Catalytic Membrane with Copper Single-Atom Catalysts for Effective Hydrogen Peroxide Activation and Pollutant Destruction
Bibliographic record
Abstract
The superior catalytic property of single-atom catalysts (SACs) renders them highly desirable in the energy and environmental fields. However, using SACs for water decontamination is hindered by their limited spatial distribution and density on engineered surfaces and low stability in complex aqueous environments. Herein, we present copper SACs (Cu 1 ) anchored on a thiol-doped reactive membrane for water purification. We demonstrate that the fabricated Cu 1 features a Cu–S 2 coordination─one copper atom is bridged by two thiolate sulfur atoms, resulting in high-density Cu-SACs on the membrane (2.1 ± 0.3 Cu atoms per nm 2 ). The Cu-SACs activate peroxide to generate hydroxyl radicals, exhibiting fast kinetics, which are 40-fold higher than those of nanoparticulate Cu catalysts. The Cu 1 -functionalized membrane oxidatively removes organic pollutants from feedwater in the presence of peroxide, achieving efficient water purification. We provide evidence that a dual-site cascade mechanism is responsible for in situ regeneration of Cu 1 . Specifically, one of the two linked sulfur atoms detaches the oxidized Cu 1 while donating one electron, and an adjacent free thiol rebinds the reduced Cu(I)–S pair, retrieving the Cu–S 2 coordination on the reactive membrane. This work presents a universal, facile approach for engineering robust SACs on water-treatment membranes and broadens the application of SACs to real-world environmental problems.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".