Ultrasound‐assisted adsorption of reactive blue 21 dye on TiO<sub>2</sub> in the presence of some rare earths (La, Ce, Pr & Gd)
Bibliographic record
Abstract
Abstract Adsorption of reactive blue (RB) 21 dye in the aqueous solution has been carried out on TiO2 alone and in combination with rare earth ions [La3+, Ce4+, Pr3+ and Gd3+] in the presence and absence of ultrasound. The formation of adsorbent (TiO2) from tetra n‐butyl orthotitanate and its characterisation has been done through X‐ray diffraction (XRD), scanning electron microscopy (SEM), diffuse reflectance spectroscopy (DRS), Raman spectroscopy and N2 adsorption techniques. Complete decolourisation was achieved in 5 min in the presence of US + TiO2 + Ce. The effects of initial concentration of dye, adsorbent dose and contact time on the decolourisation of dye have been examined under different experimental conditions. The removal of dye in the presence of ultrasound was (88–99%) compared to conventional stirring (62–69%). Adsorption behaviour has been analysed using Langmuir, Freundlich, Dubinin–Radushkevich and Temkin isotherm models. RE–TiO2 combination had higher adsorption equilibrium constant (Kc) and better adsorption capacity (qmax) than TiO2 alone, both in the absence and presence of ultrasound, indicating the formation of Dye‐RE complex before sorption. This is confirmed from the shift in λmax from 626 to 616 cm−1. The kinetic data fit well with the pseudo‐second order kinetic model, and the adsorption process was spontaneous in the presence of rare earths. The mechanism of adsorption has been explained with the help of Weber–Morris intraparticle diffusion and Boyd kinetic models. A batch adsorber has been proposed for different TiO2 dose to effluent volume ratios using the Langmuir equation.
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) | 0.001 | 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".