Chinese scientists have discovered that fragile swamp forests in the Pearl River Delta (PRD) region suddenly collapsed around 2.1 thousand years ago (ka)—with human activity as the cause.

A new review paper in Engineering delves into silicon carbide (SiC)-based pressure sensors. SiC, a third-generation semiconductor, shows great potential for high-temperature applications. The report covers SiC’s material properties, key technologies in sensor development like piezoresistive effect, ohmic contact, etching, and packaging, along with future research directions.

In the realm of smart manufacturing and digital engineering, a new technology named Data-Model Fusion (DMF) is gaining traction. A review paper in Engineering details how DMF integrates model-based and data-driven methods, addresses their limitations, and finds applications across the product lifecycle. It also explores DMF’s future directions, showing its potential to reshape industrial processes.

In a recent study published in Engineering, researchers from Xi’an Jiaotong University have developed a self-adaptive core-shell dry adhesive with a “live core”. This new adhesive addresses the long-standing issue of weak adhesion under non-parallel contact in engineering operations, offering improved performance and potential applications in various fields such as robotic gripping and optical component assembly.

A recent study in Engineering reveals that bank filtration (BF) can be a highly effective pretreatment for gravity-driven membrane (GDM) filtration. This approach addresses common issues in GDM systems, such as poor permeate quality and low stable flux when treating polluted water, offering a sustainable and practical solution.

In a paper recently published in Chinese Physics Letters, a research team from Peking University report their latest discovery in the field of high-temperature superconductivity, revealing the existence of pair density modulation within a single unit cell of iron-based superconductors. This finding provides unprecedented microscopic insights into unconventional Cooper pairing mechanisms at the atomic scale.

On February 11, the team from the Data Darkness Lab (DDL) at the Medical Imaging Intelligence and Robotics Research Center of the University of Science and Technology of China (USTC) Suzhou Institut introduced a new out-of-core mechanism, Capsule, for large-scale GNN training, which can achieve up to a 12.02× improvement in runtime efficiency, while using only 22.24% of the main memory, compared to SOTA out-of-core GNN systems. This work published on ACM Journals.

Given the multitude of conditions that must be optimized in synthesis routes, chemical synthesis remains a complex and multidimensional challenge. The rapid development of computational guidelines and machine learning (ML) techniques has brought exciting hope to this dilemma. A new study published in the journal National Science Review highlights the advancement of computationally guided and ML-assisted approaches in inorganic material synthesis.