A study published in Frontiers in Energy by researchers from Harbin Institute of Technology, Shenzhen University, Ningbo Institute of Materials Technology and Engineering, and Nanyang Technological University addresses these challenges. They developed a multi-physical model to analyze and optimize the operation of large-area SOFCs.

In a study published in Frontiers in Energy, Frederik Wiesmann and collaborators from TU Wien, Shanghai Jiao Tong University (SJTU) and Friedrich-Alexander-Universität Erlangen-Nürnberg introduced a refined oxidation mechanism for OME_1-6. The new SJTU mechanism modifies key reaction rates in the Niu mechanism based on sensitivity analysis and validation against jet-stirred reactor (JSR) experiments and shock tube IDT data from the literature. This mechanism improves the prediction of intermediate species and ignition behavior while maintaining compatibility with CFD frameworks.

A research paper by scientists at Beijing Friendship Hospital develop a systematic approach for automated ossicular-chain segmentation and measurement using ultra-high-resolution computed tomography (U-HRCT).

This article explores the potential of large language models (LLMs) in reliability systems engineering, highlighting their applications in improving industrial efficiency and flexibility. It also examines the challenges, including data deficiencies and complexity, and suggests future directions for integrating LLMs into complex engineering processes.

A new study in Engineering introduces a novel MRI technique enabling real-time, artifact-free navigation of magnetic robots. Using a multi-frequency dual-echo sequence, researchers achieved a 30-millisecond repetition time, allowing precise control and high-resolution imaging. Demonstrations in maze, phantom vessel, and in vivo trials highlight its potential for minimally invasive therapies.

New review shows that guiding pollutant motion with electric fields and 3D-printed hierarchical pores can slash fan energy and double removal rates, offering a practical blueprint for next-generation, low-resistance air purifiers.

A team of Chinese researchers has developed an AI-based modeling approach that revolutionizes the prediction of complex nonlinear dynamics in Kerr resonators. By leveraging recurrent neural networks (RNNs)—specifically gated recurrent units (GRUs)—and a hybrid convolutional neural network (CNN)-GRU model for complex scenarios, the team achieved nearly 20x faster simulations than traditional methods, while maintaining high accuracy. The work paves the way for faster design of next-generation optical systems, from optical memories to all-optical computers.

Pressure sensors are essential for a wide range of applications, including health monitoring, industrial diagnostics, etc. However, achieving both high sensitivity and mechanical ability to withstand high pressure in a single material remains a significant challenge. This study introduces a high-performance cellulose hydrogel inspired by the biomimetic layered porous structure of human skin. The hydrogel features a novel design composed of a soft layer with large macropores and a hard layer with small micropores, each of which contribute uniquely to its pressure-sensing capabilities. The macropores in the soft part facilitate significant deformation and charge accumulation, providing exceptional sensitivity to low pressures. In contrast, the microporous structure in the hard part enhances pressure range, ensuring support under high pressures and preventing structural failure. The performance of hydrogel is further optimized through ion introduction, which improves its conductivity, and as well the sensitivity. The sensor demonstrated a high sensitivity of 1622 kPa⁻¹, a detection range up to 160 kPa, excellent conductivity of 4.01 S m⁻¹, rapid response time of 33 ms, and a low detection limit of 1.6 Pa, outperforming most existing cellulose-based sensors. This innovative hierarchically porous architecture not only enhances the pressure-sensing performance but also offers a simple and effective approach for utilizing natural polymers in sensing technologies. The cellulose hydrogel demonstrates significant potential in both health monitoring and industrial applications, providing a sensitive, durable, and versatile solution for pressure sensing.