Engineering articles from across Nature Portfolio

Assessing the carbon emissions of an electronic device from raw material extraction to disposal requires time-intensive data collection for each component. Now, artificial intelligence agents autonomously generate life-cycle inventories for electronic devices in under a minute and estimate carbon emissions with less than 19% deviation from expert assessments.

A microscale gripper that can be opened and closed using light can fit into tight spaces and perform tasks linked to clinical applications.

A ring-shaped electrochemical ion-pumping architecture synchronizes fluid flow with circuit switching. The result is pseudo-continuous, unidirectional ion transport, which enables redox-free, energy-efficient and scalable desalination.

Abu Sebastian, a distinguished research scientist at IBM Research in Zurich, talks to Nature Materials about the opportunities and challenges of memristive devices for in-memory computing and their potential to commercialization.

Assessing the carbon emissions of an electronic device from raw material extraction to disposal requires time-intensive data collection for each component. Now, artificial intelligence agents autonomously generate life-cycle inventories for electronic devices in under a minute and estimate carbon emissions with less than 19% deviation from expert assessments.

A microscale gripper that can be opened and closed using light can fit into tight spaces and perform tasks linked to clinical applications.

Sapna Ramesh discusses how nuclear magnetic resonance spectroscopy can be used to characterize the solvation structure of electrolytes.

Ilia Valov, a professor at the Institute of Electrochemistry and Energy Systems, Bulgarian Academy of Sciences, and at the Forschungszentrum Jülich, talks to Nature Materials about the relationship between nanoscale structure, composition, electrochemistry and the properties and functionalities of memristors.

Reasoning capability has significantly advanced complex logical inference and robotic decision-making in general domains. However, its potential in the Artificial Intelligence (AI) copilot robot—particularly implemented based on the Vision-Language-Action (VLA) model—remains unexplored in endoscopic surgery. Effective reasoning should enable AI copilot robots to integrate multimodal cues, interpret surgical intent, and infer hidden tissue dynamics, thereby alleviating intraoperative uncertainty and cognitive burden on surgeons. Properly implemented, reasoning-driven autonomy can transform AI copilot robots from reactive executors into cognitive collaborators, enhancing precision, safety, and sustainability in clinical practice.