New Approaches to Additive Manufacturing of Polymers with Ring-Opening Metathesis Polymerization
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Additive Manufacturing
Custom-form factor batteries fabricated in non-conventional shapes can maximize the overall energy density of the systems they power, particularly when used in conjunction with energy dense materials (e.g., Li metal anodes and conversion cathodes). Additive manufacturing (AM), and specifically material extrusion (ME), have been shown as effective methods for producing custom-form cell components, particularly electrodes. However, the AM of several promising energy dense materials (conversion electrodes such as iron trifluoride) have yet to be demonstrated or optimized. Furthermore, the integration of multiple AM produced cell components, such as electrodes and separators, along with a custom package remains largely unexplored. In this work, iron trifluoride (FeF
Additive Manufacturing
As additive manufacturing (AM) has become a reliable method for creating complex and unique hardware rapidly, the quality assurance of printed parts remains a priority. In situ process monitoring offers an approach for performing quality control while simultaneously minimizing post-production inspection. For extrusion printing processes, direct linkages between extrusion pressure fluctuations and print defects can be established by integrating pressure sensors onto the print head. In this work, the sensitivity of process monitoring is tested using engineered spherical defects. Pressure and force sensors located near an ink reservoir and just before the nozzle are shown to assist in identification of air bubbles, changes in height between the print head and build surface, clogs, and particle aggregates with a detection threshold of 60–70% of the nozzle diameter. Visual evidence of printed bead distortion is quantified using optical image analysis and correlated to pressure measurements. Importantly, this methodology provides an ability to monitor the quality of AM parts produced by extrusion printing methods and can be accomplished using commonly available pressure-sensing equipment.
Additive Manufacturing
Material extrusion additive manufacturing (AM) has enabled an elegant fabrication pathway for a vast material library. Nonetheless, each material requires optimization of printing parameters generally determined through significant trial-and-error testing. To eliminate arduous, iteration-based optimization approaches, many researchers have used machine learning (ML) algorithms which provide opportunities for automated process optimization. In this work, we demonstrate the use of an ML-driven approach for real-time material extrusion print-parameter optimization through in-situ monitoring of printed line geometry. To do this, we use deep invertible neural networks (INNs) which can solve both forward and inverse, or optimization, problems using a single network. By combining in-situ computer vision and deep INNs, the printing parameters can be autonomously optimized to print a target line width in 1.2 s. Furthermore, defects that occur during printing can be rapidly identified and corrected autonomously. The methods developed and presented in this work eliminate user-intensive, time-consuming, and iterative parameter discovery approaches that currently limit accelerated implementation of extrusion-based AM processes. Furthermore, the presented approach can be generalized to provide real-time monitoring and optimization pathways for increasingly complex AM environments.
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Additive Manufacturing
Material extrusion printing of reactive resins and inks present a unique challenge due to the time-dependent nature of the rheological and chemical properties they possess. As a result, careful print optimization or process control is important to obtain consistent, high quality prints via additive manufacturing. We present the design and use of a near-infrared (NIR) flow through cell for in situ chemical monitoring of reactive resins during printing. Differences between in situ and off-line benchtop measurements are presented and highlight the need for in-line monitoring capability. Additionally, in-line extrusion force monitoring and off-line post inspection using machine vision is demonstrated. By combining NIR and extrusion force monitoring, it is possible to follow cure reaction kinetics and viscosity changes during printing. When combined with machine vision, the ability to automatically identify and quantify print artifacts can be incorporated on the printing line to enable real-time, artificial intelligence-assisted quality control of both process and product. Together, these techniques form the building blocks of an optimized closed-loop process control strategy when complex reactive inks must be used to produce printed hardware.
ACS Applied Energy Materials
The solution processability of ionogel solid electrolytes has recently garnered attention in the Li-ion battery community as a means to address the interface and fabrication issues commonly associated with most solid electrolytes. However, the trapped ionic liquid (ILE) component has hindered the electrochemical performance. Herein, we present a process to tune the properties by replacing the ILE in a silica-based ionogel after fabrication with a liquid component befitting the desired application. Electrochemical cycling under various conditions showcases gels containing different liquid components incorporated into LiFePO4 (LFP)/gel/Li cells: high power (455 W kg-1 at a 1 C discharge) systems using carbonates, low temperatures (-40 °C) using ethers, or high temperatures (100 °C) using ionic liquids. Fabrication of additive-manufactured cells utilizing the exchanged carbonate-based system is demonstrated in a planar LFP/Li4Ti5O12 (LTO) system, where a marked improvement over an ionogel is found in terms of rate capability, capacity, and cycle stability (118 vs 41 mA h g-1 at C/4). This process represents a promising route to create a separator-less cell, potentially in complex architectures, where the electrolyte properties can be facilely tuned to meet the required conditions for a wide range of battery chemistries while maintaining a uniform electrolyte access throughout cast electrodes.
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Advanced Science
The development of chemistry is reported to implement selective dual-wavelength olefin metathesis polymerization for continuous additive manufacturing (AM). A resin formulation based on dicyclopentadiene is produced using a latent olefin metathesis catalyst, various photosensitizers (PSs) and photobase generators (PBGs) to achieve efficient initiation at one wavelength (e.g., blue light) and fast catalyst decomposition and polymerization deactivation at a second (e.g., UV-light). This process enables 2D stereolithographic (SLA) printing, either using photomasks or patterned, collimated light. Importantly, the same process is readily adapted for 3D continuous AM, with printing rates of 36 mm h–1 for patterned light and up to 180 mm h–1 using un-patterned, high intensity light.
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Additive Manufacturing
Interest in 3D printing of thermoset resins has increased significantly in recent years. One approach to additive manufacturing of thermoset resins is printing dual-cure resins with direct ink write (DIW). Dual-cure resins are multi-component resins which employ an in situ curable constituent to enable net-shape fabrication while a second constituent and cure mechanism contribute to the final mechanical properties of the printed materials. In this work, the cure kinetics, green strength, printability, and print fidelity of dual-cure epoxy/acrylate thermoset resins are investigated. Resin properties are evaluated as a function of acrylate concentration and in situ UV exposure conditions. The acrylate cure kinetics are probed using photo-differential scanning calorimetry and the impacts of resin composition and UV cure profile on the acrylate extent of conversion are presented. Continuous and pulsed UV cure profiles are shown to affect total conversion due to variances in radical efficiency at different UV intensities and acrylate concentrations. The effects of acrylate concentration on the kinetics of the epoxy thermal cure and the final mechanical properties are also investigated using dynamic mechanical analysis and three-point bend measurements. The glass transition temperature is dependent on formulation, with increasing acrylate content decreasing the Tg. However, the room temperature shear moduli, flexural moduli, strength, strain-to-failure, and toughness values are relatively independent of resin composition. The similarity of the final properties allows for greater flexibility in resin formulation and in situ cure parameters, which can enable the printing of complex parts that require high green strength. We found that the in situ UV print intensities and exposure profiles that are necessary to achieve the best print quality are not, in most cases, the conditions that maximize conversion of the acrylate network. This highlights the importance of developing optimized resin compositions which enable complete cure of the acrylate network by promoting acrylate dark cure or thermal cure.
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