Preparation of novel N-CNT nanocomposite as an adsorbent for removal of As+3 toxic ions and DFT calculation
Bibliographic record
Abstract
Abstract This work aimed at developing novel adsorbents to remove arsenic ions in an aqueous solution. To this end, the use of polymeric materials and functionalized nitrogen-doped carbon nanotubes (N-CNT) was considered to adsorb arsenic ions from aqueous solutions. In this regard, the effects of experimental parameters including pH, contact time, initial concentration of metal, and the adsorption temperature were studied. According to experiments, the optimum solution pH for arsenic adsorption at 20 °C was 7. As the contact time increased, the adsorption rate increased and reached equilibrium after 10 minutes. The arsenic adsorption capacity of Polyether Sulfone/N-CNT nanocomposites increased with increasing temperature. The highest adsorption capacity was achieved at 49 °C. The equilibrium data obtained in the initial concentration range of Arsenic and the studied temperature was consistent with the Freundlich isotherm. The adsorption kinetics revealed that the arsenic adsorption followed the Pseudo-First order and Weber Morris equation. The thermodynamic parameters (ΔH, ΔG, and ΔS) indicated that the arsenic adsorption in the temperature range of 332-296 K was spontaneous and endothermic. The adsorption properties of the modified N-doped carbon nanotubes towards arsenic ions were also studied by density functional theory (DFT) calculations.
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".