How machines detect chemical patterns in air.
Every environment carries an invisible chemical signature. Machine smell is the study of reading that signature — turning the reaction of a sensor array into a stable, recognizable pattern.
What we research
We do not try to identify every molecule one by one. We read the combined response of a low-cost sensor array as a fingerprint — a pattern that a model can learn, the way a person recognizes coffee without naming each compound in it.
Technical terms: volatile organic compounds (VOCs), gas sensing, electronic nose, sensor array.
The array we are building
Our electronic-nose design combines commercially mature, low-power sensor families, chosen so no single sensor is a crutch.
The full Phase 1 rig runs multiple units of each family behind an I2C multiplexer, with diaphragm pumps and PTFE tubing for controlled sampling.
Where this stands
The bench is live: the ESP32-S3 is flashed and three sensor families — BME688, SGP40, SHT40 — are returning real readings, with MiCS-6814 in bring-up. We are completing the rig and characterizing how the array responds run to run, before any detection claim rests on it.
The open question — the reason the lab exists — is whether clean-air baselines and controlled aerosol events separate repeatably on real hardware, across humidity and background air. That is what the chamber experiments now starting are designed to answer.
Further reading
- →Controlled sniffing — how air reaches the array in a repeatable way.
- →On-device AI — turning the fingerprint into a decision on the board itself.
- →Truth gates — how each result is staged and what it is allowed to claim.