A design space, and the experiments that find it.

Formulating a nanocarrier is a search problem. There are a dozen ways to make the particle, six or seven process factors that matter for whichever one you pick, and a response surface you cannot see. This walks the loop: narrow the technique from what the molecule is, bracket the factors, build a design that can actually estimate what you want to estimate, fit the surface to your results, and find the settings that satisfy every response at once. The designs are constructed from their definitions and checked against their own properties, not copied off a table. Nothing you type leaves the page.

your formulation data stays in this browser

Read the labels on the numbers. Two different kinds of thing live on this page. The experimental designs and the statistics are mathematics: they are exact, and they are tested in front of you at the bottom. The technique ranking and the starting factor ranges are formulation heuristics: they encode how an experienced formulator would open the problem, they are not predictions, and they are not derived from your compound by any physical law. Every block says which it is. Widen or narrow any range before you run anything.

The molecule.

calculated descriptors, entered by you

Needed for a real solubility classification.

Measured, not predicted.

How to make it.

a formulation heuristic, itemised so you can disagree with it

The factors.

a starting bracket, yours to change

A design of experiments does not need the range to be right. It needs the range to be wide enough to contain the answer and narrow enough to be runnable. If you know the real limits, type them in.

The design.

constructed from its definition, then checked against its own properties

Replicated, so pure error and lack of fit can be separated.

The surface.

least squares on coded factors

The settings.

Derringer and Suich desirability across every response at once

What this does not do.

stated rather than discovered

Self-test.

the designs and the statistics, checked in front of you

A design of experiments is worth nothing if the design is wrong, and the failure is silent: you run the experiments, fit a model, and the model estimates something other than what you think it estimates. These assertions check the constructions against their defining properties on every load.

© 2026 Rizkin Labs ← back to the bench Legal · a teaching instrument, not formulation or regulatory advice