Researchers Create Ammonia-Capturing Polymer
Just before the year’s end, researchers from the Niels Bohr Institute and the Department of Chemistry at University of Copenhagen announced their designs of a porous polymer that aimed to capture small molecules, specifically ammonia.
Known as a toxic gas, ammonia is compound of nitrogen and hydrogen commonly used as a reagent in industrial processes or a result from agricultural activities. If in the presence of the gas for too long, it can cause eye damage, throat irritation, or in extreme circumstances where the ammonia reacts with other air pollutants and forms ammonium salts, the reaction can affect breathing and even result in death.
Because the particles are so fine, they’re able to travel long distances, creating a widespread problem.
“If we want to use this material in a real application to solve an important societal problem like ammonia pollution, it is important to explain how ammonia is captured by the porous network in the polymer,” said Heloisa Bordallo, an associate professor at the Niels Bohr Institute.
“This implies that we needed to come up with a technique that allows us to find out exactly how the interaction between the polymer and ammonia takes place. Being successful in answering this question, will make us able to understand better how this or other polymers can be efficient in multidisciplinary domains, including nanomedicine and protective coatings. If scaled up—which is not a simple process—this could have a significant positive impact on the working environment of many people all over the globe."
For the research, assistant professor Jiwoong Lee at the Chemistry Department and Rodrigo Lima, a former postdoc at the Niels Bohr Institute, synthesized 2 grams of the polymer, using a variety of techniques to characterize the material at the ISIS Neutron and Muon Source part of the STFC Rutherford Appleton Laboratory in the United Kingdom.
“The synthesis process often involves washing the material with solvents and it was a nice surprise to realize that the porous polymer actually kept a portion of these solvents inside,” said Lee. “This was indicative of the material’s ability to perhaps capture other pollutants, such as ammonia.”
At the lab, researchers investigated the dynamics of the hydrogen bonds by collecting neutron scattering data at low pressure to get ammonia into the polymer. This was then followed by an experiment headed my Lima using thermal analysis to demonstrate if the ammonia had been captured.
Results revealed that not only was the gas captured, but to everyone’s surprise, had also attached to the porous material.
“In order to be able to explain this seemingly strong connection between the polymer and the ammonia, we needed to know the structure of the polymer,” said Bordallo. “But since this particular polymer is amorphous, it is difficult to fully characterize its structure. In a way you could say that we had ticked the box of capturing the ammonia, but we still needed to explain how this happens—and for that we needed a better view of the structure, which was unattainable. Quite a dilemma to have full success in one part of the project, and not be able to explain exactly why.”
In finding an answer, researchers created different combinations of the polymer building blocks and were able to calculate a spectra, using a computational modelling method called DFT, from a combination that came closest to being similar to the measurements in the real sample.
“There are numerous applications for a polymer that captures ammonia,” Lee explained. “It would be useful in labs, as coating for masks to wear for personal safety, as ammonia is toxic and also very corrosive. It could be used as filters, reducing the spread of ammonia released through the exhausts from many types of industry. Thinking ahead, it is possible that the polymer technique could be applied to other types of polluents as well.”
Moving forward, Bordallo reported that she would like to apply machine learning to amorphous systems, as it could prove to be a more viable way to go about the process. Through deep learning algorithms, the team hopes that they can achieve more accurate classifications of the amorphous materials and in addition to characterizing their structural features.
The research has since been published in ACS Applied Materials & Interfaces.