Nuclear magnetic resonance spectroscopy (NMR) is a form of spectroscopy that yields detailed mechanistic information about chemical structures, reactions, and processes. Photochemistry has widespread use across many industries and holds excellent utility for additive manufacturing (AM) processes. Here, we use photoNMR to investigate three photochemical processes spanning AM relevant timescales. We first investigate the photodecomposition of a photobase generator on the slow timescale, then the photoactivation of a ruthenium catalyst on the intermediate timescale, and finally the radical polymerization of an acrylate system on the fast timescale. In doing so, we gain fundamental insights to mission relevant photochemistries and develop a new spectroscopic capability at SNL.
Frontal polymerization involves the propagation of a thermally driven polymerization wave through a monomer solution to rapidly generate high-performance polymeric materials with little energy input. The balance between latent catalyst activation and sufficient reactivity to sustain a front can be difficult to achieve and often results in systems with poor storage lives. This is of particular concern for frontal ring-opening metathesis polymerization (FROMP) where gelation occurs within a single day of resin preparation due to the highly reactive nature of Grubbs-type catalysts. In this report we demonstrate the use of encapsulated catalysts to provide remarkable latency to frontal polymerization systems, specifically using the highly active dicyclopentadiene monomer system. Negligible differences were observed in the frontal velocities or thermomechanical properties of the resulting polymeric materials. FROMP systems with encapsulated catalyst particles are shown with storage lives exceeding 12 months and front rates that increase over a well-characterized 2 month period. Moreover, the modularity of this encapsulation method is demonstrated by encapsulating a platinum catalyst for the frontal polymerization of silicones by using hydrosilylation chemistry.
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.
Plasmonic heating by nanoparticles has been used to promote a range of chemical reactions. Now, thermoplasmonic activation has been applied to latent ruthenium catalysts, enabling olefin metathesis initiated by visible and infrared light. Additionally, the desire to harness light to drive chemical transformations has surely existed as long as the study of chemistry itself. In the earliest documented applications, light was used simply as a heat source — for example, in the distillation of liquids. Since that time, our knowledge of how light and matter interact has increased exponentially, with greater mechanistic and molecular understanding enabling modern photochemists to design molecules with a myriad of finely tuned optical properties for catalysis, biochemistry, optoelectronics and more. Nonetheless, the design and optimization of molecules to achieve specific optical properties is still challenging, and for some applications, a return to the ‘simplest’ transformation — that of light to heat — can offer a more efficient approach to achieve light-mediated chemical reactions. Now, writing in Nature Chemistry, Yossi Weizmann and colleagues describe a strategy for organic and polymer synthesis driven by the conversion of light to heat.
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.
Recent progress in photoinitiated ring-opening metathesis polymerization (photoROMP) has enabled the lithographic production of patterned films from olefinic resins. Recently, we reported the use of a latent ruthenium catalyst (HeatMet) in combination with a photosensitizer (2-isopropylthioxanthone) to rapidly photopolymerize dicyclopentadiene (DCPD) formulations upon irradiation with UV light. While this prior work was limited in terms of catalyst and photosensitizer scope, a variety of alternative catalysts and photosensitizers are commercially available that could allow for tuning of thermomechanical properties, potlifes, activation rates, and irradiation wavelengths. Herein, 14 catalysts and 8 photosensitizers are surveyed for the photoROMP of DCPD and the structure-activity relationships of the catalysts examined. Properties relevant to stereolithography additive manufacturing (SLA AM)-potlife, irradiation dose required to gel, conversion-are characterized to develop catalyst and photosensitizer libraries to inform development of SLA AM resin systems. Two optimized catalyst/photosensitizer systems are demonstrated in the rapid SLA printing of complex, multidimensional pDCPD structures with microscale features under ambient conditions.
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.