Self-Assembled Nanocoatings Protect Microbial Fertilizers for Climate-Resilient Agriculture
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Chemical fertilizers have been crucial for sustaining the current global population by supplementing overused farmland to support consistent food production, but their use is unsustainable. Pseudomonas chlororaphis is a nitrogen-fixing bacterium that could be used as a fertilizer replacement, but this microbe is delicate. It is sensitive to stressors, such as freeze-drying and high temperatures. Here, we demonstrate protection of P. chlororaphis from freeze-drying, high temperatures (50 o C), and high humidity using self-assembling metal-phenolic network (MPN) coatings. The composition of the MPN is found to significantly impact its protective efficacy, and with optimized compositions, no viability loss is observed for MPN-coated microbes under conditions where uncoated cells do not survive. Further, we demonstrate that MPN-coated microbes improve germination of seeds by 150% as compared to those treated with fresh P. chlororaphis . Taken together, these results demonstrate the protective capabilities of MPNs against environmental stressors and represent a critical step towards enabling the production and storage of delicate microbes under nonideal conditions.
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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".