Integrating circular economy (CE) principles into battery design is critical for enhancing sustainability in energy storage, as lithium-ion batteries grow essential for renewable energy and electric mobility. However, raw material depletion, hazardous waste, and inefficient end-of-life (EoL) practices threaten long-term resource and environmental sustainability. This study reviews 94 sources, synthesizing material flow analyses, design innovations, recycling technologies, and policy frameworks to assess CE applications across the battery lifecycle. Fourthemes emerge: 1) recovery of critical materials like lithium, cobalt, and nickel via emerging recycling methods that reduce energy consumption and environmental impact; 2) design innovations such as modularity and disassembly-oriented approaches that enable reuse and efficient resource recovery; 3) second-life battery use in stationary renewable energy systems to extend lifespan and lower costs; and 4) regulatory mechanisms, including extended producer responsibility and digital product passports to support circular practices. Key barriers include limited recycling infrastructure, complex chemistries hindering disassembly, lack of data transparency, and fragmented regulations reducing producer accountability. Promising solutions involve low-impact recycling, standardized modular designs, blockchain-based material traceability, and harmonized policies enforcing EoL responsibility. The study proposes a forward-looking framework combining technological innovation and policy reform driven by interdisciplinary collaboration to transform batteries into regenerative assets aligned with CE goals.

As industrial automation evolves towards human-centric, adaptable solutions, collaborative robots must overcome challenges in unstructured, dynamic environments. This paper extends our previous work on developing a digital shadow for industrial robots by introducing a comprehensive framework that bridges the gap between physical systems and their virtual counterparts. The proposed framework advances toward a fully functional digital twin by integrating real-time perception and intuitive human–robot interaction capabilities. The framework is applied to a hospital test lab scenario, where a YuMi robot automates the sorting of microscope slides. The system incorporates a RealSense D435i depth camera for environment perception, Isaac Sim for virtual environment synchronization, and a locally hosted large language model (Mistral 7B) for interpreting user voice commands. These components work together to achieve bi-directional synchronization between the physical and digital environments. The framework was evaluated through 20 test runs under varying conditions. A validation study measured the performance of the perception module, simulation, and language interface, with a 60% overall success rate. Additionally, synchronization accuracy between the simulated and physical robot joint movements reached 98.11%, demonstrating strong alignment between the digital and physical systems. By combining local LLM processing, real-time vision, and robot simulation, the approach enables untrained users to interact with collaborative robots in dynamic settings. The results highlight its potential for improving flexibility and usability in industrial automation.

The circular economy (CE) is increasingly recognized as a transformative framework for fostering sustainable production and consumption. In this context, Artificial Intelligence (AI) is emerging as a key enabler for advancing both material systems and policy processes. This article investigates the potential of AI-driven policy tools to accelerate the transition towards a circular economy. Consequently, it explores AI capabilities such as systems modelling, predictive analytics, and adaptive regulation, focusing on their application within environmental governance frameworks. The article examines the integration of AI technologies including natural language processing for policy synthesis, machine learning for waste pattern detection, and digital twins for scenario testing into multi-level governance structures. Furthermore, it critically addresses the ethical, institutional, and technical challenges associated with AI deployment in policymaking, particularly in terms of data bias, transparency, and public accountability. The article concludes with a roadmap for embedding 'circular intelligence' in governance systems, emphasizing the need for a strategic, transparent, and inclusive approach to AI integration in CE policy to ensure sustainable and just transitions.