Single-Walled Carbon Nanotubes (SWCNTs), as a Novel Sorbent for Determination of Mercury in Air
Bibliographic record
Abstract
BACKGROUND: Based on the noticeable toxicity and numerous application of mercury in industries, removal of mercury vapor through sorbent is an important environmental challenge. PURPOSE OF THE STUDY: Due to their highly porous and hollow structure, large specific surface area, light mass density and strong interaction, Single-Walled Carbon Nanotubes (SWCNTs) sorbent were selected for this investigation. METHODS: In this study, instrumental conditions, method procedure and different effective parameters such as adsorption efficiency, desorption capacity, time, temperature and repeatability as well as retention time of adsorbed mercury were studied and optimized. Also, mercury vapor was determined by cold vapor atomic absorption spectrometry (CV-AAS).Obtained data were analyzed by Independent T- test, Multivariate linear regression and one way-ANOVA finally. RESULTS: For 80 mg nanotubes, working range of SWCNT were achieved 0.02-0.7 mg with linear range (R2=0.994).Our data revealed that maximum absorption capacity was 0.5 mg g-1 as well as limit of detection (LOD) for studied sorbent was 0.006 mg. Also, optimum time and temperature were reported, 10 min and 250 °C respectively. Retention time of mercury on CNTs for three weeks was over 90%. Results of repeated trials indicated that the CNTs had long life, so that after 30 cycles of experiments, efficiency was determined without performance loss. CONCLUSION: Results showed that carbon nanotubes have high potential for efficient extraction of mercury from air and can be used for occupational and environmental purposes. The study of adsorption properties of CNTs is recommended.
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 | 0.001 | 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".