Polymers are attractive materials for nanoelectromechanical (NEMS) as they can respond to various stimuli such as pH, salt concentration, electric field, etc. Polymers are easy to manufacture, cheap, disposable, biocompatible, low-power actuators and exhibit attractive sensitivity and selectivity properties. Polymer-based NEMS find applications in sensing cells, proteins, DNA and biomolecules, and as key components in microfluidic networks with applications in chemical and biological analysis. Polymer-based bio-NEMS devices when combined with microfluidics, MEMS, and control electronics can enable significant advances in the design and development of integrated systems. The objective of this proposal is to develop hierarchical design tools to enable rapid design and development of bio-NEMS. The proposed research focuses on performing quantum-mechanical studies to understand the electrostatic behavior of polymers, atomistic simulations to understand diffusion in polymers and to extract stress-strain relations characterizing the constitutive behavior of the biomaterials, continuum calculations based on coupled chemical, electrical, and mechanical theories to understand the macroscopic response, and extracting compact models for bio-NEMS devices from detailed and extensive continuum simulations.
The research proposed here is multidisciplinary and some of the applications that could benefit from this research are nanoscale sensing and actuation, DNA purification, chemical and biological processing, etc. This project will result in the education of graduate students in the highly interdisciplinary area of bio-NEMS. Students working on this project will be trained in quantum, atomistic and continuum simulation methods, physics of polymers, system-level modeling and design of large scale integrated bio-NEMS systems. The results from this project will be presented in archival journals and conferences around the world. In addition, the results from this project will be integrated into an existing course on modeling and simulation of MEMS and summer schools offered at UIUC.
Summary of Some of the Key Accomplishments: 1. We investigated the mechanical properties of graphene under shear deformation. Specifically, using molecular dynamics simulations, we computed the shear modulus, shear fracture strength, and shear fracture strain of zigzag and armchair graphene structures at various temperatures. To predict shear strength and fracture shear strain, we also developed an analytical theory based on the kinetic analysis. We showed that wrinkling behavior of graphene under shear deformation can be significant. We computed the amplitude to wavelength ratio of wrinkles using molecular dynamics and compared it with existing theory. Our results indicated that graphene can be a promising mechanical material under shear deformation. This opens up opportunities for graphene as a NEMS material. 2. We investigated diffusion of water, ions and various macromolecules (e.g. rhodamine) through polymers using extensive molecular dynamics simulations. First, we developed a systematic approach to create cross-linked polymers at nanoscale. As the cross-linking density is increased, the water diffusion decreases and the slowdown in diffusion is more severe at the polymer-water interface. The diffusion of ions and rhodamine also decreased with the increase in cross-linking density. We showed that the diffusion is closely related to the water content in the polymer. These results shed fundamental insights into membrane separation processes based on polymers. 3. We developed a self-consistent tight-binding method combined with molecular dynamics (MD) to investigate the electrostatic signals generated by DNA segments inside semiconducting single-wall carbon nanotubes (CNTs). The trajectories of DNA and water molecules are obtained from molecular dynamics simulations. The trajectories are then used in the tight-binding method to compute the electrostatic potential at any desired location. The electrostatic signals indicate that when the defective DNA molecules translocate through the CNT, it is possible to identify the number of total base pairs and the relative positions of the defective base pairs in DNA chains. Our results also indicate that it is also probable to differentiate Dickerson and hairpin DNA structures by comparing the electrical signal patterns. 4. Self-assembly of structures is an area of great interest in bio-NEMS. We recently demonstrated the self-assembly of graphene fragments in water using molecular dynamics simulation. We observed that graphene fragments dispersed in water are assembled into a single aggregate. The assembly process was investigated by using the potential of mean force analysis and the significance of the enthalpic and entropic contributions is understood. We also examined other fundamental quantities such as hydrogen bonding and excluded volume. We anticipate that this fundamental finding can be extended to design the assembly process of complex carbon-based structures. 5. We investigated the effect of surface and interior defects such as vacancies and broken bonds on the performance of nano/microelectromechanical (N/MEMS) switches. By combining multiscale electrostatic analysis with mechanical analysis, we computed the capacitance-voltage and pull-in/out voltages of N/MEMS switches in the presence of defects in the dielectric oxide layer. Our results indicated that both surface and interior defects can change the pull-in/out voltages leading to significant voltage offsets. These voltage offsets can lead to an eventual failure of the N/MEMS switch. 6. We developed a unified framework for uncertainty quantification (UQ) in nano/microelectromechanical systems (N/MEMS). The goal is to model uncertainties in the input parameters of micro/nano mechanical devices and to quantify their effect on the final performance of the device. We considered different electromechanical actuators that operate using a combination of electrostatic and electrothermal modes of actuation, for which high-fidelity numerical models have been developed. We used a data-driven framework to generate stochastic models based on experimentally observed uncertainties in geometric and material parameters. Since we are primarily interested in quantifying the statistics of the output parameters of interest, we developed an adaptive refinement strategy to efficiently propagate the uncertainty through the device model, in order to obtain quantities like the mean and the variance of the stochastic solution with minimal computational effort. We demonstrated the efficacy of this framework by performing UQ in some examples of electrostatic and electrothermomechanical microactuators. We also validated the method by comparing our results with experimentally determined uncertainties in an electrostatic microswitch. We also showed that our framework results in the accurate computation of uncertainties in nano/micromechanical systems with lower computational effort. 7. The research results obtained through this project are incorporated into graduate level courses taught by the principal investigator.
