Nanoconfined core-shell heterogeneous fenton reactor: Accelerated degradation of organic pollutants in flow-through systems
Bibliographic record
Abstract
Integrating heterogeneous catalysts into porous matrices as flow-through reactors has attracted significant attention for continuous elimination of dissolved organic contaminants. However, the catalytic activity of most flow-through reactors is severely limited by the short diffusion length of generated reactive species. Inspired by the natural biological systems that achieve remarkable rate acceleration within nanoconfined environments, we designed an ultra-fast flow-through catalytic reactor featuring nanoconfined reaction environment by incorporating engineered core–shell iron-based heterogeneous catalysts (C-MIC). The porous silica shell of C-MIC facilitates catalytic reactions in a confined environment, enhancing activity through local concentration and nanoconfinement effects. Meanwhile, the polydopamine (PDA) matrix of C-MIC promotes efficient electron transfer and preserves the structural integrity of the catalysts, accelerating the catalytic rate up to 0.505 min −1 for model dyes. Moreover, the C-MIC based composite reactor achieved a flow rate up to ∼2000 L m −2 h −1 for continuous flow-through degradation of organic dyes, antibiotics, and endocrine-disrupting compounds, and maintaining its catalytic efficiency across a wide pH range and after multiple cycles, and this performance surpasses most reported flow-through reactor. This work provides a new strategy for designing advanced oxidation process (AOP) catalyst with regulated nanoconfined structures to achieve superior catalytic performance for 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) | 0.000 | 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".