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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Length-temperature measurements for several polymers and mixtures for detection of transitions and determination of location and magnitude of expansion coefficient changes
Reinforcing polymers with discontinuous fibers improves mechanical properties, such as strength and stiffness, and in some cases achieve isotropic properties, rendering them suitable for various engineering applications. Matrix materials are generally highly engineered thermosets (e.g. crosslinked epoxies), bonded to the fiber periphery by proprietary surface and sizing chemistries. Semicrystalline thermoplastic matrices are less utilized due to poor fiber-matrix bonding resulting in inefficient interfacial load-transfer in reinforced composites. However, flexibility with melt-processing or molding conditions can be leveraged to promote non-covalent interfacial bonding between matrix and fiber via crystallization of the matrix onto fiber surface. In the present study, we utilize a co-mingle chopped carbon and isotactic polypropylene fibers to form isotropic composites, tailoring interfacial immobilized matrix or interphase morphology to optimize performance through precise control of thermal processing/molding windows. Calorimetry and optical microscopy were employed to investigate the impact of carbon fiber at various volume fractions (10, 20, and 30 %) on isotactic polypropylene crystallization and mechanical performance. Variations in mechanical properties correspond to the structural evolution of the interfacial region and are correlated to underlying microstructural attributes using wide-angle X-ray scattering, thermal analysis, and low-field nuclear magnetic resonance spectroscopy. These results provide a practical framework for the manufacturing of thermoplastic matrix composites. In conclusion, the results presented provide a guide for the strategic optimization of interphase design, showcasing tailorable tensile strengths which outperform any isotactic polypropylene carbon fiber composites previously reported in literature.
Propylene is an important petrochemical that is used in various industries, including the automobile and polymer sectors. Conventionally, propylene has been produced through fluid catalytic cracking, steam cracking, and propane dehydrogenation. In this project, a novel process to produce propylene from polypropylene plastic waste using a microwave reactor is introduced. Propylene production is simulated in Aspen Plus for both the conventional propane dehydrogenation process and the novel design, with heat integration applied to minimize utility demands and the conventional process serving as the base case for comparison. Finally, a tailored technoeconomic analysis is carried out for both simulated cases to estimate important economic indicators. Analysis of the results shows that the novel microwave-assisted process outperforms the conventional route for propylene production, achieving a single-pass conversion of almost 100%, compared with only 33.8% in the conventional plant. The Net Present Value of the novel plant is $\$1720.3$ MM, which is 3.5 times higher than the conventional process, while the Levelized Cost of Propylene is reduced by 40%. Capital and operating expenditure are also improved in the proposed scheme, with reductions of approximately $\$29$ MM and $\$222$ MM/yr, respectively. In addition to cost savings, this novel process also provides a convenient means to recycle waste polypropylene.
Low temperature measurements of length- temperature curves of polyethylene, polypropylene, mixtures of polyethylene and polypropylene, and copolymers
A new route for polypropylene (PP) deconstruction through radical pathways based on thermodynamic and kinetic considerations has been investigated. Radical polypropylene (PP) deconstruction, activated through small quantities of initiators, can enable the deconstruction of waste POs into unsaturated products. We found that stirring was detrimental to the β-scission extent because it accelerates radical termination reactions through mixing. The best results were achieved by using a semibatch process that included mechanical mixing during the temperature ramp, static heating of the polymer/initiator mixture at the final temperature, and volatile product removal with N 2 . Dicumyl peroxide and alkylated dicumene initiators produced similar liquid and solid products after a 30 min thermal treatment. Terminal alkenes were formed in the liquid products of reactions at 375 and 400 °C, with about 5% of the protons in the liquid product ascribed to terminal alkenes. Yields of liquid products increased with temperature, reaching 60% w/w at 400 °C; at the same time, yields of solid products decreased with temperature to 16% w/w at 400 °C. Although radical initiators decrease PP molecular weight during the temperature ramp, at 400 °C, initiators only marginally increased the liquid product fraction compared to control experiments. Finally, the terminal double bonds in the liquid product mixture (C8–C36) provide multiple pathways for upgrading to surfactants, plasticizers, lube oils, and other valuable products.
