Three-Dimensional Polymer Nanofiber Structures for Liquid Contamination Adsorption
Bibliographic record
Abstract
Green amphiphilic decontamination materials with high-performance, ultrafast, and highly efficient liquid contamination adsorption are of great significance to environmental protection. In this work, an ethylene-vinyl alcohol copolymer with both hydrophilic and hydrophobic chain fragments was selected as the matrix (amphiphilic adsorption), a topological grafting structure was designed for chain entanglement (to enhance fiber integrity), and then the aggregate structure was controlled with a soft dispersed phase (to facilitate fiber formation). Finally, environmental-friendly supercritical CO2 foaming is used to obtain the three-dimensional (3D) polymer nanofiber structures (to increase fiber-structure porosity) for liquid contamination adsorption. The strategy takes full advantage of the synergistic effect from the multi-scale structure, including the random copolymer structure (repeating unit scale in the molecular chain), topological structure (molecular chain scale), microphase separation structure (aggregate scale), and nanofiber structure (porous scale). The obtained adsorption amphiphilic material adsorbed liquid contamination with a high efficiency of 64.78 g/g and a large adsorption rate of 1.14 g/g·s–1 (kinetic constant) for carbon tetrachloride, attributing to its unique 3D polymer nanofiber structure with a large specific surface area and a large amount of porous space to adhere and to be filled by liquid contamination, respectively. This work provided a strategy for the green preparation of environment-friendly and high-performance decontamination materials.
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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 | 0.001 | 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) | 0.002 | 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".