Construction of three-dimensional aerogels from electrospun cellulose fibers as highly efficient and reusable oil absorbents
Bibliographic record
Abstract
The escalating environmental impact of oil pollution has necessitated the development of efficient and sustainable absorbent materials. Considering the large surface area and good flexibility of electrospun fibers, in this work, we aim to construct three-dimensional interconnected porous structures by using electrospun cellulose fibers as building blocks to allow highly efficient and repeatable oil absorption. Specifically, electrospun cellulose fibers were dispersed in water and assembled by covalently crosslinking, followed by a silanization process to improve the hydrophobicity of the fibrous matrices. The structure and properties of the assembled aerogels were studied by scanning electron microscopy (SEM), Fourier transform infrared (FT-IR) spectroscopy, water contact angle (WCA) measurement, and compression test. All the aerogels exhibited multiscale morphological structures composed of major pores (up to 20 μm), minor pores (up to 1 μm), and semi-micron scaled fibers as the building blocks, and were capable of absorbing diversified oils and organic solvents owing to their three-dimensional interconnected porous structures (porosity > 93.5 %). In particular, Aerogel 1–1 exhibited exceptional oil absorption capacities (up to 37.82 g/g) and superior reusability as evidenced by the minimal changes in its absorption capacity and compressive strength after 20 absorption-compression cycles. Therefore, this work highlights the potential of the electrospun cellulose fiber-constructed aerogels for efficient oil pollution remediation and industrial wastewater treatment.
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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 | 0.000 | 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 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".