Design of Novel Solvents for Agrochemical Formulations via Solvatochromic Methods using N-Alkyl substituted Amides as Example
Bibliographic record
Abstract
Finding new candidate solvents that can work as replacers for banned solvents, or that could be an improvement to existing solvents, is a time-consuming procedure. It is important to know how solvent structure affects interactions with active ingredients and how it will affect the performance in the application. To fully understand the interactions between solvents and solutes, there is a need to understand the types of interactions that are involved in such systems. This article discusses polar interaction between solvents and solutes and presents the methods that monitor polarity. Improved methods to monitor solute solubility and water solubility are also presented. Two classes of solvents are used as examples, namely, N,N-dimethyl alkaneamides and N-alkyl, N-methyl formamides. Tebuconazole is used as a model solute. The solubility of tebuconazole in the mentioned solvents as well as formulation stability is related to fundamental interaction parameters. It is clear from the analysis that there exists a correlation between the β-parameter (electron donating properties) and the solubility of tebuconazole for amide solvents. It can also be seen that the two classes of solvents investigated in this study interact to a different extent with water (i.e. both the solubility of solvent in water and the solubility of water in the solvent). From the results, it is also clear that the N-decyl, N-methyl formamide is the best suited solvent to obtain stable formulations and emulsions at low temperature for high concentrations of tebuconazole.
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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 | 0.001 |
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".