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Gasification and pyrolysis of plastic wastes are growing thermochemical conversion technologies to help solving plastic waste issues of environmental pollution and loss of valuable energy resources.
Non-sustainable packaging materials (polymers) are a large portion of the increasing amount of plastic waste that become environmental pollutants. While many biodegradable or compostable polymers have been developed in recent years, most fail to compete with the non-sustainable polymers dominating the market, due to lack of certain desired properties, such as thermal (high melt temperature) and mechanical (high ductility) performance. For example, the popular compostable polymer polylactic acid (PLLA) has a high melt temperature but is very brittle. Another polymer, poly(d-valerolactone) (PVL) demonstrates great ductility but has a very low melt temperature. By blending PLLA and PVL, there is an opportunity to create a new biodegradable material with synergistic desired properties for packaging applications. In this study, we produce several (9) physical blends of PVL and PLLA with varying compositions of the two polymers while applying three different materials to make them more compatible (compatibilizers). We test the success of compatibilization by scanning electron microscopy (SEM), mechanic testing (strain at break) and differential scanning calorimetry (melting temperature). Overall, we highlight several promising materials with high compatibility and desired thermomechanical properties for sustainable packaging. Ideally, these materials could help mitigate future plastic pollution in Colorado and beyond.
Neutrinos are elementary particles with many properties still unknown. Their masses so far have only upper and lower limits. Still, due to neutrino oscillations, it is clear that they are not massless, as stated by the Standard Model of Elementary Particles. Neutrinos are also present in the Universe in vast amounts, but they rarely interact with the surrounding matter. Their abundance makes them very interesting for many theories beyond the Standard Model, e.g., dark matter searches and CP symmetry violation in the leptonic sector, which could be (partially) responsible for the observed matter-antimatter asymmetry in today's Universe.\\ The Deep Underground Neutrino Experiment (DUNE) is a next-generation accelerator-based neutrino oscillation experiment that will study neutrinos with unprecedented precision and may answer many open questions. DUNE uses a powerful neutrino beam from Fermilab. It consists of a Near Detector (ND) complex to measure neutrinos before oscillat ion, and a Far Detector (FD) complex $1300\ \mathrm{km}$ away to measure them after oscillation. As part of the ND complex, measurements are also possible with different angles to the neutrino beam. This enables excellent control of systematic uncertainties of e.g., neutrino cross section measurements.\\ One detector in the near detector complex is The Muon Spectrometer (TMS), an extension of a Liquid Argon (LAr) detector that measures the charge and momentum of muons produced in neutrino interactions within the LAr. The design of this detector, which consists of alternating layers of steel and plastic scintillator bars, must be optimized for the expected muon energies.\\ In this thesis, a study of the optimal module orientation plan is presented, which is necessary for the physics performance of the near detector complex and, by extension, DUNE. As part of this study, the event reconstruction was also developed and improved. Simulated muons are then reconstructed, and the performan ce of different module orientation plans is tested.\\ As a second part, a study of neutron and gamma tagging using a Pulse Shape Discriminating (PSD) plastic scintillator is presented. The properties of this material allow particle differentiation based on the temporal distribution of emitted light. A novel approach to using the individual light signals was successfully tested using data from a small, local test setup.
Multilayer plastic film use increased in multiple packaging applications due to its versatility and overall increased performance over monolayer structures. However, the performance gains from multiple layers also make recycling difficult because they contain multiple polymers that can be immiscible and burdensome to traditional mechanical recycling operations. A possible solution is the solvent-targeted recovery and precipitation (STRAP) process, but the effect on the retrieved polymer is still unknown. Here, the STRAP process is applied to two different multilayer films and samples of recovered polymers are evaluated for physical, molecular, and thermal properties. Changes in the molecular weight are not significant, but differences in thermal properties are reported along with the coprecipitation of different polymers. Solvent retention in the polymer matrix from STRAP reduced the glass transition temperature of samples, but enhanced drying recovered it. Heavy metals, such as Cd, Cr, and Pb are not detected, indicating regulatory compliance for different applications.
