Fluorescence detection of hydrazine in an aqueous environment by a corrole derivative
Bibliographic record
Abstract
Abstract Broadly, the industrial applications of hydrazine cause environmental pollution and damage to living organisms because of the high toxicity of hydrazine. Therefore, monitoring hydrazine in the environmental system is of great significance to human health. Here, a new fluorescent probe PC‐N2H4 based on corrole dye was developed for the detection of hydrazine that had excellent specificity, low limit of detection (LOD: 88 nM), and a wide pH range (6–12). Upon addition of hydrazine into the probe solution, the strong red fluorescence was ‘turned on’ centred at 653 nm with a 127‐fold fluorescence intensity enhancement. The detection mechanism was proved using ESI‐MS, 1H NMR, and density functional theoretical calculations. Importantly, the probe was utilized to fabricate a ready‐to‐use test strip to realize the visual inspection of hydrazine. Furthermore, PC‐N2H4 was successfully applied for practical detection of hydrazine in water samples with satisfactory recoveries ranging from 96.2% to 105.0%, and indicating that the designed PC‐N2H4 is highly promising for hydrazine detection in an aqueous environment. Considering the diverse toxicological functions of hydrazine, PC‐N2H4 was also successfully used to image exogenous hydrazine in HeLa cells and zebrafish.
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 | 0.000 |
| 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 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".