Carbon Nanotubes Improved the Germination and Vigor of Plant Species from Peatland Ecosystem Via Remodeling the Membrane Lipidome
Bibliographic record
Abstract
Application of the nanopriming technique to alleviate seed dormancy has shown promising results in various agricultural crop species. However, there is a dearth of knowledge regarding its potential use in native peatland boreal forest species to alleviate seed dormancy and improve their propagation or vigor for forest reclamation activities. Herein, we demonstrate the use of nanopriming with carbon nanotubes (CNT) to alleviate seed dormancy, improved seed germination, and seedling vigor in two boreal peatland species. Bog birch (Betula pumila L.) and Labrador tea (Rhododendron groenlandicum L.) seeds with embryo or seed coat dormancy were nanoprimed with either 20 or 40 µg/mL CNT, cold stratified at 2–4 °C for 15 days, and allowed to germinate at room temperature. The emerged seedlings’ lipidome was assessed to decipher the role of lipid metabolism in alleviating seed dormancy. We observed significant (p < 0.05) improvement in seedling germination and seedling vigor in seeds primed with multiwalled carbon nanotubes functionalized with carboxylic acids. Phosphatidylcholine (PC 18:1/18:3), phosphatidylglycerol (PG 16:1/18:3), and lysophosphatidylcholine (LPC 18:3) molecular species (C18:3 enriched) were observed to be highly correlated with the increased seed germination percentages and the enhanced seedling vigor. Mechanistically, it appears that carbon nanoprimed seeds following stratification are effective in mediating seed dormancy by remodeling the seed membrane lipids (C18:3 enriched PC, PG, and LPC) in both peatland boreal forest species. The study results demonstrate that nanopriming may provide a solution to resolve seed dormancy issues by enhancing seed germination, propagation, and seedling vigor in non-resource boreal forest species ideally suited for forest reclamation following anthropogenic disturbances.
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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".