Author | Lightnews

Richard James MacCowan @rmaccowan.bsky.social · 9d

Evidence first. Results over rhetoric.
If you're at either summit, let's talk about what works.

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#cosmetics #packaging #biomimetics #AI #networks

Richard James MacCowan @rmaccowan.bsky.social · 9d

This isn't about nature being "sustainable" or "optimized."
It's about selection-shaped functions solving similar problems under similar constraints.
Then proving they deliver measurable advantage in your application.

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Richard James MacCowan @rmaccowan.bsky.social · 9d

R&D leaders ask about replicability, scaling bioinspired microstructures, and validating claims.
I've compiled the peer-reviewed answers with protocols your teams can use.

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Richard James MacCowan @rmaccowan.bsky.social · 9d

The common thread? Mechanism fidelity.
Does the biological principle map to your engineering context?
Can you measure the performance differential?
Will it survive your manufacturing constraints?

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Richard James MacCowan @rmaccowan.bsky.social · 9d

In London: perception-aware AI systems informed by biological vision.
Not mimicking eyes. Understanding how selection pressure shaped robust sensory processing under constraint.

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Richard James MacCowan @rmaccowan.bsky.social · 9d

In Paris: structural colour approaches reducing dye load in formulations.
Not copying beetle patterns. Mapping the mechanisms producing colour without pigment.

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Richard James MacCowan @rmaccowan.bsky.social · 9d

AI researchers grapple with dataset bias and perception systems breaking under real-world conditions.
Meanwhile, insect vision has been handling variable inputs for millions of generations.

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Richard James MacCowan @rmaccowan.bsky.social · 9d

The cosmetics industry faces 18-24-month development cycles.
This conflicts with the time needed to validate biological mechanisms properly.
Most teams default to traditional chemistry.

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Richard James MacCowan @rmaccowan.bsky.social · 9d

This week I'm speaking at the Sustainable Cosmetics Summit Europe and next week at the Global Summit on Open Problems for AI.
Different sectors. Same challenge.

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Richard James MacCowan @rmaccowan.bsky.social · 9d

Paris and London. Cosmetics and AI.
Two industries asking me the same question... how do we know this biological strategy works?

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Richard James MacCowan @rmaccowan.bsky.social · 23d

The sophistication isn't in the leaf alone.
It's in the leaf-environment interaction; solutions evolved over millennia to solve problems we're just beginning to measure correctly.

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Richard James MacCowan @rmaccowan.bsky.social · 23d

Moving forward requires a methodological revolution.
Couple surface chemistry, microtopography, and controlled airflow in one workflow.
Expect tighter predictions, clearer mechanisms, stronger bioinspired design choices.

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Richard James MacCowan @rmaccowan.bsky.social · 23d

For biomimetics, this changes everything.
If your surface coating models came from still-air data, they'll fail when deployed.
If your self-cleaning designs ignore airflow, they won't self-clean in REAL-WORLD conditions.

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Richard James MacCowan @rmaccowan.bsky.social · 23d

The implications extend beyond taxonomy.
This is a pattern in scientific inquiry: isolating variables at the expense of understanding HOLISTIC SYSTEMS.
Nature rarely operates through single-factor mechanisms.

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Richard James MacCowan @rmaccowan.bsky.social · 23d

This matters because Loftus leaves simultaneously exhibit contradictory properties.
Hydrophobic AND hydrophilic zones create microenvironments where water behaves unpredictably.
Environmental factors don't just influence—they transform the system.

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Richard James MacCowan @rmaccowan.bsky.social · 23d

It's not just wind as background noise.
The BOUNDARY LAYER redistributes droplets across mixed wetting surfaces, shifting adhesion and roll-off thresholds we thought were stable.
Small airflow variations create dramatically different outcomes.

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Richard James MacCowan @rmaccowan.bsky.social · 23d

Here's what changes when you introduce controlled microgusts:
The hydrophobic-hydrophilic matrix triggers REGIME CHANGES in coalescence, pinning, and self-cleaning that static trials never revealed.

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Richard James MacCowan @rmaccowan.bsky.social · 23d

STATIC tests miss this entirely.
We've been studying these leaves in still air for decades.
Bench-top results looked clean. Repeatable. Publishable.
But they don't match what happens in the field.

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Richard James MacCowan @rmaccowan.bsky.social · 23d

On Loftus leaves, there's an air film near the surface.
Turns out, this film modulates droplet behaviour as much as the microstructure does.
Small changes in shear produce different pinning, runoff, and particulate transport patterns.

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Richard James MacCowan @rmaccowan.bsky.social · 23d

The boundary layer we keep skipping

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Richard James MacCowan @rmaccowan.bsky.social · 24d

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Further reading: https://news.njit.edu/ywcc-student-faculty-win-best-presentation-award-simulating-ant-swarms

Richard James MacCowan @rmaccowan.bsky.social · 24d

Bottom line:

Switch from fighting collisions to converting them into coordinated flow.

The thresholds are published. The simulations are validated. The applications are immediate.

Time to test them in your system.

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Richard James MacCowan @rmaccowan.bsky.social · 24d

Key insight:

Biological systems aren't "optimised."

Trade-offs and constraints shape them.

That's precisely why they work at scale - they evolved under the same messy conditions your systems face.

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Richard James MacCowan @rmaccowan.bsky.social · 24d

The NJIT models are simulation-ready.

You can test the yielding and spacing parameters in your existing planning stack within MINUTES.

Not metaphors. Mechanisms with clear values you can tune.

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Richard James MacCowan @rmaccowan.bsky.social · 24d

The shift:

FROM brittle central control that breaks under edge cases

TO resilient local rules that handle noise, density spikes, and hardware variation

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