Paper-Based Bioassays Using Gold Nanoparticle Colorimetric Probes
Bibliographic record
Abstract
The majority of bioassays utilize thermosensitive reagents (e.g., biomolecules) and laboratory conditions for analysis. The developing world, however, requires inexpensive, simple-to-perform tests that do not require refrigeration or access to highly trained technicians. To address this need, paper-based bioassays using gold nanoparticle (AuNP) colorimetric probes have been developed. In the two prototype DNase I and adenosine-sensing assays, blue (or black)-colored DNA-cross-linked AuNP aggregates were spotted on paper substrates. The addition of target DNase I (or adenosine) solution dissociated the gold aggregates into dispersed AuNPs, which generated an intense red color on paper within one minute. Both hydrophobic and (poly(vinyl alcohol)-coated) hydrophilic paper substrates were suitable for this biosensing platform; by contrast, uncoated hydrophilic paper caused "bleeding" and premature cessation of the assay due to surface drying. The assays are surprisingly thermally stable. During preparation, AuNP aggregate-coated papers can be dried at elevated temperatures (e.g., 90 degrees C) without significant loss of biosensing performance, which suggests the paper substrate protects AuNP aggregate probes from external nonspecific stimuli (e.g., heat). Moreover, the dried AuNP aggregate-coated papers can be stored for at least several weeks without loss of the biosensing function. The combination of paper substrates and AuNP colorimetric probes makes the final products inexpensive, low-volume, portable, disposable, and easy-to-use. We believe this simple, practical bioassay platform will be of interest for use in areas such as disease diagnostics, pathogen detection, and quality monitoring of food and water.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".