Programmable Quantitative DNA Nanothermometers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it