Upload raw electrochemical data for instant HQ/DA prediction, or contribute labeled datasets to fine-tune
the PeakTransformer model for your specific electrode environment.
Hydroquinone (HQ) · E₀ = −0.16 V Dopamine (DA) · E₀ = +0.12 V 219-point DPV signal (−0.6 → +0.6 V)
Hydroquinone
HQ
Peak at −0.16 V
σ ≈ 0.025–0.045 V
Dopamine
DA
Peak at +0.12 V
σ ≈ 0.025–0.045 V
Peak separation (ΔE)
≈ 280 mV — overlap managed by model
1Upload your DPV signal
Drag & drop your CSV / TXT
or click to browse · 219 data points required
2Optional: known true labels
3Run model
Running PeakTransformer…
Expected File Format
# Single-column, 219 rows (µA)
-1.234
-1.187 -0.523# ← HQ peak region
0.012 3.871# ← DA peak region
0.541 ... (219 total rows)
Header row is auto-detected and skipped. Comma or tab-separated formats also accepted (first column used).
⬤ Hydroquinone (HQ)
—
µA·V peak area
⬤ Dopamine (DA)
—
µA·V peak area
Input Signal
HQ Component
DA Component
Upload a signal file and click Predict HQ & DA to see results
What is Fine-Tuning? The PeakTransformer uses
k-shot domain adaptation (Phase 3) — with as few as 6 labeled samples
from your specific electrode, the concentration head adapts to your real-world sensor environment
while the deep encoder stays frozen.
pDeep3 / Bengio et al. 2009 inspired curriculum learning strategy.
1 Upload labeled dataset CSV
Drag & drop labeled CSV
221 columns: 219 signal + area_HQ + area_DA
2 k-Shot adaptation samples:
6
1–10 samples used for domain adaptation; remaining rows used for evaluation