Utilizing tea waste for methylene blue removal: Insights from batch and fixed-bed adsorption studies
Bibliographic record
Abstract
This study explores the potential of tea waste as a cost-effective and eco-friendly biosorbent for the removal of hazardous Methylene Blue (MB) dye from aqueous solutions. The tea waste-based adsorbent has been synthesised and characterised using Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), and Scanning Electron Microscopy (SEM) to examine its functional groups, crystallinity, and surface morphology. Batch adsorption experiments evaluated the impact of initial dye concentration, contact time, and adsorbent dosage. Isotherm analysis revealed Langmuir model compatibility with a high monolayer capacity (qₘₐₓ = 454.54 mg/g), indicating effective surface interaction. Kinetic modeling showed excellent fit with the pseudo-second-order model (R² = 1.000), suggesting chemisorption as the primary mechanism.A fixed-bed column study assessed the effects of flow rate, bed height, and column diameter on breakthrough behaviour. Optimal conditions 4 mL/min flow rate, 2 cm bed height, and 1.5 cm column diameter achieved a 105 min. breakthrough time. Process optimization via Box–Behnken response surface methodology confirmed the model's predictive strength. The results demonstrate that tea waste is a promising low-cost biosorbent for the efficient removal of MB dye, with potential application in wastewater treatment systems.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".