Hello, and welcome to the Physics World weekly
podcast. I'm Hamish Johnston.
In this episode, I'm in conversation with Antonio
Rossi,
who's a researcher in two d materials
engineering
at the Italian Institute of Technology
in Genoa.
We explore how two d materials such as
graphene
are finding a wide range of applications,
including in fundamental science, quantum technologies,
and industrial
coatings.
Much of Rossi's research focuses on tungsten disulfide
and hexagonal
boron nitride,
which he explains are two d materials with
great potential for electronics and optoelectronics.
We also chat about the use of artificial
neural networks and artificial intelligence
to optimize the synthesis
of two d materials.
Before we get to that conversation,
I'd like to introduce you to a new
and quick way of discovering cutting edge science.
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this podcast.
Hi, Antonio.
Welcome to the podcast.
Hi. It's a pleasure to be here.
So, Antonio, can we start with the basics?
What are two d materials,
and why are they interesting in terms of
science and technology?
Yes. So two d materials are,
like, classical materials and arrangement of atoms.
The the the main difference with respect to,
classical three d three materials is that they
extend over
a sheet that is, three up to three
atoms
thick, which makes them extremely different in terms
of properties that they display.
And,
they
go through what's called quantum confinement thanks to
this,
low dimensionality,
which is great because usually atoms, they tend
to be in the three d world. Right?
They tend to be,
x y z orbital, but then they are
confined in a x y
space.
And packing them together,
basically,
this, offers the opportunity
to explore
a different physics with respect to,
normal Trinity compounds.
I see. And and graphene is perhaps the
most famous
two d material.
In fact, I I I had an encounter
with graphene over the weekend. My wife and
I are looking at buying a new car.
And we went to the dealership, and they
were offering
a special graphene coating
on the car.
I sort of had my doubts as to
whether it was actually graphene,
or or something
something else. But,
you know, I think a lot of listeners
will be familiar
with graphene. What what is the current
state of play with graphene? Is it being
used
in commercial technologies?
Indeed,
were they not joking at the car dealership?
Were they putting it on cars?
So they were not joking
up to a point. There's been a huge
debate lately about what graphene actually is and
what these components are made of because they
it's easy to say graphene, then when you
go look into that, it's not exactly graphene.
Graphene is mostly, let's say,
one single layer of carbon atoms arranged in
a honeycomb lattice.
Right? And that's the definition of graphene.
But then people start playing with the having,
graphene oxide that is a different structure as
an oxygen in the in the compound. Reduce
graphene oxide that was before oxidized and then
reduced, and then it's very defective,
which is not graphic because it's missing some,
some carbon atoms from from the from the
lattice from the lattice structure.
Yet, is it that different? Can we just,
like, graphing based material, all of that? That
I think it's fair to say that.
And the dealership yeah. Then
they will probably offering you, like, this new
coating that also
I don't I'm not sure if I'm allowed
to say company's name, but,
even our our, Airbus is, is developing,
for for airplane technology.
And so it's reasonable that you have, like,
a composite
material that implements some graphene
form of, these, bigger umbrella of graphene based
materials.
In that sense, there's been a lot
of development towards market. Like,
let's say the the the first years of
the what's called graphene flagship,
the big European initiative to study graphene and
two d materials,
has been prompted by the
need to understand the property of two d
two d materials. But later on, people were
like, okay.
Now we have a clear picture of what's
going on, at least, like, let's say, 80%.
Let's try to go to market. Let's try
to make a product out of it. And
so there are different sectors that you can
think of,
from composite material to electronics
to,
batteries.
And now the market the graphic market is
around 350,000,000,
380,000,000,
US dollars.
And but it's projected to reach the 1,500,000,000.0
by the 2027.
So it is being used. It's implemented. There's
a there are a lot of, like, initiatives
in that sense. For instance, like, one great
example that, came out recently is, like, this
new graphene based paint
that you can use
to paint your house walls with.
There is also a heating source. So, basically,
you are cutting down,
the power consumption to heat up and to,
to heat up your house. So you just
get the heat straight out of the paint.
