Sarah Cureton-Chinn

Traditional open or closed-cell stochastic elastomeric foams have wide-ranging applications in numerous industries: from thermal insulation, shock absorbing/gap-filling support cushions, packaging, to light-weight structural and positional components. Recent developments in 3D printing technologies by direct ink-write have opened the possibility of replacing stochastic foam parts by more controlled printed micro-structures with superior stress-distribution and longer functional life. For successful deployment as mechanical support or structural components, it is crucial to characterize the response of such printed materials to long-term external loads in terms of stress-strain behavior evolution and in terms of irreversible structural and load-bearing capacity changes over time. To this end, here we report a thermal-age-aware constitutive model for a 3D printed close-packed foam structure under compression. The model is based on the Ogden hyperfoam strain-energy functional within the framework of Tobolsky two-network scheme. It accurately describes experimentally measured stress-strain response, compression set, and load retention for various aging times and temperatures. Through the technique of time-temperature-superposition the model enables the prediction of long-term changes along with the quantification of uncertainty stemming from sample-to-sample variation and measurement noise. All aging parameters appear to possess the same Arrhenius activation barrier, which suggests a single dominant aging mechanism at the molecular/network level.

3D printing of polymeric foams by direct-ink-write is a recent technological breakthrough that enables the creation of versatile compressible solids with programmable microstructure, customizable shapes, and tunable mechanical response including negative elastic modulus. However, in many applications the success of these 3D printed materials as a viable replacement for traditional stochastic foams critically depends on their mechanical performance and micro-architectural stability while deployed under long-term mechanical strain. To predict the long-term performance of the two types of foams we employed multi-year-long accelerated aging studies under compressive strain followed by a time-temperature-superposition analysis using a minimum-arc-length-based algorithm. The resulting master curves predict superior long-term performance of the 3D printed foam in terms of two different metrics, i.e., compression set and load retention. To gain deeper understanding, we imaged the microstructure of both foams using X-ray computed tomography, and performed finite-element analysis of the mechanical response within these microstructures. This indicates a wider stress variation in the stochastic foam with points of more extreme local stress as compared to the 3D printed material, which might explain the latter’s improved long-term stability and mechanical performance.

Modulus softening and permanent set in filled polymeric materials due to cyclic loading and unloading, commonly known as the Mullins effect, can have a significant impact on their use as support cushions. A quantitative analysis of such behavior is essential to ensure the effectiveness of such materials in long-term deployment. In this work we combine existing ideas of filler-induced modulus enhancement, strain amplification, and irreversible deformation within a simple non-Gaussian constitutive model to quantitatively interpret recent measurements on a relevant PDMS-based elastomeric cushion. We find that the experimental stress-strain data is consistent with the picture that during stretching (loading) two effects take place simultaneously: (1) the physical constraints (entanglements) initially present in the polymer network get disentangled, thus leading to a gradual decrease in the effective cross-link density, and (2) the effective filler volume fraction gradually decreases with increasing strain due to the irreversible pulling out of an initially occluded volume of the soft polymer domain.

Organophosphorus compounds represent a large class of molecules that include pesticides, flame-retardants, biologically relevant molecules, and chemical weapons agents (CWAs). The detection and identification of organophosphorus molecules, particularly in the cases of pesticides and CWAs, are paramount to the verification of international treaties by various organizations. To that end, novel analytical methodologies that can provide additional support to traditional analyses are important for unambiguous identification of these compounds. We have developed an NMR method that selectively edits for organophosphorus compounds via ³¹P–¹H heteronuclear single quantum correlation (HSQC) and provides an additional chromatographic-like separation based on self-diffusivities of the individual species via ¹H diffusion-ordered spectroscopy (DOSY): ¹H–³¹P HSQC-DOSY. The technique is first validated using the CWA VX (O-ethyl S-[2-(diisopropylamino)ethyl] methylphosphonothioate) by traditional two-dimensional DOSY spectra. We then extend this technique to a complex mixture of VX degradation products and identify all the main phosphorus-containing byproducts generated after exposure to a zinc-cyclen organometallic homogeneous catalyst.

Computational studies of condensed-phase chemical reactions are challenging in part because of complexities in understanding the effects of the solvent environment on the reacting chemical species. Such studies are further complicated due to the demanding computational resources required to implement high-level ab initio quantum chemical methods when considering the solvent explicitly. Here, we use first-principles molecular dynamics simulations to examine condensed-phase decontamination reactions of V-type nerve agents in an explicit aqueous solvent. Our results include a detailed study of hydrolysis, base-hydrolysis, and nucleophilic oxidation of both VX and R-VX, as well as their protonated counterparts (i.e., VXH⁺ and R-VXH⁺). The decontamination mechanisms and chemical reaction energy barriers, as determined from our simulations, are found to be in good agreement with experiment. The results demonstrate the applicability of using such simulations to assist in understanding new decontamination technologies or other applications that require computational screening of condensed-phase chemical reaction mechanisms.