Low-Cost and Desktop-Fabricated Biosensor for Rapid and Sensitive Detection of Circulating D-Dimer Biomarker
Bibliographic record
Abstract
Disposable point-of-care biomedical applications demand low-cost biosensing systems that can provide rapid detection with accessible fabrication requirements and user-friendly operation. Here, we present an easy-to-operate and cleanroom-free desktop-fabricated biosensor that is capable of reliable detection for human D-dimer biomarkers on-site. Interdigitated electrodes are used as the biosensor, and they are fabricated using commercial compact discs and home-made gadgets. The compact disc-based electrode arrays are patterned by wet-etching and followed by electroplating of polypyrrole (PPy). The nano-porous structures of PPy enhance the immobilization of protein probe on the electrode surface. The sensor performance is further enhanced by the ac electrokinetics (ACEK) capacitive sensing method. ACEK capacitive sensing induces ACEK effects that direct analytes toward the electrode surfaces, significantly promoting probe-target binding. Our sensor can quantitatively detect D-dimer biomarkers in 1 min with a limit of detection of 1 pg/mL. In contrast, Nyquist or Bode plots can only qualitatively distinguish positive and negative samples. Furthermore, the total cost of each test, together with the lithography material and compact disk, is only around U.S. $1, and it is acceptable to be disposable. The sensitive device yields wide-ranging and significant advantages for biomedical and lab-on-a-chip applications for undeveloped areas.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".