Liners for nitric and hydrofluoric acid tanks show no sign of deterioration after 18 months of continuous use. Each side of each edge of the polypropylene sheets is chamfered, and sheets are welded from both sides with polypropylene filler rod and a special hot-air welding torch.
This study explores how a sieving step of waste cellulosic fiber and fine (WCFF) mixture affects the performance of WCFF‐loaded polypropylene (PP) composites and whether the separation of fines from fibers offers an added benefit. The WCFF samples were downsized, and four different filler size ranges were sieved using a series of mesh sizes from 4 to 0.85 mm. The WCFF/PP composites were then compounded at 20 wt.% loading of WCFF using a twin‐screw extruder. Incorporating WCFF increased the tensile strength to 41.28 MPa and the modulus to 3207 MPa, accounting for 28% and 38% enhancements, respectively. Interestingly, the greatest improvements were associated with the nonsieved WCFF case, and the sieved WCFF fibers provided only marginal enhancements over virgin PP. The outperformance of nonsieved WCFF was attributed to the synergistic reinforcement of hybrid fibers and fines as well as the maintenance of longer fibers in the system. However, the strain at break and impact strength of PP decreased after introducing WCFF. Moreover, the complex viscosity and storage modulus increased with an increase in the filler size, due to the formation of a more effective percolative network. The PP's crystallinity exhibited a relatively strong dependency on the sieving, where WCFF samples with short‐aspect‐ratio fillers promoted the crystallinity significantly. It was also found that the WCFF degradation onset temperature increased once it was incorporated into PP. This study suggests that waste cellulosic feedstocks can be utilized as a reinforcement without additional sieving to manufacture high‐performance and cost‐effective composites.
For this work, accelerated aging was used to assess environmental degradation in emerging co-extruded polypropylene (PP)-based backsheets under three different environmental conditions (65°C/20% relative humidity (RH), 75°C/20% RH, and 75°C/50% RH). Although differential scanning calorimetry did not measure crystallinity changes with exposure, spatially resolved Raman spectroscopy identified crystallinity increases in the core layer of aged samples, indicating a heterogeneous postcrystallization process. The Raman results were in agreement with synchrotron-based microfocused wide-angle X-ray scattering measurements. Cross-sectional nanoindentation was used to correlate localized crystallinity shifts with changes in Young's modulus. A similar trend was found where increased modulus was measured in the core layer, supporting the relationship between modulus and crystallinity. Finally, dielectric characterization was used to assess the impact of these material property changes on performance. While changes in the backsheet material properties and dielectric performance were observed with accelerated aging, these shifts generally equilibrated with time, indicating overall stability in response to environmental stressors. Additionally, the identified heterogeneous material property changes indicate that spatially resolved crystallinity measurements may be a valuable early failure indicator to be used in the assessment of PV backsheet long-term durability.
Modern automotive applications demand lightweight, multifunctional materials to reach mileage goals and natural fiber reinforced composites (NFRCs) are one of the classes of materials proposed as a solution. NFRCs exhibit good vibration damping properties and have low density, but are often limited by processing challenges, poor-fiber matrix compatibility and variable performance. Herein, we investigate non-woven wet-lay of comingled banana fiber (BF), recycled glass fiber (rGF), and polypropylene (PP) fibers to in situ sizing and preparation of composite feedstocks for compression molding. BF and rGF hybrids were prepared by stacking rGF layers during compression molding to produce composites with various fiber ratios. The effect of fiber content, in-situ sizing and ratio of BF to rGF on tensile, flexural and vibration damping performance are investigated. Key results are the significant increase in tensile strength by in situ sizing (40 % sized at 60 wt% BF) and in flexural modulus (+58 % sized at 60 wt% BF) and flexural strength (+41 % sized 60 wt% BF) compared to the unsized equivalent. For BF-rGF hybrid composites with40 wt% total fiber content, flexural strength and modulus were improved by 51 % and 231 % respectively for a 1:1 ratio BF:rGF compared to BF reinforced system. Lastly, identifying the cross-over point where damping and stiffness are optimized for a hybrid composite. These findings demonstrate that these composites can be used as alternative to synthetic fiber or mineral filled composites in automotive applications, particularly where weight reduction, vibration damping and stiffness are desired.
