The synergistic integration of nanomaterials with 3D printing technologies can enable the creation of architecture and devices with an unprecedented level of functional integration. In particular, a multiscale 3D printing approach can seamlessly interweave nanomaterials with diverse classes of materials to impart, program, or modulate a wide range of functional properties in an otherwise passive 3D printed object. However, achieving such multiscale integration is challenging as it requires the ability to pattern, organize, or assemble nanomaterials in a 3D printing process. This review highlights the latest advances in the integration of nanomaterials with 3D printing, achieved by leveraging mechanical, electrical, magnetic, optical, or thermal phenomena. Ultimately, it is envisioned that such approaches can enable the creation of multifunctional constructs and devices that cannot be fabricated with conventional manufacturing approaches.

The ability to seamlessly integrate functional materials into three-dimensional (3D) constructs has been of significant interest, as it can enable the creation of multifunctional devices. Such integration can be achieved with a multiscale, multi-material 3D printing strategy. This technology has enabled the creation of unique devices such as personalized tissue regenerative scaffolds, biomedical implants, 3D electronic devices, and bionic constructs which are challenging to realize with conventional manufacturing processes. In particular, the incorporation of nanomaterials into 3D printed devices can endow a wide range of constructs with tailorable mechanical, chemical, and electrical functionalities. This review highlights the advances and unique possibilities in the fabrication of novel electronic, biomedical, and bioelectronic devices that are realized by the synergistic integration of nanomaterials with 3D printing technologies.

Methacrifos (22.5 g t-l) and the three protectant combinations chlorpyrifos-methyl ( l o g t-l) plus bioresmethrin (1 g t-l), fenitrothion (12 g t-l) plus (1R)-phenothrin (2 g t-l) and pirimiphos-methyl (4 g t-I) plus carbaryl(8 g t-1) were each applied to grain that was stored in at least 15 silos. Grain temperature and levels of protectant were regularly monitored, and samples f r o m 12 storages using each treatment were taken f o r laboratory assays against Rhyzopertha dominica and Tribolium castaneum. Grain condition did not deteriorate during storage. Grain remained free of insects in 60 of the 63 storages treated; partial failure in the other 3 storages was attributed to low or irregular levels of protectant. The mean and range of residue values of all protectants were recorded as a function of time and the mean observed values corresponded to predicted values. In laboratory 27 1 Pestic. Sci. 0031-613X/87/$03.50 0 Society of Chemical Industry, 1987. Printed in Great Britain

Reinforcement learning control methods can impart robots with the ability to discover effective behavior, reducing their modeling and sensing requirements, and enabling their ability to adapt to environmental changes. However, it remains challenging for a robot to achieve navigation in confined and dynamic environments, which are characteristic of a broad range of biomedical applications, such as endoscopy with ingestible electronics. Herein, a compact, 3D‐printed three‐linked‐sphere robot synergistically integrated with a reinforcement learning algorithm that can perform adaptable, autonomous crawling in a confined channel is demonstrated. The scalable robot consists of three equally sized spheres that are linearly coupled, in which the extension and contraction in specific sequences dictate its navigation. The ability to achieve bidirectional locomotion across frictional surfaces in open and confined spaces without prior knowledge of the environment is also demonstrated. The synergistic integration of a highly scalable robotic apparatus and the model‐free reinforcement learning control strategy can enable autonomous navigation in a broad range of dynamic and confined environments. This capability can enable sensing, imaging, and surgical processes in previously inaccessible confined environments in the human body.

Robots In article number http://doi.wiley.com/10.1002/aisy.202100039, Yong Lin Kong and co‐workers present a 3D‐printed, compact robot integrated with reinforcement learning that performs adaptable, autonomous crawling in a confined environment. Without prior knowledge of the environment, the scalable robot can learn effective locomotory gaits that enable navigation through confined and dynamic environments, addressing a key challenge for robotic biomedical diagnosis.