During the course of iGEM 2021, we ran into quite a few difficulties. Thus this page is aimed at documenting the problems that we have run into, our solutions and other solutions that we could have used, but haven’t tried.
One of the biggest problems that we had encountered when dyeing cloth with indigo, is that the substance was extremely miscible with water. Even after stirring for minutes, the solution was colorless. Our solution was to use lye and sodium dithionite, both caustic materials, to reduce the indigo to leucoindigo. Then, we had to rinse it with water to convert leucoindigo back to indigo. Unfortunately, this process was both time consuming and dangerous and there wasn’t much to do to speed things up. We also tried sodium sulfite and sodium thiosulfate as a reducing agent, but it didn’t work out.
For future experiments, we suggest finding ways to dissolve the indigo in large amounts before dyeing, and no, this doesn’t mean to use DMSO or DMF for dissolving indigo, but to find a way for using indigo in water efficiently. Another method that can be used is dyeing the cloth directly with indoxyl, which could use an enzyme that protects indoxyl from oxidation, and could be washed out when the dyeing process ends.
As we mentioned in the experiment section, we could proportionally mix the indigo and silver nanoparticles. If we were to create a dye solution that contained 100 ppm of silver nanoparticles and, we would add 0.1 g of AgNP to 9.9 g of indigo and add the entire mixture to 100 mL of water. However, this would make it very hard to change the concentration of AgNP and indigo at the same time. Our proposed solution to this problem was adding AgNP and indigo separately, however, it was also hard to add appropriate amounts of AgNP solution since it was to be added in extremely small amounts.
While we were creating our computer models, we also ran into a few problems. For the model with the efficiency tests, we were only able to run about 100 different tests, but it was still hard to find out if the curve we had acquired was linear, restricted, or logistic. Because we weren’t able to find other experiments that had the same methods as us, we weren’t able to put our results into an already established model.
Completing our physics model was even harder, for our project this year, we only ran two groups of experiments to explain how the model works, however, to explain that our model is valid, we would have to do another hundred of groups of experiments. We suggest that future groups first find a database or existing model with a graph or generalization forehand.