CO<sub>2</sub>‐Responsive Smart Wood Scaffold for Natural Organic Matter Removal without Secondary Pollution
Bibliographic record
Abstract
Abstract Ensuring drinking water safety remains a critical challenge, particularly when treating complex water sources, due to secondary pollution caused by active chemical additives. Herein, a novel CO 2 ‐responsive smart wood scaffold that leverages non‐toxic CO 2 activation is developed to achieve highly efficient removal of carcinogenic natural organic matter (NOM) and broad‐spectrum microbial disinfection without requiring additional chemical agents. Unlike conventional water purification techniques that face a safety‐efficacy trade‐off, the multi‐stage CO 2 ‐responsive wood scaffold offers exceptional tunability in NOM abatement across diverse environmental conditions, including variable water chemistry, NOM composition, high salinity, and real‐world water sources. The purified water meets stringent drinking water standards (e.g., UV 254 reduction, dissolved organic carbon removal, and bacterial elimination). It is found that the highly efficient NOM adsorption mainly originates from the strong and stable CO 2 ‐triggered cation−π interaction between the scaffold surface and aromatic NOM groups, as revealed via high‐resolution mass spectrometry and direct intermolecular force measurements. This ecofriendly and contamination‐free CO 2 ‐responsive strategy provides a transformative approach to overcoming secondary pollution challenges in water purification, offering a scalable and sustainable platform for environmental applications and beyond.
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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".