Maurizio Porfiri

In a new study led by Institute Professor Maurizio Porfiri at NYU Tandon, researchers showed a novel principle of actuation — to transform electrical energy into motion. This actuation mechanism is based on solvation, the interaction between solute and solvent molecules in a solution. This phenomenon is particular important in water, as its molecules are polar: oxygen attracts electrons more than hydrogen, such that oxygen has a slightly negative charge and hydrogen a slightly positive one. Thus, water molecules are attracted by charged ions in solution, forming shells around them. This microscopic phenomenon plays a critical role in the properties of solutions and in essential biological processes such as protein folding, but prior to this study there was no evidence of potential macroscopic mechanical consequences of solvation.

The group of researchers, which also included Alain Boldini, a Ph.D. candidate in the Department of Mechanical and Aerospace Engineering at NYU Tandon, and Dr. Youngsu Cha of the Korea Institute of Science and Technology, proposed that solvation could be exploited to produce macroscopic deformations in materials. To this end, Porfiri and his group utilized ionomer membranes, unique polymeric materials in which negative charges cannot move. Positive ions can easily enter these membranes, while negative ions are repulsed by them. To demonstrate actuation, ionomer membranes were immersed in a solution of water and salt, between two electrodes. Applying a voltage across the electrodes caused the membrane to bend. The paper, "Solvation-Driven Electrochemical Actuation," is published in the American Physical Society's Physical Review Letters.

According to the model developed by Porfiri and his group, the voltage caused a current of positive ions toward the negative electrode. These ions entered the membrane from one side, along with the water molecules in their solvation shells. On the other side of the membrane, positive ions and their solvation shells were dragged outside. The membrane responded like a sponge: the side full of water expanded, while the side with less water shrank. This differential swelling produced the macroscopic bending of the membrane. Studying actuation with different ions helps understand this phenomenon, as different ions attract a different number of water molecules around them.

The discovery of macroscopic mechanical consequences of solvation paves the way for more research on membranes. The group expects applications in the field of electrochemical cells (batteries, fuel cells, and electrolyzers), which often rely on the membranes utilized in this study. These membranes also share similarities with natural membranes, such as cell membranes, on which the mechanical effects of solvation are largely unknown.

The work was supported by the National Science Foundation under grant No. OISE-1545857 and the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) under grant No. 2020R1A2C2005252.

COVID 19 has wreaked havoc across the planet. As of January 1, 2021, the WHO has reported nearly 82 million cases globally, with over 1.8 million deaths. In the face of this upheaval, public health authorities and the general population are striving to achieve a balance between safety and normalcy. The uncertainty and novelty of the current conditions call for the development of theory and simulation tools that could offer a fine resolution of multiple strata of society while supporting the evaluation of “what-if” scenarios. 

The research team led by Maurizio Porfiri proposes an agent-based modeling platform to simulate the spreading of COVID-19 in small towns and cities. The platform is developed at the resolution of a single individual, and demonstrated for the city of New Rochelle, NY — one of the first outbreaks registered in the United States. The researchers used New Rochelle not only because of its place in the COVID timeline, but because agent-based modelling for mid-size towns are relatively unexplored despite the U.S. being largely composed of small towns.

Supported by expert knowledge and informed by officially reported COVID-19 data, the model incorporates detailed elements of the spreading within a statistically realistic population. Along with pertinent functionality such as testing, treatment, and vaccination options, the model also accounts for the burden of other illnesses with symptoms similar to COVID-19. Unique to the model is the possibility to explore different testing approaches — in hospitals or drive-through facilities— and vaccination strategies that could prioritize vulnerable groups. Decision making by public authorities could benefit from the model, for its fine-grain resolution, open-source nature, and wide range of features.

The study had some stark conclusions. One example: the results suggest that prioritizing vaccination of high-risk individuals has a marginal effect on the count of COVID-19 deaths. To obtain significant improvements, a very large fraction of the town population should, in fact, be vaccinated. Importantly, the benefits of the restrictive measures in place during the first wave greatly surpass those from any of these selective vaccination scenarios. Even with a vaccine available, social distancing, protective measures, and mobility restrictions will still key tools to fight COVID-19.

The research team included Zhong-Ping Jiang, professor of electrical and computer engineering; post-docs Agnieszka Truszkowska, who led the implementation of the computational framework for the project, and Brandon Behring; graduate student Jalil Hasanyan; as well as Lorenzo Zino from the University of Groningen, Sachit Butail from Southern Illinois University, Emanuele Caroppo from the Università Cattolica del Sacro Cuore, and Alessandro Rizzo from Turin Polytechnic. The work was partially supported by National Science Foundation (CMMI1561134 and CMMI-2027990), Compagnia di San Paolo, MAECI (“Mac2Mic”), the European Research Council, and the Netherlands Organisation for Scientific Research.