The recent decades have witnessed the booming of additive manufacturing (AM), or 3D printing, not only in conventional areas, such as aviation, [1] automobile [2] and construction, [3] but also in various emerging fields, such as electronics, [4] biomedical engineering [5] and soft robotics. [6] The reason is the growing capability of AM to fabricate complex structures, which are challenging to be realized by traditional machining methods. In this advancement, the material library of 3D printing is no longer limited to static materials for structural construction and has expanded to active materials or stimuli-responsive materials, such as shape memory polymers, [7] hydrogels, [8] magnetic soft materials [9] and liquid crystal elastomers (LCEs), [10] driven by the growing need for soft robots, [10h,11] biomedical devices, [5,8a] smart wearable devices, [12] etc. The active nature of stimuli-responsive materials adds the dimension of time to 3D printing and leads to the emerging 4D printing. [8a,13] Among active materials for 4D printing, LCEs are appealing candidates due to their large, reversible and rapid actuation through a nematic-isotropic phase transition upon external stimuli, such as heat, [14] light, [10a,b,15] humidity [16] and electric fields. [17] LCEs are a class of soft active materials that inherit both the entropic elasticity of elastomers and the molecular anisotropy of liquid crystals (or mesogens). The actuation relies on the mesogen alignment, [18] which can be achieved by mechanical stretching, [19] surface shearing [10g] or external fields. [20] 3D/4D printing methods have been developed to fabricate LCE-based structures and align the mesogens. Direct ink writing (DIW) has been explored for printing LCEs. [11,15,21] In DIW, mesogens are aligned along the printing path when the LCE ink is extruded out of the syringe through the nozzle. Different inks have been developed for both high-temperature printing [10c,21a,c] and room-temperature printing. [21d,22] In addition, functionally graded LCEs were achieved by varying printing parameters, [10d,21c,g,23] such as printing temperature, printing speed and nozzle size. Although 3D structures, such as pinecone and saddle-shaped structures, [21c] can be achieved by 2D structures via different actuation strains between layers, the layer-by-layer manner of material deposition in DIW makes LCEs to be printed on the build platform or the previous layers.Liquid crystal elastomers (LCE) are appealing candidates among active materials for 4D printing, due to their reversible, programmable and rapid actuation capabilities. Recent progress has been made on direct ink writing (DIW) or Digital Light Processing (DLP) to print LCEs with certain actuation. However, it remains a challenge to achieve complicated structures, such as spatial lattices with large actuation, due to the limitation of printing LCEs on the build platform or the previous layer. Herein, a novel method to 4D print freestanding LCEs on-the-fly by using laser-assisted DIW w...

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Active composites consisting of materials that respond differently to environmental stimuli can transform their shapes. Integrating active composites and 4D printing allows the printed structure to have a pre‐designed complex material or property distribution on numerous small voxels, offering enormous design flexibility. However, this tremendous design space also poses a challenge in efficiently finding appropriate designs to achieve a target shape change. Here, a novel machine learning (ML) and evolutionary algorithm (EA) based approach is presented to guide the design process. Inspired by the beam deformation characteristics, a recurrent neural network (RNN) based ML model whose training dataset is acquired by finite element simulations is developed for the forward shape‐change prediction. EA empowered with ML is then used to solve the inverse problem of finding the optimal design. For multiple target shapes with different complexities, the ML‐EA approach demonstrates high efficiency. Combining the ML‐EA with computer vision algorithms, a new paradigm is presented that streamlines design and 4D printing process where active straight beams can be designed based on hand‐drawn lines and be 4D printed that transform into the drawn profiles under the stimulus. The approach thus provides a highly efficient tool for the design of 4D‐printed active composites.

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Cytoplasmic viscosity ( μ c ) is a key biomechanical parameter for evaluating the status of cellular cytoskeletons. Previous studies focused on white blood cells, but the data of cytoplasmic viscosity for tumour cells were missing. Tumour cells (H1299, A549 and drug-treated H1299 with compromised cytoskeletons) were aspirated continuously through a micropipette at a pressure of −10 or −5 kPa where aspiration lengths as a function of time were obtained and translated to cytoplasmic viscosity based on a theoretical Newtonian fluid model. Quartile coefficients of dispersion were quantified to evaluate the distributions of cytoplasmic viscosity within the same cell type while neural network-based pattern recognitions were used to classify different cell types based on cytoplasmic viscosity. The single-cell cytoplasmic viscosity with three quartiles and the quartile coefficient of dispersion were quantified as 16.7 Pa s, 42.1 Pa s, 110.3 Pa s and 74% for H1299 cells at −10 kPa ( n cell = 652); 144.8 Pa s, 489.8 Pa s, 1390.7 Pa s, and 81% for A549 cells at −10 kPa ( n cell = 785); 7.1 Pa s, 13.7 Pa s, 31.5 Pa s, and 63% for CD-treated H1299 cells at −10 kPa ( n cell = 651); and 16.9 Pa s, 48.2 Pa s, 150.2 Pa s, and 80% for H1299 cells at −5 kPa ( n cell = 600), respectively. Neural network-based pattern recognition produced successful classification rates of 76.7% for H1299 versus A549, 67.0% for H1299 versus drug-treated H1299 and 50.3% for H1299 at −5 and −10 kPa. Variations of cytoplasmic viscosity were observed within the same cell type and among different cell types, suggesting the potential role of cytoplasmic viscosity in cell status evaluation and cell type classification.