Removal of copper(II) ions from Aqueous Media by Chemically Modified MCM‐41 with <i>N</i>‐(3‐(trimethoxysilyl)propyl)ethylenediamine and Its 4‐hydroxysalicylidene Schiff‐base
Bibliographic record
Abstract
The adsorption capacities of mesoporous silica modified with N‐(3‐(trimethoxysilyl)propyl)ethylenediamine (MCM‐41‐NH2) and its 4‐hydroxysalicylidene Schiff‐base, MCM‐41‐N‐Hdhba, as sorbents for removal and recovery of copper(II) ions from aqueous media have been investigated. The sorption uptake is highly dependent on the pH, contact time, temperature, diverse ions, mass of sorbent as well as initial Cu(II) ions concentration. At pH 6, the two sorbents, MCM‐41‐NH2 and MCM‐41‐N‐Hdhba, show 96.4% and 99.9% Cu(II) ions removal, respectively. Langmuir isotherm gave the best fit of the experimental data with maximum adsorption capacities 138.8 and 222.2 mg g−1 for MCM‐41‐NH2 and MCM‐41‐N‐Hdhba, respectively. The uptake kinetics were modeled using a pseudo‐second‐order rate equation and the thermodynamic parameters (ΔH°, ΔG° and ΔS°) verified favorable, spontaneous, and exothermic for MCM‐41‐NH2 and endothermic for MCM‐41‐N‐Hdhba adsorption processes. Successive adsorption–desorption studies indicated that MCM‐41‐NH2 and MCM‐41‐N‐Hdhba maintain their adsorption and desorption efficiencies constant over five cycles. Of particular importance is the fact that MCM‐41‐NH2 and MCM‐41‐N‐Hdhba were able to remove 95% of Cu(II) ions from polluted river and tap water. The structures and physicochemical properties of the sorbents before and after adsorption of Cu(II) ions were characterized by using spectroscopic (FTIR and XRD), morphological (TEM‐EDX), thermal, elemental analysis, and magnetic susceptibility measurements. © 2017 American Institute of Chemical Engineers Environ Prog, 37: 746–760, 2018
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".