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Chemical energy ferroelectrics are generally solid macromolecules showing spontaneous polarization and chemical bonding energy. These materials still suffer drawbacks, including the limited control of energy release rate, and thermal decomposition energy well below total chemical energy. To overcome these drawbacks, we report the integrated molecular ferroelectric and energetic material from machine learning-directed additive manufacturing coupled with the ice-templating assembly. The resultant aligned porous architecture shows a low density of 0.35 g cm⁻³, polarization-controlled energy release, and an anisotropic thermal conductivity ratio of 15. Thermal analysis suggests that the chlorine radicals react with macromolecules enabling a large exothermic enthalpy of reaction (6180 kJ kg⁻¹). In addition, the estimated detonation velocity of molecular ferroelectrics can be tuned from 6.69 ± 0.21 to 7.79 ± 0.25 km s⁻¹by switching the polarization state. These results provide a pathway toward spatially programmed energetic ferroelectrics for controlled energy release rates.

As a facile and versatile additive manufacturing technology, direct ink writing (DIW) has attracted considerable interest in academia and industry to fabricate three-dimensional structures with unique properties and functionalities. However, so far, the physical phenomena during the DIW process are not revealed in detail, leaving a research gap between the physical experiments and the underlying theories. Here, we presented a comprehensive simulation study of non-Newtonian ink flow during the DIW process. We used the computational fluid dynamics (CFD) method and revealed the shear-thinning behavior during the extrusion process. Different nozzle geometry models were adopted in the simulation. The advantages and drawbacks of each syringe-nozzle geometry were analyzed. In addition, the ink shear stress and velocity fields were investigated and compared in the case studies. Based on these investigations and analysis, we proposed an improved syringe-nozzle geometry towards high-resolution DIW. Consequently, the high-resolution and high shape fidelity DIW could enhance the DIW product performance. The results developed in this work offer valuable guidelines and could accelerate further advancement of DIW.

Tailoring thermal transport by structural parameters could result in mechanically fragile and brittle networks. An indispensable goal is to design hierarchical architecture materials that combine thermal and mechanical properties in a continuous and cohesive network. A promising strategy to create such a hierarchical network targets additive manufacturing of hybrid porous voxels at nanoscale. Here we describe the convergence of agile additive manufacturing of porous hybrid voxels to tailor hierarchically and mechanically tunable objects. In one strategy, the uniformly distributed porous silica voxels, which form the basis for the control of thermal transport, are non-covalently interfaced with polymeric networks, yielding hierarchic super-elastic architectures with thermal insulation properties. Another additive strategy for achieving mechanical strength involves the versatile orthogonal surface hybridization of porous silica voxels retains its low thermal conductivity of 19.1 mW m⁻¹K⁻¹, flexible compressive recovery strain (85%), and tailored mechanical strength from 71.6 kPa to 1.5 MPa. The printed lightweight high-fidelity objects promise thermal aging mitigation for lithium-ion batteries, providing a thermal management pathway using 3D printed silica objects.

The macro-porous ceramics has promising durability and thermal insulation performances. A cost-effective and scalable additive manufacturing technique for the fabrication of macro-porous ceramics, with a facile approach to control the printed porosity is reported in the paper. Several ceramic inks were prepared, the foaming agent was used to generate gaseous bubbles in the ink, followed by the direct ink writing and the ambient-pressure and room-temperature drying to create the three-dimensional geometries. The experimental studies were performed to optimize the printing quality. A set of studies revealed the optimal printing process parameters for printing the foamed ceramic ink with a high spatial resolution and fine surface quality. Varying the concentration of the foaming agent enabled the controllability of the structural porosity. The maximum porosity can reach 85%, with a crack-free internal porous structure. The tensile tests showed that the printed macro-porous ceramics have enhanced durability with the addition of fiber. With a high-fidelity 3D printing process and precise control of the porosity, the printed samples exhibited a low thermal conductivity and high mechanical strength.

The conventional manufacturing process of aerogel insulation material relies largely on the supercritical drying, which suffers from issues of massive energy consumption, high-cost equipment and prolonged processing time. With the consideration of large market demand of the aerogel insulation material in the next decade, a low-cost and scalable fabrication technique is highly desired. In this paper, a direct ink writing (DIW) method is used to three-dimensionally fabricate the silica aerogel insulation material, followed by room-temperature and ambient pressure drying. Compared to the supercritical drying and freeze-drying, the reported method significantly reduces the fabrication time and costs. The cost-effective DIW technique offers the capability to print complex hollow internal structures, coupled with the porous structure, is found to be beneficial to the thermal insulation property. The addition of fiber to the ink assures the durability of the fabricated product. The foam ink preparation methods and the printability are demonstrated in this paper, along with the printed samples for characterizing thermal insulation performance and mechanical properties.

