Enzyme-immobilized hierarchically porous covalent organic framework biocomposite for catalytic degradation of broad-range emerging pollutants in water
Bibliographic record
Abstract
Efficient enzyme immobilization is crucial for the successful commercialization of large-scale enzymatic water treatment. However, issues such as lack of high enzyme loading coupled with enzyme leaching present challenges for the widespread adoption of immobilized enzyme systems. The present study describes the development and bioremediation application of an enzyme biocomposite employing a cationic macrocycle-based covalent organic framework (COF) with hierarchical porosity for the immobilization of horseradish peroxidase (HRP). The intrinsic hierarchical porous features of the azacalix[4]arene-based COF (ACA-COF) allowed for a maximum HRP loading capacity of 0.76 mg/mg COF with low enzyme leaching (<5.0%). The biocomposite, HRP@ACA-COF, exhibited exceptional thermal stability (~200% higher relative activity than the free enzyme), and maintained ~60% enzyme activity after five cycles. LCMSMS analyses confirmed that the HRP@ACA-COF system was able to achieve >99% degradation of seven diverse types of emerging pollutants (2-mercaptobenzothiazole, paracetamol, caffeic acid, methylparaben, furosemide, sulfamethoxazole, and salicylic acid)in under an hour. The described enzyme-COF system offers promise for efficient wastewater bioremediation applications. With the ever-growing global water demand, the urgent and sustainable treatment of polluted water is of extreme significance. However, it is becoming extremely challenging for water treatment plants to meet minimum standards for water quality, while considering their carbon footprint and environmental impact. Herein we present the excellent capacity of enzyme@COF biocomposite and thereby create a novel class of high-performance biocatalysts with ultrahigh enzyme loading (0.76 mg/mg), excellent catalytic activity against broad-range of pollutants, enhanced operational stability, and reusability. This study opens up a wide window of opportunities for enzyme immobilization and high-performance biocatalysts, particularly suited for water treatment.
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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".