Adsorption and heat‐energy‐aid desorption of cationic dye on a new thermo‐sensitive adsorbent: Methyl cellulose/calcium alginate beads
Bibliographic record
Abstract
The adsorption and heat‐energy‐aid desorption of methylene blue (MB) on a thermo‐sensitive adsorbent of methyl cellulose/calcium alginate beads (MC/CABs) has been studied. The addition of methyl cellulose intensified the desorption ability of adsorbent, and boosted the difference of adsorption capacity of adsorbent between low temperature and high temperature. At the mass ratio of methyl cellulose to sodium alginate of 2:1, the difference of adsorption capacity of MC/CABs between 20 and 60°C reached 20.48 mg g −1 . The effects of temperature, time and initial MB concentration on adsorption performance were investigated in detail. The MB adsorption on MC/CABs followed the pseudo‐second‐order kinetic model. The equilibrium data was fitted well with Langmuir isotherm. The maximum adsorption capacity of 336.70 mg g −1 exhibited MC/CABs had a good adsorption capability. Thermodynamic analyses showed high temperature was not favorable to MB adsorption, and MC/CABs had a distinct superiority in desorption of adsorbate with heat‐energy‐aid. Lastly, the possible mechanisms involving in adsorption and heat‐energy‐aid desorption were presented. POLYM. ENG. SCI., 56:1382–1389, 2016. © 2016 Society of Plastics Engineers
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".