August 6, 2025 • 19 mins
Welcome back to Adhesion Matters. Ever wondered if we could engineer glue so powerful it works underwater and endures tides? This episode dives into a wild, near-futuristic breakthrough that merges big data, bio-inspired chemistry, and machine learning to create a glue that’s practically unstoppable.
What you’ll discover:
- Evolution as a data source
Imagine using sequences from adhesive proteins found in bacteria, fungi—even viruses! The team mined over 24,000 such sequences across the tree of life to spot the common motifs that make things sticky, even when wet. - From nature to lab—180 new glues in one go
The researchers used random copolymer chemistry to recreate the identified protein patterns, synthesizing 180 unique hydrogels. Many outperformed the best-known natural adhesives for underwater strength. - A never-ending improvement loop via AI
Machine learning (Gaussian processes, random forests, Bayesian optimization) then took the stage—designing glue #2.0. Newly predicted formulations beat the original set, hitting underwater bond strengths beyond 1 MPa. That’s strong enough to hold a rubber duck against crashing tides—for a year. - Real-world test: Pond to pipe
In one demo, the hydrogel instantly patched a 20 mm pipe hole, preventing leaks for months. The power of inspiration meets real engineering potential.
Why this matters (and why it’s fascinating)
- Bridging disciplines — Combining bioinformatics, polymer chemistry, and AI to engineer new materials.
- Applications across fields — From marine repair to surgical adhesives, wearable devices to soft robotics.
- Storytelling gold — From protein sequence mines to ML-driven material wizardry, this is innovation you can hear.
Whether you geek out over AI, adhesives, materials innovation, or just love a great story of nature + tech, this episode will stick with you.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Lucas Adheron (00:00):
Okay, picture this with me.
A bright yellow rubber duck.
And it's stuck, really stuck,to a rock by the sea.
Right.
Not just for a little while,but for over a whole year.
It's getting hammered by tides,waves, everything.
And it just stays put.
Elena Bondwell (00:15):
That's quite an image.
And it really highlights theproblem.
Lucas Adheron (00:18):
Exactly.
For decades, making adhesivesthat work in water, especially
for soft stuff like hydrogels.
You know, the things in contactlenses or medical implants.
Elena Bondwell (00:28):
Yeah, those flexible, watery materials.
Lucas Adheron (00:30):
It's been like a holy grail for material science.
Seemed almost impossible.
Until now.
Elena Bondwell (00:36):
It's true.
Making things stick in wet,salty conditions is a huge
challenge, especially when youneed that material to be soft
and bendy.
Lucas Adheron (00:43):
It feels like a contradiction, doesn't it?
Elena Bondwell (00:45):
It really is.
A fundamental engineeringparadox that's held back a lot
of cool ideas.
Lucas Adheron (00:50):
And today, we're going to dive deep into exactly
that.
A really groundbreakingscientific development.
We're talking super adhesivehydrogels designed with help
from AI, artificialintelligence, but also getting
inspiration straight fromnature.
Elena Bondwell (01:03):
Yeah, our goal here is to unpack that whole
scientific journey for you.
We'll trace it right from thebiology that sparked the idea
through how AI helped optimizeit so you can really get how
these super glues actually cameabout.
Lucas Adheron (01:16):
And then we'll explore what they could actually
do.
Elena Bondwell (01:18):
Yeah.
Lucas Adheron (01:19):
The potential applications sound incredible
surgery, maybe even fixingthings deep under the sea.
Elena Bondwell (01:24):
The range is pretty staggering.
Lucas Adheron (01:26):
We've pulled together some great sources for
this deep dive.
Elena Bondwell (01:28):
Yeah.
Lucas Adheron (01:29):
A news piece from Nature, the actual research
paper also in Nature.
Elena Bondwell (01:32):
Right.
A press
Lucas Adheron (01:34):
release from Hokkaido University, something
from New Scientist, and even acool video from Nature's YouTube
channel.
So let's get started.
Elena Bondwell (01:42):
Let's do it.
Lucas Adheron (01:43):
Okay, so let's start right at the beginning.
Why is underwater adhesion sodifficult?
Elena Bondwell (01:49):
Yeah.
Lucas Adheron (01:49):
Especially for soft things like hydrogels.
It just seems like theyshouldn't stick.
Elena Bondwell (01:54):
It is pretty counterintuitive.
The basic problem comes down towhat hydrogels are.
They're soft, flexible, mostlywater, which is great for
contact lenses or putting thingsin the body, but those vary
properties.
Mm-hmm.
They're usually the exactopposite of what you need for
good adhesion.
Lucas Adheron (02:12):
How so?
Elena Bondwell (02:12):
Well, sticking usually needs strong close
contact.
You need to push the water outof the way to get the surfaces
to really interact.
Lucas Adheron (02:19):
Oh, OK.
And
Elena Bondwell (02:20):
that's incredibly hard to do with
something that's already full ofwater and
Lucas Adheron (02:25):
squishy.
Right.
And before AI got involved,what were the big roadblocks?
How did researchers even try tomake sticky hydrogels?
Elena Bondwell (02:33):
Well, historically, it was mostly
trial and error, reallyempirical stuff.
Lucas Adheron (02:37):
So mixing things.
Elena Bondwell (02:38):
Yeah.
Basically, you'd mix differentchemicals, make different
versions, test them out, andjust hope you stumbled onto
something that worked.
Lucas Adheron (02:44):
Sounds inefficient.
Elena Bondwell (02:45):
Oh, it was.
Incredibly expensive.
Took ages.
And it really limiteddeveloping materials that were
good enough for, say, medicaluse or big industrial jobs.
Lucas Adheron (02:55):
So it wasn't just about finding the right
ingredients list, butunderstanding the deeper
interactions, the structure.
Elena Bondwell (03:02):
Precisely.
When you're designing softmaterials, there are just
countless combinations of thebuilding blocks you can use and
how the tiny molecular structurerelates to the big picture
properties.
It's super complex, spansdifferent scales.
That complexity makes itreally, really hard to create
good predictive theories orcomputer models to guide the
(03:22):
design.
So you're stuck with that slow,painstaking experimental work.
Lucas Adheron (03:26):
Which brings us to the really cool part.
Faced with this massivechallenge, they looked to
nature.
organisms that already masteredsticking underwater.
What did they find?
Elena Bondwell (03:36):
Yeah, they looked at things like muscles,
you know, famous for clinging torocks underwater.
And they found these adhesiveproteins, the molecules
responsible for sticking, areactually everywhere in archaea,
bacteria, eukaryotes, evenviruses.
