Transition Metal Dichalcogenide Nanosheets for Visual Monitoring PCR Rivaling a Real-Time PCR Instrument
Bibliographic record
Abstract
Monitoring the progress of polymerase chain reactions (PCRs) is of critical importance in bioanalytical chemistry and molecular biology. Although real-time PCR thermocyclers are ideal for this purpose, their high cost has limited their applications in resource-poor areas. Direct visual detection would be a more attractive alternative. To monitor the PCR amplification, DNA-staining dyes, such as SYBR Green I (SG), are often used. Although these dyes give higher fluorescence when binding to double-stranded DNA products, they also yield strong background fluorescence in the presence of a high concentration of single-stranded (ss) DNA primers. In this work, we screened various nanomaterials and found that graphene oxide (GO), reduced GO, molybdenum disulfide (MoS 2 ), and tungsten disulfide (WS 2 ) can quench the fluorescence of nonamplified negative samples while still retaining strong fluorescence of positive ones. The signal ratio of positive-over-negative samples was enhanced by around 50-fold in the presence of these materials. In particular, MoS 2 and WS 2 nearly fully retained the fluorescence of the positive samples. The mechanism for MoS 2 and WS 2 to enhance PCR signaling is attributed to the adsorption of both the ssDNA PCR primers and SG with an appropriate strength. MoS 2 can also suppress nonspecific amplification caused by excess polymerase. Finally, this method was used to detect extracted transgenic soya GTS 40-3-2 DNA after PCR amplification. Compared with the samples without nanomaterials, the addition of MoS 2 could better distinguish the concentration difference of the template DNA, and the sensitivity of visual detection rivaled that from a real-time PCR instrument.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".