A portable, low-cost and high-throughput electrochemical impedance spectroscopy device for point-of-care biomarker detection
Bibliographic record
Abstract
This paper presents a portable, fast and accurate electrochemical impedance spectroscopy (EIS) device with 8-well interdigitated electrode chips for biomarker detection. The design adopts low crest factor multisine signal synthesis at low frequencies (<1 kHz) and single-tone signals at high frequencies (>1 kHz), which significantly increases measurement speed without sacrificing accuracy. In addition, the low excitation amplitude of 10 mV preserves impedance linearity and protects the biosamples. The system achieved an average magnitude accuracy error of 0.30% in the frequency range of interest and it requires only 0.46 s to scan 28 frequency points from 10 Hz to 1 MHz. Experiments were conducted to test the capability to detect antibodies against SARS-CoV-2. Gold nanoparticles bound with protein G (GNP-G) were employed as the conjugated secondary antibody probe to detect anti-SARS-CoV-2 IgG in serum. A highly statistical significance (p = 7×10−6) could be found in the impedance data at 10 kHz. The impedance magnitude alteration caused by the GNP-G of the positive and negative groups were 27.2%±13.6% and 4.1%±1.7%, respectively. The results imply that the proposed system enables rapid COVID-19 antibody biomarker detection. Moreover, the EIS system and GNPs have the potential to be modified to detect other biomarkers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".