Pushing Polymers Through Nanopores

In order to analyse and fully understand single biological molecules a method is required for separating them. A common method is translocation of the polymer, commonly DNA, through a membrane. This membrane is covered in holes called nanopores as they are in the nanoscale range of sizes. The first ever transfer of DNA into one of these nanopores was twenty years ago back in 1996. Since then these membranes have been used to characterise many different objects such as proteins, cells and DNA. There are some problems due to the fact that it takes up to a millisecond for the species to escape the pore and also because the pores can contain multiple species at once and so individual analysis is made more difficult.

A solution to this is the use nanopores that have been carved into monatomic graphene sheets. The new membranes have the advantage that they are much thinner than previous membranes and so can only contain one polymer at a time. Another problem, however, has been created by this solution. The graphene’s flexibility, normally an advantage, may hinder its ability to be a membrane as thermal and elastic fluctuations may cause disruptive vibration in the membrane. Interestingly the theoretical side of membranes is at a bit of a loss because an assumption that is almost universally made is that the membrane is immobile compared to the polymers passing into it. For thick membranes this is largely true, but in this example the vibrations could have a tangible effect on translocation time. A study has been undertaken into the effects of thermal and mechanical distortions in the graphene membranes with some interesting results. It was found that when the membrane was deformed and in motion that the translocation time sped up compared to when the membrane was immobile. However, when heated, it is believed the the nanopores distort and so an increase in thermal fluctuation slows down translocation time. Although these results and trends are clear there is still yet to be any quantitative predictions and equations developed to understand the numerical data that has been provided. Both more precise experiments and some rigorous mathematical treatment should hopefully fix this problem.

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