134 Search Results

The hydrolysis of thermoplastic poly(ester urethane) (PEU) is convoluted by its block copolymer phase structure and competing hydrolytic sensitivities of multiple functional groups. The exact pathways for water ingress, water interaction with the material and ultimately the kinetics and order of functional group hydrolysis remain to be refined. Additional diagnostics are needed to enable deeper insight and deconvolution of material changes. In combination with GPC results, a promising analytical technique – two-dimensional correlation spectroscopy (2D-COS) – has been reviewed and applied to analyze FTIR spectra of hydrolyzed PEUs aged under various conditions, such as exposure time, temperature, and relative humidity. 2D-COS allows the complex role of water with distinct intermediate steps to be established, plus it emphasizes the initial stages of PEU hydrolysis at more susceptible functional groups. As a complication for the raw material, ATR IR detected some talc on the surface of commercial PEU beads and pressed sheets thereof, which can interfere with water ingress and thereby retards PEU hydrolysis, particularly in its natural form or moderate aging at lower temperatures (e.g., below the melting point of PEU). As aging temperature increases above the melting temperature, even traces of water trapped inside the PEU are sufficient to initiate the hydrolysis, which then progresses strongly with increasing temperatures. Feedback from 2D-COS analysis confirms that PEU hydrolysis starts at esters in the soft-segments before those in the urethane linkage become susceptible. Only when the molecular weight of PEU is below a critical molar mass (M_c) will the hydrolysis occur in parallel in the hard-segments since protective morphological phase structures are then absent. The current observations demonstrate unexpected behavior that may result from 'unknown' additives in polymer degradation, the temporal and group-specific hydrolysis of PEU as a function of locally available water molecules, the order of reactivity of susceptible functional groups, and the importance of changes in molecular weight coupled with the phase structure of the polymer.

Glass fiber-reinforced polymer composites are widely used in marine applications, including renewable energy devices and submarines, due to their strength-to-weight ratios and corrosion resistance. As sustainability becomes more important, recyclable thermoplastic composites are gaining attention in marine energy applications. These materials face harsh conditions such as high chloride water, UV radiation, pressurization, and thermal cycling, which can affect their durability and recyclability. This study examines the impact of hygrothermal aging on the recyclability of an Elium glass fiber (GF) composite. Samples were mechanically ground for recycling, and mechanical and thermal properties were compared between aged and unaged samples. After seawater aging, the tensile strength and modulus of the unaged composite dropped by 25% and 13% respectively, due to water ingress along the fiber-matrix interface. The recycled composite showed reduced flexural strength as the long fiber composite was converted into short fibers during the process. Differential scanning calorimetry (DSC) revealed no change in glass transition temperature after aging. Weight loss (25%) was attributed to the thermal stability of the glass fibers. Dynamic mechanical analysis (DMA) showed that the storage modulus increased with aging, and fracture analysis confirmed a mixed mode of failure for aged composites.

In response to escalating environmental concerns, there is a pressing demand for materials capable of delivering both sustainability and robust mechanical properties, thereby substituting nonrenewable counterparts in various applications. This study presents a comprehensive investigation into the synthesis and characterization of biobased aliphatic thermoplastic polyurethanes (TPUs) that exhibit impressive mechanical properties, including tensile strength in the range of 48–41 MPa and flexural modulus up to 2.2 GPa. These biodegradable TPUs displayed high shore A and D hardness between 95 and 98 and 51–42, respectively, and thus can be categorized as “extra hard” plastics according to the durometer scale for PUs. Herein, we have prepared a series of four 100% biobased polyester polyols from biobased diacid and chain-extender as precursors with molecular weights varying from 500 to 1400 g/mol. The corresponding TPUs that were prepared by using an aliphatic diisocyanate were evaluated for their thermal stability, microphase separation, mechanical properties, and biodegradation. By leveraging renewable feedstocks, these TPUs offer a sustainable alternative to petroleum-derived materials, with their mechanical performance meeting conventional benchmarks. Furthermore, postcomposting analysis revealed significant surface degradation, affirming their biodegradability and environmental compatibility.

With over 6 million tons produced annually, thermoplastic elastomers (TPEs) have become ubiquitous in modern society, due to their unique combination of elasticity, toughness, and reprocessability. Nevertheless, industrial TPEs display a tradeoff between softness and strength, along with low upper service temperatures, typically ≤100 °C. This limits their utility, such as in bio-interfacial applications where supersoft deformation is required in tandem with strength, in addition to applications that require thermal stability (e.g., encapsulation of electronics, seals/joints for aeronautics, protective clothing for firefighting, and biomedical devices that can be subjected to steam sterilization). Thus, combining softness, strength, and high thermal resistance into a single versatile TPE has remained an unmet opportunity. Through de novo design and synthesis of novel norbornene-based ABA triblock copolymers, this gap is filled. Ring-opening metathesis polymerization is employed to prepare TPEs with an unprecedented combination of properties, including skin-like moduli (<100 kPa), strength competitive with commercial TPEs (>5 MPa), and upper service temperatures akin to high-performance plastics (≈260 °C). Here, the materials are elastic, tough, reprocessable, and shelf stable (≥2 months) without incorporation of plasticizer. Structure–property relationships identified herein inform development of next-generation TPEs that are both biologically soft yet thermomechanically durable.

