited
when technology works.
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NARRATOR:
...to save food
from going to waste.
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THARINDU MADDUMA:
Food waste is
enormous global problem.
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NARRATOR:
Creating a machine...
RESEARCHER: Rob, I'm going in.
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NARRATOR:
...to heal coral reefs.
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ARAN MOONEY:
How do we fix
the environment that's
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sort of dying in front of us?
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NARRATOR:
Or even combining
a traditional work of art...
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LEWIS STETSON ROWLES:
We see this amazing
opportunity to use pottery.
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NARRATOR:
...with modern chemistry...
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NAVID SALEH:
Could you actually make
something like that?
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Do you have something similar?
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NARRATOR:
...to provide
clean drinking water.
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I made a shape similar to that.
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NARRATOR:
"Building Stuff: Change It!"
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Right now, on "NOVA."
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♪ ♪
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NARRATOR:
Human beings have been
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changing our surroundings
for thousands of years.
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The signs are written
on the land itself.
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We're builders and makers.
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And the evidence
is plain to see.
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ADAM STELTZNER:
Our whole lives are constructed.
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We live in the modern world
in a very altered environment.
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And all of that alteration
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starts and finishes
with engineering.
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ANDREA ARMANI:
Engineering can transform
a community by
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bringing power,
bringing water, growing food.
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DEB CHACHRA:
Taking sewage away, the power
grid, telecommunications,
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these are all
engineering systems
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that are not about
making any one of us
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smarter or stronger or faster,
but making us, collectively,
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have more agency
and more capacity.
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NARRATOR:
But building the modern world
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has come with steep costs
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and changes to more
than just the land,
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like altering the chemical
composition of our atmosphere.
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But now there's a new generation
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that wants to engineer
a cleaner planet.
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So, as an engineer,
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when you see the world as it is,
you begin to think,
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"How could we make it better?"
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So that's our job,
to take the world as it is
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and make it better.
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Everyone's
engineering background,
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it comes from
that purpose of saying,
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"I want to solve a problem
that just changes the world."
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NARRATOR:
One daunting challenge
we face today
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is to reduce
the carbon emissions
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caused by burning fossil fuels.
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Electrifying transportation
offers some hope.
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On the ground,
cars, buses, trucks
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and trains are
gradually making the switch.
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But what about in the air?
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Is there a way to go green
in flight?
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At Joby Aviation
in Marina, California,
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engineers think so.
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They're testing
a new kind of aircraft.
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WILSON:
So, today,
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uh, Joby's flight test team
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is putting the aircraft
through its paces,
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flying range and
endurance missions.
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NARRATOR:
The aircraft is a hybrid--
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like a helicopter,
able to take off vertically,
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but also, like an airplane,
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able to fly horizontally
at high speeds.
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And it's completely electric.
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ARMANI:
The challenge is, you know,
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how do we make
a personal helicopter?
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How do we make them sustainable?
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Right, we don't want to bring
more jet fuel into the world.
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WILSON:
It is routine for us to fly
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three times a day, cruising
around at about 100 knots.
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NARRATOR:
Joby's ultimate dream
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is to deploy the aircraft
in cities around the world
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as flying taxis,
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reducing congestion
on the ground.
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Today they're in
the final testing stages
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of their latest prototype.
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But despite promising results,
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they're not taking chances
with humans on this round.
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WILSON:
There's actually nobody on board
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the aircraft
while it's in flight.
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The pilots are simply
sat on the ground
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in the ground control station,
flying the aircraft remotely.
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NARRATOR:
Technically, it's
known as an EVTOL,
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Electric Vertical
Take Off and Landing.
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But it's also capable
of level, forward flight.
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As we're going through our
airspeed expansion,
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we are testing a,
a certain airspeed,
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performing a bunch of tests
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to make sure
our aircraft is stable,
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and then expanding into
different airspeed regimes
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all the way
to fully wing-borne flights.
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NARRATOR:
This day's testing
is winding down.
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A sudden tilt on touchdown
is quickly corrected
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by the remote pilot.
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Something to tweak
for future flights.
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WILSON:
Our analysts look
at the data after the flight
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to make sure that
the aircraft is performing
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exactly as we expect it to.
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NARRATOR:
As Joby engineers work
to realize their dream,
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significant engineering
challenges remain
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before regular passenger flights
become a reality.
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DARAIO:
As you're trying to develop
transportation devices,
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you really need to understand
the environment
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in which these systems
need to operate
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and iterate the
engineering design,
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the components, the testing
specifically to those needs.
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NARRATOR:
Today, it's not uncommon to see
helicopters in city skies.
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But they have drawbacks.
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They're noisy, the learning
curve to fly them is steep,
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they have limited forward speed,
and they burn fossil fuels.
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Joby's design is an attempt to
address all of those problems.
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VALERO-CUEVAS:
You have identified a problem.
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Can you make an airplane
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that uses propellers like
a helicopter
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but doesn't have
that noise?
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Well, you've dreamt it up.
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The question is,
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how do you actually
bring it into existence?
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WORKER:
All right,
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one, two, three, lower.
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NARRATOR:
One of the biggest challenges
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has been to invent
a new propulsion system.
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The idea was to design a vehicle
for four passengers and a pilot
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that can rise straight
off the ground
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and then somehow transition
to fly like an airplane.
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Joby's solution--
six electric motors
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that can individually pivot,
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propelling the vehicle
up to 200 miles per hour,
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eliminating fossil fuels
and reducing noise,
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a critical improvement
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if they have any hope
of widespread adoption.
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That's what gives the aircraft
its unusual profile.
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Six smaller propellers
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that are quieter than
a single helicopter blade.
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But because they're small,
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everything depended on finding
the right propeller shape,
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a surprisingly
complicated problem,
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part art and part science,
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with much of the know-how
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handed down since the early
pioneers of powered flight.
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These propellers may seem
wholly modern.
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But if we trace their evolution,
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we can see clear connections
to the past.
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Leonardo da Vinci's notebooks
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contain one of the most famous
early conceptualizations
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of a device
resembling the modern propeller.
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Da Vinci, in turn,
may have been inspired by
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the Greek philosopher Archimedes
and his screw-shaped water pump,
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or even by nature.
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Certain plants and seeds,
like the maple and sycamore,
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have evolved similar shapes.
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When they fall from trees,
they look and work
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remarkably
like helicopter blades.
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At Joby, the design team
is looking for the best shape
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to balance power and noise.
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We went through a lot
of experimentation
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with actual propeller,
uh, prototypes.
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We needed to put real work in,
in terms of experiments,
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to really understand
this phenomenon.
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NARRATOR:
To reduce noise, it helps to
understand what causes it.
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As each propeller blade
slices through the air,
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it creates pressure vibrations.
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The strength of those vibrations
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depends in turn
on a propeller's shape,
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how fast it spins
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and the number of blades.
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MIKIC:
So we iterated
with a number of designs.
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We took blades with
a lot of blade area
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and then much thinner blades
and, uh, trying to see
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how that results
in acoustic generation.
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These propellers
are turning much slower
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than traditional
helicopter blades.
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We varied the shape,
a lot of experimentation.
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I think
this trial and error system
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is something that allows us
to ever more refine design,
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produce and, uh, test,
which, in multiple iterations,
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allows us to arrive to,
uh, to optimal solutions.
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NARRATOR:
The company has tested
several blade shapes,
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hoping to find
the best combination
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of efficiency, lightness
and durability.
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To test
each new propeller design,
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the company has built
a large circular track
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in an old quarry
near Santa Cruz.
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MIKIC:
In quarry, we have
what we call "The Whirlybird,"
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which is a track kind of like
a roller coaster track
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that goes around in circles.
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And we have to test
this propeller
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not only in hover conditions,
but through all the conditions
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that it experienced
through transition
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as well as forward flight.
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NARRATOR:
On the track,
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they test each iteration
of the propeller
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for durability and blade design,
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as well as for noise.
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MIKIC:
And then we adjust the angle
of the propeller,
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the speed of the propeller,
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the variable pitch on it
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to see how it operates
in different regimes of flight
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that the real airplane
would experience.
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And we can do this
for hours on end,
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00:11:04,966 --> 00:11:08,600
days on end, uh,
to see how the system performs.
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ARMANI:
The design of a propeller
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is a very theoretically
heavy lift.
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However, at the end of the day,
experimental results rule.
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And their ability
to build that huge test ring
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to really, you know, compare
their experimental results
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with the,
the theoretical predictions
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are really
what allowed them to advance
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and push
their entire plane forward.
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NARRATOR:
Ultimately, they discovered
that their original design,
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which was wider,
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actually performed better than
subsequent slimmer designs.
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The greater surface area
allowed them to slow down
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the propeller's rotation speed,
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00:11:49,466 --> 00:11:52,600
reducing noise while
meeting power requirements.
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00:11:52,600 --> 00:11:53,833
MIKIC:
When you do the experiments,
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you realize you're going down
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00:11:55,100 --> 00:11:56,766
the wrong path, then you
start to go back and see,
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00:11:56,766 --> 00:11:59,466
like, well, why is
the thing that I tried to do
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00:11:59,466 --> 00:12:01,066
that makes things better
actually worse?
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00:12:01,066 --> 00:12:02,666
So you challenge
your own assumptions.
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00:12:04,333 --> 00:12:06,666
DARAIO:
Challenging assumption
is something that
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00:12:06,666 --> 00:12:08,900
is an essential component
in engineering.
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00:12:08,900 --> 00:12:12,666
Being able to harvest the
advances of divergent thinking
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00:12:12,666 --> 00:12:14,233
and creative thinking
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00:12:14,233 --> 00:12:16,500
is something that, in the end,
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00:12:16,500 --> 00:12:19,233
promotes innovation
and allows us
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00:12:19,233 --> 00:12:21,266
to advance technology
much faster.
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00:12:21,266 --> 00:12:24,100
NARRATOR:
A change to the shape
of the propeller
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00:12:24,100 --> 00:12:25,966
helps with
the nature of turbulence
248
00:12:25,966 --> 00:12:28,433
generated by the blade.
249
00:12:28,433 --> 00:12:29,766
Exactly how they did it,
250
00:12:29,766 --> 00:12:34,433
a Joby representative said,
is a trade secret.
251
00:12:34,433 --> 00:12:38,400
But the result is a vehicle
that the company says
252
00:12:38,400 --> 00:12:44,566
produces 100 times less acoustic
power than a helicopter.
253
00:12:47,133 --> 00:12:50,066
Eventually, they're hoping
to expand their test program
254
00:12:50,066 --> 00:12:52,100
to include passengers
255
00:12:52,100 --> 00:12:54,533
and move toward
full certification
256
00:12:54,533 --> 00:12:58,633
from the
Federal Aviation Administration.
257
00:12:58,633 --> 00:13:01,566
DIDIER PAPADOPOULOS:
Safety is non-negotiable.
258
00:13:01,566 --> 00:13:04,633
Look, I'm gonna put my kids on
these airplanes,
259
00:13:04,633 --> 00:13:06,566
and so this is, this is
close to me,
260
00:13:06,566 --> 00:13:08,200
just as it is close to
everybody else.
261
00:13:11,900 --> 00:13:13,133
WILSON:
Now being able to travel
262
00:13:13,133 --> 00:13:16,333
routinely with an aircraft
like this,
263
00:13:16,333 --> 00:13:18,733
and be able to do it
relatively low cost
264
00:13:18,733 --> 00:13:23,133
and super available
to the masses, is so exciting.