And so this is like a great, great,
use of, of graphene in my opinion.
But
I know, like, many people ask,
so is do you find, like, a real
application to graphene? Because as the conversation started
many years ago, everybody was expecting
new electronics. Right? Exactly. Yeah. They say graphene
is the new silicon,
and so everybody was waiting for that.
But there are many problems in that direction.
First of all, it does not show the
zero one,
signal the silicon
produces.
And so that makes, like, the the the
logic the computer logic a little bit hard
to to parse.
And at the same time, the quality of
the material that,
people use to make science from the
famous Scotch tape technique
used to be
much higher than the,
the quality of the material that you can
produce via other methods and scalable methods.
Now this gap has been
closed,
I would say. Like, lately, like, you can
see that chemical vapor deposition graphing is famous
technique that,
employs,
copper
as a subject to grow graphene,
delivered, like, very high quality samples. And you
can see the quantum physics that was that
was,
at first just in the exfoliated material.
And so
that that
that gap is causing, but this is something
that is really recent. There are a number
of scientists across the the globe,
that are working into integrating graphene into silicon
technology, and I think that's a safer route
because you don't have to reinvent the whole
supply chain. You're just placing, like, a piece
of,
carbon, the graphene, into something that already exists
that can make it better.
On the other hand, there are some properties
that only graphene has, like its optical absorption.
They makes it they make it extremely suitable
for optoelectronic application, for extra fast auto optoelectronic
application. And there are a number of startups
that are, like, start,
becoming famous in that sense, like, becoming,
delivering to the market some products that can
actually be used to
detect and motivate flight, which I think it's
the closest thing to having something entirely graphene
based, that is rephrasing electronics, of
course. I see. So so it's still we
we can still call graphene a wonder material.
It's just that,
maybe it's taken longer than expected for some
of those applications to come to fruition.
Yes. Yes.
I wanna say that we are a little
bit biased. Right? Because we knew what silicon
could do, and we could not see anything
different. So we wanna have silicon but made
of graphene.
So it requires, like, a,
let's say, a bigger leap
to reinvent
electronics, to reinvent logic. And in that sense,
that might require a little bit more time,
but graphene is still, like, a perfect candidate
in that sense
if you ask me.
I see. And, Antonio,
the graphene is by no means the only
two d material.
There are others,
that have great scientific and technical interest.
You work on
tungsten disulfide
and
hexagonal
boron
nitride.
Can you can you give us an introduction
to those materials? Absolutely. I I'm really excited
to talk about these two materials. Taq's and
sulfide first because it was my
PhD project. You know? I started, like, synthesizing,
tanks and sulfide.
Packs and sulfide is basically like graphene. It's
still like a two d materials, but instead
of being made by
one layer of material, it's made by three
layers. So the top and the bottom is
sulfide, so sulfur sulfur layer.
It is sandwiching
a tanks and atom layer. So it's like
a really thin sandwich.
And,
the
with respect to graphene,
it displays
that zero one
characteristic that is typical
of of silicon. Okay? And has also great,
optical properties. It absorbs light. It emits light.
It
it works really well in that sense. And
given that it's three atoms, and we're still
talking about something that is a very limited
number of atoms interacting with light. So
given that, it works extremely well.
And recently,
TSMC in Taiwan delivered, like, one
technology that was entirely based on a cousin
of tanks and sulfide. It was molybdenum sulfide.
Basically, the same properties just like different numbers,
but same properties.
So that in that sense, like, if you
if you think about, like, regular electronics, that's
like a strong candidate to actually outperform sync
silicon,
in the two d material world and can
do things that graphing cannot do at the
same time.
I see. Just to make things clear for,
if our listeners aren't that familiar with the
product properties of semiconductors,
when you talk about zero one, you're talking
about,
the fact that you can make
a a a a a transistor or a
switch or something that can toggle between
two distinct
states. And that sort of forms the basis
of of all our computers.
Whereas with graphene,
there there aren't the
two distinct states
if you make a transistor. Is that right?
That's right. That's right. So
let's say,
yes, that you can with some tweaks. You
can do that in graphene as well. But
let's say that the zero and one, the
the two bits that they use to process
regular information nowadays in our computers,
are not so well defined.
