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Dow: Optimizing Emulsion Stability of Solvent and Surfactant Packages Using High Throughput Techniquesqrcode

Jun. 22, 2020

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Jun. 22, 2020

Co-authors: Michael Paul Tate1, Laura Havens1, Jeff Michalowski1, Matt Entorf1, Britton Romain1, Matt Benedict1, Thomas Boomgaard1, Emily Bellairs1, Bethany Karl1
1The Dow Chemical Company, Midland, MI USA

Emulsifiable concentrates (EC) represent a significant fraction of all formulation types applied to the field globally and are valued for their ability to deliver oil soluble actives as well as the ease of transport and storage of the actives.  Despite the ubiquitous nature within the crop protection industry, the prediction of oil-in-water emulsion separation time from the multiple structures within the formulation remains limited and drives an experimental approach to development of emulsifiable concentrates.  In particular, the presence of various concentrations of electrolytes within the aqueous phase challenges, i.e., hard water, challenge development of a single surfactant package that provides adequately reduced separation time of the oil and water phase from the starting dispersion.1  Further, surfactant properties are dependent upon the electrolytes present adding to the complicated nature of development of surfactant package for emulsifiable concentrates.

Here, we describe a high throughput experimentation approach that enables development of solvent and surfactant packages that limit the separation of emulsions to <1% after 24 hours at room temperature under various electrolyte concentrations.  The approach enables rapid elucidation of the impact of hard water at 3 levels (20, 342, and 1500 ppm CaCO3 eq.) on a given surfactant package that contains not just a single surfactant, but 3 different surface active agents including an anionic surfactant, a nonionic wetting surfactant, and a nonionic dispersing copolymer. 

High Throughput Formulation Preparation

All these formulations are evaluated at constant total surfactant concentration (0.1% (w/w)), but with varying levels of each component.  In addition, the immiscible oil phase type are changed, but the oil concentration remains the same (0.9%).  An ABCD design of experiments is used to vary the surfactant concentration amongst the 3 individual surfactants resulting a 10 data point design (plus replicates) for each combination of materials (1 solvent plus 3 surfactants at a given water hardness). 

An overview of the workflow to prepare the samples at the 1 milliliter (mL) scale is shown in figure 1 starting with concentrates of individual surfactants and solvents which are then mixed together at various ratios to prepare the EC using an 8-channel Hamilton Microlab Start liquid handler.  The EC is then diluted into water of varying hardness using the same liquid handler, capped by hand, and aged at room temperature.  After 24 hours, the extent of separation of the emulsion is determined.

Figure 1. Overview of high throughput preparation process for emulsions

Machine Learning Method to Analyze Emulsion Stability

Determination of the extent of emulsion separation begins with capturing an image of the 1 mL glass vial using a custom-built imaging robot that takes images under well controlled, uniform lighting conditions that remain constant over time.  These lighting conditions enable direct spatial and temporal comparison of multiple samples.  These images are then analyzed by an in-house developed machine learning algorithm based on human evaluation of a training set of 636 samples and a test set of 158 samples.  The confusion matrix of the test set is shown in figure 2 along with example images of each identified state of the dispersion after 24 hours.  Overall, the classification process results in >89% of the images classified into the correct categories.

Figure 2. (a) Output confusion matrix of HTR analysis, and (b) example images of each output class

Effect of Nonionic Surfactant and Dispersant

High levels of water hardness accelerate destabilization of the emulsions due to shielding of the electrostatic repulsion of anionic surfactants.  The addition of nonionic surfactants enables use of steric repulsion as a mechanism to slow separation of the emulsion.  The extent to which nonionic surfactants are affected by electrolyte concentration is less than anionic surfactants.  Thus mixing anionic surfactants and nonionic surfactants enables a blending of approaches in order to achieve limited separation of the emulsion over different electrolyte concentrations in the water. 

The example in figure 3 highlights the effect of nonionic dispersant and nonionic wetting surfactant hydrophilic-lipophilic balance (HLB) on emulsion separation time.  The formulation contains 0.9% immiscible solvent in lieu of a traditional aromatic or aliphatic oil with the anionic surfactant, calcium alkylbenzene sulfonate, and a nonionic wetting surfactant and nonionic dispersing copolymer where the sum of the surfactants always equals 0.1%.  The nonionic wetting surfactants are secondary alcohol based ethoxylates with the molecular formula of C11-15H25-31O[CH2CH2O]xH with HLB that ranged from 12.1 to 14.5.  The nonionic dispersant copolymers are short chain alkyl initiated alkoxylates with the molecular formula of C2-4H5-9O[C2H4O]x[C3H6O]yH with resulting HLB of 12.5 to 14.5.

As the HLB of the nonionic dispersant increases from 12.5 to 14.5, the operating window of different surfactant ratios that resulted in a stable emulsion, which we define as <1% separation of the emulsion after 24 hours, increased substantially.  For example, at the lowest HLB for both nonionic components (12.1 and 12.5), there is no data point that resulted in a stable emulsion.  Note, that replication of the 50/50 anionic surfactant/wetting surfactant HLB of 12.1 data shows that 2 out of 3 replicated creamed, but 1 indicated stability.

If the HLB of the wetting surfactant is increased, the number of data points that resulted in stable emulsions is increased only to two points.  In contrast, increasing the HLB of the nonionic dispersant from 12.5 to 14.5 increased the number of observed stable emulsion data point to 5 for all values of the wetting surfactant HLB. 

The surfactant ratios that result in stable emulsions when the HLB of the nonionic dispersant is 14.5 create a region where the stable emulsion is likely to occur.  This region is bound as follows: <67% anionic surfactant, >16% nonionic dispersant, and >16% nonionic wetting surfactant.  This trend holds true when the data set for this solvent and anionic surfactant is expanded to include 6 different nonionic dispersant copolymers and 11 different nonionic co-surfactants (not shown). 

Figure 3. Matrix of output results as a function of HLB of the nonionic dispersant and wetting surfactant at 1500 ppm CaCO3 eq. water hardness

Future work will continue to advance the capture and machine learning analysis methods to more accurately assess the separation of the emulsions over time in order to elucidate structure/property relationships.

Dow materials used here include POWERBLOXTM; SV-17 Solvent, TERGITOLTM; 15-S-7 Surfactant, TERGITOLTM; 15-S-9, TERGITOLTM; 15-S-12, TERGITOLTM; XJ, TERGITOLTM; XD, and TERGITOLTM; XH.  Visit www.dowcropdefense.com for more information.
TMTrademark of The Dow Chemical Company ("Dow") or an affiliated company of Dow


The authors would like to acknowledge our coworkers and statisticians Fabio D' Ottaviano and Matthew Malloure.


1. Whitford, F.; Lindner, G.; Young, B.; Penner, D.; Deveau, J.; Linscott, D.; Zhu, H.; Zollinger, R.; Spandl, E.; Johnson, B.; Wise, K.; Patton, A.; Champion, C.; Harre, N.; Wagoner, N.; Smith, K. L., Adjuvants and the Power of the Spray Droplet: Improving the Performance of Pesticide Applications. Purdue University Cooperative Extension Service: West Lafayette, IN, 2014; Vol. PPP-107.

If you are interested in cooperation with Dow▕ The Materials Science Company, pls contact: Lwu4@dow.com

This article was initially published in AgroPages '2020 Formulation & Adjuvant Technology' magazine. Download it to read more articles.

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