Lucas Adheron (03:49):
Wow.
All across life.
Elena Bondwell (03:51):
Exactly.
And despite all that diversity,what's really fascinating is
that these proteins share commonpatterns in their sequences,
underlying blueprints forsticking in the wet.
Lucas Adheron (03:59):
Nature figured it out multiple times.
Elena Bondwell (04:01):
It really did.
It had to be a solved problemin the natural world.
Lucas Adheron (04:04):
Okay, so they had this biological clue.
How did they then go about likereverse engineering nature's
recipe?
You mentioned data mining.
Elena Bondwell (04:12):
That's right.
They essentially went on a hugedigital expedition.
They put together this massivedata set, over 24,000 adhesive
protein sequences.
Lucas Adheron (04:21):
24,000?
Elena Bondwell (04:22):
Yep, from almost 4,000 different organisms, all
pulled from the NCBI proteindatabase.
That's the National Center forBiotechnology Information.
Lucas Adheron (04:32):
That's a ton of data.
What were they looking for?
What were the key nuggets theytried to pull out?
Elena Bondwell (04:36):
Well, first they narrowed it down, focusing on
the top 200 species known foradhesion.
From those, they generated whatthey called consensus
sequences, basically finding thecommon patterns.
And then they did a keysimplification.
They grouped all the differentamino acids, the building blocks
of proteins, into just sixfunctional classes, things like
hydrophobic, caseinic, aromatic.
Lucas Adheron (04:57):
So simplifying the complexity.
Elena Bondwell (04:59):
Exactly, based on chemical function.
Interestingly, they left outglycine, alanine, and proline
from the hydrophobic group,thinking their smaller size
wasn't as important forsticking.
Lucas Adheron (05:09):
Even with that simplification, did they find
anything surprising in the data,anything unexpected about how
nature does this?
Elena Bondwell (05:16):
Oh, absolutely.
What was fascinating was thateven when you looked at just
those broad functional classes,there was still a lot of
variation heterogeneity in thesequences.
Lucas Adheron (05:26):
So not just one magic pattern.
Elena Bondwell (05:28):
No, not at all.
And different species had theirown distinct ways these
functional classes paired up.
Also, they found that stretchesof the same functional class,
what they called block links,were usually very short,
typically less than three aminoacids in a row.
Lucas Adheron (05:44):
Interesting.
So it's more like a subtle mixthan big chunks of the same
stuff?
Elena Bondwell (05:48):
Precisely.
A really nuanced design, itseems, not just long, repetitive
sections.
Lucas Adheron (05:53):
Okay, this is a big jump now.
Elena Bondwell (05:54):
Yeah.
Lucas Adheron (05:55):
How do you take those complex, subtle patterns
from natural proteins andactually build something similar
in the lab using syntheticpolymers?
That sounds really hard,controlling sequences like that.
Elena Bondwell (06:05):
It was definitely the big conceptual
leap, but their strategy wasquite clever.
Lucas Adheron (06:10):
Yeah.
Elena Bondwell (06:10):
They decided to use six specific chemical
building blocks monomers torepresent those six amino acid
classes.
Lucas Adheron (06:17):
Okay, a synthetic translation.
Elena Bondwell (06:18):
Right.
And since getting exactsequence control in polymers is
notoriously difficult.
Lucas Adheron (06:24):
Yeah, I can imagine.
Elena Bondwell (06:25):
They aimed to statistically replicate the
natural patterns.
They used a technique calledideal random copolymerization.
Lucas Adheron (06:33):
ideal random.
What does that mean?
Elena Bondwell (06:35):
It basically means the different monomers get
incorporated into the polymerchain randomly, but in a way
that keeps the overallproportions consistent
throughout the chain formation.
It lets you mimic thosestatistical features of nature
sequences without needingperfect placement.
Lucas Adheron (06:49):
That is clever.
A statistical mimic.
So, okay, theory's one thing.
Did it actually work?
What happened when they madethis first batch of hydrogels
based on the data mining, theDM-driven ones?
Elena Bondwell (07:01):
It was a pretty significant success, actually.
This whole approach led them tosynthesize 180 unique
hydrogels.
Lucas Adheron (07:08):
Wow, 180?
Elena Bondwell (07:09):
And many of them performed better than
previously reported underwateradhesives, one which they called
G042 or GMAX.
Lucas Adheron (07:17):
GMAX, okay.
Elena Bondwell (07:17):
It reached an adhesive strength of 147
kilopascals, which was, youknow, quite impressive at the
time.
Lucas Adheron (07:24):
That's definitely a solid start.
But how did they double check?
How did they make sure thisdata mining approach was really
the reason for the success, notjust luck?
Elena Bondwell (07:33):
Good question.
They did two crucial validationtests.
Lucas Adheron (07:37):
Okay.
Elena Bondwell (07:37):
First, they designed some gels based on
resalin proteins.
These are natural proteins, butthey aren't adhesive.
Lucas Adheron (07:44):
A negative control.
Elena Bondwell (07:45):
Exactly.
And as expected, thoseresalin-based gels were not
sticky at all.
That confirmed the specificfeatures from the adhesive
proteins were key.
Lucas Adheron (07:54):
Makes sense.
What was the second test?
Elena Bondwell (07:55):
Second, they tried making gels using a
different synthesis method, onecalled non-ideal
copolymerization.
This tends to create blockysequences, clumps of the same
monomer together, less random.
Lucas Adheron (08:07):
Right, not like the subtle mix they saw in
nature.
Elena Bondwell (08:09):
Precisely.
And those blocky gels showedsignificantly lower adhesion.
That really drove home thepoint that mimicking the
statistical nature of thesequences using that ideal
random copolymerization wascritical.
Lucas Adheron (08:22):
OK, that really does seem to validate the whole
approach.
Yeah.
So now they have this data setof 180 gels designed based on
nature and they know theapproach works.
Enter the A.I.
Elena Bondwell (08:30):
Exactly.
That initial set of 180DM-driven hydrogels was a
high-quality data set, perfectfodder for machine learning.
Lucas Adheron (08:38):
What did the AI do?
Elena Bondwell (08:39):
They tested nine different machine learning
models to see which could bestpredict adhesive strength just
based on the monomeringredients.
Lucas Adheron (08:46):
And the winners were?
Elena Bondwell (08:47):
Gaussian process, or GP, and random force
regression, RFR.
Those two came out on top formaking accurate predictions.
Lucas Adheron (08:55):
Okay, so the AI could predict stickiness.