265
00:13:24,166 --> 00:13:27,833
NARRATOR:
Today, air travel accounts
for an estimated 10%
266
00:13:27,833 --> 00:13:31,733
of the carbon produced
by all transportation.
267
00:13:31,733 --> 00:13:33,500
It's this kind of
experimentation
268
00:13:33,500 --> 00:13:38,100
that could lead to
bigger changes in air travel.
269
00:13:38,100 --> 00:13:40,533
Electrifying aviation
is one of the hardest
270
00:13:40,533 --> 00:13:42,866
engineering challenges we face.
271
00:13:42,866 --> 00:13:47,533
But not every problem requires
such a difficult solution.
272
00:13:47,533 --> 00:13:51,200
When it comes to finding ways
to reduce carbon emissions,
273
00:13:51,200 --> 00:13:54,833
there is some
lower-hanging fruit.
274
00:13:56,433 --> 00:13:58,000
Over thousands of years,
275
00:13:58,000 --> 00:13:59,600
we've gotten
more and more efficient
276
00:13:59,600 --> 00:14:02,800
at growing food
for an ever-growing population.
277
00:14:04,466 --> 00:14:08,333
But the road from farm to table
can be long and wasteful.
278
00:14:09,333 --> 00:14:11,766
Globally,
a third of all crops go bad
279
00:14:11,766 --> 00:14:14,066
before they reach the table.
280
00:14:14,066 --> 00:14:16,600
And with food production
accounting for about 30%
281
00:14:16,600 --> 00:14:18,866
of global
greenhouse gas emissions,
282
00:14:18,866 --> 00:14:21,800
reducing food waste
could be one solution
283
00:14:21,800 --> 00:14:23,533
to our climate problem.
284
00:14:23,533 --> 00:14:28,566
At least, that's the idea behind
a Norwegian rot-sniffing robot.
285
00:14:31,366 --> 00:14:33,500
The BAMA food warehouse
in Oslo, Norway.
286
00:14:36,300 --> 00:14:41,066
NARRATOR:
Anne Person is the director
of quality assurance.
287
00:14:41,066 --> 00:14:43,100
We get about 2,000 pallets
288
00:14:43,100 --> 00:14:44,400
in here every night.
289
00:14:45,566 --> 00:14:47,600
NARRATOR:
The produce comes in
from 80 countries.
290
00:14:48,533 --> 00:14:50,233
They're being scanned here.
291
00:14:50,233 --> 00:14:53,766
And then they go straight
to the quality control tower.
292
00:14:53,766 --> 00:14:56,600
This is the first control
that is being done
293
00:14:56,600 --> 00:14:59,066
when it comes to Norway.
294
00:14:59,066 --> 00:15:01,166
NARRATOR:
Inspectors screen the produce
295
00:15:01,166 --> 00:15:04,733
for spoilage, as best they can,
before sending it
296
00:15:04,733 --> 00:15:05,933
to the supermarket.
297
00:15:07,300 --> 00:15:08,666
The problem is we don't have
very much time
298
00:15:08,666 --> 00:15:09,766
to inspect the
pallets.
299
00:15:09,766 --> 00:15:11,400
It's maximum
60 seconds.
300
00:15:13,500 --> 00:15:16,900
And also, due to the setup
of the quality stations,
301
00:15:16,900 --> 00:15:20,700
we are only able to control
the two upper layers, maximum.
302
00:15:22,133 --> 00:15:24,700
NARRATOR:
That means,
even with experience,
303
00:15:24,700 --> 00:15:26,900
visual inspection
only goes so far.
304
00:15:26,900 --> 00:15:31,066
Inevitably, some spoiled produce
goes undetected
305
00:15:31,066 --> 00:15:34,466
and gets shipped along
with the rest of the produce
306
00:15:34,466 --> 00:15:37,166
all over Norway
to local supermarkets.
307
00:15:38,266 --> 00:15:39,566
PERSON:
So our question was,
308
00:15:39,566 --> 00:15:42,400
how can we check
the whole pallets?
309
00:15:42,400 --> 00:15:45,366
So that's when we started
to look at the new technology.
310
00:15:46,833 --> 00:15:51,133
The goal is increased freshness
and reduced food waste.
311
00:15:51,133 --> 00:15:54,200
If you can detect spoilage
earlier in the value chain,
312
00:15:54,200 --> 00:15:55,533
we are also able to do more
313
00:15:55,533 --> 00:15:57,266
with the products
that we might reject.
314
00:15:57,266 --> 00:16:01,100
We can sort them,
we can give them to food banks.
315
00:16:02,266 --> 00:16:07,266
NARRATOR:
BAMA connected with Tunable,
a small tech company in Oslo,
316
00:16:07,266 --> 00:16:09,200
inventors of an artificial nose,
317
00:16:09,200 --> 00:16:11,833
or machine olfaction device,
318
00:16:11,833 --> 00:16:14,266
that is already
in use monitoring
319
00:16:14,266 --> 00:16:16,300
the amount of greenhouse gasses
320
00:16:16,300 --> 00:16:18,233
emitted by container ships.
321
00:16:18,233 --> 00:16:23,166
Tharindu Madduma is Tunable's
business development manager.
322
00:16:23,166 --> 00:16:25,000
MADDUMA:
BAMA came to us.
323
00:16:25,000 --> 00:16:26,800
They explained
that they had this problem
324
00:16:26,800 --> 00:16:29,966
of determining the quality
of the fruits and vegetables,
325
00:16:29,966 --> 00:16:33,300
being able to do it at
a large scale
326
00:16:33,300 --> 00:16:34,566
and being accurate.
327
00:16:34,566 --> 00:16:36,600
VALERO-CUEVAS:
There's a long history
328
00:16:36,600 --> 00:16:38,433
of inventions
329
00:16:38,433 --> 00:16:41,000
that allow us to extend
our senses.
330
00:16:41,000 --> 00:16:43,433
So we've done that
for sight.
331
00:16:43,433 --> 00:16:45,166
We've done that for hearing.
332
00:16:46,166 --> 00:16:48,800
MADDUMA:
So, we have microscopes,
we have hearing aid,
333
00:16:48,800 --> 00:16:51,533
but smell is still a sense
334
00:16:51,533 --> 00:16:53,133
that we haven't digitalized.
335
00:16:53,133 --> 00:16:55,000
And that's what we're doing.
336
00:16:55,000 --> 00:16:58,300
NARRATOR:
Kristian Hovet
is Tunable's C.E.O.
337
00:16:58,300 --> 00:16:59,533
HOVET:
When you take a breath,
338
00:16:59,533 --> 00:17:01,766
you're doing a
multi-gas analysis.
339
00:17:01,766 --> 00:17:03,100
You're pulling in
molecules,
340
00:17:03,100 --> 00:17:06,033
and those molecules are detected
by your nose,
341
00:17:06,033 --> 00:17:08,266
and then it's
detected by your brain
342
00:17:08,266 --> 00:17:09,633
to tell you
what you're smelling.
343
00:17:09,633 --> 00:17:11,733
NARRATOR:
The challenge for Tunable
344
00:17:11,733 --> 00:17:15,500
was to take their existing
analyzer for emission analysis
345
00:17:15,500 --> 00:17:17,400
and increase its sensitivity
346
00:17:17,400 --> 00:17:20,300
without making the device
too big and cumbersome
347
00:17:20,300 --> 00:17:23,366
to be useful
on a warehouse floor.
348
00:17:23,366 --> 00:17:25,966
So why use smell?
349
00:17:27,100 --> 00:17:29,366
Our noses are
sensitive detectors,
350
00:17:29,366 --> 00:17:31,733
able to identify a wide variety
351
00:17:31,733 --> 00:17:35,766
of chemicals in the air,
even at low concentrations.
352
00:17:35,766 --> 00:17:39,266
Airborne molecules
can also potentially reveal
353
00:17:39,266 --> 00:17:41,333
what's hidden in the pallets.
354
00:17:41,333 --> 00:17:44,800
These molecules tell
a chemical story
355
00:17:44,800 --> 00:17:48,100
of fruits and vegetables
as they rot.
356
00:17:48,100 --> 00:17:51,166
But the device would have to be
far more sensitive
357
00:17:51,166 --> 00:17:53,866
than a human nose,
and able to detect spoilage
358
00:17:53,866 --> 00:17:58,300
more reliably than a human eye.
359
00:17:58,300 --> 00:18:01,200
Produce, like all living things,
360
00:18:01,200 --> 00:18:02,833
decays after death
361
00:18:02,833 --> 00:18:05,666
as microbes consume dead cells,
362
00:18:05,666 --> 00:18:08,966
releasing
volatile organic compounds.
363
00:18:08,966 --> 00:18:12,466
In theory, the team should be
able to tune their machine
364
00:18:12,466 --> 00:18:15,600
to recognize those molecules.
365
00:18:15,600 --> 00:18:18,600
We knew that we could look
at complex gasses.
366
00:18:20,466 --> 00:18:25,400
We redesigned emission analyzer,
and then we started testing.
367
00:18:26,933 --> 00:18:29,600
NARRATOR:
Eivind Jülke Røer
368
00:18:29,600 --> 00:18:32,933
is the lead engineer on
the Tunable e-nose project.
369
00:18:32,933 --> 00:18:35,500
RØER:
So now I'm going
to measure fresh grapes
370
00:18:35,500 --> 00:18:37,033
and then some spoiled grapes.
371
00:18:37,033 --> 00:18:38,766
See our e-nose can smell
the difference.
372
00:18:38,766 --> 00:18:39,866
I'll start with collecting
373
00:18:39,866 --> 00:18:41,900
a sample from
the ambient air
374
00:18:41,900 --> 00:18:43,466
as a baseline for
the measurement.
375
00:18:43,466 --> 00:18:45,233
(machine whirring)
376
00:18:46,766 --> 00:18:49,466
And the noise you can hear now
is actually the compressor pump
377
00:18:49,466 --> 00:18:52,833
pulling air, uh,
into the analyzer.
378
00:18:54,766 --> 00:18:57,666
So now I'm going to take a
sample from the fresh grapes
379
00:18:57,666 --> 00:19:00,900
to see if there is anything
present there.
380
00:19:00,900 --> 00:19:02,233
NARRATOR:
The probe pulls in air
381
00:19:02,233 --> 00:19:03,733
and then compresses it
382
00:19:03,733 --> 00:19:05,400
by a factor of five,
383
00:19:05,400 --> 00:19:07,733
which increases
the density of the sample
384
00:19:07,733 --> 00:19:11,233
and makes molecules
easier to detect.
385
00:19:11,233 --> 00:19:14,633
Next, infrared light
shines through the sample.
386
00:19:14,633 --> 00:19:16,800
The light then passes
through a chip
387
00:19:16,800 --> 00:19:19,166
that sorts different types
of molecules
388
00:19:19,166 --> 00:19:21,000
based on
the specific wavelengths
389
00:19:21,000 --> 00:19:23,133
of light they absorb,
390
00:19:23,133 --> 00:19:25,266
which ultimately
allows the analyzer
391
00:19:25,266 --> 00:19:28,100
and accompanying software
to reliably detect
392
00:19:28,100 --> 00:19:29,633
the presence and concentration
393
00:19:29,633 --> 00:19:31,733
of molecules
that signal spoilage
394
00:19:31,733 --> 00:19:34,200
with extreme sensitivity.
