David William Rosen

Shape memory polymers capable of remote actuation are promising for soft robotics, adaptive structures, and biomedical devices, yet efficient photothermal actuation remains limited by particle aggregation, localized thermal hotspots, and poor energy utilization. Here, we introduce a microfluidic architecture‐engineering strategy that transforms MXene nanosheets into spatially distributed patchy core–shell particles for efficient near‐infrared (NIR)‐triggered actuation at ultralow loadings. Flow‐focusing microfluidics assembles Ti ₃ C ₂ T _x MXene nanosheets into 7–10 nm patchy shells around polymeric cores, suppressing restacking while maximizing accessible photothermal interfaces. Incorporated into poly(L‐lactic acid), these particles enable the composite to reach its glass‐transition temperature within 10 s under NIR irradiation (850 nm, 1.8 W cm ⁻² ) at MXene loading of only 0.0024 wt%, resulting in ≈95% shape recovery within 25 s. In contrast, composites containing directly dispersed MXene exhibit localized heating and achieve only ≈63% recovery after 150 s. Combining Monte Carlo photon transport and finite‐element thermal simulations, we show that the patchy‐shell architecture enhances photon harvesting and volumetric heat generation by spatially distributing photothermal interfaces throughout the material. This work establishes architecture‐controlled photothermal transport as a design principle for next‐generation smart composites and demonstrates microfluidic engineering of functional particle architectures as a scalable route toward high‐performance remotely actuated materials.

In this paper, we present a new phase-transition constitutive model for the thermomechanical behavior of shape memory polymers (SMPs). The model decomposes stress contributions into netpoint and switching domains based on experimental observations. We employ explicit expressions to characterize the evolution of the elastic modulus, storage strain and thermal strain during thermomechanical cycles. The model consists of only eight parameters yet precisely describes the temperature-dependent shape memory and thermal expansion effects observed experimentally. We implement the constitutive model in a 3D finite element procedure using Abaqus UEL user subroutines. The finite element procedure exhibits excellent convergence performance and has been applied to analyze a range of SMP structures under various programming loads, including blocks, cantilever beams, composite beams, thin-walled sheets, and origami structures. The simulation results demonstrate the accuracy and robustness of the model for large-scale SMP structures. We make the key computational source files available as supplementary materials.

Additive manufacturing (AM) represents a category of manufacturing processes that fabricates parts in a layer-by-layer manner. As such, AM provides unique advantages over conventional manufacturing processes such as the ability to fabricate highly complex geometries, to minimize material waste, and to enable mass customization, while having some limitations, such as high costs and complexities. Advances in artificial intelligence (AI) and machine learning (ML) enable these limitations to be addressed due to the data-rich environment in modern commercial AM machines with multiple sensors. This paper surveys papers that apply AI/ML techniques to the topics of defect detection, AM process surrogate models and their application, generative design, and design for manufacturing in metal AM processes. The approach taken is to introduce these topics, provide a coarse survey, and then discuss specific applications in some depth, rather than to provide a fine-grained, comprehensive survey.