How did they use that toactually improve the gels Was it
just one prediction or moredynamic?
Elena Bondwell (09:03):
Oh, much more dynamic.
They set up something called asequential model-based
optimization workflow, SMBO.
Lucas Adheron (09:11):
SMBO.
Sounds fancy.
Elena Bondwell (09:12):
It's basically an iterative loop.
The AI analyzes the currentdata, then proposes a new batch
of hydrogel recipes.
Things will be even better.
Lucas Adheron (09:20):
Ah, like suggesting experiments.
Elena Bondwell (09:21):
Exactly.
Then those get made in the lab,tested, and the new results are
fed back into the AI model.
It learns and suggests again.
Lucas Adheron (09:29):
So it's a learning cycle.
Trying to cut down on that slowlab work.
Elena Bondwell (09:32):
Precisely.
The goal was to massively speedup the discovery process and
find the truly optimalformulations without doing
thousands of experiments byhand.
Lucas Adheron (09:42):
And connecting this back to the big picture,
what was the ultimate result?
How much better did the AI makethese hydrogels?
Did it tell them why they werebetter?
Elena Bondwell (09:51):
The results were, frankly, astounding.
This ML optimization led to anew generation, the ML-driven
hydrogels, and their underwateradhesive strength went over one
megapascal.
Lucas Adheron (10:02):
Whoa.
One megapascal.
How much stronger is that?
Elena Bondwell (10:05):
That's an order of magnitude stronger.
Roughly 10 times stickier thanthe best previously reported
underwater hydrogels or evenelastomers.
Lucas Adheron (10:12):
10 times.
That's incredible.
Elena Bondwell (10:14):
It really is.
To give you a visual, a littlepatch the size of a postage
stamp, maybe 2.5 by 2.5centimeters, could theoretically
hold up around 63 kilograms,like an adult human's weight.
Lucas Adheron (10:25):
Get out.
That's unbelievable.
Elena Bondwell (10:26):
It's pretty mind-blowing performance.
Now, as for the why, the AI wasbrilliant at finding the best
ingredients But
Lucas Adheron (10:34):
not necessarily the deep scientific reason why
those ratios work so well.
Elena Bondwell (10:38):
Not entirely.
It identified the crucialcomponents, but the fundamental
physics or chemistry behind thatextreme stickiness.
That's actually still an activearea of research.
The AI found the solution, butwe're still unpacking exactly
how it works at the most basiclevel.
Lucas Adheron (10:54):
That is fascinating.
Yeah.
AI outpaces our understandingsometimes.
So what did the AI point to?
What were the key ingredientsor design principles it
highlighted?
Elena Bondwell (11:04):
Looking at the data the AI generated, a clear
principle emerged.
You needed high amounts of twomonomers, BA, which is
hydrophobic.
Lucas Adheron (11:12):
Water repelling.
Elena Bondwell (11:13):
Right.
And PEA, which is aromatic,plus a moderate amount of ATAC,
which is cationic or positivelycharged.
Lucas Adheron (11:20):
Okay.
So that specific combo was thesecret sauce.
Elena Bondwell (11:23):
That seemed to be the key.
The thinking is that BA and PEAhelp kick water out from the
interface between the gel andthe surface it's sticking to.
That's vital for wet adhesion.
Lucas Adheron (11:33):
Right.
Got to get rid of the waterbarrier.
Elena Bondwell (11:34):
Exactly.
And then the ATAC, the Kishinokpart, helps form electrostatic
bonds with surfaces that aretypically negatively charged,
like glass.
So it's a one-two punch.
Lucas Adheron (11:43):
Hydrophobic push, electrostatic pull.
Elena Bondwell (11:45):
Something like that.
A powerful synergy that the AIreally zeroed in on and
optimized.
Lucas Adheron (11:50):
When they compared these top AI design
gels, the R1 Max, R2 Max, R3Max, to the best one from the
first phase, G-Max.
Elena Bondwell (11:58):
Yeah.
Lucas Adheron (11:59):
What were the differences?
Were they just stickier?
Elena Bondwell (12:01):
They were definitely stickier, but also
different in other ways.
The ML gels were more opaque,more viscoelastic, meaning sort
of stretchy, but also able toflow a bit and significantly
stronger and toughermechanically.
Lucas Adheron (12:13):
And why was that?
Elena Bondwell (12:15):
It's thought to be mainly due to that higher
hydrophobic content we justtalked about.
It allows the material todissipate energy better when
stretched or stressed, making itmore resilient.
Lucas Adheron (12:24):
So not just super sticky, but also tough.
Did they hold up over time orunder stress?
Elena Bondwell (12:29):
Their durability was remarkable.
Take R1 Max.
It hit over one MPa on glassand saltwater, which is
impressive enough.
Lucas Adheron (12:36):
Yeah.
Elena Bondwell (12:36):
But it kept strong adhesion even after 200
cycles of sticking andunsticking it.
Lucas Adheron (12:42):
Wow.
200 times.
Elena Bondwell (12:43):
And it wasn't just glass.
It stuck strongly to all sortsof things, plastics, metals,
other inorganic stuff.
They even showed it holdingtogether joints between
different materials like ceramicand saponium under a one kilo
shear load, For over a year.
Lucas Adheron (12:57):
For a year.
Under load.
Elena Bondwell (12:59):
Yes.
That kind of long-termperformance in wet conditions is
just, it's really exceptionalfor adhesives like this.
Lucas Adheron (13:05):
Okay.
That's the serious labvalidation.
But let's get back to thoseamazing demos.
The rubber duck.
I mean, how did that actuallywork sticking through ocean
waves?
Elena Bondwell (13:15):
Ah, yes.
The rubber duck heard aroundthe world.
Well, maybe not quite, but itwas effective.
They used the R1 Max gel.
Okay.
And they literally just stuck arubber duck onto a rock in the
splash zone at the seaside.
And it stayed there, enduringconstant tides, wave impacts,
proving it could handle reallyharsh, real-world marine
environments.
Lucas Adheron (13:35):
That's just brilliant.
A perfect visual.
What about the leaky pipeexample?
That sounded more practical.
Elena Bondwell (13:39):
Extremely practical.
For that, they used the R2 Maxgel, which was particularly good
in deionized water, like tapwater.
They had this tallpolycarbonate pipe, three meters
high, filled with water.
And they put a 20-millimeterhole that's pretty big right at
the bottom.
Lucas Adheron (13:54):
Okay, so high pressure coming out.
