Highly Robust, Compressible, Anisotropic, and Fire-Retardant Polyimide/Hydroxyapatite Nanowires/Reduced Graphene Oxide Aerogel for Rapid Adsorption of Viscous Oil Assisted by Sunlight
Bibliographic record
Abstract
Improving the adsorption efficiency of porous adsorbent materials for organic liquids with high viscosity is crucial for addressing oil spill incidents. In this study, a high-performance aerogel adsorbent composed of polyimide (PI), hydroxyapatite nanowires (HAPnws), and reduced graphene oxide (rGO) has been fabricated, which leverages reduced flow tortuosity through anisotropic structures and solar-assisted viscosity reduction via photothermal materials. The prepared anisotropic PI/HAP/rGO aerogel, with directional channels, shows unique mechanical properties with high stiffness along the axial direction and compressibility along the radial direction. PI/HAP/rGO, featuring vertically aligned channels, demonstrated superior adsorption efficiency (the adsorption coefficient K s reached 0.37 kg m −1 s −1/2 for an engine oil with a viscosity of ~144 mPa·s) for oil of varying viscosities compared to similar aerogels with uniform pores, because of the substantially reduced flow tortuosity. The photothermal properties of rGO further enhance the adsorption speed of PI/HAP/rGO for viscous oil under sunlight, including crude oil with ultrahigh viscosity. In addition, PI/HAP/rGO exhibits excellent fire resistance, allowing for reusability via both adsorption–compression and adsorption–combustion cycles. The robust and compressible PI/HAP/rGO aerogels with high adsorption efficiency for viscous oil and fire resistance represent an ideal solution for practical oil spill treatment, and this approach also offers inspiration for the development of advanced adsorbent materials.
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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) | 0.000 | 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 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".