In a new study published in Engineering, researchers led by Xinxia Cai, Zhaojie Xu, and Yirong Wu explore the latest progress in intracortical neural interface technologies for freely moving animals. These technologies focus on four key areas, including higher spatial density, improved biocompatibility, enhanced multimodal detection, and more effective neural modulation, with potential to benefit neuroscience research and clinical applications.

Breakthrough in N₂ activation! This study unveils an electron catalysis strategy that directly converts N₂ into azo compounds under mild conditions—a game-changer for nitrogen fixation. By harnessing electron flow, it lowers activation energy from 3.44 eV to 0.14 eV, making azo synthesis faster, greener, and highly efficient. This innovative approach revolutionizes nitrogen chemistry, unlocking new possibilities for sustainable chemical synthesis.

A new study in Engineering explores the future of AI after large language models (LLMs). LLMs have their limits, so researchers are looking at knowledge empowerment, model collaboration, and model co-evolution. This article will explain these directions and their potential impacts, as well as future research trends in the AI field.

A new study in Engineering has developed an acoustofluidics-based method for intracellular nanoparticle delivery. This approach overcomes limitations of traditional methods, efficiently transports various nanomaterials into different cells, and offers potential benefits for therapeutic and biophysical research.

Women's health has long been neglected, causing delayed diagnoses and poor care. Now, AI is transforming women's healthcare by improving decision making, accuracy, and treatment outcomes.

A recent study in Engineering presents LearningEMS, a unified framework and open-source benchmark for electric vehicle (EV) energy management. It offers a platform to compare multiple EMS algorithms, evaluate their performance from various aspects, and aims to optimize EV energy efficiency.

The Hong Kong Polytechnic University (PolyU) is committed to promoting research excellence to address societal challenges and opportunities by providing strategic insights. In response to intensifying competition in the global shipping market, PolyU scholars have conducted a comprehensive study of Hong Kong’s shipping ecosystem and developed policy recommendations aimed at fostering collaborative efforts to enhance the shipping ecosystem within the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). This project aligns with the long-term initiatives outlined in the 2024 Chief Executive Policy Address and has been supported under the Strategic Public Policy Research Funding Scheme (SPPRFS) 2024/25 of the Chief Executive’s Policy Unit of the HKSAR Government. This year, only two out of 37 applications received funding.

A research team from Taiyuan University of Technology and Zhejiang University has made headway in enhancing polypropylene (PP)’s performance. Their study, published in Engineering, reveals that blending PP with high-density polyethylene (HDPE) and polystyrene–polyethylene–polypropylene–polystyrene (SEPS) to form a SEPS@HDPE core–shell structure can adjust PP’s brittle–ductile transition temperature and improve its low-temperature toughness, with potential applications across various industries.

This study explores the potential of oncomicrobial vaccines in mitigating tumor progression by targeting specific cancer-promoting microbes in mice. The researchers developed and tested vaccines against Campylobacter jejuni (C. jejuni) and enterotoxigenic Bacteroides fragilis (ETBF), both of which are associated with colorectal cancer (CRC). The vaccines effectively reduced the colonization of these oncomicrobes, suppressed tumor development, and mitigated inflammation without significantly disrupting the gut microbiota. The findings suggest that oncomicrobial vaccines could be a promising strategy for preventing and treating CRC, particularly in individuals at high risk due to family history or other factors.

Accurate estimation of the State of Charge (SoC) in lithium-ion batteries is a pivotal factor for optimizing the performance, safety, and longevity of electric vehicles (EVs). Traditional methods often fail to capture the dynamic, nonlinear nature of battery behavior, resulting in inaccuracies that undermine battery management systems (BMS). A new hybrid model that combines deep neural networks (DNNs) with a teaching-learning based optimization (TLBO) algorithm is set to revolutionize SoC estimation, offering a much-needed solution to enhance the efficiency and reliability of EVs.