Elena Bondwell (13:55):
Serious pressure.
A burst flow rate around 5.4meters per second.
Water just gushing out.
Lucas Adheron (14:00):
Yeah.
Elena Bondwell (14:01):
They slapped a patch of the R2 Max gel over the
hole and it instantly sealedit.
Stopped the leak completely.
Lucas Adheron (14:08):
Instantly.
Under that pressure.
Elena Bondwell (14:10):
Instantly.
Lucas Adheron (14:10):
Yeah.
Elena Bondwell (14:11):
And just for comparison, they tried a
commercial adhesive sealantunder the exact same condition.
Lucas Adheron (14:16):
Oh, and does that go?
Elena Bondwell (14:17):
It failed.
Gave way in about an hour and ahalf.
The hydrogel just held strong.
Lucas Adheron (14:22):
That is genuinely game-changing performance for
emergency repairs, potentially.
Elena Bondwell (14:26):
Absolutely.
Think about underwater repairs,emergency plumbing fixes,
situations where commonadhesives just can't cope.
Lucas Adheron (14:34):
And beyond sticking ducts and fixing pipes,
what about inside the body?
You mentioned biomedicalpotential.
Were they safe?
Elena Bondwell (14:42):
That's a crucial question, of course.
They did biocompatibilitytests, including implanting the
hydrogels under the skin inmice.
And they found goodbiocompatibility, no significant
adverse reactions, which reallydoes open the door for
potential uses like surgicalglues.
Lucas Adheron (14:59):
Closing wounds without stitches.
Elena Bondwell (15:01):
Potentially, yes.
Or maybe for fixing implantssecurely inside the body where
things are obviously very wet.
Lucas Adheron (15:08):
It really seems these materials are incredibly
versatile.
Does performance change muchdepending on the water, like
saltwater versus freshwater?
Does nature do that too?
Elena Bondwell (15:18):
That's a really sharp observation.
And yes, they found that smalltweaks in the hydrogels
composition did change how wellit's stuck in different
environments, like deionizedwater versus artificial
seawater.
Lucas Adheron (15:28):
Interesting.
Elena Bondwell (15:29):
And that absolutely mirrors what we see
in nature, right?
Organisms evolved to be reallygood at sticking in their
specific environment, notnecessarily to be the best
everywhere.
Lucas Adheron (15:38):
So adaptability rather than one size fits all.
Elena Bondwell (15:41):
Exactly.
these AI-designed gels might becapturing some of that natural
principle too, differentformulations for different
conditions.
Lucas Adheron (15:49):
Okay, stepping back then, what's the big
takeaway here for materialscience?
This feels like more than justfinding a new glue.
Oh,
Elena Bondwell (15:56):
absolutely.
This whole approach, blendingthe protein data, the smart
polymer synthesis, the iterativeAI learning loop, It really
represents a, well, a paradigmshift.
Lucas Adheron (16:08):
A new way of doing things.
Elena Bondwell (16:09):
A new way of designing high-performance soft
materials.
Much more systematic, muchfaster than before.
Lucas Adheron (16:15):
And presumably this method isn't just for
making things sticky, right?
Could it be used for othermaterial properties?
Elena Bondwell (16:21):
Precisely.
This is a framework.
A systematic, scalable,start-to-finish method for
developing all kinds offunctional soft materials.
Lucas Adheron (16:29):
Like what?
What else could we design thisway?
Elena Bondwell (16:32):
Well, imagine next generation flexible
electronics that can stretch orconform to complex shapes, or
new kinds of soft robots thatmove more like natural
organisms, advanced biomedicaldevices.
The list goes on.
Lucas Adheron (16:44):
Custom designing materials on demand almost.
Elena Bondwell (16:47):
That's the dream.
Tailoring materials for veryspecific, very challenging jobs.
Lucas Adheron (16:52):
Of course, it can't all be smooth sailing.
Even with this breakthrough,what challenges are still out
there?
What are the researchers stillworking on?
Elena Bondwell (16:59):
They're very upfront about the limitations,
which is good science.
One is just the sheer diversityof monomers, the chemical
building blocks that arecurrently available and well
understood for this kind ofsynthesis.
Lucas Adheron (17:10):
Need more Lego bricks, essentially.
Elena Bondwell (17:11):
Kind of, yeah.
Also improving the polymersynthesis techniques themselves
to get even finer control overthe sequence and structure and
just scaling up the data sets,making sure the AI has enough
high quality data to learn from,especially as they target even
more complex material functions.
Lucas Adheron (17:29):
So what's the path forward?
How do we tackle those issues?
Elena Bondwell (17:31):
It'll likely involve expanding those
libraries of functionalmonomers, pushing polymer
chemistry forward and cruciallydeveloping even smarter AI
models.
Lucas Adheron (17:40):
More how?
Elena Bondwell (17:41):
Maybe physics informed AI Models that don't
just see patterns in data, buthave some built-in understanding
of the underlying chemistry andphysics.
Lucas Adheron (17:49):
Ah, so they can generalize better, maybe predict
things even with less data?
Elena Bondwell (17:54):
That's the hope.
Making the whole design processeven more powerful and
efficient.
Moving beyond just finding whatworks to really understanding
why it works computationally.
Lucas Adheron (18:03):
Okay, so here's something fascinating to leave
our listeners with.
Something to really thinkabout.
Despite this incredible successstory, the AI...
Finding the recipe.
The material sticking ducts torocks.
Fixing pipes.
According to the sources, theresearchers admit they still
don't fully understand thefundamental reason why this
(18:24):
material is so incrediblysticky.
Elena Bondwell (18:26):
It's true.
The deep mechanism isn't fullynailed down.
Lucas Adheron (18:30):
Think about that.
AI helped create this amazingthing.
It works incredibly well.
But the absolute rock bottomscience of why.
It's still a bit of a mystery.
Still more digging to do.
Elena Bondwell (18:42):
It's a fantastic point.
It shows how powerful thesetools are, but also that there's
always more to learn, morefundamental science to uncover.
Nature and AI working together,but still holding some secrets.
Lucas Adheron (18:52):
So we've gone from nature's own sticky
proteins all the way to theseAI-crafted hydrogels, materials
that can literally glue a duckto a seaside rock for a year or
stop a high-pressure leak in itstracks.
Elena Bondwell (19:03):
It really showcases what's possible when
you combine biologicalinspiration with cutting-edge AI
and material science.
Lucas Adheron (19:10):
And this isn't just some obscure lab curiosity.
