Systematic synthesis of high surface area silica nanoparticles in the sol–gel condition by using the central composite design (CCD) method
Bibliographic record
Abstract
The sol–gel method is employed for producing high surface area silica nanoparticles from a cheap precursor, that is water glass (sodium silicate). To design the experiments systematically, the response surface method (RSM) combined with the central composite design (CCD) approach is used. Four major factors including the concentration of sodium silicate solution, solution pH, reaction temperature and reaction time are identified as the major controlling parameters and the particle surface area is considered as the response or the output parameter. A total of 31 experiments are designated by the CCD. The experiments are conducted at a centre point chosen based on experience and at its vicinity to investigate how the response changes as the factors change. Nanoparticles with a surface area as high as 630 m2/g and a particle size as low as 8 nm are produced at the optimum parameters of sodium silicate concentration of 1.5 × 10−4 g/L, pH of 4, a reaction temperature of 25°C and a reaction time of 1.5 h. A regression analysis is performed on the experimental data and a correlation is obtained that may be used to predict the particle surface area and investigate the effect of varying the factors on the response. Within the range of parameters studied here, it is found that the solution pH and then the process temperature have a profound effect on the surface area and concentration and reaction time have a moderate effect. An increase of the solution pH from 4 to higher values results in a rapid drop in the particle surface area.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".