Ionic liquid-assisted synthesis of mesoporous polymers and carbon materials: the self-assembly mechanism
Bibliographic record
Abstract
Soft-templating synthesis has been widely employed to fabricate ordered mesoporous polymer and carbon materials with effectively tuneable pore sizes. However, the commonly used templating agents, block copolymers, are normally decomposed during the process, thus are barely recyclable; this increases the costs and hampers the scale-up feasibility. Therefore, it becomes imperative to seek promising alternatives; amphiphilic ionic liquids (ILs) are excellent candidates due to their good recyclability. This study explored the templating behaviour of IL templates for preparing mesoporous polymers and carbons. In details, the self-assembly of ternary systems (comprising of IL templates, precursors and solvent) were investigated by a combination of coarse-grained molecular dynamics (CGMD) simulations, density function theory (DFT) calculations and experimental techniques. The results indicate that the morphologies of IL templates are tuneable not only by the adjustment of water content in the mixture but also by the selection of suitable precursors. Material precursors containing increasing numbers of hydroxyl moieties also induce various precursor-template spatial correlations, resulting in different topological structures of nanomaterials. This work presents a fundamental investigation into the mechanisms of templating synthesis with amphiphilic ILs as recyclable templates and gives insight into the effective design of coveted carbon nanomaterials for targeted applications.
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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) | 0.001 | 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".