As we heard, it's a realglimpse into a future where
materials can be designed almoston demand for incredible tasks,
solving problems we used tothink were just, well,
impossible.
Elena Bondwell (19:25):
A future built on understanding nature better
and using AI to translate thatunderstanding into reality.
Lucas Adheron (19:30):
So we really hope this gets you thinking.
What other huge challenges outthere might be solvable if we
look closely at nature andcleverly apply tools like AI?
The possibilities, when youthink about it, seem truly
boundless.
Okay, picture this with me.
A bright yellow rubber duck.
And it's stuck, really stuck,to a rock by the sea.
Right.
Not just for a little while,but for over a whole year.
It's getting hammered by tides,waves, everything.
And it just stays put.
Elena Bondwell (00:15):
That's quite an image.
And it really highlights theproblem.
Lucas Adheron (00:18):
Exactly.
For decades, making adhesivesthat work in water, especially
for soft stuff like hydrogels.
You know, the things in contactlenses or medical implants.
Elena Bondwell (00:28):
Yeah, those flexible, watery materials.
Lucas Adheron (00:30):
It's been like a holy grail for material science.
Seemed almost impossible.
Until now.
Elena Bondwell (00:36):
It's true.
Making things stick in wet,salty conditions is a huge
challenge, especially when youneed that material to be soft
and bendy.
Lucas Adheron (00:43):
It feels like a contradiction, doesn't it?
Elena Bondwell (00:45):
It really is.
A fundamental engineeringparadox that's held back a lot
of cool ideas.
Lucas Adheron (00:50):
And today, we're going to dive deep into exactly
that.
A really groundbreakingscientific development.
We're talking super adhesivehydrogels designed with help
from AI, artificialintelligence, but also getting
inspiration straight fromnature.
Elena Bondwell (01:03):
Yeah, our goal here is to unpack that whole
scientific journey for you.
We'll trace it right from thebiology that sparked the idea
through how AI helped optimizeit so you can really get how
these super glues actually cameabout.
Lucas Adheron (01:16):
And then we'll explore what they could actually
do.
Elena Bondwell (01:18):
Yeah.
Lucas Adheron (01:19):
The potential applications sound incredible
surgery, maybe even fixingthings deep under the sea.
Elena Bondwell (01:24):
The range is pretty staggering.
Lucas Adheron (01:26):
We've pulled together some great sources for
this deep dive.
Elena Bondwell (01:28):
Yeah.
Lucas Adheron (01:29):
A news piece from Nature, the actual research
paper also in Nature.
Elena Bondwell (01:32):
Right.
A press
Lucas Adheron (01:34):
release from Hokkaido University, something
from New Scientist, and even acool video from Nature's YouTube
channel.
So let's get started.
Elena Bondwell (01:42):
Let's do it.
Lucas Adheron (01:43):
Okay, so let's start right at the beginning.
Why is underwater adhesion sodifficult?
Elena Bondwell (01:49):
Yeah.
Lucas Adheron (01:49):
Especially for soft things like hydrogels.
It just seems like theyshouldn't stick.
Elena Bondwell (01:54):
It is pretty counterintuitive.
The basic problem comes down towhat hydrogels are.
They're soft, flexible, mostlywater, which is great for
contact lenses or putting thingsin the body, but those vary
properties.
Mm-hmm.
They're usually the exactopposite of what you need for
good adhesion.
Lucas Adheron (02:12):
How so?
Elena Bondwell (02:12):
Well, sticking usually needs strong close
contact.
You need to push the water outof the way to get the surfaces
to really interact.
Lucas Adheron (02:19):
Oh, OK.
And
Elena Bondwell (02:20):
that's incredibly hard to do with
something that's already full ofwater and
Lucas Adheron (02:25):
squishy.
Right.
And before AI got involved,what were the big roadblocks?
How did researchers even try tomake sticky hydrogels?
Elena Bondwell (02:33):
Well, historically, it was mostly
trial and error, reallyempirical stuff.
Lucas Adheron (02:37):
So mixing things.
Elena Bondwell (02:38):
Yeah.
Basically, you'd mix differentchemicals, make different
versions, test them out, andjust hope you stumbled onto
something that worked.
Lucas Adheron (02:44):
Sounds inefficient.
Elena Bondwell (02:45):
Oh, it was.
Incredibly expensive.
Took ages.
And it really limiteddeveloping materials that were
good enough for, say, medicaluse or big industrial jobs.
Lucas Adheron (02:55):
So it wasn't just about finding the right
ingredients list, butunderstanding the deeper
interactions, the structure.
Elena Bondwell (03:02):
Precisely.
When you're designing softmaterials, there are just
countless combinations of thebuilding blocks you can use and
how the tiny molecular structurerelates to the big picture
properties.
It's super complex, spansdifferent scales.
That complexity makes itreally, really hard to create
good predictive theories orcomputer models to guide the
(03:22):
design.
So you're stuck with that slow,painstaking experimental work.
Lucas Adheron (03:26):
Which brings us to the really cool part.
Faced with this massivechallenge, they looked to
nature.
organisms that already masteredsticking underwater.
What did they find?
Elena Bondwell (03:36):
Yeah, they looked at things like muscles,
you know, famous for clinging torocks underwater.
And they found these adhesiveproteins, the molecules
responsible for sticking, areactually everywhere in archaea,
bacteria, eukaryotes, evenviruses.
Lucas Adheron (03:49):
Wow.
All across life.
Elena Bondwell (03:51):
Exactly.
And despite all that diversity,what's really fascinating is
that these proteins share commonpatterns in their sequences,
underlying blueprints forsticking in the wet.
Lucas Adheron (03:59):
Nature figured it out multiple times.
Elena Bondwell (04:01):
It really did.
It had to be a solved problemin the natural world.
Lucas Adheron (04:04):
Okay, so they had this biological clue.
How did they then go about likereverse engineering nature's
recipe?
You mentioned data mining.
Elena Bondwell (04:12):
That's right.
They essentially went on a hugedigital expedition.
They put together this massivedata set, over 24,000 adhesive
protein sequences.
Lucas Adheron (04:21):
24,000?
Elena Bondwell (04:22):
Yep, from almost 4,000 different organisms, all
pulled from the NCBI proteindatabase.
That's the National Center forBiotechnology Information.
Lucas Adheron (04:32):
That's a ton of data.
What were they looking for?
What were the key nuggets theytried to pull out?
Elena Bondwell (04:36):
Well, first they narrowed it down, focusing on
the top 200 species known foradhesion.
