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Smart Objects With Embodied Logic 3D Printed By Engineers

Even with no nervous system or brain, the Venus flytrap seems to make complicated decisions about when to snap down on possible prey, as well as to start when it has unintentionally caught something that it cannot consume.

Scientists at the School of Engineering and Applied Science of University of Pennsylvania have taken motivation from these kinds of systems. Employing geometric principles and stimuli-responsive materials, they have developed structures that have “personified logic.” Via their chemical and physical makeup alone, they are capable of determining which of various possible answers to make in response to their surroundings.

In spite of having no batteries, motors, processors, or circuits of any kind, they can toggle between various configurations in response to pre-determined environmental hints, such as oil-based chemicals or humidity.

Employing 3D multi-material printers, the scientists can make these active structures with equipped then logic gates, and can manage each gate’s timing, permitting for complex mechanical behaviors in response to easy modifications in the surrounding.

On a related note, similar to fingerprints, no 3D printer is precisely the same. That is the takeaway from a new study led by University at Buffalo. The study describes what it believed to be the first precise technique for tracking a 3D-printed object to the device it originated from. The improvement, which the research group dubs as “PrinTracker,” can eventually assist intelligence agencies and law enforcement track the source of fake products, 3D-printed guns, and other goods.

“3D printing has a number of wonderful applications, but it is also a counterfeiter’s dream. Even more worrying, it has the capability to make firearms more readily obtainable to individuals who are not permitted to own them,” claims Wenyao Xu, the lead author of the study and associate professor in UB’s School of Engineering and Applied Sciences for computer science and engineering. The study will be shown at the Association for Computing Machinery’s Conference in Toronto. The conference includes coauthors from Northeastern University and Rutgers University.

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