Wood Biomolecules as Agricultural Adjuvants for Effective Suppression of Droplet Rebound from Plant Foliage
Bibliographic record
Abstract
The agrochemical run-off associated with crop control is an unintended consequence of droplet rebound from plant foliage, which negatively affects crop performance and the environment. This is most critical in water-based formulations delivered on plant surfaces that are typically waxy and nonwetting. This study introduces an alternative to synthetic surfactants and high molecular weight polymers that are used as spreading agents for agrochemicals. Specifically, biopolymeric adjuvants (hemicelluloses and oligomeric lignin) extracted from wood by pressurized hot water are shown for their synergistic pinning capacity and surface activity that can effectively suppress droplet rebound from hydrophobic surfaces. Hemicellulose and lignin mixtures, alongside several model compounds, are investigated for understanding the dynamics of droplet impact and its correlation with biomacromolecule formations. The benefit of utilizing lean solutions (0.1 wt.% concentration) is highlighted for reducing droplet rebounding from leaves, outperforming synthetic systems in current use. For instance, a tenfold deposition improvement is demonstrated on citrus leaves, because of a significantly suppressed droplet roll-off. These results establish the excellent prospects of wood extracts to improve crop performance.
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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 | 0.000 |
| 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) | 0.000 | 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 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".