Difference between revisions of "Team:BostonU HW/Model"
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<small class="h6 text-default">In order to determine the correction factor we ran a series of experiments to calculate experimental droplet sizes at 15 different flow rates. For statistical significance we measured 30 droplet volumes per flow rate by measuring the time interval between droplets. Using a MATLAB script, the droplet volumes were used to calculate the correction factor for each flow rate. The average correction factor was then calculated to see how they differed between flow rates. The plots blow display the results of our modeling experiments. </p></small> | <small class="h6 text-default">In order to determine the correction factor we ran a series of experiments to calculate experimental droplet sizes at 15 different flow rates. For statistical significance we measured 30 droplet volumes per flow rate by measuring the time interval between droplets. Using a MATLAB script, the droplet volumes were used to calculate the correction factor for each flow rate. The average correction factor was then calculated to see how they differed between flow rates. The plots blow display the results of our modeling experiments. </p></small> | ||
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<div class="col-10"><img src="https://static.igem.org/mediawiki/2018/e/ea/T--BostonU_HW--model_droplet_avg.png" style="width: 80%;" class="img-fluid"></div> | <div class="col-10"><img src="https://static.igem.org/mediawiki/2018/e/ea/T--BostonU_HW--model_droplet_avg.png" style="width: 80%;" class="img-fluid"></div> | ||
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<small class="h6 text-default">Figure 1. Plot of the average experimental droplet volume versus the predicted volume from the static model.</p></small> | <small class="h6 text-default">Figure 1. Plot of the average experimental droplet volume versus the predicted volume from the static model.</p></small> | ||
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<div class="col-10"><img src="https://static.igem.org/mediawiki/2018/a/a9/T--BostonU_HW--model_cf_avg.png" style="width: 80%;" class="img-fluid"></div> | <div class="col-10"><img src="https://static.igem.org/mediawiki/2018/a/a9/T--BostonU_HW--model_cf_avg.png" style="width: 80%;" class="img-fluid"></div> | ||
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<small class="h6 text-default">Figure 2. Plot of the correction factors calculate based on the experimental volumes at various flow rates against the average correction factor. </small> | <small class="h6 text-default">Figure 2. Plot of the correction factors calculate based on the experimental volumes at various flow rates against the average correction factor. </small> | ||
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Revision as of 20:29, 13 October 2018
Modeling
Droplet Characterization
Due to the relatively low flow rate that fluid moves at in microfluidic chips, outputs offloaded from microfluidic chips typically form droplets as opposed to a continuous stream. Therefore, in order to accurately dispense these droplets into wells, we created a model to predict their volume so that users can control the end sample volume in the well of interest. To develop the model we used a free-body diagram to identify the forces present right before a droplet falls off the nozzle. Using the sum of the forces we were able to model the volume of the droplet. To test the model we ran preliminary experiments to calculate volumes and compared them against the theoretical value given by the model. Because we can’t directly measuring the volume of the droplet, we used the time interval between droplets and the given flow rate to calculate what the experimental volume is.
- the theoretical volume from the model was less than the experimental
- this difference, on average, increased with flow rate
A potential reason for this missing component is that the model assumed that the fluid is static instead of dynamic, due to the constant flow rate of fluid moving through the chip (Zhang). In a dynamic model, the total droplet volume is characterized by the static volume and the pinching volume (Zhang). The pinching volume is formed during the droplet’s fall when the droplet breaks off the tube (Zhang).
However, after researching the physical phenomenon behind dynamic droplets we realized that a complete dynamic model, as described in Zhang et al, required measuring many parameters, making it difficult to implement. Therefore, we decided to create our dynamic model empirically by adding a correction factor, which would depend on flow rate.
Zhang et al.
In order to determine the correction factor we ran a series of experiments to calculate experimental droplet sizes at 15 different flow rates. For statistical significance we measured 30 droplet volumes per flow rate by measuring the time interval between droplets. Using a MATLAB script, the droplet volumes were used to calculate the correction factor for each flow rate. The average correction factor was then calculated to see how they differed between flow rates. The plots blow display the results of our modeling experiments.
