Nanotechnology decision support

Match every water sample to the right green nanomaterial — explained, not guessed.

AquaNano AI reads a water-quality profile, weighs contaminant load against a library of sustainable nanomaterials, and assembles a full treatment train — with the reasoning shown, not hidden.

Run the analyzer ↓ See a treatment train
Step 1 — Water quality input

Describe the sample

Set physical, chemical and biological parameters. In a full deployment these fields are populated automatically from IoT probes or lab reports.

WHO guideline: below 5 NTU for drinking use
Safe drinking range: 6.5 – 8.5
WHO acceptable limit: ~500–1000 ppm
WHO guideline: below 10 ppb
WHO guideline: below 10 ppb
WHO guideline: below 1.5 ppm
Scales cost and capacity estimates below
Microbial contamination detected (E. coli / coliform)
Organic pollutants present (dye, pesticide, industrial effluent)
Adjust parameters, then run the model.
Step 2 — Recommended treatment train

Assembled purification sequence

Each stage names a real green-nanomaterial class, matched against the contaminant profile above and scored for sustainability.

Step 3 — Explainability

Why the model chose this

Every recommendation traces back to specific thresholds — no black box between the sample and the suggested materials.

    —
    Composite sustainability index
    (biodegradability · low toxicity · energy efficiency · cost)
    Step 4 — Real-world deployment estimate

    Scale & cost projection

    Illustrative planning figures for the selected deployment context — indicative only, for pre-feasibility discussion.