Cell-Targeted Metal-Phenolic Nanoalgaecide in Hydroponic Cultivation to Enhance Food Sustainability
Bibliographic record
Abstract
The growing global population necessitates substantial increases in food production. Hydroponic cultivation systems afford a critical alternative for food sustainability and enable stable annual production regardless of the climatic and geographical variations. However, the overgrowth of harmful algal blooms significantly threatens the crop yield by competing with nutrition in the solution and producing contaminants. The conventional practice of algaecides fails to control algal proliferation due to the limited efficiency and food safety concerns. Nanopesticides can deliver active ingredients responsively to suppress crop diseases and offer solutions to current practical challenges and difficulties. Inspired by prospects of nanotechnology for agricultural applications, we have utilized natural polyphenols and copper ions (Cu 2+ ions) to develop self-assembled nanoalgaecides referred to as CuBes. The nanoalgaecide attached to algal cells via phenolic surface interactions, enabling localized Cu 2+ ion release. This cell-targeted delivery suppressed Chlorella vulgaris for over 30 days (99% inhibition). Transcriptomics revealed that the nanoalgaecide disrupted algal metabolism by downregulating photosynthesis and chlorophyll pathways. In a solar-illuminated plant factory, the nanoalgaecide showed higher algal inhibition and lettuce biosafety versus the commercial Kocide 3000. Notably, the use of nanoalgaecide can enhance the nutrient value of lettuces, which meets the daily supply of Cu for adults. By integrating smart nanotechnology design with selective delivery mechanisms, this metal-phenolic nanoalgaecide provides a nanoenabled solution for controlling harmful algal blooms in hydroponics to advance food production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 | 0.001 |
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 teacher head, 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".