Good's buffers have various affinities to gold nanoparticles regulating fluorescent and colorimetric DNA sensing
Bibliographic record
Abstract
Citrate-capped gold nanoparticles (AuNPs) are highly important for sensing, drug delivery, and materials design. Many of their reactions take place in various buffers such as phosphate and Good's buffers. The effect of buffer on the surface properties of AuNPs is critical, yet this topic has not been systematically explored. Herein, we used halides such as fluoride, chloride, and bromide as probes to measure the relative adsorption strength of six common buffers. Among them, HEPES had the highest adsorption affinity, while MES, citrate and phosphate were weakly adsorbed with an overall ranking of HEPES > PIPES > MOPS > MES > citrate, phosphate. The adsorption strength was reflected from the inhibited adsorption of DNA and from the displacement of pre-adsorbed DNA. This conclusion is also supported by surface enhanced Raman spectroscopy. Furthermore, some buffer molecules did not get adsorbed instantaneously, and the MOPS buffer took up to 1 h to reach equilibrium. Finally, a classic label-free AuNP-based colorimetric sensor was tested. Its sensitivity increased by 15.7-fold when performed in a MES buffer compared to a HEPES buffer. This study has articulated the importance of buffer for AuNP-based studies and how it can improve sensors and yield more reproducible experimental systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".