395
00:19:34,200 --> 00:19:35,966
RØER:
The reading I got now
396
00:19:35,966 --> 00:19:38,433
doesn't really show
any molecules present at all
397
00:19:38,433 --> 00:19:40,433
compared to ambient air,
398
00:19:40,433 --> 00:19:43,466
which is more or less what I
would expect from fresh fruit.
399
00:19:43,466 --> 00:19:45,266
(machine whirring)
400
00:19:45,266 --> 00:19:47,566
So now I'm going
to take a sample
401
00:19:47,566 --> 00:19:49,566
for the, um, spoiled grapes.
402
00:19:49,566 --> 00:19:50,866
We see a clear difference.
403
00:19:50,866 --> 00:19:53,200
We see up to 12% absorption
404
00:19:53,200 --> 00:19:56,066
at ethanol wavelength,
which is a good indication
405
00:19:56,066 --> 00:19:58,000
that we actually smell
the rotten grapes.
406
00:19:58,000 --> 00:20:00,633
So, uh, this looks
really promising.
407
00:20:01,633 --> 00:20:03,266
HOVET:
The fumes we were able
to collect,
408
00:20:03,266 --> 00:20:05,666
we were able to see the,
the kind of the signatures.
409
00:20:05,666 --> 00:20:07,633
NARRATOR:
The engineers then tested
410
00:20:07,633 --> 00:20:10,733
different kinds of
fruits and vegetables
411
00:20:10,733 --> 00:20:15,633
as they decayed, building up a
database of chemical profiles.
412
00:20:15,633 --> 00:20:20,533
HOVET:
We saw a tomato was different,
somewhat, from a banana.
413
00:20:21,666 --> 00:20:26,033
Grapes were different
from avocado, for example.
414
00:20:27,066 --> 00:20:30,500
And we thought, well,
this must be interesting.
415
00:20:30,500 --> 00:20:32,133
(laughs)
416
00:20:32,133 --> 00:20:33,400
(compressed air can sprays)
417
00:20:33,400 --> 00:20:34,800
NARRATOR:
Thor Bakke
418
00:20:34,800 --> 00:20:38,000
is the founder and Chief
Technology Officer of Tunable.
419
00:20:38,000 --> 00:20:41,566
He's been working with
microelectromechanical systems
420
00:20:41,566 --> 00:20:43,433
for over 30 years.
421
00:20:43,433 --> 00:20:47,166
BAKKE:
Tunable is a component,
uh, inside our analyzers.
422
00:20:47,166 --> 00:20:50,133
That's the Tunable filter.
423
00:20:50,133 --> 00:20:51,966
It's used to change the
wavelength of light
424
00:20:51,966 --> 00:20:54,166
so we can scan the wavelength
and do spectroscopy.
425
00:20:54,166 --> 00:20:56,833
(radio playing static
between stations)
426
00:20:56,833 --> 00:20:58,733
Spectroscopy is
very much like, uh,
427
00:20:58,733 --> 00:21:01,766
tuning a radio to find
a particular station.
428
00:21:01,766 --> 00:21:04,866
The gasses are separated
in the infrared spectrum,
429
00:21:04,866 --> 00:21:06,433
just like radio stations.
430
00:21:06,433 --> 00:21:08,766
And then you can basically
detect each one of them.
431
00:21:08,766 --> 00:21:11,500
So that's where the word
Tunable comes from.
432
00:21:12,566 --> 00:21:15,400
NARRATOR:
After extensive fine tuning
in the lab,
433
00:21:15,400 --> 00:21:19,133
it's time for the very first
field test in the warehouse.
434
00:21:20,166 --> 00:21:21,433
STELTZNER:
Sometimes you can't learn
435
00:21:21,433 --> 00:21:23,333
about all of the variables
436
00:21:23,333 --> 00:21:25,333
that will be involved
in an engineered system
437
00:21:25,333 --> 00:21:27,533
sitting on a desk
438
00:21:27,533 --> 00:21:30,033
with a pen and paper or at
a computer screen.
439
00:21:30,033 --> 00:21:31,333
You need to go out
into the field.
440
00:21:31,333 --> 00:21:33,533
You need to put it
in the actual environment
441
00:21:33,533 --> 00:21:36,766
and see how it interacts,
learn from that, make changes,
442
00:21:36,766 --> 00:21:38,300
and move forward.
443
00:21:38,300 --> 00:21:41,600
RØER:
Now I'm capturing; I'm in there.
444
00:21:41,600 --> 00:21:44,966
Now I'm ready to do
the measurement on the grapes.
445
00:21:47,300 --> 00:21:49,500
NARRATOR:
Eivind watches the screen,
446
00:21:49,500 --> 00:21:53,400
waiting to see
the telltale grape waveform.
447
00:21:53,400 --> 00:21:56,566
But the pump just whirrs away.
448
00:21:56,566 --> 00:21:59,600
And eventually he gives up.
449
00:22:01,933 --> 00:22:04,866
Uh, I don't really know
what happened here.
450
00:22:04,866 --> 00:22:07,400
Uh...
451
00:22:07,400 --> 00:22:11,366
For some reason, um,
the results wasn't as expected.
452
00:22:11,366 --> 00:22:14,466
NARRATOR:
The first time
definitely wasn't the charm.
453
00:22:14,466 --> 00:22:15,766
Murphy's law.
454
00:22:15,766 --> 00:22:17,066
Yeah.
455
00:22:18,133 --> 00:22:20,700
HOVET:
We know that it works in a
laboratory environment.
456
00:22:20,700 --> 00:22:22,633
So the big thing now
457
00:22:22,633 --> 00:22:24,700
is showing that
it actually works...
458
00:22:24,700 --> 00:22:29,166
(chuckling):
...in real life, and as you see,
there's been some challenges.
459
00:22:30,166 --> 00:22:32,100
CHACHRA:
We tend to think of failure
as a bad thing, right?
460
00:22:32,100 --> 00:22:34,800
That something that is not
supposed to happen, happens.
461
00:22:34,800 --> 00:22:37,333
But if you're doing
anything new,
462
00:22:37,333 --> 00:22:39,700
failure is an integral
part of the process.
463
00:22:39,700 --> 00:22:42,200
And the reason for that
is because we can't
464
00:22:42,200 --> 00:22:44,633
perfectly predict or understand
how things are gonna work
465
00:22:44,633 --> 00:22:46,266
in the real world
until we try them.
466
00:22:47,566 --> 00:22:50,233
NARRATOR:
Turns out
the warehouse temperature,
467
00:22:50,233 --> 00:22:52,600
a chilly 41 degrees Fahrenheit,
468
00:22:52,600 --> 00:22:55,866
affected the test result.
469
00:22:55,866 --> 00:22:57,533
HOVET:
The cold part.
470
00:22:57,533 --> 00:23:01,033
We did know that
it was cold in that area,
471
00:23:01,033 --> 00:23:03,833
but did we take it
on account enough?
472
00:23:03,833 --> 00:23:05,166
No, we didn't.
473
00:23:05,166 --> 00:23:07,400
We should, of course,
have thought about that.
474
00:23:07,400 --> 00:23:09,766
But, uh, but that's
the kind of the learning,
475
00:23:09,766 --> 00:23:10,800
that's the process.
476
00:23:12,766 --> 00:23:13,933
NARRATOR:
Back in the lab,
477
00:23:13,933 --> 00:23:17,000
the Tunable team
recalibrated their chip
478
00:23:17,000 --> 00:23:20,066
to account for
the BAMA warehouse temperature.
479
00:23:20,066 --> 00:23:22,433
They also adjusted the design
480
00:23:22,433 --> 00:23:25,833
to include the pumps
that compress the sample,
481
00:23:25,833 --> 00:23:27,566
increasing the density
of the gas
482
00:23:27,566 --> 00:23:29,933
to compensate for
the lower metabolic rate
483
00:23:29,933 --> 00:23:33,000
of the food in
the refrigerated environment.
484
00:23:34,200 --> 00:23:36,300
RØER:
It will be
really interesting to see
485
00:23:36,300 --> 00:23:38,900
if the alterations
we have, uh, made,
486
00:23:38,900 --> 00:23:40,266
will actually do
the difference in the field.
487
00:23:41,633 --> 00:23:45,733
NARRATOR:
Eivind is back with the latest
iteration of the e-nose.
488
00:23:45,733 --> 00:23:47,733
Further testing in the lab
489
00:23:47,733 --> 00:23:49,500
showed that,
even with the changes,
490
00:23:49,500 --> 00:23:53,733
the machine needs time
to adjust to the conditions
491
00:23:53,733 --> 00:23:55,266
in the warehouse.
492
00:23:59,366 --> 00:24:01,900
RØER:
Now, I'll let the instrument
stay here for the night
493
00:24:01,900 --> 00:24:03,600
to reach a steady temperature,
494
00:24:03,600 --> 00:24:05,200
and then we'll do
measurements tomorrow.
495
00:24:08,666 --> 00:24:13,400
♪ ♪
496
00:24:13,400 --> 00:24:15,933
Well, after a long cold night,
497
00:24:15,933 --> 00:24:17,566
the system
should be ready to go.
498
00:24:17,566 --> 00:24:22,066
(machine whirring)
499
00:24:27,100 --> 00:24:28,733
Now we see absorption of light
500
00:24:28,733 --> 00:24:31,700
at more or less 9.5,
ten microns,
501
00:24:31,700 --> 00:24:34,633
which, um, indicate
ethanol being present.
502
00:24:34,633 --> 00:24:37,966
This really shows that
our new chip is working
503
00:24:37,966 --> 00:24:41,133
in this real environment.
504
00:24:41,133 --> 00:24:43,233
NARRATOR:
Eivind uses the e-nose
505
00:24:43,233 --> 00:24:45,566
to sample the air
from various locations
506
00:24:45,566 --> 00:24:47,766
on the entire pallet stack.
507
00:24:47,766 --> 00:24:52,166
RØER:
Actually, we see a spike at the
ethanol absorption wavelength,
508
00:24:52,166 --> 00:24:53,733
so that might be something.
509
00:24:53,733 --> 00:24:56,366
NARRATOR:
They've taken an important step.
510
00:24:56,366 --> 00:24:58,200
A successful real-world test
511
00:24:58,200 --> 00:25:02,733
of the newest version
of the Tunable e-nose.
512
00:25:03,700 --> 00:25:05,700
I'm not the most
excited guy, but, um...
513
00:25:05,700 --> 00:25:07,666
(giggles)
514
00:25:07,666 --> 00:25:09,233
...this is, uh,
this is exciting.
515
00:25:09,233 --> 00:25:11,500
(e-nose humming)
516
00:25:11,500 --> 00:25:14,733
I expected it,
although you never know.
517
00:25:14,733 --> 00:25:15,933
It's a big win.
518
00:25:18,133 --> 00:25:20,300
I get very excited when
technology works.
519
00:25:21,900 --> 00:25:25,666
NARRATOR:
Still, there is work ahead to
make the technology viable
520
00:25:25,666 --> 00:25:29,000
and, most importantly, scalable.
521
00:25:29,000 --> 00:25:32,300
MADUMMA:
We hope that we can
make them more efficient.
522
00:25:32,300 --> 00:25:35,600
Food waste is enormous global
problem.
523
00:25:35,600 --> 00:25:40,300
8% of all greenhouse gasses
comes from food waste.
524
00:25:40,300 --> 00:25:44,600
So if we can be a part
of the solution, it's huge.
525
00:25:46,466 --> 00:25:49,200
NARRATOR:
Reducing food waste
is one of many ways
526
00:25:49,200 --> 00:25:52,533
engineers are trying
to slow climate change.