The influence of the internal structure at micrometer length scales on the deformation of polycrystalline materials can be effectively captured using crystal plasticity finite element methods (CPFEM). However, the complexity and nonlinearity of the deformation equations CPFEM solves demand significant computational power and resources to achieve accurate predictions, limiting its broader application. To address this challenge, we have identified a reduced-order representation of the complex data in order to establish a computationally efficient reduced-order models (ROM) and drastically reduce the computational expense of CPFEM. Specifically, in this work, we developed a parametric, data-driven, and non-intrusive ROM framework for CPFEM using proper orthogonal decomposition (POD) and sparse variational Gaussian process (SVGP) regression for single-crystal microstructures under tensile loading conditions. The developed protocol enables one to compress field into a latent/low-dimensional space described by principal component analysis (PCA) via the singular value decomposition (SVD) algorithm. As a result, the high-dimensional data are reduced to a significantly smaller amount of dimensions with POD bases and POD coefficients. Furthermore, we deployed an ensemble of SVGPs—extended from the classical Gaussian process (GP) regression for scalability and handling big data—in a massively parallel manner to train and predict latent POD coefficients using known POD bases from a set of previously obtained simulations results. Lastly, using the predicted POD coefficients, we reconstructed the full-field results and showed reasonable agreement compared with the true values obtained from running CPFEM. The developed framework is validated with a set of CPFEM simulations of a single embedded void in single-crystal aluminum alloy. While the framework is broadly applicable, this work specifically focuses on single-crystal microstructures, a single load case (e.g., tensile), and a specific void geometry (spherical).
Uniaxial compression tests of 99.9% pure polycrystalline $β$-tin (Sn) were conducted at various strain rates (10 -3 /s, 10 -1 /s and 1/s) and temperatures (294 K and 193 K) to understand the effects of these variables on the stress–strain response, microstructure evolution, and stress relaxation behavior. Multiple Sn specimens were subjected to complex compressive loading/unloading/reloading paths at different strain rates. Specimens initially compressed at higher strain rates showed strain rate-dependent texture evolution and more pronounced relaxation upon reloading, as compared to those pre-strained at lower rates at room temperature (RT). Compression tests conducted at low temperature (193 K) revealed increased strength, similar to the enhanced strength observed when the strain rate was increased at RT. Neutron diffraction was employed to characterize the initial and final bulk textures of the RT specimens. Electron backscatter diffraction was utilized to examine the crystallographic grain orientation and morphology, thereby identifying the signatures of dislocation-mediated deformation, grain refinement, recrystallization, and twinning behaviors. Elastic-plastic self-consistent simulations were performed to investigate the deformation modes responsible for the strain rate-dependent macroscopic stress–strain response and texture evolution. Effects of crystallographic orientation on stress relaxation behavior was also examined. In conclusion, the model predictions are in reasonable agreement with experimental observations.
In this work, an approach to upcycling plastic waste (PW) products is presented. The method relies on flash Joule heating (FJH) to convert PW into flash graphene (FG). In addition to FG, the process results in the formation of carbon oligomers, hydrogen, and light hydrocarbons. In order to make high quality graphene, a sequential alternating current (AC) and direct current (DC) flash is used. Here, the FJH process requires no catalyst and works for PW mixtures, which makes the process suitable for handling landfill PW. The energy required to convert PW to FG is ~ 23 kJ/g or ~ $125 in electricity per ton of PW, potentially making this process economically attractive for scale-up. The FG was characterized by Raman spectroscopy and had a I 2D /I G peak ratio up to 6 with a low-intensity D band. Moreover, transmission electron microscopy and X-ray diffraction analysis show that the FG is turbostratic with an interlayer spacing of 3.45 Å. The large interlayer spacing will facilitate its dispersion in liquids and composites. Analysis of FG dispersions in 1% Pluronic aqueous solution shows that concentrations up to 1.2 mg/mL can be achieved. The carbon oligomers that distilled from the process were characterized by Fourier-transform infrared spectroscopy and have chemical structures similar to the starting PW. Initial analysis of gas phase products shows the formation of considerable amounts of hydrogen along with other light hydrocarbons. Since graphene is naturally occurring and it shows a low toxicity profile, this could be an environmentally beneficial method to upcycle PE.
