Bibliographic record
Abstract
High-performance graphene aerogels with well-developed internal structures are generally obtained by means of introducing additive materials such as carbon nanotubes, cellulose, and lignin into the aerogel network, which not only enhances the cost but also complicates the preparation process. Therefore, tailoring the internal structure of pristine graphene aerogel in a feasible way to achieve high performance is of great significance to the practical applications. Herein, a novel cysteamine/l-ascorbic acid graphene aerogel (CLGA) was fabricated by a simple one-step hydrothermal method followed by freeze-drying. Through the creative combination of the reducing agent l-ascorbic acid and cross-linking agent cysteamine, a dual-network structure was constructed by both layered physical stacking and vertical chemical cross-linking. The addition of cysteamine not only enhanced the reduction degree but also assisted the formation of more vertical connections between graphene nanosheets, resulting in more abundant pores with smaller sizes compared with graphene aerogels prepared by the traditional hydrothermal reduction method. CLGA possessed an ultra-low density of 4.2 mg/cm<sup>3</sup> and a high specific surface area of 397.9 m<sup>2</sup>/g. As expected, this dual-network structure effectively improved the absorption capacity toward a variety of oil and organic solvents, with an outstanding oil absorption capacity up to 310 g/g. Furthermore, CLGA possessed good mechanical properties and oil/water selectivity. The absorbed oil could be recovered by both continuous absorption-removal process and mechanical squeezing, making the as-prepared aerogel superior absorbent material for a variety of applications, such as selective oil absorption and water treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".