Continuous Additive Manufacturing using Olefin Metathesis

Volumetric Additive Manufacturing of Dicyclopentadiene by Solid-State Photopolymerization

Polymerization in the solid state is generally infeasible due to restrictions on mobility. However, in this work, the solid-state photopolymerization of crystalline dicyclopentadiene is demonstrated via photoinitiated ring-opening metathesis polymerization. The source of mobility in the solid state is attributed to the plastic crystal nature of dicyclopentadiene, which yields local short-range mobility due to orientational degrees of freedom. Polymerization in the solid state enables photopatterning, volumetric additive manufacturing of free-standing structures, and fabrication with embedded components. Solid-state photopolymerization of dicyclopentadiene offers a new paradigm for advanced and freeform fabrication of high-performance thermosets.

PhotoROMP: The Future Is Bright

Since the earliest investigations of olefin metathesis catalysis, light has been the choice for controlling the catalyst activity on demand. From the perspective of energy efficiency, temporal and spatial control, and selectivity, photochemistry is not only an attractive alternative to traditional thermal manufacturing techniques but also arguably a superior manifold for advanced applications like additive manufacturing (AM). In the last three decades, pioneering work in the field of ring-opening metathesis polymerization (ROMP) has broadened the scope of material properties achievable through AM, particularly using light as both an activating and deactivating stimulus. In this Perspective, we explore trends in photocontrolled ROMP systems with an emphasis on approaches to photoinduced activation and deactivation of metathesis catalysts. Recent work has yielded a myriad of commercial and synthetically accessible photosensitive catalyst systems, although comparatively little attention has been paid to achieving precise control over polymer morphology using light. Metal-free, photophysical, and living ROMP systems have also been relatively underexplored. To take fuller advantage of both the thermomechanical properties of ROMP polymers and the operational simplicity of photocontrol, clear directions for the field are to improve the reversibility of activation and deactivation strategies as well as to further develop photocontrolled approaches to tuning cross-link density and polymer tacticity.

Adaptive Data-Driven Deep-Learning Surrogate Model for Frontal Polymerization in Dicyclopentadiene

Frontal polymerization (FP) is a self-sustaining curing process that enables rapid and energy-efficient manufacturing of thermoset polymers and composites. Computational methods conventionally used to simulate the FP process are time-consuming, and repeating simulations are required for sensitivity analysis, uncertainty quantification, or optimization of the manufacturing process. In this work, we develop an adaptive surrogate deep-learning model for FP of dicyclopentadiene (DCPD), which predicts the evolution of temperature and degree of cure orders of magnitude faster than the finite-element method (FEM). The adaptive algorithm provides a strategy to select training samples efficiently and save computational costs by reducing the redundancy of FEM-based training samples. The adaptive algorithm calculates the residual error of the FP governing equations using automatic differentiation of the deep neural network. A probability density function expressed in terms of the residual error is used to select training samples from the Sobol sequence space. The temperature and degree of cure evolution of each training sample are obtained by a 2D FEM simulation. The adaptive method is more efficient and has a better prediction accuracy than the random sampling method. With the well-trained surrogate neural network, the FP characteristics (front speed, shape, and temperature) can be extracted quickly from the predicted temperature and degree-of-cure fields.

3D printing of bone and cartilage with polymer materials

Damage and degeneration to bone and articular cartilage are the leading causes of musculoskeletal disability. Commonly used clinical and surgical methods include autologous/allogeneic bone and cartilage transplantation, vascularized bone transplantation, autologous chondrocyte implantation, mosaicplasty, and joint replacement. 3D bio printing technology to construct implants by layer-by-layer printing of biological materials, living cells, and other biologically active substances in vitro, which is expected to replace the repair mentioned above methods. Researchers use cells and biomedical materials as discrete materials. 3D bio printing has largely solved the problem of insufficient organ donors with the ability to prepare different organs and tissue structures. This paper mainly discusses the application of polymer materials, bio printing cell selection, and its application in bone and cartilage repair.

Additive Manufacturing of Degradable Materials via Ring-Opening Metathesis Polymerization (ROMP)

Thermoset materials comprise a significant proportion of high-performance plastics due to their shape permanence and excellent thermal and mechanical properties. However, these properties come at the expense of degradability. Here, we show for the first time that the industrial thermoset polydicyclopentadiene (PDCPD) can be additively manufactured (AM) with degradable 2,3-dihydrofuran (DHF) linkages using a photochemical approach. Treatment of the manufactured objects with acid results in rapid degradation to soluble byproducts. This work highlights the potential of ring-opening metathesis polymerization (ROMP) chemistry to create degradable materials amenable to advanced manufacturing processes.