Synthetic polymers have found widespread use, but their ineffective end-of-life treatment is causing a significant environmental and human health crisis. Here, we demonstrate the upcycling of polynorbornene derivatives (pNBEs) through their deconstruction into distinct oligomeric buildings blocks that can be repolymerized into chemically recyclable pNBEs-like multiblock polymers via dehydrogenative polymerization. The resulting materials exhibit diverse mechanical properties, while integrating high melting temperatures (T m as high as 133 °C). Notably, this method could also enable the selective deconstruction of permanently cross-linked polydicyclopentadiene (pDCPD) thermosets into telechelic-OH functionalized oligomers, overcoming the significant challenges posed by their robust network structure in recycling and degradation. The resulting pDCPD oligomers can subsequently be repolymerized with macrodiols to create multiblock thermosets with tunable mechanical properties, including Young's modulus and tensile elongation. After use, upcycled plastics could be effectively deconstructed back to the oligomers for recovery and repolymerization. Overall, this work establishes an approach that can be utilized to upcycle pNBEs into previously inaccessible multiblock thermosets and thermoplastics with full recyclability, and may be generalizable to a range of polymers to shift their end-of-life waste disposal toward sustainable recovery and reuse.
The rational design of effective polymeric binders for the formulation of plastic-bonded explosives (PBX) is challenging due to their inherent compositional complexity. The composites comprise irregularly shaped energetic material (EM) powders coated with low weight fractions of polymer via non-equilibrium processes such as slurry coating. Defects can deleteriously affect PBX stability and performance: nano- to micrometer-scale voids can act as loci for hot spots, lowering deflagration and detonation temperatures in unpredictable ways. Furthermore, some nominally desirable polymer properties are at odds with each other: e. g. good flow characteristics are desirable for coating and adhesion, but mechanical stiffness is needed to prevent deformation and cracking of PBX under mechanical stress. Good binder adhesion is critical, but the best means to predict and measure adhesion in PBX is not obvious. Experimental methods of determining binder adhesion on model surfaces may not capture polymer structural configurations relevant to deposition during coating. Molecular dynamics-based computational models have predicted key observables in PBX formulation, suggesting that they may be powerful tools for binder selection. In this review, primarily recent (~2006 and later) literature on polymeric binders for insensitive HE (IHE) is surveyed. We focus on how binder properties influence observable PBX properties as resistance to irreversible volume growth and void formation in PBX formulations mainly (but not exclusively) featuring 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) as the EM (e. g. PBX 9502, LX-17 and others). Conclusions from these studies yield useful guidelines for choosing HE binder candidates, as well as for the general design of highly-filled polymer composite materials. Finally, studies describing challenges in PBX formulation with the newer and more energetically dense high explosive, LLM-105, will be discussed.
Advanced explosive formulations and methods of production require rapid characterization for both processes and materials. Here, we present results for ultrasound characterization of a plastic bonded explosive (PBX) formulation as a function of binder weight percent and pressing density using through-transmission sound speed measurements. The explosive in this study was composed of 1,3,5-triamino-2,4,6-trinitrobenzene (TATB) combined with the fluoropolymer binder Kynar Ultraflex B. Binder content ranged from 2.6 wt.% to 11.5 wt.% with associated explosive formulation densities from 1.69 g/cc to 1.89 g/cc. We find linear and nonlinear changes in longitudinal and shear sound speeds, peak frequencies, and measured elastic properties for the different PBX formulations. These measured changes can differentiate the tested formulations, highlighting the potential for ultrasound as a nondestructive characterization method for newly-formulated PBXs. We also demonstrate through-transmission measurements using different coupling media and present results from an ultrasound technique called resonant ultrasound spectroscopy (RUS). Both transmission and RUS approaches may allow for rapid and even automated characterization of PBX components suitable for high-throughput manufacturing operations.
Abstract Rieske oxygenases (ROs) are a diverse metalloenzyme class with growing potential in bioconversion and synthetic applications. We postulated that ROs are nonetheless underutilized because they are unstable. Terephthalate dioxygenase (TPA DO PDB ID 7Q05 ) is a structurally characterized heterohexameric α 3 β 3 RO that, with its cognate reductase (TPA RED ), catalyzes the first intracellular step of bacterial polyethylene terephthalate plastic bioconversion. Here, we showed that the heterologously expressed TPA DO /TPA RED system exhibits only ~300 total turnovers at its optimal pH and temperature. We investigated the thermal stability of the system and the unfolding pathway of TPA DO through a combination of biochemical and biophysical approaches. The system's activity is thermally limited by a melting temperature ( T m ) of 39.9°C for the monomeric TPA RED , while the independent T m of TPA DO is 50.8°C. Differential scanning calorimetry revealed a two‐step thermal decomposition pathway for TPA DO with T m values of 47.6 and 58.0°C (Δ H = 210 and 509 kcal mol −1 , respectively) for each step. Temperature‐dependent small‐angle x‐ray scattering and dynamic light scattering both detected heat‐induced dissociation of TPA DO subunits at 53.8°C, followed by higher‐temperature loss of tertiary structure that coincided with protein aggregation. The computed enthalpies of dissociation for the monomer interfaces were most congruent with a decomposition pathway initiated by β‐β interface dissociation, a pattern predicted to be widespread in ROs. As a strategy for enhancing TPA DO stability, we propose prioritizing the re‐engineering of the β subunit interfaces, with subsequent targeted improvements of the subunits.
