Fabrication of graphene-based porous materials: traditional and emerging approaches
Bibliographic record
Abstract
The anisotropic nature of 'graphenic' nanosheets enables them to form stable three-dimensional porous materials. The use of these porous structures has been explored in several applications including electronics and batteries, environmental remediation, energy storage, sensors, catalysis, tissue engineering, and many more. As method of fabrication greatly influences the final pore architecture, and chemical and mechanical characteristics and performance of these porous materials, it is essential to identify and address the correlation between property and function. In this review, we report detailed analyses of the different methods of fabricating porous graphene-based structures - with a focus on graphene oxide as the base material - and relate these with the resultant morphologies, mechanical responses, and common applications of use. We discuss the feasibility of the synthesis approaches and relate the GO concentrations used in each methodology against their corresponding pore sizes to identify the areas not explored to date.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".