Revealing the Hydrolysis Mechanism of a Hg<sup>2+</sup>-Reactive Fluorescein Probe: Novel Insights on Thionocarbonated Dyes
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide As one of the most toxic metal pollutants, mercury is the subject of extensive research to improve current detection strategies, notably to develop sensitive, selective, fast, and affordable Hg 2+ -responsive fluorescent probes. Comprehending the sensing mechanism of these molecules is a crucial step in their design and optimization of their performance. Herein, a new fluorescein-based thionocarbonate-appended Hg 2+ -sensitive probe was synthesized to study the hydrolysis reactions involved in the sensing process. Autohydrolysis was revealed as a significant component of the signal generation mechanism, occurring concurrently with Hg 2+ -catalyzed hydrolysis. This knowledge was used to investigate the effects of key experimental conditions (pH, temperature, chloride ions) on sensing efficiency. Overall, the chemical and physical properties of this new thionocarbonated dye and the insights into its sensing mechanism will be instrumental in designing reliable and effective portable sensing strategies for mercury and other heavy metals.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".