Stroke often results in significant upper limb impairment, affecting patients' ability to perform daily activities. Robotic therapy for individuals after stroke has been proven as evidence-based practice in rehabilitation because it allows high frequency and intensity of training to the individual with guided movement and standardized behavioural protocols. However, current rehabilitation methods are often limited by access to clinical facilities and high cost of robotic therapies.

Researchers from University of Shanghai for Science and Technology, China have developed a twisted double-layer graphene plasmonic metasurface that achieves unprecedented confinement of terahertz waves into nanoscale volumes, theoretically enabling fingerprint detection of molecular monolayers as thin as 1 nm. This system overcomes the critical challenge in terahertz sensing where the long wavelength (hundreds of micrometers) weakly interacts with nanoscale molecules. By engineering acoustic plasmon nanocavities through precise twist angles between graphene layers, the team demonstrated a mode volume as small as 10⁻¹³λ₀³ and sensitivity 48 times higher than conventional single-layer graphene and non-twist double-layer graphene structures. The platform provides a new insight for ultra-strong light-matter interaction at terahertz frequencies and opens possibilities for single-molecule spectroscopy and on-chip biosensing applications.

Ulcerative colitis (UC), a prevalent chronic inflammatory bowel disease, faces limitations in current treatments due to poor efficacy and side effects. Emodin (EMO), a natural anti-inflammatory compound, shows promise but is hindered by low solubility and bioavailability. A research team led by Prof. Xueye Chen from Ludong University developed a novel micromixer combined with machine learning to enable one-step synthesis of targeted emodin liposomes (Apt-EMO@Lip), significantly enhancing therapeutic efficiency and paving new avenues for UC treatment.

While artificial intelligence (AI) is designed to enhance the quality of our lives, there is an increase in AI-mediated harm. Since AI systems operate semi-autonomously, neither their developers nor users typically intend or foresee such harm. Thus, it seems absurd to hold AI systems and stakeholders responsible, giving rise to a responsibility gap. To address this gap, researchers have now developed new notions of responsibility tailored to cover unintended and unforeseen consequences caused by AI.