Crystal plasticity finite element model (CPFEM) is a powerful numerical simulation in the integrated computational materials engineering toolboxes that relates microstructures to homogenized materials properties and establishes the structure–property linkages in computational materials science. However, to establish the predictive capability, one needs to calibrate the underlying constitutive model, verify the solution and validate the model prediction against experimental data. Bayesian optimization (BO) has stood out as a gradient-free efficient global optimization algorithm that is capable of calibrating constitutive models for CPFEM. Here in this paper, we apply a recently developed asynchronous parallel constrained BO algorithm to calibrate phenomenological constitutive models for stainless steel 304 L, Tantalum, and Cantor high-entropy alloy.
Crystal plasticity finite element method (CPFEM) has been an integrated computational materials engineering (ICME) workhorse to study materials behaviors and structure-property relationships for the last few decades. These relations are mappings from the microstructure space to the materials properties space. Due to the stochastic and random nature of microstructures, there is always some uncertainty associated with materials properties, for example, in homogenized stress-strain curves. For critical applications with strong reliability needs, it is often desirable to quantify the microstructure-induced uncertainty in the context of structure-property relationships. However, this uncertainty quantification (UQ) problem often incurs a large computational cost because many statistically equivalent representative volume elements (SERVEs) are needed. In this article, we apply a multi-level Monte Carlo (MLMC) method to CPFEM to study the uncertainty in stress-strain curves, given an ensemble of SERVEs at multiple mesh resolutions. By using the information at coarse meshes, we show that it is possible to approximate the response at fine meshes with a much reduced computational cost. We focus on problems where the model output is multi-dimensional, which requires us to track multiple quantities of interest (QoIs) at the same time. In conclusion, our numerical results show that MLMC can accelerate UQ tasks around 2.23x, compared to the classical Monte Carlo (MC) method, which is widely known as ensemble average in the CPFEM literature.
Herein, phase evolution in FCC metals with strongly interacting alloy components during severe plastic deformation is investigated using molecular dynamics simulations. Specifically, we study the alloy microstructure in steady state, nucleation and growth of precipitates in supersaturated alloys, and the decomposition of precipitates in undersaturated alloys. The results are compared to a modified effective temperature model, providing a physical understanding for the atomic processes underlying the model and a perspective on its strengths and weaknesses. Key observations in this work are nucleation and growth of precipitates during SPD at a temperature of 100 K; Gibbs-Thomson-like behavior relating solubility to precipitate size under steady-state shearing; a direct relationship between the effective temperature and the shear modulus; and the importance of cluster agglomeration during precipitate growth. The study also reveals that the mechanisms of forced chemical mixing depends on precipitate size, adding complications for effective temperature models describing inhomogeneous systems. The simulations are shown to provide good semiquantitative agreement with experimental findings reported in the literature.
Our experimental mechanical testing data demonstrated that the additively manufactured (AM) laser powder bed fusion (L-PBF) Haynes-214 alloy exhibits non-linear mechanical properties as the temperature rises from ambient to 870 °C. Crystal plasticity (CP) simulations provide an effective approach to gaining deeper insights into microstructure-property linkages under thermomechanical loading. This method can reduce the need for costly high-temperature mechanical testing while accounting for the effects of crystallographic texture and grain morphology on the mechanical behavior of AM materials. However, calibrating a CP model is time-consuming because individual simulations are computationally expensive and hundreds (or more) of iterations over parameter sets may be required. To address this issue, we have designed a machine learning-differential evolution (ML-DE) CP framework that can accurately interpolate the tensile properties of AM L-PBF Haynes-214 alloy across a wide temperature range from ambient to 870 °C, with minimal reliance on experimental data. The framework uses electron backscatter diffraction (EBSD) measurements to generate statistically equivalent microstructural volume elements to serve as inputs to the CP modeling framework. Stress–strain curves were generated from 1000 CP simulations, which serve as the training data set for the three ML regression algorithms explored: linear, extra-trees, and multi-layer perceptron. These three regression models were independently evaluated to compare their efficiency and identify the most suitable algorithm for the given problem. Results revealed that the extra-trees ML regressor outperforms the other models in both qualitative and quantitative aspects with an R 2 of 0.98. Subsequently, the differential evolution optimization approach is employed to calibrate the ML-based CP material parameters with experimental results obtained at various temperatures. Finally, temperature-dependent CP material parameters are formulated. The effectiveness and efficiency of the designed framework are validated through comparison with experimental results, demonstrating a high degree of agreement. These calibrated parametric constitutive equations enable further use of the CP model to study the deformation behavior of this alloy under a wide range of thermo-mechanical loading conditions.
