A Microfluidic Paper‐Based Origami Nanobiosensor for Label‐Free, Ultrasensitive Immunoassays
Bibliographic record
Abstract
Microfluidic paper-based analytical devices (μPADs) represent a promising platform technology for point-of-care diagnosis. Highly sensitive, rapid, and easy-to-perform immunoassays implemented on μPADs are desirable to fulfill the promise of the μPAD technology. This article reports the first microfluidic paper-based origami nanobiosensor (origami μPAD), which integrates zinc oxide nanowires (ZnO NWs) and electrochemical impedance spectroscopy (EIS) biosensing mechanism, for label-free, ultrasensitive immunoassays. The EIS mechanism features simple and label-free assay operations which take less than 25 min to be finished, while the ZnO NWs allow covalent bonding for immobilizing probe proteins and improve the biosensing performance with such features as high surface-area-to-volume ratios and high sensitivity to surface binding. The calibration of the device reveals an ultralow limit of detection (LOD) of 60 fg mL(-1) (>100 times lower than those of existing μPADs) for rabbit immunoglobulin G in phosphate-buffered saline. The detection of human immunodeficiency virus p24 antigen in human serum with a low LOD of 300 fg mL(-1) (>33 times lower than that of a commercial p24 antigen test kit) is also demonstrated. This novel μPAD design offers ultrahigh sensitivity, short assay time, and ease of operation, and thus possesses significant potential for low-cost, rapid molecular diagnosis of early-stage diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".