The effects of nanoparticles on polymerase chain reaction
Bibliographic record
Abstract
The ability of synthesizing nanomaterials marked the beginning of the Nanotechnology era. Due to their extremely small sizes, nanomaterials present unique properties that are not seen in their bulk counterparts. However, understanding how nanomaterials behave in all kinds of biochemical reactions is the key to utilize them in potential applications. In this paper, the effects of gold, titanium dioxide and silver nanoparticles on polymerase chain reaction (PCR) are reported. It is found that they all cause PCR inhibition. Surface interaction between nanoparticles and PCR components such as Taq polymerase should account for the inhibition. PCR inhibition caused by gold nanoparticles can be reversed by adding chemical reagents to block surface of the nanoparticles from interacting with Taq polymerase. Surface modification of nanoparticles has a large impact on PCR. Titanium dioxide nanoparticles modified with different functional groups show different PCR inhibition behavior. It is also found that mixing titanium dioxide nanoparticles with silver nanoparticles at a certain ratio can reduce PCR inhibition caused by both nanoparticles.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".