Polymers — molecules of repeating chemicals — are the basis of many materials: plastic water bottles, rubber tires, even the keratin in your hair. When certain kinds of polymers are sensitive to changes in external stimuli such as temperature, they become helpful, particularly in biomedical applications like drug delivery, tissue engineering, and gene delivery.
A team of researchers led by Sanket Deshmukh, assistant professor of chemical engineering, has developed a method to investigate the structures of polymers that are sensitive to external stimuli. In a recently published journal article in the Journal of Physical Chemistry Letters, the group developed a first-of-its-kind, temperature-independent computational model for one particular polymer that is sensitive to temperature. Simulation trajectories of this computational model were analyzed by using a data-driven machine-learning method.
The group chose the polymer poly(N-isopropylacrylamide), also known as PNIPAM, which is temperature sensitive. Unlike most materials, this thermosensitive polymer dissolves in water at temperatures below 32 ℃ and is insoluble at higher temperatures — the reverse of most materials. The temperature at which the polymer’s behavior changes is known as a lower critical solution temperature.
The thermosensitive polymer’s uniquely lower critical solution temperature can be altered, however, by incorporating groups of atoms that control the way the polymer reacts to changes in the surrounding temperature. Adding atoms to the thermosensitive polymer that like or dislike water allows the polymer to change its lower critical solution temperature to be close to the human body temperature of 37 ℃ — valuable for controlled drug delivery applications.
A type of computational model Deshmukh’s team has developed for the thermosensitive polymer is called a coarse-grained model, where a group of atoms is arranged together in the model in what’s known as a bead. Moreover, this is a first-ever attempt to utilize a specific data-driven machine-learning approach, called a non-metric multidimensional scaling method, to analyze molecular dynamics simulation trajectories of a coarse-grained model of a temperature-sensitive polymer.