Practical considerations for DNA sensing using Faradaic electrochemical impedance spectroscopy on ENIG PCB electrodes
Bibliographic record
Abstract
Electroless nickel immersion gold (ENIG) finish printed circuit board (PCB) substrates offer a cost-effective solution for electrochemical sensing of biomolecules. Electrochemical impedance spectroscopy (EIS) is highly sensitive and can differentiate between bioelectrochemical circuit elements over a wide frequency range. Traditional Faradaic EIS measurements often focus on charge transfer resistance ( R c t ) to determine analyte concentration. However, the long measurement time required to perform such EIS measurements down to very low frequencies (typically 0.1 Hz) can adversely affect the ENIG PCB due to gold layer degradation and copper corrosion. To overcome these issues, we propose using the constant phase element (CPE) in the electrical equivalent circuit in EIS measurements for DNA sensing. Our approach employs ENIG PCB electrodes functionalized with thiolated single-stranded DNA probes targeting the uidA gene of E. coli . We evaluate the specificity of this sensing scheme to a 166 bp complementary amplicon from E. coli against non-complementary amplicons of different lengths from E. coli and bacteriophage Phi6. By operating at higher frequencies ( > 10 Hz), CPE analysis reduces measurement times and minimizes the risk of PCB degradation. Preliminary findings indicate that the CPE impedance exhibits concentration-dependent changes with increasing target DNA concentrations. We also present preliminary results for utilizing this sensing mechanism to detect E. coli from wastewater samples. • Faradaic EIS analysis on ENIG PCB substrates for electrochemical DNA sensors. • CPE-based rapid measurement safeguards PCB integrity against corrosion. • CPE impedance changes with target DNA concentration, effective for complex samples. • CPE shows reversal in trend with increasing DNA concentration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".