527
00:25:54,033 --> 00:25:56,100
But the negative changes
we've made to our climate
528
00:25:56,100 --> 00:25:59,200
are already damaging
some environments
529
00:25:59,200 --> 00:26:01,500
like coral reefs.
530
00:26:01,500 --> 00:26:03,200
MOONEY:
Coral reefs are in decline.
531
00:26:03,200 --> 00:26:04,933
So one of the things
that I really think about
532
00:26:04,933 --> 00:26:06,200
is how do we fix
the environment
533
00:26:06,200 --> 00:26:07,833
that's sort of dying
in front of us?
534
00:26:09,600 --> 00:26:13,033
NARRATOR:
Healthy coral reefs can be
stunningly beautiful
535
00:26:13,033 --> 00:26:16,233
and play a critical role
in coastal ecosystems.
536
00:26:16,233 --> 00:26:20,700
They harbor a tremendous
diversity of marine life
537
00:26:20,700 --> 00:26:22,100
and contribute
to the overall health
538
00:26:22,100 --> 00:26:25,633
of the world's oceans
and their coastlines.
539
00:26:25,633 --> 00:26:29,233
A quarter of all marine species
depend on them for survival.
540
00:26:29,233 --> 00:26:32,566
They're also important
to humans.
541
00:26:32,566 --> 00:26:34,600
Often located in shallow water,
542
00:26:34,600 --> 00:26:36,900
they can protect coastal
communities
543
00:26:36,900 --> 00:26:39,633
from damaging storm surges.
544
00:26:39,633 --> 00:26:42,400
And the reefs host a primary,
sustainable food source
545
00:26:42,400 --> 00:26:46,733
for hundreds of millions of
people around the world.
546
00:26:46,733 --> 00:26:49,666
But as the oceans warm,
547
00:26:49,666 --> 00:26:53,033
corals are struggling
to survive.
548
00:26:53,033 --> 00:26:56,466
Excessive heat drives away
the microscopic algae
549
00:26:56,466 --> 00:26:59,200
the coral depend on.
550
00:26:59,200 --> 00:27:01,066
That leads to
a dramatic loss of color,
551
00:27:01,066 --> 00:27:03,300
known as coral bleaching--
552
00:27:03,300 --> 00:27:07,266
a powerful visual indicator
of an unhealthy reef.
553
00:27:07,266 --> 00:27:12,133
But bleaching isn't the only
indicator of a reef in peril...
554
00:27:12,133 --> 00:27:13,500
MOONEY:
Not only it looks brown
555
00:27:13,500 --> 00:27:15,200
and is lacking these beautiful,
vibrant colors,
556
00:27:15,200 --> 00:27:16,700
but it just sounds dead.
557
00:27:16,700 --> 00:27:18,466
(underwater ambient noise)
558
00:27:18,466 --> 00:27:23,333
NARRATOR:
That's where sensory biologist
Aran Mooney comes in.
559
00:27:23,333 --> 00:27:26,366
MOONEY:
My background is in hearing
and in bioacoustics.
560
00:27:26,366 --> 00:27:29,166
And I study how animals
perceive the world around them.
561
00:27:29,166 --> 00:27:30,866
(wildlife chittering)
562
00:27:30,866 --> 00:27:32,966
Coral reefs are kind of
rainforest of the sea,
563
00:27:32,966 --> 00:27:34,666
and just like
a really rich forest
564
00:27:34,666 --> 00:27:36,166
might have
a lot of birds calling,
565
00:27:36,166 --> 00:27:38,433
and you might hear the monkeys
calling in the background,
566
00:27:38,433 --> 00:27:41,066
coral reefs are really the same.
567
00:27:41,066 --> 00:27:42,666
So basically
a healthy coral reef
568
00:27:42,666 --> 00:27:44,366
has a really
healthy rich soundscape.
569
00:27:44,366 --> 00:27:46,633
(crackling, snapping)
570
00:27:46,633 --> 00:27:49,100
NARRATOR:
Snapping shrimp, lobster,
and fish
571
00:27:49,100 --> 00:27:53,000
create a symphony indicative
of a biodiverse community.
572
00:27:54,766 --> 00:27:58,333
MOONEY:
And a degraded coral reef is
just an impoverished soundscape.
573
00:27:58,333 --> 00:28:01,466
It sounds quiet,
kind of desolate.
574
00:28:01,466 --> 00:28:03,166
So, by listening to
the soundscape,
575
00:28:03,166 --> 00:28:05,033
we can kind of
track that biodiversity
576
00:28:05,033 --> 00:28:07,200
and understand when
that change is happening.
577
00:28:07,200 --> 00:28:08,800
♪ ♪
578
00:28:08,800 --> 00:28:11,733
NARRATOR:
Off the coast of St. John
in the Caribbean,
579
00:28:11,733 --> 00:28:14,633
a team from the Woods Hole
Oceanographic Institution
580
00:28:14,633 --> 00:28:16,066
in Massachusetts
581
00:28:16,066 --> 00:28:18,100
conducts bleaching surveys,
582
00:28:18,100 --> 00:28:21,366
finding evidence of
degraded reefs.
583
00:28:21,366 --> 00:28:23,233
(water splashing)
584
00:28:23,233 --> 00:28:25,033
To your right,
there's some bleached coral.
585
00:28:28,200 --> 00:28:30,666
You knew there's going to be
bleaching here, right?
586
00:28:30,666 --> 00:28:33,600
But then it's freaking
everywhere, right?
587
00:28:33,600 --> 00:28:34,733
YOGI GIRDHAR:
I've been coming here
588
00:28:34,733 --> 00:28:35,933
five or six years now,
589
00:28:35,933 --> 00:28:38,833
this was the first time
590
00:28:38,833 --> 00:28:40,533
I have seen such bleaching.
591
00:28:40,533 --> 00:28:44,000
NARRATOR:
Yogi Girdhar is a roboticist
592
00:28:44,000 --> 00:28:46,300
and computer scientist
at Woods Hole.
593
00:28:46,300 --> 00:28:47,300
GIRDHAR:
I am working on
594
00:28:47,300 --> 00:28:48,866
robots and A.I.
595
00:28:48,866 --> 00:28:50,133
and machine learning-based
techniques
596
00:28:50,133 --> 00:28:54,233
to understand complex ecosystems
in the ocean,
597
00:28:54,233 --> 00:28:55,366
such as coral reefs.
598
00:28:58,800 --> 00:29:00,533
NARRATOR:
A question they pose:
599
00:29:00,533 --> 00:29:03,700
is it possible to build a robot
600
00:29:03,700 --> 00:29:05,566
that can seek out and
find healthy reefs
601
00:29:05,566 --> 00:29:06,900
on its own?
(electronic beeping)
602
00:29:06,900 --> 00:29:09,166
If they succeed,
603
00:29:09,166 --> 00:29:12,533
the robot could provide an
efficient and cost-effective way
604
00:29:12,533 --> 00:29:16,066
to find healthy coral reefs,
map them,
605
00:29:16,066 --> 00:29:18,700
and monitor their health.
606
00:29:18,700 --> 00:29:21,366
(electronic crackling)
607
00:29:21,366 --> 00:29:23,466
The soundscapes recorded
by the robot
608
00:29:23,466 --> 00:29:26,833
could be a vital tool
in diagnosing reef health
609
00:29:26,833 --> 00:29:30,366
and tracking decline
or improvement.
610
00:29:30,366 --> 00:29:31,966
♪ ♪
611
00:29:31,966 --> 00:29:34,433
MOONEY:
Good job, team!
612
00:29:34,433 --> 00:29:36,900
♪ ♪
613
00:29:36,900 --> 00:29:40,166
NARRATOR:
The team has been collecting
data on reefs
614
00:29:40,166 --> 00:29:42,266
for over a decade.
615
00:29:42,266 --> 00:29:43,400
You're going through this.
Yeah.
616
00:29:43,400 --> 00:29:45,500
I might be able to thread
it through here.
617
00:29:45,500 --> 00:29:47,633
NARRATOR:
They have mountains
of information;
618
00:29:47,633 --> 00:29:50,566
including audio and video.
619
00:29:50,566 --> 00:29:53,500
They've even created 3D models
of the reefs
620
00:29:53,500 --> 00:29:55,200
for further study.
621
00:29:55,200 --> 00:29:56,400
Helping them gather this data
622
00:29:56,400 --> 00:30:00,200
is this third-generation robot.
623
00:30:00,200 --> 00:30:01,833
GIRDHAR:
We call it CUREE--
624
00:30:01,833 --> 00:30:04,533
C-U-R-E-E.
625
00:30:04,533 --> 00:30:08,833
It stands for Curious Underwater
Robot for Ecosystem Exploration.
626
00:30:08,833 --> 00:30:12,900
NARRATOR:
It's equipped with sensors,
microphones, and cameras
627
00:30:12,900 --> 00:30:15,266
and is still very much
under development.
628
00:30:15,266 --> 00:30:17,300
GIRDHAR:
The design of a robot
629
00:30:17,300 --> 00:30:18,566
is always evolving.
630
00:30:18,566 --> 00:30:20,166
Our robot is never finished.
631
00:30:20,166 --> 00:30:22,666
NARRATOR:
It's an engineering challenge
632
00:30:22,666 --> 00:30:24,733
with a lot of moving parts.
633
00:30:24,733 --> 00:30:28,366
So they've broken it down
into many small steps.
634
00:30:28,366 --> 00:30:29,733
MARIA YANG:
There are many, many problems
635
00:30:29,733 --> 00:30:32,466
that you can solve
with an engineering solution.
636
00:30:32,466 --> 00:30:33,666
But I think you have to
637
00:30:33,666 --> 00:30:34,900
really understand what
the problem is
638
00:30:34,900 --> 00:30:36,500
and sort of pick the two
or three
639
00:30:36,500 --> 00:30:38,533
that really you want
to address.
640
00:30:38,533 --> 00:30:40,533
Otherwise, you kind of fall into
this trap of
641
00:30:40,533 --> 00:30:41,866
trying to solve all
the problems all at once
642
00:30:41,866 --> 00:30:43,166
and you run out of resources.
643
00:30:43,166 --> 00:30:44,866
♪ ♪
644
00:30:44,866 --> 00:30:48,000
NARRATOR:
This morning, the team is
prepping for its latest test
645
00:30:48,000 --> 00:30:50,300
right off the dock.
646
00:30:50,300 --> 00:30:51,300
MOONEY:
All right, Dr. Girdhar.
647
00:30:51,300 --> 00:30:53,900
Are you ready?
648
00:30:53,900 --> 00:30:54,933
Always.
649
00:30:59,966 --> 00:31:02,500
GIRDHAR:
I'll manage the tether.
Got it?
650
00:31:02,500 --> 00:31:06,133
NARRATOR:
To start, they'll place a
speaker on the ocean floor,
651
00:31:06,133 --> 00:31:10,000
playing a recording
of a healthy coral reef.
652
00:31:10,000 --> 00:31:11,600
A sound file they captured
653
00:31:11,600 --> 00:31:12,833
from a previous trip.
654
00:31:13,833 --> 00:31:14,866
SETH McCAMMON:
It should be on.
655
00:31:14,866 --> 00:31:15,866
GIRDHAR: Yeah.
All right.
656
00:31:15,866 --> 00:31:16,866
We hear it.
657
00:31:16,866 --> 00:31:18,600
(electronic crackling)
658
00:31:18,600 --> 00:31:19,766
NARRATOR:
They're hoping the robot
659
00:31:19,766 --> 00:31:22,266
will recognize the sound
through the water
660
00:31:22,266 --> 00:31:24,066
and be able to record it.
