Robust MXene aerogel assisted by cellulose nanofiber for efficient crude oil spill remediation
Bibliographic record
Abstract
Efficient cleanup of crude oil continues to be a global challenge owing to its inherent high viscosity, which makes it difficult to remove with conventional porous adsorbents. Here, a novel multifunctional aerogel was fabricated through directional freeze-drying, leveraging its photothermal properties and directional structure for the rapid cleanup of crude oil. The aerogel incorporates 2,2,6,6-tetramethyl-1-piperidinyloxy oxidized nanocellulose to enhance Ti 3 C 2 T x (MXene) to construct functional networks, MXene/gold nanoparticles (MX/AuNPs) as photothermal absorbers, and methyltrimethoxysilane for hydrophobic coatings. After 30 compression-release cycles at 90% strain, the strain retention of the aerogel is 85.7%, indicating its mechanical super-elasticity. The as-prepared aerogel showed durable hydrophobicity (145°), high oil/organic solvent absorption capacity (45.7–85.6 g/g), and efficient photothermal conversion, rapidly attaining and sustaining 76 °C. Interestingly, for viscous crude oil that cannot be absorbed for a long time, the aerogel completed the absorption within 10 s after illumination, demonstrating an improved absorption ability of viscous crude oil. Furthermore, the obtained aerogel successfully achieved controlled and rapid light-driven motion, as well as long-lasting photothermal sterilization performance. This work presents a feasible strategy for developing multifunctional composite aerogels, addressing the increasing demands in crude oil separation applications.
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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) | 0.000 | 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 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".