The electrocatalytic CO
2
reduction reaction (CO
2
RR) to useful multicarbon (C
2+
) products offers a way to help achieve carbon neutrality. Copper‐based catalysts are uniquely capable of achieving this efficiently; however, when used as single atoms or dual‐atom pairs, their structures change significantly during reactions. This change makes it difficult to apply traditional models that link a catalyst's shape to its performance, which hinders the design of high‐performance catalysts. To bridge this gap, this review presents a comprehensive framework focused on the behaviors and mechanisms of such dynamically evolving copper‐based catalysts in CO
2
RR. First, it explains how C
2+
products are formed at the atomic level, describing three main ways in which carbon atoms bond: symmetric, asymmetric, and single‐site dynamic coupling. Second, it summarizes the primary features that affect the degree to which these catalysts form C
2+
products, especially the changing environment and oxidation state (Cu
+
/Cu
0
ratio) during the reaction. Next, the discussion elucidates how external factors, such as electrolyte and electrolysis conditions, influence their dynamic surface reconstruction. Finally, emphasis is placed on the shift from passive observation to active catalyst design, driven by multiscale theoretical simulations and automated AI chemists. This offers practical insights into developing efficient atomically dispersed Cu‐based catalysts suitable for industrial use.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
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Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026
The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.
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