661
00:31:27,300 --> 00:31:28,566
In this outing,
662
00:31:28,566 --> 00:31:31,300
the robot is
not moving autonomously.
663
00:31:31,300 --> 00:31:33,366
Researcher Seth McCammon
664
00:31:33,366 --> 00:31:34,900
is operating the robot remotely
665
00:31:34,900 --> 00:31:39,166
to steer and position it
for the test.
666
00:31:39,166 --> 00:31:41,033
I'm getting it in line
with the thing
667
00:31:41,033 --> 00:31:43,066
so we can start to look
at the data.
668
00:31:45,166 --> 00:31:47,000
GIRDHAR:
If the robot doesn't work
with this sound,
669
00:31:47,000 --> 00:31:50,000
it's probably not going to work
on the real coral reef,
670
00:31:50,000 --> 00:31:52,433
so it's a good, good test.
671
00:31:52,433 --> 00:31:55,566
NARRATOR:
Experimenting with sound
underwater
672
00:31:55,566 --> 00:31:58,700
is not a new idea.
673
00:31:58,700 --> 00:32:00,566
In the 1800s,
674
00:32:00,566 --> 00:32:03,366
a Swiss physicist
and a French mathematician,
675
00:32:03,366 --> 00:32:05,666
armed with a bell and stopwatch,
676
00:32:05,666 --> 00:32:09,600
measured the speed at which
sound traveled underwater.
677
00:32:09,600 --> 00:32:11,633
On one side of Lake Geneva,
678
00:32:11,633 --> 00:32:14,866
Charles François Sturm
rang a submerged bell,
679
00:32:14,866 --> 00:32:17,400
(bell ringing)
while Jean-Daniel Colladon
680
00:32:17,400 --> 00:32:21,300
used a long tube to listen
underwater across the lake...
681
00:32:21,300 --> 00:32:22,933
(watch clicks)
...pressing his stopwatch
682
00:32:22,933 --> 00:32:24,466
to keep track of how long
it took
683
00:32:24,466 --> 00:32:27,166
the sound to travel across.
684
00:32:27,166 --> 00:32:29,433
Surprisingly,
they found that water
685
00:32:29,433 --> 00:32:32,433
is a better conduit for sound
than air.
686
00:32:32,433 --> 00:32:34,666
Sound travels through water
687
00:32:34,666 --> 00:32:36,433
roughly five times faster.
688
00:32:37,600 --> 00:32:39,333
Today, the Woods Hole team
689
00:32:39,333 --> 00:32:41,833
will be using
the speed of sound underwater
690
00:32:41,833 --> 00:32:44,300
as part of their calculations.
691
00:32:44,300 --> 00:32:45,933
The robot is equipped
692
00:32:45,933 --> 00:32:49,533
with four microphones
designed for underwater use
693
00:32:49,533 --> 00:32:50,933
called hydrophones.
694
00:32:50,933 --> 00:32:54,066
As the sound from the speaker
speeds through the water
695
00:32:54,066 --> 00:32:55,633
in all directions,
696
00:32:55,633 --> 00:32:59,766
it reaches the hydrophones at
slightly different times--
697
00:32:59,766 --> 00:33:02,633
just milliseconds apart.
698
00:33:02,633 --> 00:33:05,066
The researchers look at
a computer display
699
00:33:05,066 --> 00:33:06,600
that shows
the signals recorded...
700
00:33:06,600 --> 00:33:09,433
(electronic chirping)
...on each hydrophone.
701
00:33:09,433 --> 00:33:11,733
McCAMMON:
And so it will hit one
hydrophone before the others
702
00:33:11,733 --> 00:33:13,900
and by looking at
the relative time of arrival
703
00:33:13,900 --> 00:33:15,366
at those different hydrophones,
704
00:33:15,366 --> 00:33:17,566
we can figure out which
direction it came from first
705
00:33:17,566 --> 00:33:19,733
and then steer the robot
in that direction.
706
00:33:19,733 --> 00:33:23,400
♪ ♪
707
00:33:23,400 --> 00:33:26,600
NARRATOR:
The robot correctly identifies
708
00:33:26,600 --> 00:33:28,200
the direction of the sound--
709
00:33:28,200 --> 00:33:32,533
an important first step
toward autonomous navigation.
710
00:33:32,533 --> 00:33:34,533
♪ ♪
711
00:33:34,533 --> 00:33:37,833
A small but important victory.
712
00:33:37,833 --> 00:33:41,266
♪ ♪
713
00:33:41,266 --> 00:33:42,400
McCAMMON:
It's like you're building
out of LEGOS
714
00:33:42,400 --> 00:33:43,533
and you're building up a house,
715
00:33:43,533 --> 00:33:44,966
brick by brick by brick.
716
00:33:44,966 --> 00:33:46,100
And it only works
717
00:33:46,100 --> 00:33:47,500
when the house is
fully done.
718
00:33:47,500 --> 00:33:48,666
But you need to know
719
00:33:48,666 --> 00:33:50,300
that each single brick
in that
720
00:33:50,300 --> 00:33:51,766
works on its own
in isolation
721
00:33:51,766 --> 00:33:53,466
before you're willing to add it
to the larger picture.
722
00:33:53,466 --> 00:33:55,833
MABRY:
And so, you have this
massive goal
723
00:33:55,833 --> 00:33:57,200
that you're trying to achieve,
724
00:33:57,200 --> 00:33:59,500
but there needs to be attainable
goals along the way
725
00:33:59,500 --> 00:34:01,266
because ultimately,
726
00:34:01,266 --> 00:34:03,566
you're dealing with a system
of components,
727
00:34:03,566 --> 00:34:05,700
a system of elements
728
00:34:05,700 --> 00:34:06,900
that need to work together
729
00:34:06,900 --> 00:34:08,166
in order for this
730
00:34:08,166 --> 00:34:09,233
to be successful.
731
00:34:09,233 --> 00:34:12,200
NARRATOR:
CUREE is ready to step up
732
00:34:12,200 --> 00:34:13,866
to a bigger challenge.
733
00:34:13,866 --> 00:34:16,900
Locating an actual healthy reef
by sound--
734
00:34:16,900 --> 00:34:20,000
something less predictable than
what the speaker provided.
735
00:34:20,000 --> 00:34:23,000
One of the healthier reefs
in St. John
736
00:34:23,000 --> 00:34:24,800
is in nearby Joel's Shoal.
737
00:34:24,800 --> 00:34:26,700
GIRDHAR:
I propose we drop the robot
738
00:34:26,700 --> 00:34:28,033
like 20 meters...
739
00:34:28,033 --> 00:34:29,133
MOONEY:
We're like ten meters
740
00:34:29,133 --> 00:34:30,600
off the reef right now.
741
00:34:30,600 --> 00:34:32,533
NARRATOR:
They'll place CUREE
742
00:34:32,533 --> 00:34:34,433
approximately 20 meters
from the reef.
743
00:34:34,433 --> 00:34:35,900
(electronic chirping)
To succeed,
744
00:34:35,900 --> 00:34:37,833
it just needs to orient itself
745
00:34:37,833 --> 00:34:39,700
toward the sound.
746
00:34:39,700 --> 00:34:41,566
Robot going in.
747
00:34:44,100 --> 00:34:47,600
All right, cast away!
(electronic melody)
748
00:34:47,600 --> 00:34:48,833
McCAMMON:
So the test today
749
00:34:48,833 --> 00:34:50,933
is mostly just trying
to figure out
750
00:34:50,933 --> 00:34:53,200
if the robot can
accurately determine
751
00:34:53,200 --> 00:34:55,500
which direction
the reef sound is in.
752
00:34:55,500 --> 00:34:58,466
NARRATOR:
It's a more complex test.
753
00:34:58,466 --> 00:35:00,900
This time CUREE is untethered
754
00:35:00,900 --> 00:35:03,233
and the boat is drifting
with the ocean current.
755
00:35:03,233 --> 00:35:07,300
NARRATOR:
If they lose contact,
756
00:35:07,300 --> 00:35:09,500
they could easily lose
the robot entirely,
757
00:35:09,500 --> 00:35:12,833
and all of the engineering
that went into it.
758
00:35:12,833 --> 00:35:14,766
♪ ♪
759
00:35:14,766 --> 00:35:17,600
MABRY:
When they began to design
this autonomous robot
760
00:35:17,600 --> 00:35:18,900
that would go underwater,
761
00:35:18,900 --> 00:35:21,500
there is a need
to make sure that
762
00:35:21,500 --> 00:35:23,666
this thing is able to behave
763
00:35:23,666 --> 00:35:25,566
in an environment where,
if it doesn't,
764
00:35:25,566 --> 00:35:26,600
we can retrieve it...
765
00:35:28,200 --> 00:35:30,600
NARRATOR:
CUREE locates the direction
of the healthy reef.
766
00:35:30,600 --> 00:35:32,900
Which is encouraging.
767
00:35:32,900 --> 00:35:34,933
NARRATOR:
It's another successful test.
768
00:35:34,933 --> 00:35:36,066
(electronic crackling)
769
00:35:36,066 --> 00:35:38,300
The next big hurdle,
770
00:35:38,300 --> 00:35:39,700
can CUREE not only locate,
771
00:35:39,700 --> 00:35:43,400
but then move towards
a healthy reef autonomously.
772
00:35:43,400 --> 00:35:47,366
This will be a crucial milestone
in the mission,
773
00:35:47,366 --> 00:35:49,966
which is to ultimately build
a fleet of robots
774
00:35:49,966 --> 00:35:51,733
to map, monitor, and record
775
00:35:51,733 --> 00:35:56,500
the health of corals
around the globe.
776
00:35:56,500 --> 00:35:58,533
While reefs are under serious
threat all over,
777
00:35:58,533 --> 00:36:01,166
there are some signs of hope,
778
00:36:01,166 --> 00:36:04,500
and some surprising ideas
for ways to protect them;
779
00:36:04,500 --> 00:36:08,000
including one that came from
this team's research.
780
00:36:08,000 --> 00:36:09,533
♪ ♪
781
00:36:09,533 --> 00:36:12,600
In their work, they discovered
that the sound of a healthy reef
782
00:36:12,600 --> 00:36:15,400
might actually have
an indirect healing effect
783
00:36:15,400 --> 00:36:16,800
on a stressed reef.
784
00:36:16,800 --> 00:36:20,833
It has to do with
the coral animal's life cycle.
785
00:36:20,833 --> 00:36:23,100
Newly born baby corals--
786
00:36:23,100 --> 00:36:25,300
tiny larvae--
drift in the ocean,
787
00:36:25,300 --> 00:36:27,500
searching for somewhere
to settle.
788
00:36:27,500 --> 00:36:30,400
It turns out the sound of
a thriving coral reef
789
00:36:30,400 --> 00:36:33,233
signals them
to settle into place.
790
00:36:33,233 --> 00:36:34,833
Once they find a spot,
791
00:36:34,833 --> 00:36:36,466
they can be very resilient
792
00:36:36,466 --> 00:36:38,700
and grow for centuries.
793
00:36:38,700 --> 00:36:40,633
So the more larvae a reef
can attract,
794
00:36:40,633 --> 00:36:43,233
the healthier it will be.
795
00:36:43,233 --> 00:36:46,666
And that gave the team an idea.
