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.
Sensor array
Our electronic-nose array combines five commercially mature, low-power families. Together they produce 114 features per sample.
Findings
Our analysis pipeline already separates clean air from post-use air and tells event families apart on a scenario-heldout split — while raising no false alarms on clean air. That baseline sets the bar the on-bench system has to clear.
What we are figuring out next: whether these separations hold on real sensor hardware, across changing humidity and background air — the focus of the controlled chamber experiments now starting.
References
- 01
AERALYTE_RESEARCH_LAB_BRIEF.md— research pillars & technical direction. - 02
PHASE1_CONTROLLED_ENOSE_EXPERIMENT.md— the controlled chamber experiment plan. - 03
Kimi_Agent · Sensor Evolution & Power Trade-offs— sensor selection (BME688/690, SGP40, SHT40, MiCS-6814, PMS5003). - 04GitHub —
XoAnonXo/aeralyte. Explore the research corpus.