Rapid and efficient inactivation of viruses in seawater by LIG electrodes
Bibliographic record
Abstract
Abstract Water-borne viral diseases are a significant concern for public health. In particular, they threaten the health of people and animals in countries that lack proper water treatment facilities. Novel water treatment technology may efficiently improve water quality and prevent the spread of waterborne viral pathogens. Laser-induced graphene (LIG) has been shown to inactivate viruses and bacteria with its photothermal properties, electrochemical reaction, and rough surface texture. However, LIG's activity to prevent virus transmission via contaminated water has not been fully explored. Here, we demonstrated that enveloped and non-enveloped viruses in seawater could be rapidly inactivated by LIG technology. After being activated by 3 V of electricity, the LIG electrodes inactivated both types of viruses spiked in water within 30 min. In addition, the electrolyzed seawater exhibited virucidal effects even after the cessation of the electrical charge. The generation of different oxidants, such as chlorine, chlorine dioxide, and hydrogen peroxide, may play an essential role in the antiviral mechanism of the LIG electrodes. Furthermore, after 10 min of electrolysis, the pH of the seawater dropped from approximately 8–5, which may also have contributed to the virucidal effects of the LIG technology. The virucidal activity of LIG technology highlighted its potential for preventing the spread of viral infections via seawater systems which may have public health implications in areas where seawater is used in the sewage system. It may also have applications in aquaculture, where viral diseases do not have treatments and can cause high fish mortality.
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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".