Carbon Nanoparticles Functionalized with Carboxylic Acid Improved the Germination and Seedling Vigor in Upland Boreal Forest Species
Bibliographic record
Abstract
Nanopriming has been shown to significantly improve seed germination and seedling vigor in several agricultural crops. However, this approach has not been applied to native upland boreal forest species with complex seed dormancy to improve propagation. Herein, we demonstrate the effectiveness of carbon nanoparticles functionalized with carboxylic acids in resolving seed dormancy and improved the propagation of two native upland boreal forest species. Seed priming with carbon nanoparticles functionalized with carboxylic acids followed by stratification were observed to be the most effective in improving germination to 90% in green alder (Alnus viridis L.) compared to 60% in the control. Conversely, a combination of carbon nanoparticles (CNPs), especially multiwall carbon nanoparticles functionalized with carboxylic acid (MWCNT–COOH), cold stratification, mechanical scarification and hormonal priming (gibberellic acid) was effective for buffaloberry seeds (Shepherdia canadensis L.). Both concentrations (20 µg and 40 µg) of MWCNT–COOH had a higher percent germination (90%) compared to all other treatments. Furthermore, we observed the improvement in germination, seedling vigor and resolution of both embryo and seed coat dormancy in upland boreal forest species appears to be associated with the remodeling of C18:3 enriched fatty acids in the following seed membrane lipid molecular species: PC18:1/18:3, PG16:1/18:3, PE18:3/18:2, and digalactosyldiacylglycerol (DGDG18:3/18:3). These findings suggest that nanopriming may be a useful approach to resolve seed dormancy issues and improve the seed germination in non-resource upland boreal forest species ideally suited for forest reclamation following resource mining.
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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".