Microbubble-Enhanced Cold Plasma Activation (MB-CPA) for Promoting Vegetable Growth in Hydroponics
Bibliographic record
Abstract
Increasing global population, climate change, environmental pollution, and rapid urbanization have dramatically increased the global food demand. Innovative technologies minimizing the use of scarce freshwater resources while still boosting crop production are highly desirable. This study introduces microbubble-enhanced cold plasma activation (MB-CPA) technology to generate plasma-activated water (PAW) and investigates its effects on plant seedling growth in hydroponic systems. Coupling of the Venturi tube helped to efficiently transfer the reactive nitrogen and oxygen species (RONS) generated by air cold plasma discharge via microbubble formation. Notably, a high and stable concentration of nitrate (NO 3 –; 28 mg/L) and nitrite (NO 2 –; 5 mg/L) was estimated in PAW. Lab-scale investigations showed that garlic seedlings treated for 15 min MB-CPA have 1.5 times longer shoots and heavier weights than those irrigated with tap water and chemically prepared N-containing (NO 3 – and NO 2 – ) water. Interestingly, the treated garlic showed an almost 1.3-fold increase in sulfur content (10 4 mg/kg) compared to control, indicating an increase of beneficial natural sulfur compounds. Similar growth enhancements were observed in peanuts, garlic, and soybean sprouts, with PAW-treated plants showing 1.66, 1.5, and 1.8 times longer sprouts, respectively, compared to untreated groups. Application of MB-CPA to a large-scale commercial hydroponic system (108 units of garlic) demonstrated the accelerated rate of germination, improved shoot growth, and a higher root-to-shoot ratio. Furthermore, 32-day study comprising three growth cycles utilizing recycled PAW water resulted in a 1.53-fold increase in fresh weight and a 1.46-fold increase in dry weight of garlic during the final growth cycle. These findings demonstrate that MB-CPA is a promising, sustainable, and environmentally friendly fertilizing technology and can be an alternative for the improvement of agricultural production.
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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".