Carbon Nanotube-Silver Composite for Mercury Capture and Analysis
Bibliographic record
Abstract
The carbon nanotube-silver composite (Ag-CNT) is a new class of multifunctional materials with potential applications such as sensors, catalysts, biodisinfection, and sorbents. A simple method combining wet-chemistry and thermal reduction was adopted to synthesize silver on the surface of the CNT. The synthesized Ag-CNT was tested as a sorbent for the removal of elemental mercury from flue gases of coal-fired power plants and as a mercury trap for elemental mercury analysis. A complete capture of mercury by the Ag-CNT was achieved up to a capture temperature of 150 °C, similar to the temperature of flue gases in coal-fired power plants. The captured mercury could be quickly and completely released by simple heating at 330 °C, to restore its mercury adsorption capacity. Silver on the Ag-CNT was shown to be the main active component for the mercury capture via an amalgamation mechanism in contrast to simple physical adsorption on the undoped CNT . Compared to silver-coated quartz beads (Ag-beads) and gold-coated quartz beads (Au-beads), which is conventionally used as a mercury trap for mercury measurements, the Ag-CNT showed a much higher mercury capture capacity and a minimal memory effect. With the Ag-CNT as a mercury preconcentration trap, calibration results showed a satisfactory linear coefficient of ≥0.9998 between known amounts of standard mercury and their corresponding fluorescence signals of cold vapor atomic fluorescence spectrophotometry (CVAFS). The presence of SO 2, NO x, CO 2, or O 2 showed a negligible impact on the mercury capture performance of the Ag-CNT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".