Mylar and polypropylene laminates with aluminum as materials for communication satellites
Appearing as one of the key-components of lithium-ion batteries (LIBs), this work specifically focuses on the additive manufacturing (AM) of custom-shape separators, facilitated by the filament material extrusion process, also called fused deposition modeling (FDM). The development and optimization of composite thermoplastic filament feedstocks combining polypropylene and paraffin wax, followed by the 3D printing of the separator membranes is shown. A post-processing step, based on thermal induced phase separation (TIPS), is introduced to promote porosity formation through removal of the paraffin wax sacrificial phase within the 3D printed items. Separators with different polypropylene/paraffin wax ratios are developed and the impact on printability, mechanical strength, porosity, and electrochemical performances, is thoroughly discussed. X-ray micro-computed tomography is employed to assess the geometric fidelity and to detect printing defects in a complex 3D lattice structure. The performance of the 3D printed porous separators is also compared to a commercial separator. This pioneering research establishes a foundation for the creation of porous separators that can adapt to and conform into 3D printed battery architectures with novel form factors, and also creates opportunities for the use of FDM and TIPS for a wide range of applications that employ porous structures beyond the energy storage field.
Abstract Recalcitrant waste plastics such a polyethylene, polypropylene, and polystyrene are difficult to recycle and are mostly disposed of in landfills and eventually leached into the environmental as micro‐ and nano‐plastics. This review explores how photo‐, electro‐, and combined photoelectro‐catalytic processes can assist in the degradation and upcycling of waste plastic into different chemicals and mitigate their release to the environment. In this work, we discuss how the different reaction mechanisms proceed, explore the current relevant literature, and highlight the developments needed to advance the field.
Abstract Polypropylene (PP) is one of the most widely used plastics, yet its recycling remains limited, with less than 1% of solid waste PP being reprocessed. Mechanical recycling through extrusion is the most practical method, but inconsistent reprocessing conditions introduce variability in material properties. While temperature, screw speed, and residence time influence the thermomechanical stress applied during reprocessing, there are no standardized guidelines for optimizing these parameters. This study examines how these factors shape the properties of recycled PP, using conditions designed to mimic post‐industrial recycled (PIR) scrap. Residence time was measured using colorimetric tracking and correlated with molecular weight, viscosity, and mechanical properties over multiple extrusion cycles. Data‐driven modeling, including response surface methodology, support vector machines, and artificial neural networks, identified processing temperature as the dominant factor in material degradation, followed by residence time. Mechanical properties remained stable, while viscosity decreased predictably with increasing residence time. By linking reprocessing conditions to property evolution, this study provides a method to optimize processing parameters and reduce variability in recycled PP. These findings help manufacturers improve process control, making recycled PP more predictable for reuse in manufacturing. Highlights Study of PIR‐quality PP without additives or compatibilizers. Residence time analysis shows processing temperature drives PP property changes. Mark‐Houwink enables quick molecular weight checks for quality control. Models predict mechanical and rheological shifts in reprocessing. Optimized processing parameters minimize property degradation in recycling.
