Inactivation of Rhizoctonia solani in fertigation water using regenerative in situ electrochemical hypochlorination
Bibliographic record
Abstract
Abstract The capture and re-use of greenhouse fertigation water is an efficient use of fertilizer and limited water resources, although the practice is not without risk. Plant pathogens and chemical contaminants can build up over successive capture and re-use cycles; if not properly managed they can lead to reduced productivity or crop loss. There are numerous established and emerging water treatment technologies available to treat fertigation water. Electrochemical processes are emerging as effective means for controlling pathogens via in situ regenerative hypochlorination; a process that is demonstrated here to achieve pathogen control in fertigation solutions without leading to the accumulation of potentially phytotoxic free chlorine residuals associated with other chlorination processes. An electrochemical flow cell (EFC) outfitted with ruthenium dioxide (RuO2) dimensionally stable anodes (DSA) was characterized and evaluated for free chlorine production and Rhizoctonia solani inactivation in both irrigation and fertigation solutions. Pathogen inactivation was achieved at low current densities and short residence or cell contact times. Effluent free chlorine concentrations were significantly lower than commonly reported phytotoxic threshold values (approximately 2.5 mg/L) when fertilizer (containing ammonium) was present in the test solution; an effect attributable to reactions associated with breakpoint chlorination, including chloramine formation, as well as the presence of other oxidizable compounds in the fertilizer. Chloride concentrations were stable under the test conditions suggesting that the EFC was operating as a regenerative in situ electrochemical hypochlorination system. No significant changes to macronutrient concentrations were found following passage through the EFC.
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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".