A starch slurry dries. Cracks form. The pattern of cracks depends on the solvent — water, ethanol, acetone, and mixtures produce distinct geometries. A neural network can identify which solvent was used with 96% accuracy just from a photograph of the dried residue.

The cracks are not random. Each solvent has a characteristic evaporation rate, surface tension, and interaction with the starch matrix. These physical properties translate into specific stress distributions during drying, which translate into specific crack patterns. The network isn’t learning arbitrary correlations — it’s reading the physics of the drying process written into the geometry of the fracture.

This is forensic chemistry through fracture mechanics. The dried residue is a record of its liquid past. The information about the solvent’s identity persists in the solid long after the solvent has evaporated. The liquid writes its signature in the cracks it leaves behind, and the signature is specific enough to be decoded.

The practical applications are obvious (drug identification, industrial quality control). But the conceptual point is more interesting: a destructive process (drying, cracking) is also an information-preserving process. The crack doesn’t erase the liquid’s identity — it encodes it. Destruction and documentation happen in the same physical act.