Adsorption and Desorption of Methylene Blue on Porous Carbon Monoliths and Nanocrystalline Cellulose
Bibliographic record
Abstract
The dynamic batch adsorption of methylene blue (MB), a widely used and toxic dye, onto nanocrystalline cellulose (NCC) and crushed powder of carbon monolith (CM) was investigated using the pseudo-first- and -second-order kinetics. CM outperformed NCC with a maximum capacity of 127 mg/g compared to 101 mg/g for NCC. The Langmuir isotherm model was applicable for describing the binding data for MB on CM and NCC, indicating the homogeneous surface of these two materials. The Gibbs free energy of -15.22 kJ/mol estimated for CM unravelled the spontaneous nature of this adsorbent for MB, appreciably faster than the use of NCC (-4.47 kJ/mol). Both pH and temperature exhibited only a modest effect on the adsorption of MB onto CM. The desorption of MB from CM using acetonitrile was very effective with more than 94 % of MB desorbed from CM within 10 min to allow the reusability of this porous carbon material. In contrast, acetonitrile was less effective than ethanol in desorbing MB from NCC. The two solvents were incapable of completely desorbing MB on commercial granular coal-derived activated carbon.
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 | 0.000 | 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 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".