Electrospray Preparation of Robust Aramid Nanofiber Microbead Adsorbents for Wastewater Treatment
Bibliographic record
Abstract
Adsorption is one of the promising techniques for effectively removing dyes from wastewater due to its low cost and high efficiency. However, most reported adsorbents still suffer from low adsorption capacity and stability under harsh conditions, which severely limit their practical application. Herein, we report a novel method to prepare robust aramid nanofiber (ANF) microbead adsorbents using electrospraying technology and solvent replacement method. The structure of the ANF microbeads can be easily manipulated by changing the coagulation baths, thus achieving fabricated ANF microbeads with a uniform and highly porous microstructure. The as-prepared ANF microbeads show high removal efficiency toward methylene blue (MB) with a maximum adsorption capacity of up to 267 mg/g, which can be attributed to the strong π–π stacking, hydrogen bonding, and electrostatic attraction between MB and the microbeads. Moreover, ANF microbeads show outstanding stability within a wide pH range (from 2 to 12) with an MB removal efficiency over 90% even after five adsorption–desorption cycles at pH = 12. Additionally, the ANF microbeads can maintain their structure integrity after the adsorption process and can be easily separated from the solution by static sedimentation because of their robust structure. This work provides a new strategy to design robust microbeads with excellent adsorption performance and stability, demonstrating their great potential in various environmental engineering applications.
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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) | 0.001 | 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".