BURA Collection:

Unravelling the regulatory role of supercritical CO₂ in CH₄/O₂ combustion: Linking reaction pathways to performance

2026-10-17T17:04:40Z

Title: Unravelling the regulatory role of supercritical CO₂ in CH₄/O₂ combustion: Linking reaction pathways to performance Authors: Liu, Yuqing; Sun, Mingrui; Luo, Kai Hong; Xia, Jun; Jiang, Xi Zhuo Abstract: In the context of global energy transition and net-zero emission goals, supercritical carbon dioxide (sCO₂) technology has emerged as a core solution for next-generation low-carbon energy conversion systems. However, the combustion characteristics of fuel/oxidizer in sCO₂ environments remain underexplored. To better understand the fuel performance under sCO₂ conditions, this study takes methane combustion (CH₄) as an example, employing reactive force field (ReaxFF) molecular dynamics method, to examine the combustion performance of CH₄/O₂ under varying gas blend ratios, α (the ratio of number of sCO₂ molecules to the number of CH₄ molecules). At a representative pressure of 200 atm, the results show that when a small amount of sCO₂ is introduced (0 < α ≤ 1), the consumption rate of CH₄ is markedly accelerated, as sCO₂ lowers the activation energy for CH₄ decomposition and shortens the initial decomposition time. However, when the amount of sCO₂ exceeds that of CH₄, the system exhibits an inhibitory effect on CH₄ combustion. Notably, sCO₂ addition can reduce the net formation of CO₂ in the system, contributing to emission reduction. This work elucidates the critical pathways through which sCO₂ affects CH₄ combustion reactions and intermediate product conversion. To further investigate the dependence of pressure, simulations are extended to 100 atm - 250 atm, revealing that the optimal α range varies with pressure. This study, for the first time, clarifies the mechanism by which sCO₂ concentration regulates the CH₄/O₂ combustion, provides valuable insights for optimizing combustion chamber design in sCO₂ atmospheres. Description: Data availability: Data will be made available on request.

Robotic path planning for NDT using neuroevolution of augmenting topologies

2026-10-17T17:06:29Z

Title: Robotic path planning for NDT using neuroevolution of augmenting topologies Authors: Zhang, Mengyuan; Sutcliffe, M; Yang, Qingping; Nicholson, I Abstract: Path planning in ultrasonic non-destructive testing (NDT) must simultaneously optimise coverage integrity and motion efficiency under complex geometric constraints. However, traditional methods primarily focus on shortest-path computation, which struggles to adapt to complex topologies, particularly in inspection scenarios where surface continuity and movement costs vary. These methods often fail to balance path quality and obstacle avoidance. In this study, a neuroevolutionary framework is introduced that combines graph-based modelling with the dynamic topology evolution of the neuroevolution of augmenting topologies (NEAT) algorithm, aiming to improve adaptability in complex inspection environments. The method models the inspection environment as a weighted graph and employs neural networks to optimise the path generation strategy that ensures complete coverage while minimising redundant motion. The experimental results demonstrate that the proposed method achieves 100% coverage while improving path smoothness, reducing sharp turns and enhancing obstacle avoidance. Overall, it outperforms A*, Dijkstra and a multi-layer perceptron (MLP).

Large language models in the patent lifecycle: A systematic review

2026-10-16T02:01:11Z

Title: Large language models in the patent lifecycle: A systematic review Authors: Jiang, Pingfei; Arena, Andrea Pietro; Sorce, Salvatore; Atherton, Mark Abstract: Patents constitute the world's largest repository of codified technical knowledge, yet their length, legal terminology, and linguistic ambiguity have limited the effectiveness of traditional text mining and keyword-based retrieval. Large Language Models (LLMs) offer new capabilities for processing this complexity, but the resulting literature is fragmented across disciplines, with no comprehensive synthesis of how these tools are being applied across the patent lifecycle. This study systematically reviews the literature on LLMs and Generative AI in patent analytics, following the PRISMA 2020 protocol, to map current applications, characterise dominant architectural trends, and identify priority research gaps. Using Scopus and Web of Science, we identified and screened a corpus of 129 peer-reviewed studies published from 2018 to early 2026, organised according to three patent lifecycle stages: creation and pre-filing (N = 36), examination and prosecution (N = 34), and post-grant management (N = 60). The review shows that research activity has grown sharply since 2024, accompanied by a shift from exploratory, single-task studies toward applied, multi-component systems. Across this literature, Retrieval-Augmented Generation (RAG) has become the dominant architectural response to hallucination risk, grounding generated content in verified external sources. The review further identifies persistent gaps in domain-specific evaluation metrics, multimodal reasoning, and governance frameworks for AI-generated patent content. For patent professionals and institutions, the findings indicate that LLMs are best positioned as assistive tools requiring human oversight rather than autonomous replacements, and that the central challenge going forward is not whether but how the patent system governs this transition. Description: Data availability: No data was used for the research described in the article.

Smart Bioinks for 4D Bioprinting: Requirements, Design, and Applications

2026-10-01T02:01:08Z

Title: Smart Bioinks for 4D Bioprinting: Requirements, Design, and Applications Authors: Chen, Shangsi; Lai, Jiahui; Zeng, Qiongjiao; Zhou, Liangbin; Yang, Boguang; Zhang, Bin; Wang, Min; Zhou, Jiajing; Lau, Kieran; Lim, Khoon S; Li, Zhong Alan; Tuan, Rocky S Abstract: 3D bioprinting is known for its high precision and reproducibility in fabricating complex and customized biomedical constructs. However, its applications are limited by their static nature; i.e., unlike native tissues, they cannot change shape or functionality over time. To overcome this, 4D bioprinting has emerged as a groundbreaking strategy by incorporating time as the fourth dimension, enabling dynamic structures that adapt in response to stimuli, thereby more accurately replicating living tissues. The success of 4D bioprinting hinges on the development of advanced smart bioinks, as their physicochemical properties uniquely dictate the shape-morphing behavior, functionality, and performance of bioprinted constructs. These bioinks must be precisely engineered to respond to specific stimuli. This review first introduces 4D bioprinting technologies for tissue engineering scaffolds. We then outline essential requirements for smart bioinks and highlight how AI, particularly machine learning, is revolutionizing their design. Additionally, we examine widely used biomaterials for 4D bioprinting and discuss promising candidates for 4D printing. We also present cutting-edge bioink applications in tissue engineering, drug screening, and disease modeling, showcasing their potential in regenerative medicine and personalized therapeutics. Finally, we discuss current challenges and future perspectives, underscoring the transformative impact of smart bioinks and 4D bioprinting on biomedical innovation. Description: Data Availability Statement: Data will be available upon reasonable request.; Supporting Information is avaoilable online at: https://advanced.onlinelibrary.wiley.com/doi/10.1002/advs.77791#support-information-section .