796
00:36:46,666 --> 00:36:47,766
We know these reefs
are degraded
797
00:36:47,766 --> 00:36:49,100
and we want
to rebuild them
798
00:36:49,100 --> 00:36:51,200
by attracting the larvae, the
baby coral.
799
00:36:51,200 --> 00:36:53,833
NARRATOR:
In a past experiment,
800
00:36:53,833 --> 00:36:55,366
the team found that larvae
801
00:36:55,366 --> 00:36:59,166
could be drawn to recordings
of healthy reefs.
802
00:36:59,166 --> 00:37:02,200
So by placing speakers in
strategic locations,
803
00:37:02,200 --> 00:37:05,533
they could give a boost
where it's needed most.
804
00:37:05,533 --> 00:37:06,700
MOONEY:
And that system actually
805
00:37:06,700 --> 00:37:08,466
leverages the healthy landscape
806
00:37:08,466 --> 00:37:10,200
and plays it back into
the environment
807
00:37:10,200 --> 00:37:12,600
and the idea is that
it induces coral larvae
808
00:37:12,600 --> 00:37:14,066
to kind of choose that
environment and settle.
809
00:37:15,600 --> 00:37:17,300
NARRATOR:
The result?
810
00:37:17,300 --> 00:37:19,466
Up to seven times more
larvae settlement
811
00:37:19,466 --> 00:37:21,300
compared to a degraded reef
812
00:37:21,300 --> 00:37:23,566
without the audio boost.
813
00:37:23,566 --> 00:37:26,533
A very encouraging sign.
814
00:37:26,533 --> 00:37:28,666
♪ ♪
815
00:37:28,666 --> 00:37:31,066
But back to St. John and CUREE.
816
00:37:31,066 --> 00:37:34,433
The team is ready for
the final test of the day.
817
00:37:34,433 --> 00:37:36,466
McCAMMON:
The robot is going
to use the direction
818
00:37:36,466 --> 00:37:38,100
that it's finding
from its hydrophones
819
00:37:38,100 --> 00:37:39,400
and then drive itself
820
00:37:39,400 --> 00:37:42,200
to whatever the nearest
acoustic source is,
821
00:37:42,200 --> 00:37:44,500
which we're hoping is going
to be Joel's Shoal Reef.
822
00:37:44,500 --> 00:37:47,833
NARRATOR:
This time,
since CUREE will pilot itself,
823
00:37:47,833 --> 00:37:50,200
it's tethered for safety.
824
00:37:50,200 --> 00:37:53,000
They put CUREE in the water
and give it the green light.
825
00:37:53,000 --> 00:37:54,133
NATE FORMEL:
Are we expecting it
826
00:37:54,133 --> 00:37:55,800
to be moving or not?
McCAMMON: We are.
827
00:37:55,800 --> 00:37:57,800
NARRATOR:
It looks at first
as though it's orienting
828
00:37:57,800 --> 00:37:58,866
toward the sound of the reef.
829
00:37:58,866 --> 00:38:01,033
It thinks it's moving.
830
00:38:01,033 --> 00:38:03,266
NARRATOR:
But after a few minutes
it's clear
831
00:38:03,266 --> 00:38:05,466
that CUREE isn't making
much headway.
832
00:38:05,466 --> 00:38:07,333
It's just dumb stuff
in the way that I wrote.
833
00:38:07,333 --> 00:38:11,166
NARRATOR:
It seems there's an issue
with the software.
834
00:38:11,166 --> 00:38:12,266
♪ ♪
835
00:38:12,266 --> 00:38:14,966
All right, bring it back.
836
00:38:14,966 --> 00:38:16,600
(ratcheting)
837
00:38:18,866 --> 00:38:21,300
It's coming up.
838
00:38:21,300 --> 00:38:22,933
FORMEL:
I can now see it.
839
00:38:22,933 --> 00:38:25,033
NARRATOR:
They're starting to
lose the light.
840
00:38:25,033 --> 00:38:27,466
It's getting dark.
(indistinct chatter)
841
00:38:27,466 --> 00:38:30,100
NARRATOR:
They weren't able
to check off everything
842
00:38:30,100 --> 00:38:31,866
on the day's to-do list,
843
00:38:31,866 --> 00:38:34,333
yet they remain upbeat.
844
00:38:34,333 --> 00:38:36,533
GIRDHAR:
Overall, I am happy
right now because...
845
00:38:36,533 --> 00:38:37,600
McCAMMON:
We ended the day
with as many robots
846
00:38:37,600 --> 00:38:39,033
as we started
the day with.
847
00:38:39,033 --> 00:38:41,266
NARRATOR:
It's frustrating in
the moment,
848
00:38:41,266 --> 00:38:43,266
but they're making progress.
849
00:38:45,266 --> 00:38:48,433
STELTZNER:
The creative act of engineering
850
00:38:48,433 --> 00:38:51,333
has got disappointment,
851
00:38:51,333 --> 00:38:53,733
has got failure,
852
00:38:53,733 --> 00:38:55,933
and that's how we learn.
853
00:38:55,933 --> 00:39:00,200
(chuckling):
So, it is a big ball of, of...
854
00:39:00,200 --> 00:39:02,166
...two steps forward and
one step back.
855
00:39:02,166 --> 00:39:04,800
When you have
a very massive "Why"
856
00:39:04,800 --> 00:39:07,533
and a very massive purpose for
what you're trying to do,
857
00:39:07,533 --> 00:39:09,966
such as save the coral reefs,
858
00:39:09,966 --> 00:39:12,800
it allows you to experience
the disappointment
859
00:39:12,800 --> 00:39:14,433
but not be defeated by it,
860
00:39:14,433 --> 00:39:16,666
and continue to try the process
of moving it forward.
861
00:39:16,666 --> 00:39:18,166
♪ ♪
862
00:39:18,166 --> 00:39:20,400
If you're not failing
you're not trying hard enough.
863
00:39:20,400 --> 00:39:21,400
(voiceover):
Yeah, it's very frustrating
864
00:39:21,400 --> 00:39:22,833
but when it works,
865
00:39:22,833 --> 00:39:24,466
it's very satisfying.
866
00:39:24,466 --> 00:39:27,933
NARRATOR:
Engineering solutions to
the climate crisis
867
00:39:27,933 --> 00:39:31,200
will require creativity,
innovation,
868
00:39:31,200 --> 00:39:34,000
and a global commitment
to making smart choices.
869
00:39:34,000 --> 00:39:37,333
But we face many other
challenges as well;
870
00:39:37,333 --> 00:39:40,000
like restoring balance
to the land
871
00:39:40,000 --> 00:39:43,133
after decades of
industrial pollution.
872
00:39:43,133 --> 00:39:44,533
♪ ♪
873
00:39:44,533 --> 00:39:46,866
On Navajo land in Arizona,
874
00:39:46,866 --> 00:39:50,700
an Indigenous artist and
engineers are collaborating
875
00:39:50,700 --> 00:39:53,500
on a unique, local approach
876
00:39:53,500 --> 00:39:57,100
to purifying
contaminated drinking water.
877
00:39:57,100 --> 00:40:00,300
(birds chirping)
This pristine-seeming landscape
878
00:40:00,300 --> 00:40:02,900
conceals a serious problem.
879
00:40:02,900 --> 00:40:06,800
30% of the population in
the Navajo Nation
880
00:40:06,800 --> 00:40:10,266
lacks access to
clean drinking water.
881
00:40:10,266 --> 00:40:11,500
Decades of uranium mining
882
00:40:11,500 --> 00:40:14,000
has polluted the land.
883
00:40:14,000 --> 00:40:15,466
The United States government
884
00:40:15,466 --> 00:40:16,900
used the heavy metal
885
00:40:16,900 --> 00:40:18,566
to develop the atomic bomb
886
00:40:18,566 --> 00:40:21,700
and power its
nuclear weapons program
887
00:40:21,700 --> 00:40:24,733
after World War II.
888
00:40:24,733 --> 00:40:25,900
CHACHRA:
When we think of engineering,
889
00:40:25,900 --> 00:40:29,733
people are suspicious
of it because,
890
00:40:29,733 --> 00:40:32,166
for a good part of
the 20th century,
891
00:40:32,166 --> 00:40:34,033
one of the stories
of engineering
892
00:40:34,033 --> 00:40:37,266
was engineers making
decisions about systems
893
00:40:37,266 --> 00:40:40,033
that affected
a lot of other people.
894
00:40:40,033 --> 00:40:42,466
And often those effects
were not positive.
895
00:40:42,466 --> 00:40:45,066
NARRATOR:
Byproducts of uranium mining,
896
00:40:45,066 --> 00:40:46,700
such as strontium,
897
00:40:46,700 --> 00:40:48,766
can mimic calcium in the body,
898
00:40:48,766 --> 00:40:51,466
causing it to be absorbed
by bones.
899
00:40:51,466 --> 00:40:55,133
The E.P.A. has awarded
$3.8 million
900
00:40:55,133 --> 00:40:57,500
to support
three drinking water projects
901
00:40:57,500 --> 00:40:59,366
to benefit the Navajo Nation.
902
00:41:01,466 --> 00:41:05,433
Some are proposing other, more
homegrown solutions, as well.
903
00:41:05,433 --> 00:41:07,500
(stone grinding)
904
00:41:07,500 --> 00:41:10,233
Deanna Tso is a third-generation
905
00:41:10,233 --> 00:41:12,966
Navajo artist who works in clay.
906
00:41:12,966 --> 00:41:15,000
TSO:
People always ask me,
907
00:41:15,000 --> 00:41:17,200
"When'd you learn how
to do pottery?"
908
00:41:17,200 --> 00:41:18,466
I always say,
909
00:41:18,466 --> 00:41:19,800
"I was born making it."
910
00:41:19,800 --> 00:41:22,900
Both my parents,
my mother and my father,
911
00:41:22,900 --> 00:41:25,366
both did Navajo pottery.
912
00:41:25,366 --> 00:41:29,100
(car doors closing)
913
00:41:29,100 --> 00:41:30,700
NARRATOR:
She has been collaborating
with scientists
914
00:41:30,700 --> 00:41:33,366
Navid Saleh
and Stetson Rowles...
915
00:41:33,366 --> 00:41:34,400
(knocks on door)
916
00:41:35,600 --> 00:41:36,600
Hey!
Good morning.
917
00:41:36,600 --> 00:41:37,733
Hey, Deanna.
918
00:41:37,733 --> 00:41:38,833
NARRATOR:
...on a project meant to address
919
00:41:38,833 --> 00:41:40,766
the water contamination problem
920
00:41:40,766 --> 00:41:44,133
on a very human scale.
921
00:41:44,133 --> 00:41:47,133
SALEH (voiceover):
I believe that engineering
without people
922
00:41:47,133 --> 00:41:48,766
is destined to fail.
923
00:41:48,766 --> 00:41:50,700
Good. Good.
Long drive.
924
00:41:50,700 --> 00:41:53,166
SALEH (voiceover):
There is this
experiential knowledge,
925
00:41:53,166 --> 00:41:55,833
knowledge that is housed
within people's lives,
926
00:41:55,833 --> 00:41:56,900
yet to be unlocked.
927
00:41:56,900 --> 00:41:59,766
NARRATOR:
Not all people here use
928
00:41:59,766 --> 00:42:02,466
or have access
to municipal water,
929
00:42:02,466 --> 00:42:05,400
so the goal is
to call upon local knowledge
930
00:42:05,400 --> 00:42:07,700
to find a sustainable way
to purify water
931
00:42:07,700 --> 00:42:08,833
closer to the home.
