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Lab Notes 5 min read

Behind the Lab: Our First Experiment

What happens when you take amino acids, sustainable waxes, and a kitchen-scale setup into the lab for the first time.

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Cornelius van Heerden

From Theory to Practice

After months of computational screening, literature review, and formulation design, we finally stepped into the lab. The goal: validate our amino acid catalysis approach on a real substrate with a real coating formulation.

The Setup

Our first experiment focused on the W.R.A.P. track — sustainable protective coatings. The formulation:

  • Amino acid catalyst: L-Lysine (food-grade, GRAS-listed)
  • Wax base: Soy wax (renewable, biodegradable)
  • Solvent system: Ethanol/water blend (non-toxic, evaporative)
  • Substrate: Corrugated cardboard samples

What We Learned

The first thing you learn in the lab is that theory and practice are distant cousins. Our computational models predicted good adhesion — and they were right, mostly. But they didn't predict:

  • Temperature sensitivity. The formulation performed differently at room temperature vs. slightly elevated temperatures. A 10°C difference changed the coating uniformity significantly.
  • Application method matters. Brush application gave different results than spray. The amino acid distribution in the coating was more uniform with spray application.
  • Drying time is critical. Too fast and you get surface defects. Too slow and the amino acid begins to crystallize before the wax sets.

The Results

Despite the learning curve, the results were encouraging:

  • Water resistance improved measurably compared to uncoated cardboard
  • The coating was uniform and adherent after optimization
  • No toxic solvents, no hazardous waste, no special ventilation required
The best part? Every observation, every data point, every failure mode is being published on our research platform. That's what open science means in practice.

What's Next

This first experiment validated the approach. Now we're scaling up the testing matrix — different amino acids, different substrates, different environmental conditions. Each experiment feeds back into our computational models, making the next prediction more accurate.

Science isn't a straight line. But it moves faster when everyone can see where you've been.