Recent technological advances in additive manufacturing (AM) have focused on incorporating stimuli-responsive “smart” materials to extensively broaden the functionalities of printed objects beyond their shapes and structures. In this context, advancements toward soft actuators that can convert external ambient stimuli into actuation, i.e., self-driven soft actuators, have attracted tremendous interest, as they can promote sustainable energy autonomy for compact integrated systems requiring dynamic stimuli–responses such as soft robotics, wearable healthcare, and environmental monitoring. However, to practically implement such functionalities through AM, the printing process should be capable of accommodating stimuli–responsive adaptations of the physical/chemical properties of materials; this requires the use of specific processing schemes, depending on the targeted stimulus–response pairs. Therefore, it is crucial to create innovations in materials, geometries, and fabrication processes that suit various stimulus–response and application fields. To help guide such research efforts, this article provides a comprehensive overview of technological frameworks for materials, chemical/physical architectures, and corresponding process design strategies for self-driven actuators through AM. Firstly, we classify the material prerequisites for reliable and facile emulation of various stimuli–responses and the compatible printing techniques for them. Secondly, recent progress in various three-dimensional(3D)-printed untethered stimuli-responsive actuators has been categorized and summarized, depending on the targeted stimuli and energy sources. Thirdly, as a rapidly growing strategy for increasing the stability and precise control of various functional actuations, AM of functionally graded materials and multi-materials is overviewed. Finally, we propose an outlook for future research directions to resolve the remaining challenges of achieving scalable, reproducible, and multifunctional systems. Future directions are proposed to address limitations and unlock the full potential of these materials for autonomous systems.

Metals, indispensable since the Bronze Age, remain pivotal in modern technologies due to their exceptional properties and versatility. Beyond traditional machining, advanced nano/micro-machining techniques enable the fabrication of metallic nano/micro structures with high precision in shape, size, and pattern. These structures endow flexible electrodes with outstanding electrical, mechanical, optical, and electrochemical performance, enabling growing applications in flexible optoelectronics, epidermal electronics, energy harvesting, and biochemical sensing. This review provides a comprehensive overview of the fabrication strategies for flexible electrodes made from metal meshes, metal nanowires, and liquid metals. The current advancements, existing challenges, and emerging technologies are systematically discussed. Furthermore, the progression toward ultra-thin, soft epidermal electrodes is explored, with an emphasis on novel in situ and transfer fabrication methods. We examine the underlying mechanisms, performance indicators, and their integration for on-skin applications, including bioelectric sensing, electrical stimulation, and energy harvesting. Finally, we highlight the remaining challenges in performance improvement and industrialization of flexible and epidermal electrodes, along with future opportunities for integrating multimodal systems and leveraging artificial intelligence to enhance their functionalities.

Neuromorphic computing extends beyond sequential processing modalities and outperforms traditional von Neumann architectures in implementing more complicated tasks, e.g., pattern processing, image recognition, and decision making. It features parallel interconnected neural networks, high fault tolerance, robustness, autonomous learning capability, and ultralow energy dissipation. The algorithms of artificial neural network (ANN) have also been widely used because of their facile self-organization and self-learning capabilities, which mimic those of the human brain. To some extent, ANN reflects several basic functions of the human brain and can be efficiently integrated into neuromorphic devices to perform neuromorphic computations. This review highlights recent advances in neuromorphic devices assisted by machine learning algorithms. First, the basic structure of simple neuron models inspired by biological neurons and the information processing in simple neural networks are particularly discussed. Second, the fabrication and research progress of neuromorphic devices are presented regarding to materials and structures. Furthermore, the fabrication of neuromorphic devices, including stand-alone neuromorphic devices, neuromorphic device arrays, and integrated neuromorphic systems, is discussed and demonstrated with reference to some respective studies. The applications of neuromorphic devices assisted by machine learning algorithms in different fields are categorized and investigated. Finally, perspectives, suggestions, and potential solutions to the current challenges of neuromorphic devices are provided.

Recent advances in functionally graded additive manufacturing (FGAM) technology have enabled the seamless hybridization of multiple functionalities in a single structure. Soft robotics can become one of the largest beneficiaries of these advances, through the design of a facile four-dimensional (4D) FGAM process that can grant an intelligent stimuli-responsive mechanical functionality to the printed objects. Herein, we present a simple binder jetting approach for the 4D printing of functionally graded porous multi-materials (FGMM) by introducing rationally designed graded multiphase feeder beds. Compositionally graded cross-linking agents gradually form stable porous network structures within aqueous polymer particles, enabling programmable hygroscopic deformation without complex mechanical designs. Furthermore, a systematic bed design incorporating additional functional agents enables a multi-stimuli-responsive and untethered soft robot with stark stimulus selectivity. The biodegradability of the proposed 4D-printed soft robot further ensures the sustainability of our approach, with immediate degradation rates of 96.6% within 72 h. The proposed 4D printing concept for FGMMs can create new opportunities for intelligent and sustainable additive manufacturing in soft robotics.