So
somehow it's hard to understand whether you're looking
at a zero or a one. At least
it's harder than it's in tanks and sulfide
where those two states are very well separated.
And so you can actually it's like the
the Morse code. You know? You can have
a, like, a zero one sequence that is
encoding a message, and you are sure that
the message you're seeing is the right one.
I see. And and what about hexagonal
boron nitride? Is that is that a a
newer material for you that you've you've just
started working on, or have you been working
on that for a while as well?
So it depends because hexagonal
boron nitride
is the the elephant in the room in
the in the two d materials business.
So we have seen in the past years
a number of physical phenomena that are that
are hosted by two d materials. Okay?
Reason for in 2018, there was, like, the
superconductivity
that emerged when you have twisted
layers of graphene.
There's nothing of that that you can see
if you don't send with your
between hexagonal and boron nitride.
Exagger and boron nitride, like, let's say you
have your,
your layer and you have hexagonal boron nitride
on top and on bottom, and then you
find the perfect environment for two d materials
to to thrive. Okay?
Now that's the caveat, though, because all of
that is done with, exfoliated
boron nitride.
So there's, one
big group in Japan and professor,
Watanabe and Taniguchi from the National Institute of
Material Science
that basically provided
that boron nitride to the community to the
whole community, which was great because it allowed
us to really do a lot of exciting
physics
with technically
a scotch tape.
Okay? The this is like a very democratic
way of doing science. You don't you don't
need big machinery to access exotic physics, exotic
phenomena. And
but then what if you wanna bring that
that that effects, those phenomena
to the market, to the scalable application?
There's no scalable boron nitride. Right?
So one of the, I used boron nitride
Scotch tape voice. Like, I've done some of
my devices with, like, a escalated boron nitride.
But
now I'm working on the I just started
working on the synthesis
of,
boron nitride large scale.
And,
that's being done something in in the past.
People have been working on scaling,
their boron nitride
production.
The quality is still not quite there yet.
Many, many attempts have been done. There are
some, like, reports of high quality,
CBD,
chemical vapor deposition boron
nitride, but it's still not there. And this
is something that,
I think it's like the the the the
holy grail of the of two d materials
at this point is to find the right
let's call it the dielectric,
the right substrate. The three materials are
ideally, like, the floating vacuum. Right? But they
need to be
put onto something.
And that's something has been always at boron
nitride. Exceptionally,
other strategies have been adopted. There are other
flat substrates.
But if you really wanna have the
pristine properties of any material,
so far, the boron nitride is unmatched.
I see. So that's so so you so
so to create a practical device, you you
have to be able to manufacture it
in a similar way in in a chip,
processing facility. And
I suppose the the
the the problem that you have is that
once you put your
lovely two d material
onto a substrate,
it's not two d anymore.
So you have to find a substrate that
will hold
the two d material, but not hold it
so tightly
that it changes it into a three d
material. Is that is that a big challenge?
It is it is
a a a big deal. Yes. And,
so the fact that as as I said
at the beginning, having a two d materials
means that it is actually really sensitive to
the surrounding,
to its surroundings. So if you place it
on silicon oxide so transistor these days are
made of, silicon and silicon oxide
structures.
Silicon oxide is rough.
Like, not rough as we might think of,
like mountains rough. But it for for something
that is one atom thick, it is rough.
And so they create this creates inhomogeneities
that make the two d material they're still
two d in that sense because it you
will still have, like,
their their properties being confined to the graphene
layer if you're talking graphene,
but
it will be
affected a lot by the substrate
being so inhomogeneous.
Boronitride, on the other sense, is as flat
as as graphene.
And if you have many main layers, it
can protect it from the surrounding environment. So
it's basically unperturbed graph.
Now, again, depends what
one wants to see because if we are
interested in just like the transport properties of
graphene,
it still is an incredible conductor even on
silicon oxide. No problem.
But if you're interested in taking advantage of
the superconductivity
phase as we as we talked before for
the twisted system,
that might be much more challenging on a
silicon oxide.
Ship. I see. And,
my understanding
is that
a lot of your research
is,
looking at ways to to address this challenge,
and you use
artificial intelligence
and other computational techniques
to develop
new two d materials and in such a
way that they can be scaled up,
for,
I suppose, commercial applications.
Can you can you describe that work?
Absolutely. So this is something that, I've been
working on for the past couple of years.
Just to give context, we,
developed this technique onto
something that we already knew how to synthesize,
but we knew we needed a proof of
concept that the the approach was actually valuable.
So we
no surprise, we synthetize some graph.
Something that people know,
how to do for for a long for
a long time. And
but then the same approach can be also
applied to,
boron nitride. What am I talking about? I'm
talking about the use of,
neural network in that sense.
AI is a really wide,
domain. Let's talk about neural network
that can propose a recipe.
Okay? They learn,
how to propose recipes
material. You provide a feedback
to the to the neural network.
And the neural network be as its parameter
adjusted in with the method that is called
Monte Carlo. That is,
let's say, a stochastic
approach. It it's a it's a method that
has been using physics for fifty years now
to find
the
minimum
of some of some states. Let's say it's
optimization problem. So you wanna find the the
the
most efficient,
configuration of your neural network that provides the
best,
neural network configuration for the best
recipe. Yes. So this is basically the the
the
the overall approach,
and it works really well for the graphing
synthesis.
We we we recently published that. It's
also an easy problem, though, because for the
graphene, we use,
silicon carbide,
as
a precursor for our graphene. So, basically, how
it works is that you take a piece
of silicon carbide. You heat it up in
a furnace.
The silicon atoms,
leave the surface. The carbon atoms that are
left behind
form
this carbon rich subs carbon,
rich layer that,
we call graphene.
And,
it's just like a temperature profile, so it's
an easy task for for a neural network
because we will also know.
But, technically, this method that has been, developed
by,
doctor White Lamb at
Berkeley National Lab
is,
can be applied for multiple parameters. So you
can tune temperature, mass flow, pressure,
and
we're trying to implement that for this synthesis
of boron nitride. Yes.
I see. And and can you talk a
bit more about,
about your work in the lab? What sort
of experimental techniques that you use to create
and characterize
two d materials? I mean, I'm guessing that
you're you're using things like electron microscopes
and and various
surface science techniques.
Yes. Exactly. So as a matter of fact,
the surface science is my background,
and, I've been working on photomission spectroscopy for
many years now. Spectroscopy in general, but photomission
spectroscopy is my core business. So we have,
an ultrasound for the mission spectroscopy,
that is coupled to a X-ray photomission
microscopy that is also coupled to a scanning
tunneling microscopy and a low energy electron diffraction.
Many names, many names, but they
they are basically
You must have a huge vacuum chamber then.
We we do. We do. We do. As
a matter of fact, yes. To be honest,
we have
three, four vacuum chambers. They're all connected.
So that's a major plus for us because
we need to prepare the sample once, and
then we can can use all of those
techniques all at once.
And,
yeah, we can basically have
my old supervisor used to say we we
we
are capable of getting the full Hamiltonian of
the system. So the electronic state, the structure,
everything that is necessary to characterize a a
material,
which is great. It's a little bit slow,
though, because you need to go to ultra
high vacuum. We need to pump overnight. So
in those in that feedback loop that I
was mentioning before,
I'm actually using,
Raman spectroscopy.
There is an optical spectroscopy technique,
extremely famous for two d materials,
which is extremely reliable in assessing
the quality of the of the system you're
looking at. So in that in that loop
where I need to give a feedback to
their to the neural network,
I use Raman spectroscopy just because it's fast.
So I can produce a sample scanning under
Raman spectroscopy.
Am I happy with it? Am I not
happy with it? They get a better result,
a worse result?
I tell that
in a way to the neural network, and
the neural network adjust
its parameters and produces a new recipe. And
so that that's that's extremely,
powerful in that sense. Of course, we need
to post validate that because Raman can also
hide some stuff.
So you need to make sure that they
you're interpreting the signal, in a in a
in a proper way.
So then we use the surface science characterization
tool to have a full understanding of what
if we're going in the right direction or
not. But this is something that happens afterwards.
Yes. Okay. And and so does that mean
you're sort of operating in a loop in
the sense that
your AI system will
say,
try this, try this, and try that. You
will do it. Feed that information back into
the AI.
It will come up with a better,
prediction
of a material that you want, and you're
sort of continuously
improving and and feeding back into the machine
learning system. Is that is that how it
works? Yeah. Correct. Exactly. So it starts with
a random guess by the neural network,
and,
I test it, and the the loop begins.
And then I start, like, measuring and then
provide this number, the score function to the
to the
to the system and then so on and
so forth.
It is interesting because
it, we say we don't give any prior
knowledge to the system. The the the main
so
lately, there's been a lot of, hype
be be behind the neural net the AI
for material science, especially be because of the
autonomous labs
that promise to deliver new materials, thousands of
new materials,
and new and since which is great,
but it's also, like,
heavy in terms of data. So it needs
to be trained on hundreds of thousands
of published results
to understand
what can be done, what are what crystals
can be synthesized, how they you can
do that. The thing is that if you're
training something
onto something that already exist,
it's likely to produce something that already exists.
So to rediscover
a material that people already know, and this
is something that's been,
reported lately as a as a, let's say,
bottleneck of this approach.
Our approach
is,
it doesn't it doesn't go through that kind
of training.
So the the neural network learns by doing.
So it's like a baby learning how to
walk.
And,
surprisingly, it does not require so many iteration
to reach, like, a,
a satisfying outcome.
And,
but it's extremely light in terms of data
consumption.
And,
this also makes it a little bit more
transferable to
university,
labs that are not,
funded with that kind of resources
and, of the autonomous labs, of course. And,
that makes it extremely
appealing for for us with the scientists still
in the loop, not outside.
I see.
Okay.
And and finally,
Antonio, I wanted to ask you about
quantum applications because 2025
is the international
year of quantum science and technology.
And I mean, I suppose in many ways,
two d materials are sort of ideal for
tweaking
the, you know, quantum properties and and discovering
new and exciting and useful
quantum properties. Can you can you just talk
a little bit about how
two d two d materials are being used,
to both explore,
I suppose, quantum physics and also develop
new technologies for, I don't know, quantum computing
or quantum sensors.
Yes. Absolutely. So the
quantum
is a is
a powerful word in that sense. Right? So
you can say that everything is quantum. But,
when you talk about quantum technology, you really
want to,
narrow down
your field
to something that is really explainable
by
something that's been accessible to people after 1925.
Some something that
it's really, really described by Schrodinger equation and
everything that came after that.
There's
a whole field of that. The quantum sensing
is one of these. For instance, you can
use two d materials like boron nitride and
the defects,
which is interesting because now you're not really
interested in the boron nitride per se. You're
interested into the defects that the boron nitride
can host, and they act as a, basically,
a spin sensor, magnetic field sensor.
So you can basically detect
the presence of a magnetic field very locally,
very precisely
onto a material that, as we said, is
really sensitive to the external environment. So it's
really powerful in that sense.
And this is just like one of a
one of a one example. Of course, defects
are
present in any of those two d materials.
And some of the TMDs, transition metal that
you're co organized, like tanks and sulfide that
we
discussed earlier,
is also a system that can host interesting
defects because they are,
so called two level system.
So the two level system now if we
go back to the to the electronics and
how it works,
it it sounds like zero and one. Right?
It's two states, zero and one.
One thumb tells you that those zero one
are not just like zero or one, but
are zero and one. So it's what's called
the qubit.
That that that way of processing information that
it's not
easy to interpret because it goes beyond our
common knowledge and our common sense of the
physics around us. So you need to get
familiar with this concept of superposition
of states that you can have multiple
combination of zero ones.
And tags and so five, for instance, is
one of those system that can host defects
that display those two those two states and
can be accessed. It can be manipulated. It
can be, used to, process quantum information.
And then there are, like, another other,
two d base quantum system, like, graphing based
Josephson junction that they used to detect microwave.
