Okay okay.

I get it.

You want a different mystery gift. No more games for you to play.

This one is a “simulation”. One I spent hours making for you.

But only the first 10 people who answer this week’s quiz get it!

Q1: You cool a metal below its critical temperature. Its magnetic field lines:
B) Get expelled

Q2: You increase strain in a semiconductor quantum well. Hole mobility:
B) Increases

Q3: You split a multilayer material into isolated 2D planes. Electron correlation effects:
A) Strengthen

Congratulations Greg, Melissa, Shannon, and Shalaby!
Make sure to check your inboxes!

This week’s mystery gift is a bit special so make sure to try out the quiz below!

Q1: To make a spring STIFFER, engineers use:
Thicker wire OR More coils?

Q2: To make a cantilever beam deflect LESS under load, engineers:
Double the width OR Double the height?

Q3: In a pressure vessel, stress concentrates at:
Curved sections OR Flat sections?

Know the answers?
Reply HERE 👇

A quick shoutout to a friend of mine whose posts you guys might enjoy. I guarantee some things to learn paired with some laughs to share.

LOTS of memes like the one below!

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You know the drill at the fish counter: poke it, check if the eyes are cloudy, give it a suspicious sniff.

The problem?

By the time the fish LOOKS or SMELLS bad, it's been decomposing for a while.

Researchers built a sensor covered in tiny microneedles that you press into fish flesh.

The needles are coated with an enzyme that only reacts to hypoxanthine, a molecule that starts building up the moment a fish dies. When the enzyme hits hypoxanthine, it triggers an electrical signal.

More hypoxanthine = stronger signal = less fresh fish.

Press it in, wait 100 seconds, get a number. It detected freshness down to 500 parts per billion. That's "just came out of the ocean" territory.

Just press and know.

TLDR: Your nose has been lying to you this whole time 😢

Read more here

Training GPT-3 uses about as much electricity as powering a US household for 120 years.

Most of that energy doesn't even do computing. It gets wasted as heat, which then requires MORE energy to cool.

Data centers are basically expensive space heaters that occasionally do math.

The culprit? Dielectrics.

These are the insulating layers inside chips that prevent short circuits. Traditional dielectrics have high permittivity, meaning they store a little electrical charge.

Stored charge = released heat. More heat = slower chips (because electrons move sluggishly when hot) = massive cooling bills.

University of Houston engineers built a new dielectric from 2D carbon-based sheets with a porous crystalline structure.

The material has ultralow permittivity, so it barely stores any charge.

Less stored charge means less heat generated. Less heat means chips run faster AND data centers stop burning electricity just to keep servers from melting.

Same chip design. Swap out the insulator.
Suddenly AI doesn't need its own power grid.

Read more here

Carbon capture plants cost a fortune because of two things: giant fans to suck in air, and complex chemical systems to separate the CO2.

University of Toronto engineers got rid of both.

They dip polypropylene strings (yes, basically household string) into potassium hydroxide solution. The liquid wicks up the fibers like water climbing a paper towel.

Wind blows across the wet strings and evaporates the water, concentrating the solution until it's so alkaline it grabs CO2 right out of the air.

The captured carbon forms solid potassium carbonate crystals directly on the string, like rock candy growing on a stick.

Wash off the crystals with water. Run them through an electrochemical cell where electricity splits the compound: CO2 gas comes out one side (ready for storage), potassium hydroxide comes out the other (goes right back into the system).

It’s amazing how just string, wind, and crystallization are doing the work.

Capital costs could drop 40%.

Read more here

Watch the video on Japan’s first human washing machine 👇

Ever dropped a glass and wondered why it shatters into THAT specific mess?

Not "why did it break." We all know gravity doesn't care about your favorite mug.

I mean why does it always produce that same chaotic mix: a few chunky pieces, some medium bits, and a million tiny shards that hide in your carpet for months?

Here's what's weird: when researchers studied broken things (glass plates, ceramic tubes, even water droplets and bubbles) they kept finding the same pattern.

No matter WHAT breaks, the size distribution of fragments looks almost identical.

Something universal was clearly going on.

But nobody could explain it.

Until now.

NATURE PICKS THE MESSIEST OPTION

Emmanuel Villermaux at Aix-Marseille University proposed something beautifully simple: when things break, nature picks the most chaotic outcome possible.

He calls this "maximal randomness."

Think about it like this: a plate COULD theoretically break into four perfectly equal pieces.

That's the tidy outcome. But that requires everything to go exactly right.

The cracks would need to form in precisely the right spots at precisely the right angles.

The messy outcome? Way more likely.

There are millions of ways to get uneven shards, but only a handful of ways to get perfect quarters. So nature, being lazy, goes with the mess.

BUT CHAOS HAS A BUDGET

Pure randomness would mean anything goes. Fragments of any size in any combination. But that's not what actually happens.

Villermaux combined his maximal randomness principle with a conservation law his team discovered years earlier.

This law constrains how fragment sizes can be distributed. It's like an invisible budget that the chaos has to work within.

When you put these two ideas together mathematically, you get a power law that predicts exactly what mix of big, medium, and tiny pieces you'll get.

And the exponent changes based on dimensions: 1D objects (like rods), 2D objects (like plates), and 3D objects (like cubes) each follow their own specific number.

PROOF BY SUGAR CUBE

To test it, Villermaux crushed individual sugar cubes and measured every fragment. The equation predicted the size distribution perfectly.

(Apparently this was a summer project with his daughters from years ago. He kept the data and came back to it when he realized it proved his point. Love that.)

WHY THIS MATTERS BEYOND BROKEN DISHES

Mining operations need to know how ore will break to optimize energy use.
Engineers designing car windshields want to control how glass shatters in crashes.
Geologists studying rockfalls need to predict debris sizes.

The law has limits. It doesn't work well for soft materials that deform instead of crack, or for highly ordered breakups like a water stream splitting into equal droplets.

But for chaotic shattering? Seems pretty much universal.

Next time you drop a plate, you can be annoyed AND scientifically informed about exactly WHY the mess looks the way it does (i don’t think it’ll help that argument at home though lol)

Mechanical Engineer, Advanced Containment Systems, Inc
Turning "it worked in CAD" into products that survive the real world.
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Sustainability Engineer - Bala Consulting, Wayne, PA
Building carbon therapist helping structures shed emissions and earn eco badges.
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Associate Civil Engineer - Water Resources, WEBB, Riverside, CA
Municipal plumber designing how water arrives clean and leaves dirty.
Apply Now

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