932
00:42:08,833 --> 00:42:10,000
YANG:
We often think
933
00:42:10,000 --> 00:42:12,433
of engineering as
only being
934
00:42:12,433 --> 00:42:15,233
the latest and greatest
technology.
935
00:42:15,233 --> 00:42:18,700
But, people have practices that
are very effective now
936
00:42:18,700 --> 00:42:20,733
and, and have been for,
937
00:42:20,733 --> 00:42:23,300
you know,
decades, centuries longer.
938
00:42:23,300 --> 00:42:27,133
And so what can we learn from
those, existing approaches
939
00:42:27,133 --> 00:42:28,766
that are already effective?
940
00:42:28,766 --> 00:42:31,700
So Deanna,
this was something that...
941
00:42:31,700 --> 00:42:34,866
NARRATOR:
On this trip,
the scientists want to build
942
00:42:34,866 --> 00:42:36,566
a new prototype clay filter
943
00:42:36,566 --> 00:42:39,766
for use in household
water containers.
944
00:42:39,766 --> 00:42:44,300
The hope is to integrate
locally sourced minerals
945
00:42:44,300 --> 00:42:47,500
so that the finished filter
will remove uranium byproducts,
946
00:42:47,500 --> 00:42:50,600
like strontium, from the water.
947
00:42:50,600 --> 00:42:52,466
SALEH:
Could you actually make
something like that?
948
00:42:52,466 --> 00:42:53,533
Do you have something similar?
949
00:42:53,533 --> 00:42:55,566
I have one that I make
950
00:42:55,566 --> 00:42:57,366
with the cone shape.
951
00:42:57,366 --> 00:42:59,000
NARRATOR:
Navajo potters like Deanna
952
00:42:59,000 --> 00:43:01,733
use a local tree sap
as a glaze.
953
00:43:01,733 --> 00:43:04,733
Navid and his team wondered
if the sap could be used
954
00:43:04,733 --> 00:43:06,700
as part of a
decontamination filter.
955
00:43:07,900 --> 00:43:10,466
SALEH (voiceover):
What we found was how
much knowledge
956
00:43:10,466 --> 00:43:14,133
the Navajos already had
about the sap.
957
00:43:14,133 --> 00:43:18,266
They already knew it has
health benefits.
958
00:43:18,266 --> 00:43:20,000
So this is a printout
of the...
959
00:43:20,000 --> 00:43:22,200
NARRATOR:
Navid and his team
recently conducted tests
960
00:43:22,200 --> 00:43:24,533
that translated
Indigenous knowledge
961
00:43:24,533 --> 00:43:27,233
into the language of
biochemistry;
962
00:43:27,233 --> 00:43:32,000
quantifying the extent of the
sap's antimicrobial properties.
963
00:43:32,000 --> 00:43:35,500
Now, they hope to expand
the filter's capabilities
964
00:43:35,500 --> 00:43:38,333
to radioactive contaminants.
965
00:43:38,333 --> 00:43:40,600
YANG:
They worked together,
collaboratively,
966
00:43:40,600 --> 00:43:42,300
to make something new and better
967
00:43:42,300 --> 00:43:45,833
that serves her community
in a really, powerful
968
00:43:45,833 --> 00:43:48,800
and very collaborative way.
969
00:43:48,800 --> 00:43:51,166
We can engineer
a shape or a design
970
00:43:51,166 --> 00:43:53,300
that's going to work well,
not only to filter water,
971
00:43:53,300 --> 00:43:56,066
but people will want to use.
972
00:43:56,066 --> 00:43:58,133
(voiceover):
We see this amazing opportunity
973
00:43:58,133 --> 00:43:59,766
to be able to use pottery,
974
00:43:59,766 --> 00:44:02,400
or ceramics, as filters,
975
00:44:02,400 --> 00:44:04,033
because it's so a part
976
00:44:04,033 --> 00:44:05,133
of people's everyday life.
977
00:44:06,233 --> 00:44:08,400
Particularly in places like
the Navajo Nation
978
00:44:08,400 --> 00:44:11,200
where traditional practices
are so important.
979
00:44:11,200 --> 00:44:13,100
TSO:
Okay.
980
00:44:13,100 --> 00:44:14,966
ROWLES:
Which way?
981
00:44:14,966 --> 00:44:17,700
NARRATOR:
Navid and Stetson want to learn
the process of making pottery
982
00:44:17,700 --> 00:44:20,700
the way Deanna's mother
taught her--
983
00:44:20,700 --> 00:44:22,766
because collaboration
is strongest
984
00:44:22,766 --> 00:44:24,933
when it is
truly interdisciplinary.
985
00:44:24,933 --> 00:44:26,133
TSO:
Yes.
986
00:44:26,133 --> 00:44:28,266
You see that
gray spot?
987
00:44:28,266 --> 00:44:29,666
NARRATOR:
Deanna starts from scratch,
988
00:44:29,666 --> 00:44:32,000
harvesting clay from
a rocky outcropping
989
00:44:32,000 --> 00:44:33,233
on Navajo land.
990
00:44:33,233 --> 00:44:34,733
Okay,
so this portion is what?
991
00:44:34,733 --> 00:44:37,466
That portion is clay.
Okay.
992
00:44:37,466 --> 00:44:40,100
SALEH (voiceover):
We often as scientists believe
993
00:44:40,100 --> 00:44:42,333
that we know a lot.
994
00:44:42,333 --> 00:44:43,366
But we forget,
995
00:44:43,366 --> 00:44:45,133
science as a discipline
996
00:44:45,133 --> 00:44:47,400
has only been around
for 500 years.
997
00:44:47,400 --> 00:44:50,433
NARRATOR:
There are many ways of
generating knowledge
998
00:44:50,433 --> 00:44:52,166
besides
the modern scientific process.
999
00:44:53,466 --> 00:44:54,700
CHACHA:
These are all different ways
1000
00:44:54,700 --> 00:44:56,300
in which we interact
with the physical world.
1001
00:44:56,300 --> 00:44:58,433
That diversity gives
you new ideas.
1002
00:44:58,433 --> 00:45:00,166
And thinking about how
to put together
1003
00:45:00,166 --> 00:45:01,766
old technologies and
new technologies
1004
00:45:01,766 --> 00:45:03,500
might lead to entirely
new paths.
1005
00:45:03,500 --> 00:45:05,933
It creates a symbiotic
effect,
1006
00:45:05,933 --> 00:45:07,933
because the more people
feel included
1007
00:45:07,933 --> 00:45:10,033
in what is being produced
by something,
1008
00:45:10,033 --> 00:45:12,600
the more people see themselves
being a part of
1009
00:45:12,600 --> 00:45:14,066
the producing of that thing.
1010
00:45:14,066 --> 00:45:17,666
NARRATOR:
Next-- they source sap
from pinyon trees.
1011
00:45:17,666 --> 00:45:20,500
There's one right here,
let's check this one.
1012
00:45:20,500 --> 00:45:24,066
♪ ♪
1013
00:45:24,066 --> 00:45:25,366
(crunches)
ROWLES: Whoo!
1014
00:45:25,366 --> 00:45:28,166
We hit the jackpot
with this tree.
1015
00:45:28,166 --> 00:45:30,466
TSO:
We were blessed for the day.
1016
00:45:31,466 --> 00:45:33,433
Come on in.
(keys jangling)
1017
00:45:33,433 --> 00:45:36,533
I usually just take
this much out.
1018
00:45:36,533 --> 00:45:38,333
NARRATOR:
Deanna demonstrates how
to grind minerals
1019
00:45:38,333 --> 00:45:41,466
into the fine grains
that make up her clay.
1020
00:45:41,466 --> 00:45:44,333
One of you want to go ahead
and give it a try?
1021
00:45:44,333 --> 00:45:46,600
ROWLES:
I think there's a lot
of engineering
1022
00:45:46,600 --> 00:45:49,100
that goes into creating pottery.
1023
00:45:49,100 --> 00:45:52,600
The freedom that it allows
to make any shape.
1024
00:45:52,600 --> 00:45:54,066
(squeaking)
1025
00:45:54,066 --> 00:45:55,900
STELTZNER:
The fusion of art and
engineering.
1026
00:45:55,900 --> 00:46:01,966
Or maybe even the boundaries
between art and engineering...
1027
00:46:01,966 --> 00:46:03,700
...perhaps they don't exist.
1028
00:46:03,700 --> 00:46:05,366
Perhaps they're really
the same thing,
1029
00:46:05,366 --> 00:46:08,200
painted with a different
palette.
1030
00:46:08,200 --> 00:46:11,300
NARRATOR:
Stetson and Navid
are working with Deanna
1031
00:46:11,300 --> 00:46:15,633
to prototype a shape for
the clay filter.
1032
00:46:15,633 --> 00:46:17,066
I don't know if you know
Deanna,
1033
00:46:17,066 --> 00:46:19,533
but I've been making
some pottery since high school,
1034
00:46:19,533 --> 00:46:21,900
and I made this shape
to try and see
1035
00:46:21,900 --> 00:46:24,400
if maybe we can explore
making some shapes together.
1036
00:46:24,400 --> 00:46:28,666
I made a shape
similar to that...
1037
00:46:30,233 --> 00:46:32,233
...and it looks like this.
1038
00:46:32,233 --> 00:46:36,166
And we do make these
traditional Navajo pipes.
1039
00:46:36,166 --> 00:46:37,433
Do you think you can
make some grooves
1040
00:46:37,433 --> 00:46:40,166
similar to something
like this?
1041
00:46:40,166 --> 00:46:41,966
Kind of like an accordion
basically,
1042
00:46:41,966 --> 00:46:45,233
so it has the same surface area
but in a smaller size.
1043
00:46:45,233 --> 00:46:47,300
♪ ♪
1044
00:46:47,300 --> 00:46:52,633
NARRATOR:
Adding grooves increases the
total surface area of the shape.
1045
00:46:52,633 --> 00:46:55,766
More surface area
will mean more contact
1046
00:46:55,766 --> 00:46:58,500
with the water inside.
1047
00:46:58,500 --> 00:46:59,566
TSO:
I'm going to show you
1048
00:46:59,566 --> 00:47:03,333
an option we have
that we can try:
1049
00:47:03,333 --> 00:47:04,800
Coil.
Yeah. Making a coil.
1050
00:47:04,800 --> 00:47:05,966
Making a coil.
1051
00:47:05,966 --> 00:47:07,500
♪ ♪
1052
00:47:07,500 --> 00:47:10,133
NARRATOR:
Next, the new prototypes
1053
00:47:10,133 --> 00:47:11,500
need to be fired.
1054
00:47:13,500 --> 00:47:14,500
SALEH:
We have been working
with Deanna
1055
00:47:14,500 --> 00:47:16,600
for almost nine years now.
1056
00:47:16,600 --> 00:47:19,700
TSO:
Make sure we have it covered
nice and good.
1057
00:47:21,333 --> 00:47:23,333
SALEH:
Working with her side-by-side
1058
00:47:23,333 --> 00:47:25,433
as an equal partner
intellectually,
1059
00:47:25,433 --> 00:47:30,000
only opens opportunities that
are more meaningful
1060
00:47:30,000 --> 00:47:32,933
than we scientists would
ever find
1061
00:47:32,933 --> 00:47:34,366
sitting at our desks.
1062
00:47:36,733 --> 00:47:38,633
NARRATOR:
The last step:
1063
00:47:38,633 --> 00:47:42,900
heat and strain the pinyon sap,
1064
00:47:42,900 --> 00:47:44,900
creating
the microbe-resistant resin,
1065
00:47:44,900 --> 00:47:48,500
which acts as a glaze
to coat the pottery.