Mechanistic investigation of the catalytic conversion of model compounds for plastic pyrolysis oil (1-octene, octadiene, octane, and toluene) over ZSM-5 in a fixed-bed reactor was studied. 1-Octene breaks down into smaller olefins, which undergo further cracking, oligomerization, cyclization, and hydrogen transfer to eventually produce benzene, toluene, xylene (BTX), coke, and hydrogen. The effect of contact time on 1-octene conversion was further investigated and compared with thermodynamics analyses to elucidate the reaction network. Under the reaction conditions (500 oC, 1 atm), octadiene undergoes thermal coking, significantly contributing to reactor fouling. The products from octane cracking are similar to the products from 1-octene conversion whereas toluene undergoes disproportionation, dealkylation and coking. The analysis of spent catalyst showed long-chain hydrocarbons created by oligomerization reactions filled the pores and covered the surface of the catalyst. As a result, when mesoporous ZSM-5 is used instead of conventional, product selectivity is maintained for 70 hours in time-on-stream experiments.
The polymer reaction media and its properties can be altered by recycling a fraction of liquid products or adding alkane solvents. Less clear is whether this strategy affects hydrogenolysis. Herein, we investigated the effect of short-chain alkanes C n consisting of n carbons (n=8, 16, and 32) on the upcycling of high-density polyethylene (HDPE) plastic waste to lubricant-range products over Ru/TiO 2 catalysts by multiscale simulations and experiments. First, we trained a force field for polymer/surface interactions on a Ru 22 nanoparticle (NP) supported on TiO 2 . Using replica exchange molecular dynamics simulations, we studied the effect of small hydrocarbons on the adsorption of a surrogate polymer, C 142 , on the catalyst. We found segregation of long chains (C 142 ) at the catalyst surface due to the enthalpy gained by adsorbing more C-C bonds of the long chains, compensating for entropic losses upon adsorption. Short-chain molecules decrease the adsorbed carbons of long chains on the Ru NP due to blocking Ru active sites. Compared to the bulk chains, competitive adsorption results in a broader, heavy-tailed distribution of end-to-end distance of adsorbed chains. Our experiments demonstrated that catalyst activity declines significantly beyond simple dilution due to changes in polymer adsorption, and tuning the reaction media by creating suitable blends impacts hydrogenolysis. Density distributions for a 50:50%wt mixture of PP and PE show that PE chains are segregated at the surface, so they are prone to C-C bond breaking much faster than PP chains. H/D exchange experiments show preferential deuteration of PE, while CH 3 groups of PP remain undeuterated. Furthermore, this may be explained by the preferential sorption of PE over PP, leading to specific distribution in the polymer blend.
We report graphene nanosheets (GNS) can enhance the strength and ductility of metal-based composites as they can obstruct the propagation of dislocations. The present article employs Molecular Dynamics (MD) simulations to investigate dislocation-GNS interaction mechanisms and possible influencing factors, including the number of GNS layers, the thickness of the metallic amorphous layer and the C - C bond strength. The results indicated that the shear strength of the metal/GNS interface and the bending stiffness of GNS determined the ability of GNS to block dislocation transmission. A physically based phenomenological parameter that can capture such dislocation-GNS interactions is the mechanical interface energy that has been put forth within gradient plasticity. By fitting the theoretical expressions to the simulation data, it was possible to obtain estimates for the mechanical interface energy for the GNS. It was found that increasing the GNS layers and adding an amorphous layer resulted in a strengthening in the stress–strain response and increased the value of this interfacial parameter. This indicates that the mechanical interfacial energy can be a unified measure for capturing and tuning the strength of various interfaces such as grain boundaries, GNS, amorphous-crystalline interface and bimetal interfaces.