Thermochemical recycling of plastic waste (PW) into chemicals and energy vectors requires coupling particle and reactor-scale simulations to accurate condensed phase pyrolysis mechanisms for each constituent. This work proposes a methodology to derive reduced-order condensed-phase kinetic models from validated semi-detailed kinetic mechanisms. Two types of kinetic models are obtained for polyethylene (PE), polypropylene (PP) and polystyrene (PS): reduced semi-detailed models and multi-step fully lumped ones. These families offer different compromises between accuracy and computational cost. The former employ 50–100 gas + liquid species and describe both the radical degradation and the detailed carbon distribution of the products. Conversely, the latter involves 5–10 species per polymer tracking only the main petroleum cuts. The kinetic mechanisms are complemented by the definition of thermochemical properties of gas, liquid, and solid-phase species, accounting for phase-transitions through pseudo-chemical reactions. Model validations are performed by comparison with experimental data and the original semi-detailed mechanisms in terms of mass loss, heat fluxes and product distribution profiles. The resulting CHEMKIN-like condensed-phase models are attached as Supplementary Material and as a GitHub repository. Extending the proposed approach to other polymers and coupling it with existing subsets in the CRECK kinetic framework (e.g., biomass, PVC, PET) offers a powerful tool to model thermochemical recycling of PW and biomass/PW mixtures.
Although different zeolites impregnated with transition metals have been used in catalytic deconstruction of waste plastics to other products, the influence of hierarchical structures on conversion and product selectivity in plastic upcycling has not been thoroughly investigated. This research explores a transition metal free zeolite-based hierarchical composite, synthesized by incorporating SBA-15 in ZSM-5 framework, for efficient thermos-catalytic upcycling of polypropylene (PP). Thermogravimetric analysis reveals that ZSM-5@SBA-15 can break down PP at a significantly lower temperature, 240 °C, compared to that of pristine PP using ZSM-5 or SBA-15. In addition, pyrolysis of PP at 400 °C using ZSM-5@SBA-15 composite outperforms ZSM-5 or SBA-15 used separately and yields 90 % conversion with 70 % selectivity to propene. The presence of mesopores in the catalyst appears to reduce the diffusion resistance of intermediate products for successive scission, thereby reducing the activation energy of reaction and improving catalytic activity. In conclusion, the hierarchical composite ZSM-5@SBA-15 has significant potential for upcycling of PP into valuable monomers and lighter olefins, with lower energy demands, enhancing the economic feasibility of the process.
The synthesis of copolymers from high-density polyethylene (HDPE) and isotactic polypropylene (iPP) has gained increasing attention due to their ability to improve the recycling of incompatible mixed polyolefin waste feed streams. Herein, we report a new radical grafting process that yields HDPE-g-iPP copolymers from HDPE and iPP by using a commercially available peroxide. Tensile testing of brittle 70/30 HDPE/iPP mixtures with these graft copolymers added showed promising compatibilization, improving the elongation at break of the blends from 20% up to 1080%. Detailed kinetic studies coupled with thermal and rheological characterization revealed optimized conditions for HDPE and iPP macroradical coupling and a deeper understanding of the grafting reaction. This optimization yielded HDPE-g-iPP copolymers that compatibilize HDPE and iPP blends at loadings as low as 2.5 wt %. In conclusion, the versatility of this macroradical grafting reaction was demonstrated by preparing an effective compatibilizer from untreated postconsumer waste plastics.
Polypropylene (PP) comprises 21% of global plastics production and 18% of plastics waste, yet less than 1% of solid-waste PP is recycled in the United States (U.S.), representing significant environmental and economic challenges. Mechanical recycling, the most prevalent recycling method, subject's materials to thermomechanical stresses, which typically degrade polymer properties, affecting the quality of polymer products. This study replicates the impact of mechanical recycling through multiple extrusion cycles to examine the effects on PP's processing behavior. Dynamic scanning calorimetry (DSC) measurements showed stable melting behavior across all processing conditions, while crystallization analysis exhibited consistent shifts in kinetic parameters. Rheological characterization demonstrated progressive viscosity reductions through successive cycles, particularly pronounced at elevated reprocessing temperatures. Here, the integration of this experimental data into injection molding simulations showed that recycled PP maintains viable processing characteristics. Our findings establish quantitative correlations between processing history and material behavior, enabling optimization of processing parameters directly rather than relying on trial-and-error approaches. While these results reflect idealized recycling conditions with minimal contamination, they provide a framework for understanding fundamental property evolution during mechanical recycling.
The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials.