Diffusion-Controlled Simultaneous Sensing and Scavenging of Heavy Metal Ions in Water Using Atomically Precise Cluster–Cellulose Nanocrystal Composites
Bibliographic record
Abstract
Development of a system that can simultaneously sense and scavenge toxic heavy metal ions at low concentrations is an ideal solution for in situ monitoring and purification of contaminated water. In this paper, we report on the synthesis and application of a novel system, luminescent atomically precise cluster–cellulose nanocrystal composite, namely, bovine serum albumin-protected gold nanoclusters (Au@BSA NCs)-loaded cellulose nanocrystal–alginate hydrogel beads, that can simultaneously sense and scavenge heavy metal ions, specifically mercury ions in water. Characterization of the system performed using scanning electron microscopy coupled with energy dispersive spectroscopy and X-ray photoelectron spectroscopy elucidated the physical and chemical characteristics of the system. Additionally, we proposed a new method to visualize the diffusion phenomenon and calculate the effective diffusion coefficient of heavy metal ions in hydrogel beads by monitoring the fluorescence-quenching dynamics of Au@BSA NCs upon binding with mercury ions. Finally, practical applications of this nanocomposite were demonstrated using batch adsorption experiments as well as using a dip pen device loaded with the hydrogel beads for in situ monitoring of heavy metal ions in water.
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".