Inkjet 3D printing has broad applications in areas such as health and energy due to its capability to precisely deposit micro-droplets of multi-functional materials. However, the droplet of the inkjet printing has different jetting behaviors including drop initiation, thinning, necking, pinching and flying, and they are vulnerable to disturbance from vibration, material inhomogeneity, etc. Such issues make it challenging to yield a consistent printing process and a defect-free final product with desired properties. Therefore, timely recognition of the droplet behavior is critical for inkjet printing quality assessment. In-situ video monitoring of the printing process paves a way for such recognition. In this paper, a novel feature identification framework is presented to recognize the spatiotemporal feature of in-situ monitoring videos for inkjet printing. Specifically, a spatiotemporal fusion network is used for droplet printing behavior classification. The categories are based on inkjet printability, which is related to both the static features (ligament, satellite, and meniscus) and dynamic features (ligament thinning, droplet pinch off, meniscus oscillation). For the recorded droplet jetting video data, two streams of networks, the frames sampled from video in spatial domain (associated with static features) and the optical flow in temporal domain (associated with dynamic features), are fusedmore »in different ways to recognize the droplet evolving behavior. Experiments results show that the proposed fusion network can recognize the droplet jetting behavior in the complex printing process and identify its printability with learned knowledge, which can ultimately enable the real-time inkjet printing quality control and further provide guidance to design optimal parameter settings for the inkjet printing process.

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Electrospinning is a promising process to fabricate functional parts from macrofibers and nanofibers of bio-compatible materials including collagen, polylactide (PLA), and polyacrylonitrile (PAN). However, the functionality of the produced parts highly rely on quality, repeatability, and uniformity of the electrospun fibers. Due to the variations in material composition, process settings, and ambient conditions, the process suffers from large variations. In particular, the fiber formation in the stable regime (i.e., Taylor cone and jet) and its propagation to the substrate plays the most significant role in the process stability. This work aims to designing a fast process monitoring tool from scratch for monitoring the dynamic electrospinning process based on the Taylor cone and jet videos. Nevertheless, this is challenging since the videos are of high frequency and high dimension, and the monitoring statistics may not have a parametric distribution. To achieve this goal, a framework integrating image analysis, sketch-based tensor decomposition, and non-parametric monitoring, is proposed. In particular, we use Tucker tensor-sketch (Tucker-TS) based tensor decomposition to extract the sparse structure representations of the videos. Additionally, the extracted monitoring variables are non-normally distributed, hence non-parametric bootstrap Hotelling T2 control chart is deployed to handle this issue during the monitoring. Themore »framework is demonstrated by electrospinning a PAN-based polymeric solution. Finally, it is demonstrated that the proposed framework, which uses Tucker-TS, largely outperformed the computational speed of the alternating least squares (ALS) approach for the Tucker tensor decomposition, i.e., Tucker-ALS, in various anomaly detection tasks while keeping the comparable anomaly detection accuracy.

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Freeze nano 3D printing is a novel process that seamlessly integrates freeze casting and inkjet printing processes. It can fabricate flexible energy products with both macroscale and microscale features. These multi-scale features enable good mechanical and electrical properties with lightweight structures. However, the quality issues are among the biggest barriers that freeze nano printing, and other 3D printing processes, need to come through. In particular, the droplet solidification behavior is crucial for the product quality. The physical based heat transfer models are computationally inefficient for the online solidification time prediction during the printing process. In this paper, we integrate machine learning (i.e., tensor decomposition) methods and physical models to emulate the tensor responses of droplet solidification time from the physical based models. The tensor responses are factorized with joint tensor decomposition, and represented with low dimensional vectors. We then model these low dimensional vectors with Gaussian process models. We demonstrate the proposed framework for emulating the physical models of freeze nano 3D printing, which can help the future real-time process optimization.

Achieving a mesoporous structure in superinsulation materials is pivotal for guaranteeing a harmonious relationship between low thermal conductivity, high porosity, and low density. Herein, we report silica-based cryogel and aerogel materials by implementing freeze-drying and ambient-pressure-drying processes respectively. The obtained freeze-dried cryogels yield thermal conductivity of 23 mW m −1 K −1 , with specific surface area of 369.4 m 2 g −1 , and porosity of 96.7%, whereas ambient-pressure-dried aerogels exhibit thermal conductivity of 23.6 mW m −1 K −1 , specific surface area of 473.8 m 2 g −1 , and porosity of 97.4%. In addition, the fiber-reinforced nanocomposites obtained via freeze-drying feature a low thermal conductivity (28.0 mW m −1 K −1 ) and high mechanical properties (∼620 kPa maximum compressive stress and Young's modulus of 715 kPa), coupled with advanced flame-retardant capabilities, while the composite materials from the ambient pressure drying process have thermal conductivity of 28.8 mW m −1 K −1 , ∼200 kPa maximum compressive stress and Young's modulus of 612 kPa respectively. The aforementioned results highlight the capabilities of both drying processes for the development of thermal insulation materials for energy-efficient applications.