Urchin-like CO2-responsive magnetic microspheres for highly efficient organic dye removal
Bibliographic record
Abstract
CO2-responsive materials have emerged as promising adsorbents for the remediation of refractory organic dyes-contaminated wastewater without the formation of byproducts or causing secondary pollution. However, realizing the simultaneous adsorption−separation or complete removal of both anionic and cationic dyes, as well as achieving deeper insights into their adsorption mechanism, still remains a challenge for most reported CO2-responsive materials. Herein, a novel type of urchin-like CO2-responsive Fe3O4 microspheres (U-Fe3O4@P) has been successfully fabricated to enable ultrafast, selective, and reversible adsorption of anionic dyes by utilizing CO2 as a triggering gas. Meanwhile, the CO2-responsive U-Fe3O4@P microspheres exhibit the capability to initiate Fenton degradation of non-adsorbable cationic dyes. Our findings reveal exceptionally rapid adsorption equilibrium, achieved within a mere 5 minutes, and an outstanding maximum adsorption capacity of 561.2 mg g−1 for anionic dye methyl orange upon CO2 stimulation. Moreover, 99.8% of cationic dye methylene blue can be effectively degraded through the Fenton reaction. Furthermore, the long-term unresolved interaction mechanism of organic dyes with CO2-responsive materials is deciphered through a comprehensive experimental and theoretical study by density functional theory. This work provides a novel paradigm and guidance for designing next-generation eco-friendly CO2-responsive materials for highly efficient purification of complex dye-contaminated wastewater in environmental engineering. Massive discharge of organic dyes contaminated wastewater into the ecosystem threatens human health and the aquatic environment, ascribed to the hazardous and toxic natures of dyes. Thus, designing advanced materials for the highly efficient removal of organic dyes from wastewater is extremely promising. Herein, novel urchin-like CO2-responsive magnetic microspheres were devised to enable highly efficient selective adsorption−separation of anionic dyes and simultaneous removal of anionic/cationic dyes, even in the complex synthetic dye effluent by combining CO2-triggered adsorption and Fenton degradation. This work provides insights for developing 'smart' materials for the purification of complex dye-contaminated wastewater in environmental engineering.
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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".