The National Renewable Energy Laboratory worked with Verdant Power to manufacture, and characterize, novel thermoplastic composite blades on their Gen5d 5 m diameter turbines at the Roosevelt Island Tidal Energy (RITE) site in the East River in N.Y. to demonstrate a low-cost manufacturing process for marine energy structures. Verdant had designed, manufactured, and deployed epoxy thermoset composite blades on three Gen5d turbines in October 2020. At a maintenance cycle in May 2021, a Gen5d turbine equipped with the NREL thermoplastic blades was deployed and retrieved in October 2021. Modal, static and fatigue structural characterization was performed on both blade types before and after the deployment. The static and modal test results showed that the thermoplastic blades were slightly stiffer than the epoxy blades both dry and after the deployment. The epoxy blades had about 8% increase in strain at the applied load after the deployment, whereas the thermoplastic blade strains were not changed significantly. Ultimately the thermoplastic blade failed during fatigue testing due to a crack which is thought to have initiated between the internal foam and the laminate at the location of the internal instrumentation. The Verdant Power Gen5d epoxy blades did not have this internal instrumentation and did not have this failure, performing adequately through the operational period. A lack of design information and fatigue data for both the thermoplastic laminates and the adhesive used means that further work is needed to fully understand his failure and the root cause analysis is ongoing. This paper provides details on the thermoplastic blade manufacturing, materials, test methodology and results.

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

Unlike existing literature that primarily concentrates on either the plasma treatment of adherends alone or solely on adhesive surfaces, this work leverages plasma modification of both adhesive in tape form and adherend surfaces to largely enhance the interfacial bonding between a thermoset-based adhesive tape and carbon-fiber-reinforced thermoplastic polymer (CFRTP) for structural bonding applications. Here, by conducting single lap shear tests on adhesively-bonded AA6061-CFRPPA (carbon-fiber-reinforced polyphthalamide) dissimilar joints, it is shown that the plasma treatment of adherends alone can increase the lap shear strength (LSS) of the joints by approximately 200% compared to non-treated counterparts. An additional plasma treatment of adhesive tape surfaces leads to even higher LSS improvement, reaching up to 315%, due to the formation of a denser crosslinked network of covalent bonds and a reduced area fraction of interfacial voids at the CFRPPA/adhesive interface. The highest plasma-enhanced LSS of the metal-CFRTP dissimilar joints rivals that of metal-metal joints, which is typically stronger than the joints associated with fiber-reinforced polymers. This study is important for achieving strong CFRTP-related structural components bonded using adhesive tape, providing better compatibility with plasma treatment and other joining methods like riveting compared to adhesive paste or liquid.

Grid stiffened continuous fiber reinforced composite panels are an attractive option for creating lightweight structures due to the tailorability for various applications and the resulting high specific properties. However, the panel stiffeners and stiffener intersections result in high tooling complexity and correspondingly high cost of implementation. These factors have limited the impact of such structures in the composites industry. Previous research has demonstrated the ability to produce high quality, high aspect ratio beams, representative of individual grid stiffeners, using E-glass/PET comingled tow via direct digital manufacturing. Further, prior preliminary efforts have demonstrated the potential to use the same approach to manufacture grid intersections that have continuous fiber in both directions. To expand on the previous efforts in grid stiffeners produced by direct digital manufacture with radically reduced tooling requirements, this effort compares two methods of providing positioning and consolidation, nozzle vs. roller. Both processes are based on a commingled yarn feedstock. The extrusion through a nozzle has been shown to enable grid intersection control through local variations in applied consolidation and serves as the baseline process. However, this approach requires a continuous placement path to create the complete grid stiffened panel as no mechanism for cutting and restarting has been implemented. Alternatively, a newly developed placement head incorporating cut and refeed, mounted to a 6-axis robot, offers the potential of improved path placement efficiency. Furthermore, the two techniques are used to produce similar grid composite stiffeners to evaluate the effectiveness of producing the grid intersections. Rate of deposition of the two end effectors are compared and the quality of the associated grid stiffeners, and intersections, are determined through measurement of geometry, fiber volume fraction and void fraction.

The promise of ABC triblock terpolymers for improving the mechanical properties of thermoplastic elastomers is demonstrated by comparison with symmetric ABA/CBC analogs having similar molecular weights and volume fraction of B and A/C domains. The ABC architecture enhances elasticity (up to 98% recovery over 10 cycles) in part through essentially full chain bridging between discrete hard domains leading to the minimization of mechanically unproductive loops. In addition, the unique phase space of ABC triblocks also enables the fraction of hard-block domains to be higher (f_hard ≈ 0.4) while maintaining elasticity, which is traditionally only possible with non-linear architectures or highly asymmetric ABA triblock copolymers. These advantages of ABC triblock terpolymers provide a tunable platform to create materials with practical applications while improving our fundamental understanding of chain conformation and structure–property relationships in block copolymers.

Plastic scintillators are widely used as radiation detection media in homeland security and nuclear physics applications. Their attributes include low cost, scalability to large detector volumes, and additive compounding to enable additional material and detection features, such as pulse shape discrimination (PSD), gamma-ray spectroscopy, aging resistance, and coincidence timing. However, traditional chemically cured plastic scintillators (CCS) require long reaction times, and hazardous wet chemical procedures performed by specially trained personnel, and can leave residual monomer, resulting in deleterious optical and material properties. Here, we synthesize melt blended scintillators (MBSs) in 2.5 days using easily accessible solid-state compounding of commercially-available poly(styrene) with 30–60 wt% fluorene-based compound “P2” to create monolithic detectors with < 100 ppm residual monomer, in several form factors. Further, the best scintillation performance was recorded for 60 wt% P2 in Styron 665, including gamma-ray light yield 139% of EJ- 200 commercial scintillator and PSD figure of merit (FOM) value of 2.65 at 478 keVee, approaching P2 organic glass scintillator (OGS). The capability of MBS to generate fog-resistant scintillators and poly(methyl methacrylate) (PMMA)-based scintillators for use in challenging environments is also demonstrated.