From those, they generated whatthey called consensus
sequences, basically finding thecommon patterns.
And then they did a keysimplification.
They grouped all the differentamino acids, the building blocks
of proteins, into just sixfunctional classes, things like
hydrophobic, caseinic, aromatic.
Lucas Adheron (04:57):
So simplifying the complexity.
Elena Bondwell (04:59):
Exactly, based on chemical function.
Interestingly, they left outglycine, alanine, and proline
from the hydrophobic group,thinking their smaller size
wasn't as important forsticking.
Lucas Adheron (05:09):
Even with that simplification, did they find
anything surprising in the data,anything unexpected about how
nature does this?
Elena Bondwell (05:16):
Oh, absolutely.
What was fascinating was thateven when you looked at just
those broad functional classes,there was still a lot of
variation heterogeneity in thesequences.
Lucas Adheron (05:26):
So not just one magic pattern.
Elena Bondwell (05:28):
No, not at all.
And different species had theirown distinct ways these
functional classes paired up.
Also, they found that stretchesof the same functional class,
what they called block links,were usually very short,
typically less than three aminoacids in a row.
Lucas Adheron (05:44):
Interesting.
So it's more like a subtle mixthan big chunks of the same
stuff?
Elena Bondwell (05:48):
Precisely.
A really nuanced design, itseems, not just long, repetitive
sections.
Lucas Adheron (05:53):
Okay, this is a big jump now.
Elena Bondwell (05:54):
Yeah.
Lucas Adheron (05:55):
How do you take those complex, subtle patterns
from natural proteins andactually build something similar
in the lab using syntheticpolymers?
That sounds really hard,controlling sequences like that.
Elena Bondwell (06:05):
It was definitely the big conceptual
leap, but their strategy wasquite clever.
Lucas Adheron (06:10):
Yeah.
Elena Bondwell (06:10):
They decided to use six specific chemical
building blocks monomers torepresent those six amino acid
classes.
Lucas Adheron (06:17):
Okay, a synthetic translation.
Elena Bondwell (06:18):
Right.
And since getting exactsequence control in polymers is
notoriously difficult.
Lucas Adheron (06:24):
Yeah, I can imagine.
Elena Bondwell (06:25):
They aimed to statistically replicate the
natural patterns.
They used a technique calledideal random copolymerization.
Lucas Adheron (06:33):
ideal random.
What does that mean?
Elena Bondwell (06:35):
It basically means the different monomers get
incorporated into the polymerchain randomly, but in a way
that keeps the overallproportions consistent
throughout the chain formation.
It lets you mimic thosestatistical features of nature
sequences without needingperfect placement.
Lucas Adheron (06:49):
That is clever.
A statistical mimic.
So, okay, theory's one thing.
Did it actually work?
What happened when they madethis first batch of hydrogels
based on the data mining, theDM-driven ones?
Elena Bondwell (07:01):
It was a pretty significant success, actually.
This whole approach led them tosynthesize 180 unique
hydrogels.
Lucas Adheron (07:08):
Wow, 180?
Elena Bondwell (07:09):
And many of them performed better than
previously reported underwateradhesives, one which they called
G042 or GMAX.
Lucas Adheron (07:17):
GMAX, okay.
Elena Bondwell (07:17):
It reached an adhesive strength of 147
kilopascals, which was, youknow, quite impressive at the
time.
Lucas Adheron (07:24):
That's definitely a solid start.
But how did they double check?
How did they make sure thisdata mining approach was really
the reason for the success, notjust luck?
Elena Bondwell (07:33):
Good question.
They did two crucial validationtests.
Lucas Adheron (07:37):
Okay.
Elena Bondwell (07:37):
First, they designed some gels based on
resalin proteins.
These are natural proteins, butthey aren't adhesive.
Lucas Adheron (07:44):
A negative control.
Elena Bondwell (07:45):
Exactly.
And as expected, thoseresalin-based gels were not
sticky at all.
That confirmed the specificfeatures from the adhesive
proteins were key.
Lucas Adheron (07:54):
Makes sense.
What was the second test?
Elena Bondwell (07:55):
Second, they tried making gels using a
different synthesis method, onecalled non-ideal
copolymerization.
This tends to create blockysequences, clumps of the same
monomer together, less random.
Lucas Adheron (08:07):
Right, not like the subtle mix they saw in
nature.
Elena Bondwell (08:09):
Precisely.
And those blocky gels showedsignificantly lower adhesion.
That really drove home thepoint that mimicking the
statistical nature of thesequences using that ideal
random copolymerization wascritical.
Lucas Adheron (08:22):
OK, that really does seem to validate the whole
approach.
Yeah.
So now they have this data setof 180 gels designed based on
nature and they know theapproach works.
Enter the A.I.
Elena Bondwell (08:30):
Exactly.
That initial set of 180DM-driven hydrogels was a
high-quality data set, perfectfodder for machine learning.
Lucas Adheron (08:38):
What did the AI do?
Elena Bondwell (08:39):
They tested nine different machine learning
models to see which could bestpredict adhesive strength just
based on the monomeringredients.
Lucas Adheron (08:46):
And the winners were?
Elena Bondwell (08:47):
Gaussian process, or GP, and random force
regression, RFR.
Those two came out on top formaking accurate predictions.
Lucas Adheron (08:55):
Okay, so the AI could predict stickiness.
How did they use that toactually improve the gels Was it
just one prediction or moredynamic?
Elena Bondwell (09:03):
Oh, much more dynamic.
They set up something called asequential model-based
optimization workflow, SMBO.
Lucas Adheron (09:11):
SMBO.
Sounds fancy.
Elena Bondwell (09:12):
It's basically an iterative loop.
The AI analyzes the currentdata, then proposes a new batch
of hydrogel recipes.
Things will be even better.
Lucas Adheron (09:20):
Ah, like suggesting experiments.
Elena Bondwell (09:21):
Exactly.
Then those get made in the lab,tested, and the new results are
fed back into the AI model.
It learns and suggests again.
Lucas Adheron (09:29):
So it's a learning cycle.
Trying to cut down on that slowlab work.
Elena Bondwell (09:32):
Precisely.
The goal was to massively speedup the discovery process and
find the truly optimalformulations without doing
thousands of experiments byhand.
Lucas Adheron (09:42):
And connecting this back to the big picture,
what was the ultimate result?
How much better did the AI makethese hydrogels?
Did it tell them why they werebetter?
Elena Bondwell (09:51):
The results were, frankly, astounding.
This ML optimization led to anew generation, the ML-driven
hydrogels, and their underwateradhesive strength went over one
megapascal.
Lucas Adheron (10:02):
Whoa.
One megapascal.
How much stronger is that?
Elena Bondwell (10:05):
That's an order of magnitude stronger.
Roughly 10 times stickier thanthe best previously reported
underwater hydrogels or evenelastomers.
Lucas Adheron (10:12):
10 times.
That's incredible.
Elena Bondwell (10:14):
It really is.
To give you a visual, a littlepatch the size of a postage
stamp, maybe 2.5 by 2.5centimeters, could theoretically
hold up around 63 kilograms,like an adult human's weight.
Lucas Adheron (10:25):
Get out.
That's unbelievable.
Elena Bondwell (10:26):
It's pretty mind-blowing performance.
Now, as for the why, the AI wasbrilliant at finding the best
ingredients But
Lucas Adheron (10:34):
not necessarily the deep scientific reason why
those ratios work so well.
Elena Bondwell (10:38):
Not entirely.
It identified the crucialcomponents, but the fundamental
physics or chemistry behind thatextreme stickiness.
That's actually still an activearea of research.
The AI found the solution, butwe're still unpacking exactly
how it works at the most basiclevel.
Lucas Adheron (10:54):
That is fascinating.
Yeah.
AI outpaces our understandingsometimes.
So what did the AI point to?
What were the key ingredientsor design principles it
highlighted?
Elena Bondwell (11:04):
Looking at the data the AI generated, a clear
principle emerged.
You needed high amounts of twomonomers, BA, which is
hydrophobic.
Lucas Adheron (11:12):
Water repelling.
Elena Bondwell (11:13):
Right.
And PEA, which is aromatic,plus a moderate amount of ATAC,
which is cationic or positivelycharged.
Lucas Adheron (11:20):
Okay.
So that specific combo was thesecret sauce.
Elena Bondwell (11:23):
That seemed to be the key.
The thinking is that BA and PEAhelp kick water out from the
interface between the gel andthe surface it's sticking to.
That's vital for wet adhesion.
Lucas Adheron (11:33):
Right.
Got to get rid of the waterbarrier.
Elena Bondwell (11:34):
Exactly.
And then the ATAC, the Kishinokpart, helps form electrostatic
bonds with surfaces that aretypically negatively charged,
like glass.
So it's a one-two punch.
Lucas Adheron (11:43):
Hydrophobic push, electrostatic pull.
Elena Bondwell (11:45):
Something like that.
A powerful synergy that the AIreally zeroed in on and
optimized.
Lucas Adheron (11:50):
When they compared these top AI design
gels, the R1 Max, R2 Max, R3Max, to the best one from the
first phase, G-Max.
Elena Bondwell (11:58):
Yeah.
Lucas Adheron (11:59):
What were the differences?
Were they just stickier?
Elena Bondwell (12:01):
They were definitely stickier, but also
different in other ways.
The ML gels were more opaque,more viscoelastic, meaning sort
of stretchy, but also able toflow a bit and significantly
stronger and toughermechanically.
Lucas Adheron (12:13):
And why was that?
Elena Bondwell (12:15):
It's thought to be mainly due to that higher
hydrophobic content we justtalked about.
It allows the material todissipate energy better when
stretched or stressed, making itmore resilient.
Lucas Adheron (12:24):
So not just super sticky, but also tough.
Did they hold up over time orunder stress?
Elena Bondwell (12:29):
Their durability was remarkable.
Take R1 Max.
It hit over one MPa on glassand saltwater, which is
impressive enough.
Lucas Adheron (12:36):
Yeah.
Elena Bondwell (12:36):
But it kept strong adhesion even after 200
cycles of sticking andunsticking it.
Lucas Adheron (12:42):
Wow.
200 times.
Elena Bondwell (12:43):
And it wasn't just glass.
It stuck strongly to all sortsof things, plastics, metals,
other inorganic stuff.
They even showed it holdingtogether joints between
different materials like ceramicand saponium under a one kilo
shear load, For over a year.
Lucas Adheron (12:57):
For a year.
Under load.
Elena Bondwell (12:59):
Yes.
That kind of long-termperformance in wet conditions is
just, it's really exceptionalfor adhesives like this.
Lucas Adheron (13:05):
Okay.
That's the serious labvalidation.
But let's get back to thoseamazing demos.
The rubber duck.
I mean, how did that actuallywork sticking through ocean
waves?
Elena Bondwell (13:15):
Ah, yes.
The rubber duck heard aroundthe world.
Well, maybe not quite, but itwas effective.
They used the R1 Max gel.
Okay.
And they literally just stuck arubber duck onto a rock in the
splash zone at the seaside.
And it stayed there, enduringconstant tides, wave impacts,
proving it could handle reallyharsh, real-world marine
environments.
Lucas Adheron (13:35):
That's just brilliant.
A perfect visual.
What about the leaky pipeexample?
That sounded more practical.
Elena Bondwell (13:39):
Extremely practical.
For that, they used the R2 Maxgel, which was particularly good
in deionized water, like tapwater.
They had this tallpolycarbonate pipe, three meters
high, filled with water.
And they put a 20-millimeterhole that's pretty big right at
the bottom.
Lucas Adheron (13:54):
Okay, so high pressure coming out.
Elena Bondwell (13:55):
Serious pressure.
A burst flow rate around 5.4meters per second.
Water just gushing out.
Lucas Adheron (14:00):
Yeah.
Elena Bondwell (14:01):
They slapped a patch of the R2 Max gel over the
hole and it instantly sealedit.
Stopped the leak completely.
Lucas Adheron (14:08):
Instantly.
Under that pressure.
Elena Bondwell (14:10):
Instantly.
Lucas Adheron (14:10):
Yeah.
Elena Bondwell (14:11):
And just for comparison, they tried a
commercial adhesive sealantunder the exact same condition.
Lucas Adheron (14:16):
Oh, and does that go?
Elena Bondwell (14:17):
It failed.
Gave way in about an hour and ahalf.
The hydrogel just held strong.
Lucas Adheron (14:22):
That is genuinely game-changing performance for
emergency repairs, potentially.
Elena Bondwell (14:26):
Absolutely.
Think about underwater repairs,emergency plumbing fixes,
situations where commonadhesives just can't cope.
Lucas Adheron (14:34):
And beyond sticking ducts and fixing pipes,
what about inside the body?
You mentioned biomedicalpotential.
Were they safe?
Elena Bondwell (14:42):
That's a crucial question, of course.
