Sandwich‐Type Nanocomposite of Reduced Graphene Oxide and Periodic Mesoporous Silica with Vertically Aligned Mesochannels of Tunable Pore Depth and Size
Bibliographic record
Abstract
Abstract Sandwich‐type nanocomposites of graphene oxide (GO) and periodic mesoporous silica (PMS) with vertically aligned mesochannels of different pore depth and size are synthesized and characterized, and their formation modes are examined. The existence of mesoscale ordered structure in the mixture of GO and surfactant solutions is confirmed for the first time by in situ small angle X‐ray scattering measurement using synchrotron radiation. The mesochannel depth and pore wall ripening of PMS in the nanocomposites are controlled by the reaction time of the hydrolysis of tetraethyl orthosilicate. The pore size of PMS in the nanocomposites can be varied in the range of 1–5 nm by varying the chain length of alkyltrimethylammonium (CnTA+) template and high specific surface area (≈1000 m2 g−1) is achieved for all samples. Nanocomposites with vertically aligned PMS mesochannels can be synthesized by applying CnTA+ templates of n ≥ 12, whereas with CnTA+ of n ≤ 10, either PMS nanoparticle deposited GO structure or the structure with incomplete coverage of GO surface with imperfect PMS is found. The aggregation behaviors of surfactant molecules on GO depend on surfactant concentration relative to critical micelle concentration and reaction temperature, and result in the peculiar nanocomposites of different structural styles.
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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 | 0.000 | 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".