“Cellulose Spacer” Strategy: Anti-Aggregation-Caused Quenching Membrane for Mercury Ion Detection and Removal
Bibliographic record
Abstract
A membrane decorated with fluorescent dyes has a great potential in detection and removal of contaminant from wastewater. However, traditional fluorescent dyes suffer from the aggregation-caused quenching effect, which could compromise their sensing efficiency. Here, a new “cellulose spacer” strategy is developed to conquer this challenge. Specifically, the nanocellulose has a hydrogen bond interaction with hydroxyl-containing coumarin, which serves as a spacer that prevented the π–π stacking of coumarin. In such a manner, a fluorescent cellulose membrane with anti-aggregation-caused quenching is obtained. As a demonstration of as-developed materials, the fluorescent cellulose membrane is used for mercury ion recognition and removal, and the membrane shows great sensing and adsorption performance. Moreover, excellent cytocompatibility of the membrane is verified by cell proliferation of live/dead viability assays. This fabrication method is expected to provide a new concept for the construction of fluorescent and biocompatible membranes for a large variety of relevant applications.
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) | 0.001 | 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 | 0.001 | 0.000 |
| 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".