Bacterial microcompartment-mimicking Pickering emulsion droplets for detoxification of chemical threats under sweet conditions
Bibliographic record
Abstract
Abstract Chemical warfare agents represent a severe threat to mankind and their efficient decontamination is a global necessity. However, traditional disposal strategies have limitations, including high energy consumption, use of aggressive reagents and generation of toxic byproducts. Here, inspired by the compartmentalized architecture and detoxification mechanism of bacterial micro-compartments, we constructed oil-in-water Pickering emulsion droplets stabilized by hydrogen-bonded organic framework immobilized cascade enzymes for decontaminating mustard gas simulant (2-chloroethyl ethyl sulfide, CEES) under sweet conditions. Two exemplified droplet systems were developed with two-enzyme (glucose oxidase/chloroperoxidase) and three-enzyme (invertase/glucose oxidase/chloroperoxidase) cascades, both achieving over 6-fold enhancement in decontamination efficiency compared with free enzymes and >99% selectivity towards non-toxic sulfoxide. We found that the favored mass transfer of sugars and CEES from their respective phases to approach the cascade enzymes located at the droplet surface and the facilitated substrate channeling between proximally immobilized enzymes were key factors in augmenting the decontamination efficacy. More importantly, the robustness of immobilized enzymes enabled easy reproduction of both the droplet formation and detoxification performance over 10 cycles, following long-term storage and in far-field locations.
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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".