Organic Solvent-Free Cloud Point Extraction-like Methodology Using Aggregation of Graphene Oxide
Bibliographic record
Abstract
Because of its unique properties and capability of formation of well-dispersed aqueous colloids in aqueous phase, graphene oxide can be used for the efficient preconcentration of heavy metal ions prior to their determination. The complete collection of graphene oxide colloids from water has generally been considered to be insurmountable. Here, graphene oxide aggregation triggered by introducing NaCl was used to develop a novel organic solvent-free cloud point extraction-like method for the determination of trace toxic metals. The graphene oxide sheets were uniformly dispersed in aqueous samples or standard solutions for a fast and efficient adsorption of Pb(II), Cd(II), Bi(III), and Sb(III) owing to its hydrophilic character and the electrostatic repulsion among the graphene oxide sheets, and its aggregation immediately occurred when the electrostatic repulsion was eliminated via adding NaCl to neutralize the excessive negative charges on the surface of graphene oxide sheets. The aggregates of graphene oxide and analytes ions were separated and treated with hydrochloric acid to form a slurry solution. The slurry solution was pumped to mix with KBH4 solution to generate hydrides, which were subsequently separated from the liquid phase and directed to an atomic fluorescence spectrometer or directly introduced to an inductively coupled plasma optical emission spectrometer for detection. On the basis of a 50 mL sample volume, the limits of detection of 0.01, 0.002, 0.01, and 0.006 ng mL(-1) were obtained for Pb, Cd, Bi, and Sb, respectively, when using atomic fluorescence spectrometry, providing 35-, 8-, 36-, and 37-fold improvements over the conventional method. Detection limits of 0.6, 0.15, 0.1, and 1.0 ng mL(-1) were obtained with the use of slurry sampling inductively coupled plasma optical emission spectrometry. The method was applied for analysis of two Certified Reference Materials and three water samples for these elements.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".