So you have superconductors
that is this new,
state of matter.
Well, no.
It's been known for a while, but it's
a,
it's a state of matter where current can
flow with no resistance into a material.
And you can create this junction where you
place one piece of that material, connect it
to graphene, and then connect it against with
another superconductor. This is a Josephson junction with
the weak link. It's called weak link. This
graphene in the middle.
What happens is that it's extremely sensitive
to
microwave radiation.
So exploiting this
intricate,
collective phenomena called superconductivity,
you can actually detect microwave radiation in a
extremely
powerful
and sensitive way. And so this is something
that is, like, unprecedented with respect to non
quantum technologies. So it's really promising. And this
is just to name a few.
But there's, something that I'm really excited about
is the,
what's called
quantum,
matter. That is, these these
new physics that arise when you place two
different material, one on top of the other.
And when you have
either the same material with the lattice,
misorientation,
so there's a twist angle between between the
two system or the entire different lattices, so
with different periodicity,
super structure arises. Okay? So you can imagine
like an ad like a crystal. Again, it's
a periodic arrangement of of of atoms. Okay?
So you have, let's say, a carbon atom
to every x angstrom.
Okay? And that periodically.
Now if you place another,
carbon lattice on top of this one and
you twist it a little bit, you will
see
a more structure. So a super periodicity, something
that has a longer periodicity
than,
than your than your constituent crystals.
It may look like just an optical effect,
but that's in fact,
electronic potential that the
electrons feel. Okay? So they are basically immersing
this new environment that the new periodic environment
that they explore.
Some of these system,
are formed in a way that this molecule
potential is essentially a quantum well. So
a well that can trap
electronic states,
excited states, any kind of states. So you
are making like an artificial lattice out of
two real lattices.
But now it's way more tunable because you
are not limited by the chemical bond that
keep atoms together. You can twist,
the twist angle as much as you want
and change its periodicity or combine different lattice
together. You have will have an entire new
periodicity.
And so this opens up, like, a number
of possibilities.
And one of these, I think I find
very exciting is, for instance, the quantum polaridonic,
external polaridonic,
field that is when
to to have to use, like, a catchphrase
is when light becomes matter
or when light matters,
meaning that, you will have a strong interaction
between a crystal and light, and then you
can trap the state of light and translate
the state of light into electronic state. You
can manipulate it, and then you can release
it as a new form of light.
And this is like a very exciting
way of,
working with light encoding information and quantum information.
I see. Yeah. I mean, it is a
I mean, I think the whole moire thing
is incredible because, you know, as you say,
you can just with a little twist, you
can create
a custom lattice almost with, you know, the
separation that you want to match, I don't
know, the the wavelength of a photon or
or whatever. It's I mean, it is it
is an amazing thing. And It is. It
is. It's extremely interesting also because, again, this
is something that you can do only with
two d materials, at least in an easy
way.
Exactly. Yeah. And, this makes, this is really
unique to the two d material world that
other systems
struggle to do.
Well, that's great, Antonio. Thanks so much for
coming on to the podcast and talking about,
two d materials. And,
I hope that your your research,
using machine learning
and,
all that stuff you've got in the lab
goes well.
Thank you. Thank you so much. It's been
a pleasure being here.
Discover cutting edge science in minutes.
IOP Publishing's new
Progress In series,
Research Highlights
website offers quick, accessible summaries
of top papers from leading journals, like reports
on progress in physics and progress in energy.
Whether you're short on time or just want
the essentials,
these highlights help you expand your knowledge on
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Perfect for busy researchers,
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without the deep dive.
To start reading,
just type progress in series
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into your favorite search engine
or follow the link in the notes for
this podcast.
I'm afraid that's all the time we have
for this week's podcast.
Thanks to Antonio Rossi
for his fascinating insights into two d materials.
And a special thanks to our producer, Fred
Ailes.
We'll be back again next week when Physics
World's Margaret Harris
presents the first in a series of episodes
recorded at the Heidelberg
Laureate Forum,
a meeting that attracts some of the best
and brightest
mathematicians
and computer scientists.
See you then.