A Molecular Thermometer Based on Fluorescent Protein Blinking
Bibliographic record
Abstract
With the present trend toward a miniaturization of chemical systems comes the need for a precise characterization of physicochemical parameters in very small fluid volumes. We describe here an original approach for small-scale temperature measurements based on the detection of fluorescent protein blinking. We observed that the characteristic time associated with the reversible protonation reaction responsible for the blinking of the enhanced green fluorescent protein is strongly temperature dependent at low pH. The blinking characteristic time can easily be detected by fluorescence correlation spectroscopy, and therefore provides the means for noninvasive, spatially resolved, absolute temperature measurements. We applied this approach to the quantification of laser-heating effects in thin liquid samples. As expected, we observed a linear dependence between the temperature increase at the laser focus and both the laser power and the sample extinction coefficient. In addition, we were able to measure the laser induced temperature increase at the glass/liquid interface, a value difficult to predict and hard to access experimentally, demonstrating the usefulness of our approach to study surface effects in microfluidic chips. The use of GFP derivatives as genetically encoded molecular thermometers should have direct applications for both microfluidics and single-cell calorimetry.
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.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".