1066
00:47:48,500 --> 00:47:51,500
And now, a new addition
to the filter.
1067
00:47:51,500 --> 00:47:52,966
ROWLES:
Can you grab the zeolite?
1068
00:47:52,966 --> 00:47:55,400
NARRATOR:
The scientists are using
powdered chabazite,
1069
00:47:55,400 --> 00:47:57,666
a type of
naturally occurring zeolite,
1070
00:47:57,666 --> 00:48:01,533
found abundantly on Navajo land.
1071
00:48:01,533 --> 00:48:02,700
♪ ♪
1072
00:48:02,700 --> 00:48:04,900
Chabazite is a porous crystal
1073
00:48:04,900 --> 00:48:08,533
made of sodium, calcium,
and aluminum silicates
1074
00:48:08,533 --> 00:48:10,166
that has the ability to trap
1075
00:48:10,166 --> 00:48:12,433
and absorb contaminants.
1076
00:48:12,433 --> 00:48:14,233
♪ ♪
1077
00:48:14,233 --> 00:48:17,200
Finally, Deanna applies
the resin.
1078
00:48:17,200 --> 00:48:20,500
TSO:
The pottery itself
has to be hot.
1079
00:48:20,500 --> 00:48:22,333
The sap has to be hot.
1080
00:48:22,333 --> 00:48:24,533
NARRATOR:
The team hopes the chabazite
1081
00:48:24,533 --> 00:48:27,566
will add function to the resin,
1082
00:48:27,566 --> 00:48:30,000
removing uranium byproducts,
like strontium,
1083
00:48:30,000 --> 00:48:32,800
from any water that comes
into contact with it.
1084
00:48:32,800 --> 00:48:34,766
ROWLES:
Wow, the colors are beautiful.
1085
00:48:34,766 --> 00:48:36,733
♪ ♪
1086
00:48:36,733 --> 00:48:39,633
NARRATOR:
Back at the University of Texas
at Austin,
1087
00:48:39,633 --> 00:48:43,266
it's time to test their water
filter prototypes in the lab.
1088
00:48:43,266 --> 00:48:44,300
We've got some of
the clay.
1089
00:48:44,300 --> 00:48:45,300
NARRATOR:
Using the materials
1090
00:48:45,300 --> 00:48:46,600
they sourced with Deanna,
1091
00:48:46,600 --> 00:48:50,133
the scientists create
small clay discs...
1092
00:48:50,133 --> 00:48:51,800
ROWLES:
...try and just punch out,
1093
00:48:51,800 --> 00:48:53,966
a little disc like that...
1094
00:48:53,966 --> 00:48:56,766
NARRATOR:
And coat them with the same
chabazite-enriched resin.
1095
00:48:56,766 --> 00:49:01,500
These are tiny lab versions
of Deanna's pottery.
1096
00:49:01,500 --> 00:49:03,766
To test the discs,
1097
00:49:03,766 --> 00:49:05,566
the researchers expose them
1098
00:49:05,566 --> 00:49:08,200
to strontium-contaminated water
1099
00:49:08,200 --> 00:49:11,200
to see if the resin
will successfully absorb
1100
00:49:11,200 --> 00:49:12,366
the uranium byproduct.
1101
00:49:12,366 --> 00:49:14,233
♪ ♪
1102
00:49:14,233 --> 00:49:16,966
If the filter works as expected,
1103
00:49:16,966 --> 00:49:20,000
the chabazite will capture
strontium from the water
1104
00:49:20,000 --> 00:49:24,000
through ion exchange
as the water passes through.
1105
00:49:24,000 --> 00:49:25,633
♪ ♪
1106
00:49:25,633 --> 00:49:26,633
ROWLES: Hey, Andrei.
ANDREI DOLOCAN: What's up, bud?
1107
00:49:26,633 --> 00:49:28,300
Here's the sample.
1108
00:49:28,300 --> 00:49:29,333
Yeah, thank you.
1109
00:49:29,333 --> 00:49:31,666
NARRATOR:
Senior research scientist
1110
00:49:31,666 --> 00:49:33,400
Andrei Dolocan
1111
00:49:33,400 --> 00:49:35,733
loads a sample into
an ion mass spectrometer.
1112
00:49:36,800 --> 00:49:37,800
It scans the sample
1113
00:49:37,800 --> 00:49:39,200
on the molecular level,
1114
00:49:39,200 --> 00:49:42,933
layer by layer,
over several hours.
1115
00:49:42,933 --> 00:49:44,566
When it's done,
1116
00:49:44,566 --> 00:49:46,233
the result
is a map of the elements
1117
00:49:46,233 --> 00:49:48,233
within the scanned
sample surface.
1118
00:49:48,233 --> 00:49:51,200
When the clay disc
is completely scanned,
1119
00:49:51,200 --> 00:49:53,100
it's time
to check the results.
1120
00:49:53,100 --> 00:49:55,700
This is the
strontium signal.
1121
00:49:55,700 --> 00:49:58,300
NARRATOR:
The data show that
the strontium is found
1122
00:49:58,300 --> 00:50:01,366
in the same places as chabazite
in the resin...
1123
00:50:01,366 --> 00:50:04,000
DOLOCAN:
We have the zeolite,
obviously sodium,
1124
00:50:04,000 --> 00:50:05,500
aluminum-silicon.
1125
00:50:05,500 --> 00:50:07,500
Uh-huh.
DOLOCAN: Okay.
1126
00:50:07,500 --> 00:50:10,266
And now the, strontium is
increasing exactly like...
1127
00:50:10,266 --> 00:50:12,066
NARRATOR:
It's an encouraging sign
1128
00:50:12,066 --> 00:50:14,233
that the chabazite is working
as expected
1129
00:50:14,233 --> 00:50:17,933
when used with
Deanna's pottery technique.
1130
00:50:17,933 --> 00:50:18,966
SALEH:
So I guess it was a really,
1131
00:50:18,966 --> 00:50:20,700
successful run, Andrei.
1132
00:50:20,700 --> 00:50:22,333
Yeah.
We can see association
1133
00:50:22,333 --> 00:50:23,466
of strontium
with the zeolite.
1134
00:50:23,466 --> 00:50:25,966
DOLOCAN:
I agree, this is a good start.
1135
00:50:25,966 --> 00:50:27,266
ZILEVU:
One thing that I've learned
1136
00:50:27,266 --> 00:50:28,400
from the research and
design process
1137
00:50:28,400 --> 00:50:29,666
is that kind of doing
1138
00:50:29,666 --> 00:50:31,233
co-creation activities
with the end user,
1139
00:50:31,233 --> 00:50:33,200
it's really a way to kind
of bridge and create
1140
00:50:33,200 --> 00:50:34,366
new, innovative process,
1141
00:50:34,366 --> 00:50:35,400
because you're bringing
the people
1142
00:50:35,400 --> 00:50:36,900
who are using the technology
1143
00:50:36,900 --> 00:50:37,933
throughout the whole journey.
1144
00:50:38,933 --> 00:50:40,700
So this is the one
that Deanna made...
1145
00:50:40,700 --> 00:50:43,700
NARRATOR:
Now a few steps closer
to their goal,
1146
00:50:43,700 --> 00:50:44,833
the researchers will work
1147
00:50:44,833 --> 00:50:47,166
to incorporate Deanna's spiral
1148
00:50:47,166 --> 00:50:49,433
and the chabazite's
filtering power
1149
00:50:49,433 --> 00:50:53,133
into their final design.
1150
00:50:53,133 --> 00:50:54,500
So moving forward,
1151
00:50:54,500 --> 00:50:56,133
I think the most difficult
engineering challenge
1152
00:50:56,133 --> 00:50:57,133
is yet to come.
1153
00:50:57,133 --> 00:50:58,700
And I think it's going to be
1154
00:50:58,700 --> 00:51:01,366
translating our results from,
1155
00:51:01,366 --> 00:51:03,800
you know, a lab scale experiment
1156
00:51:03,800 --> 00:51:05,866
to something that's going
to be usable
1157
00:51:05,866 --> 00:51:07,833
in households throughout
the Navajo Nation.
1158
00:51:07,833 --> 00:51:08,866
♪ ♪
1159
00:51:08,866 --> 00:51:11,333
(birds chirping)
1160
00:51:11,333 --> 00:51:12,466
MABRY:
At the end of the day,
1161
00:51:12,466 --> 00:51:14,500
we want
to unlock human potential.
1162
00:51:14,500 --> 00:51:17,333
And in order
to unlock human potential,
1163
00:51:17,333 --> 00:51:19,700
we are not doing
ourselves a justice
1164
00:51:19,700 --> 00:51:22,800
if we continue to only demand
certain solutions
1165
00:51:22,800 --> 00:51:25,733
from a subset of
our populations,
1166
00:51:25,733 --> 00:51:27,766
the more we can get
more people included,
1167
00:51:27,766 --> 00:51:29,466
the more we can unlock
1168
00:51:29,466 --> 00:51:32,266
not just solutions to problems
that we now see,
1169
00:51:32,266 --> 00:51:35,266
but things that are yet to come.
1170
00:51:35,266 --> 00:51:37,233
♪ ♪
1171
00:51:37,233 --> 00:51:39,466
NARRATOR:
As we change our world through
engineering,
1172
00:51:39,466 --> 00:51:41,566
it's up to us to make changes;
1173
00:51:41,566 --> 00:51:45,700
for all of us, by all of us.
1174
00:51:45,700 --> 00:51:47,966
ARMANI:
I think we're all engineers.
1175
00:51:47,966 --> 00:51:49,933
We all build things,
1176
00:51:49,933 --> 00:51:52,633
we all design things.
1177
00:51:52,633 --> 00:51:53,766
(chuckling):
We all break things
1178
00:51:53,766 --> 00:51:54,966
and then have to fix them
1179
00:51:54,966 --> 00:51:56,000
and put them back together.
1180
00:51:56,000 --> 00:51:57,600
NARRATOR:
And we get to decide
1181
00:51:57,600 --> 00:52:00,300
what comes next.
1182
00:52:00,300 --> 00:52:01,466
What if we were to design this?
1183
00:52:01,466 --> 00:52:02,900
What if the world was
to look like this
1184
00:52:02,900 --> 00:52:04,933
in 50, 100 years?
What could that look like?
1185
00:52:04,933 --> 00:52:06,933
♪ ♪
1186
00:52:06,933 --> 00:52:08,866
ALI HAJIMIRI:
The engineer's work
is never done...
1187
00:52:08,866 --> 00:52:10,933
If you're not failing,
you're not trying hard enough.
1188
00:52:10,933 --> 00:52:13,466
You can always create
something new.
1189
00:52:13,466 --> 00:52:15,133
♪ ♪
1190
00:52:15,133 --> 00:52:16,733
NARRATOR:
Building stuff
1191
00:52:16,733 --> 00:52:18,833
to change the world.
1192
00:52:18,833 --> 00:52:20,966
♪ ♪
1193
00:52:41,166 --> 00:52:44,033
♪ ♪
1194
00:52:44,966 --> 00:52:52,500
♪ ♪
1195
00:52:56,333 --> 00:53:03,933
♪ ♪
1196
00:53:07,766 --> 00:53:15,300
♪ ♪
1197
00:53:16,933 --> 00:53:24,466
♪ ♪
1198
00:53:26,100 --> 00:53:33,633
♪ ♪
92180
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