Bibliographic record
Abstract
In pesticide and fertilizer applications, large spray droplet sizes (300-500 um) are commonly used in the field for reduced spray drift. However, retention of spray droplets after they reach the target surface can be limited by droplets splashing, rebounding, or rolling off of the surface due to their high impacting velocity and inertial energy. While polymer additives were proposed to dissipate energy during the droplet impact process, whether they can enhance the retention efficiency of crop sprays in practical field conditions is still not clear. This research work focuses on enhancing the retention efficiency with polymer additives, which is carried out in four major steps: The first step is to screen polymers suitable for spray applications. The second step is to develop an approach that provides detailed physical insight in a setup that is representative of real spray conditions to quantify retention efficiency. The third step determines the effect of extensional rheological properties on retention efficiency at various spray conditions. The work carried out in the first, second, and third steps forms a comprehensive study on the relationship between extensional rheological properties of polymer solution and retention efficiency. Then the fourth step focuses on exploring the extensional rheological properties of selected polymer additives at different solvent conditions and in agrochemical solutions. The results demonstrate that increasing the extensional relaxation time of the spray solution can increase the retention efficiency by up to 20% and in some cases achieve a total efficiency greater than 95%. It’s also suggested for a particular polymer, surface, and droplet size, the extensional relaxation time alone could be sufficient to predict retention efficiency. The results also relate the extensional relaxation time to important influencing parameters including pH, ionic strength, type of ions, etc.
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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 | 0.001 |
| 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".