Gold Nanoparticles Synthesized Using Various Reducing Agents and the Effect of Aging for DNA Sensing
Bibliographic record
Abstract
Gold nanoparticles (AuNPs) are one of the most commonly used reagents in colloidal science and biosensor technology. In this work, we first compared AuNPs prepared using four different reducing agents including citrate, glucose, ascorbate, and 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES). At the same absorbance at the surface plasmon peak of 520-530 nm, citrate-AuNPs and glucose-AuNPs adsorbed more DNA and achieved higher affinity to the adsorbed DNA. In addition, citrate-AuNPs had better sensitivity than glucose-AuNPs for label-free DNA detection. Then, using citrate-AuNPs, the effect of aging was studied by incubation of the AuNPs at 22 °C (room temperature) and at 4 °C for up to 6 months. During aging, the colloidal stability and DNA adsorption efficiency gradually decreased. In addition, the DNA sensing sensitivity using a label-free method also dropped around 4-fold after 6 months. Heating at boiling temperature of the aged citrate-AuNPs could not rejuvenate the sensing performance. This study shows that while citrate-AuNPs are initially better than the other three AuNPs in their colloid properties and sensing properties, this edge in performance might gradually decrease due to constantly changing surface properties caused from the aging effect.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".