Adsorptive Removal of Dyes: A Comparison of Graphene Oxide to Granular Activated Carbon and Zeolite NaY
Bibliographic record
Abstract
Synthetic carbon-based compounds are a prevalent wastewater contaminant that can adversely impact water resources due to their potential carcinogenic and toxic effects on aquatic biota and human health. This research investigates the versatility of graphene oxide (GO) as an alternative to commonly used adsorbents (zeolite NaY (NaY) and granular activated carbon (GAC)) for removal of synthetic cationic dyes. Rhodamine B (RhB) and methylene blue (MB), were selected as the target contaminants to represent cationic synthetic dyes with differing molecular sizes and structural compositions. Batch experiments using GO, NaY, and GAC as adsorbents were used to assess both physicochemical interactions between adsorbent surfaces and contaminants, and removal efficiency. GO demonstrated the highest removal efficiency for both target contaminants—at 99% and 86%, respectively—while the lowest removal efficiency was observed for NaY. Langmuir, Freundlich, Temkin, and BET isotherm models were used to describe the adsorption isotherms. Overall, GO demonstrated a more robust and higher removal efficiency of cationic dyes compared with GAC and NaY, indicating the potential of graphene oxide for the removal of complex structured organic contaminants in wastewater treatment.
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 | 0.001 | 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) | 0.001 | 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".