Sorptive Removal of Trinitroglycerin (TNG) from Water Using Nanostructured Silica‐based Materials
Bibliographic record
Abstract
Trinitroglycerin (TNG), a nitrate ester, is widely used in the pharmaceutical industry for the treatment of angina pectoris (chest pain) and by the military for the manufacturing of dynamite and propellants. Currently, TNG is considered as a key environmental contaminant due to the discharge of wastewater tainted with the chemical from various military and pharmaceutical industries. The present study describes the use of a nanostructured silica material (Mobil Composite Material no. 48 [MCM-48]) prepared by mixing tetraethylorthosilicate (TEOS) and cetyltrimethylammonium bromide (CTAB) to remove TNG from water. The sorption of TNG onto MCM-48 rapidly reached equilibrium within 1 h. Sorption kinetics were best described using a pseudo-second order model, whereas sorption isotherms were best interpreted using the Langmuir model. The latter gave a maximum sorption capacity of 55.2 mg g(-1) at 40 degrees C. The enthalpy and entropy of TNG sorption onto MCM-48 were 1.89 kJ mol(-1) and 79.0 J mol(-1).K(-1), indicating the endothermic nature of the TNG sorption onto MCM-48. When MCM-48 was heated at 540 degrees C for 5 h, the resulting calcined material (absence of the surfactant) did not sorb TNG, suggesting that the surfactant component of the nanomaterial was responsible for TNG sorption. Finally, we found that MCM-48 lost approximately 30% of its original sorption capacity after five sorption-desorption cycles. In conclusion, the nanostructured silica based sorbent, with high sorption capacity and remarkable reusability, should constitute the basis for the development of an effective technology for the removal of TNG from contaminated water.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.003 | 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 teacher head, 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".