Kyoto, Japan -- Shisei Tei claims he is clumsy with technology and doesn't even own a smartphone, yet he has found himself thinking a lot about what we call generative AI.

Tei is cautious rather than optimistic about AI. As a researcher, he uses it to help with analyzing psychiatric data, and outside work it helps him plan personalized hikes. But Tei is concerned that AI will change how we think about death, which he discusses in a chapter he wrote for the book SecondDeath: Experiences of Death Across Technologies.

"Today, I often see how AI reframes grief and remembrance," says Tei. Though he thinks mental health chatbots have the potential to lower barriers to care, maladaptive use of chatbots that reconstruct deceased individuals can distort our perceptions of death and existence.

Strategically coupling nanoparticle hybrids and internal thermosensitive molecular switches establishes an innovative paradigm for constructing micro/nanoscale-reconfigurable robots, facilitating energy-efficient CO₂ management in life-support systems of confined space. Here, a micro/nano-reconfigurable robot is constructed from the CO₂ molecular hunters, temperature-sensitive molecular switch, solar photothermal conversion, and magnetically-driven function engines. The molecular hunters within the molecular extension state can capture 6.19 mmol g⁻¹ of CO₂ to form carbamic acid and ammonium bicarbonate. Interestingly, the molecular switch of the robot activates a molecular curling state that facilitates CO₂ release through nano-reconfiguration, which is mediated by the temperature-sensitive curling of Pluronic F127 molecular chains during the photothermal desorption. Nano-reconfiguration of robot alters the amino microenvironment, including increasing surface electrostatic potential of the amino group and decreasing overall lowest unoccupied molecular orbital energy level. This weakened the nucleophilic attack ability of the amino group toward the adsorption product derivatives, thereby inhibiting the side reactions that generate hard-to-decompose urea structures, achieving the lowest regeneration temperature of 55 °C reported to date. The engine of the robot possesses non-contact magnetically-driven micro-reconfiguration capability to achieve efficient photothermal regeneration while avoiding local overheating. Notably, the robot successfully prolonged the survival time of mice in the sealed container by up to 54.61%, effectively addressing the issue of carbon suffocation in confined spaces. This work significantly enhances life-support systems for deep-space exploration, while stimulating innovations in sustainable carbon management technologies for terrestrial extreme environments.

In a remarkable stride towards environmental sustainability, researchers at the Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, India, have developed a novel approach to predict the adsorption capacity of biochar using machine learning. This breakthrough, detailed in their latest study titled "Machine Learning-Driven Prediction of Biochar Adsorption Capacity for Effective Removal of Congo Red Dye," offers a powerful solution to combat dye pollution.