Synthesis of mesoporous silica materials (MCM‐41) using silica fume as the silica source in a binary surfactant system assisted by post‐hydrothermal treatment and its Pb<sup>2+</sup> removal properties
Bibliographic record
Abstract
Abstract Mesoporous silica materials (MCM‐41) were successfully prepared by substituting silica fume for silicate in different surfactant systems. The effects of binary surfactant systems and post‐hydrothermal treatment on the morphology and structural properties of MCM‐41 were investigated based on X‐ray diffraction (XRD), N2 sorption/desorption, and transmission electron microscopy analyses (TEM). The results confirmed that polyethylene glycol (PEG‐6000) is an effective additive template in a binary surfactant system for the synthesis of mesoporous silica materials. Meanwhile, the hydrothermal stability of the mesoporous silica materials can be improved by post‐hydrothermal treatment and the optimal experimental parameters are a temperature of 140 °C and a treatment time of 48 h. The formation of high‐quality MCM‐41 is based on the balance between the ordered assembly of the inorganic anionic species and cationic surfactant, in accordance with the electrostatic interactions and hydrogen bonds between PEG‐6000 and the inorganic species. Meanwhile, Pb(II) removal from aqueous solution has also been examined using MCM‐41 as an adsorbent. The results of adsorption showed that the as‐synthesized MCM‐41 demonstrated a high capacity of Pb2+.
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".