In situ generation of highly localized chlorine by laser-induced graphene electrodes during electrochemical disinfection
Bibliographic record
Abstract
Laser-induced graphene (LIG) has gained popularity for electrochemical water disinfection due to its efficient antimicrobial activity when activated with low voltages. However, the antimicrobial mechanism of LIG electrodes is not yet fully understood. This study demonstrated an array of mechanisms working synergistically to inactivate bacteria during electrochemical treatment using LIG electrodes, including the generation of oxidants, changes in pH-specifically high alkalinity associated with the cathode, and electro-adsorption on the electrodes. All these mechanisms may contribute to the disinfection process when bacteria are close to the surface of the electrodes where inactivation was independent of the reactive chlorine species (RCS); however, RCS was likely responsible for the predominant cause of antibacterial effects in the bulk solution (i.e., ≥100 mL in our study). Furthermore, the concentration and diffusion kinetics of RCS in solution was voltage-dependent. At 6 V, RCS achieved a high concentration in water, while at 3 V, RCS was highly localized on the LIG surface but not measurable in water. Despite this, the LIG electrodes activated by 3 V achieved a 5.5-log reduction in Escherichia coli (E.coli) after 120-min electrolysis without detectable chlorine, chlorate, or perchlorate in the water, suggesting a promising system for efficient, energy-saving, and safe electro-disinfection.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".