They did biocompatibilitytests, including implanting the
hydrogels under the skin inmice.
And they found goodbiocompatibility, no significant
adverse reactions, which reallydoes open the door for
potential uses like surgicalglues.
Lucas Adheron (14:59):
Closing wounds without stitches.
Elena Bondwell (15:01):
Potentially, yes.
Or maybe for fixing implantssecurely inside the body where
things are obviously very wet.
Lucas Adheron (15:08):
It really seems these materials are incredibly
versatile.
Does performance change muchdepending on the water, like
saltwater versus freshwater?
Does nature do that too?
Elena Bondwell (15:18):
That's a really sharp observation.
And yes, they found that smalltweaks in the hydrogels
composition did change how wellit's stuck in different
environments, like deionizedwater versus artificial
seawater.
Lucas Adheron (15:28):
Interesting.
Elena Bondwell (15:29):
And that absolutely mirrors what we see
in nature, right?
Organisms evolved to be reallygood at sticking in their
specific environment, notnecessarily to be the best
everywhere.
Lucas Adheron (15:38):
So adaptability rather than one size fits all.
Elena Bondwell (15:41):
Exactly.
these AI-designed gels might becapturing some of that natural
principle too, differentformulations for different
conditions.
Lucas Adheron (15:49):
Okay, stepping back then, what's the big
takeaway here for materialscience?
This feels like more than justfinding a new glue.
Oh,
Elena Bondwell (15:56):
absolutely.
This whole approach, blendingthe protein data, the smart
polymer synthesis, the iterativeAI learning loop, It really
represents a, well, a paradigmshift.
Lucas Adheron (16:08):
A new way of doing things.
Elena Bondwell (16:09):
A new way of designing high-performance soft
materials.
Much more systematic, muchfaster than before.
Lucas Adheron (16:15):
And presumably this method isn't just for
making things sticky, right?
Could it be used for othermaterial properties?
Elena Bondwell (16:21):
Precisely.
This is a framework.
A systematic, scalable,start-to-finish method for
developing all kinds offunctional soft materials.
Lucas Adheron (16:29):
Like what?
What else could we design thisway?
Elena Bondwell (16:32):
Well, imagine next generation flexible
electronics that can stretch orconform to complex shapes, or
new kinds of soft robots thatmove more like natural
organisms, advanced biomedicaldevices.
The list goes on.
Lucas Adheron (16:44):
Custom designing materials on demand almost.
Elena Bondwell (16:47):
That's the dream.
Tailoring materials for veryspecific, very challenging jobs.
Lucas Adheron (16:52):
Of course, it can't all be smooth sailing.
Even with this breakthrough,what challenges are still out
there?
What are the researchers stillworking on?
Elena Bondwell (16:59):
They're very upfront about the limitations,
which is good science.
One is just the sheer diversityof monomers, the chemical
building blocks that arecurrently available and well
understood for this kind ofsynthesis.
Lucas Adheron (17:10):
Need more Lego bricks, essentially.
Elena Bondwell (17:11):
Kind of, yeah.
Also improving the polymersynthesis techniques themselves
to get even finer control overthe sequence and structure and
just scaling up the data sets,making sure the AI has enough
high quality data to learn from,especially as they target even
more complex material functions.
Lucas Adheron (17:29):
So what's the path forward?
How do we tackle those issues?
Elena Bondwell (17:31):
It'll likely involve expanding those
libraries of functionalmonomers, pushing polymer
chemistry forward and cruciallydeveloping even smarter AI
models.
Lucas Adheron (17:40):
More how?
Elena Bondwell (17:41):
Maybe physics informed AI Models that don't
just see patterns in data, buthave some built-in understanding
of the underlying chemistry andphysics.
Lucas Adheron (17:49):
Ah, so they can generalize better, maybe predict
things even with less data?
Elena Bondwell (17:54):
That's the hope.
Making the whole design processeven more powerful and
efficient.
Moving beyond just finding whatworks to really understanding
why it works computationally.
Lucas Adheron (18:03):
Okay, so here's something fascinating to leave
our listeners with.
Something to really thinkabout.
Despite this incredible successstory, the AI...
Finding the recipe.
The material sticking ducts torocks.
Fixing pipes.
According to the sources, theresearchers admit they still
don't fully understand thefundamental reason why this
(18:24):
material is so incrediblysticky.
Elena Bondwell (18:26):
It's true.
The deep mechanism isn't fullynailed down.
Lucas Adheron (18:30):
Think about that.
AI helped create this amazingthing.
It works incredibly well.
But the absolute rock bottomscience of why.
It's still a bit of a mystery.
Still more digging to do.
Elena Bondwell (18:42):
It's a fantastic point.
It shows how powerful thesetools are, but also that there's
always more to learn, morefundamental science to uncover.
Nature and AI working together,but still holding some secrets.
Lucas Adheron (18:52):
So we've gone from nature's own sticky
proteins all the way to theseAI-crafted hydrogels, materials
that can literally glue a duckto a seaside rock for a year or
stop a high-pressure leak in itstracks.
Elena Bondwell (19:03):
It really showcases what's possible when
you combine biologicalinspiration with cutting-edge AI
and material science.
Lucas Adheron (19:10):
And this isn't just some obscure lab curiosity.
As we heard, it's a realglimpse into a future where
materials can be designed almoston demand for incredible tasks,
solving problems we used tothink were just, well,
impossible.
Elena Bondwell (19:25):
A future built on understanding nature better
and using AI to translate thatunderstanding into reality.
Lucas Adheron (19:30):
So we really hope this gets you thinking.
What other huge challenges outthere might be solvable if we
look closely at nature andcleverly apply tools like AI?
The possibilities, when youthink about it, seem truly
boundless.
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New Heights with Jason & Travis Kelce
Football’s funniest family duo — Jason Kelce of the Philadelphia Eagles and Travis Kelce of the Kansas City Chiefs — team up to provide next-level access to life in the league as it unfolds. The two brothers and Super Bowl champions drop weekly insights about the weekly slate of games and share their INSIDE perspectives on trending NFL news and sports headlines. They also endlessly rag on each other as brothers do, chat the latest in pop culture and welcome some very popular and well-known friends to chat with them. Check out new episodes every Wednesday. Follow New Heights on the Wondery App, YouTube or wherever you get your podcasts. You can listen to new episodes early and ad-free, and get exclusive content on Wondery+. Join Wondery+ in the Wondery App, Apple Podcasts or Spotify. And join our new membership for a unique fan experience by going to the New Heights YouTube channel now!