Functional porous graphene materials by pickering emulsion templating: From emulsion stabilization to structural design and fabrication
Bibliographic record
Abstract
domains, GO flakes are amphiphilic. Thus, GO can stabilize Pickering emulsions where GO-stabilized droplets are dispersed in another immiscible continuous liquid phase. By tuning GO Pickering emulsion templates and removing the liquid phases, PGMs with variable architecture, such as different pore size distribution, pore shape, pore volume, and interconnectivity, can be achieved. Furthermore, both the composition and the distribution of functional additives within PGMs can be tuned via emulsion templating. Emulsion-templated PGMs have high surface area, low mass density, tunable mechanical properties and permeability, and many sites for functionalization, which make them promising materials for a variety of applications, e.g., energy storage, biomedical engineering, sensing, absorption, and separation. This paper reviews the factors affecting GO amphiphilicity, the assembly of GO flakes at emulsion interfaces, the resulting emulsion stabilization by the flakes, and the treatments, such as drying and reduction of GO emulsions, that can be used to obtain PGMs with desirable composition and architecture using Pickering emulsion templating. The latest applications of PGMs are discussed, and research challenges and future opportunities are also proposed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".