Programmable Quantitative DNA Nanothermometers
Bibliographic record
Abstract
Developing molecules, switches, probes or nanomaterials that are able to respond to specific temperature changes should prove of utility for several applications in nanotechnology. Here, we describe bioinspired strategies to design DNA thermoswitches with programmable linear response ranges that can provide either a precise ultrasensitive response over a desired, small temperature interval (±0.05 °C) or an extended linear response over a wide temperature range (e.g., from 25 to 90 °C). Using structural modifications or inexpensive DNA stabilizers, we show that we can tune the transition midpoints of DNA thermometers from 30 to 85 °C. Using multimeric switch architectures, we are able to create ultrasensitive thermometers that display large quantitative fluorescence gains within small temperature variation (e.g., > 700% over 10 °C). Using a combination of thermoswitches of different stabilities or a mix of stabilizers of various strengths, we can create extended thermometers that respond linearly up to 50 °C in temperature range. Here, we demonstrate the reversibility, robustness, and efficiency of these programmable DNA thermometers by monitoring temperature change inside individual wells during polymerase chain reactions. We discuss the potential applications of these programmable DNA thermoswitches in various nanotechnology fields including cell imaging, nanofluidics, nanomedecine, nanoelectronics, nanomaterial, and synthetic biology.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".