As cities continue to expand and face rising environmental pressure, scientists are seeking innovative solutions that can help urban areas become cleaner, greener, and more resilient. A new perspective paper highlights biochar, a carbon rich material made from organic waste, as a transformative tool that could significantly improve the environmental future of cities around the world.

A common, charcoal-like material made from plant waste could become an unexpected ally in making nuclear technology cleaner and safer, according to a new review in the journal Biochar. The study highlights how “biochar” can help trap radioactive elements, improve radiation shielding, and support cleaner nuclear processes, all while storing carbon in the ground.

In an exciting exploration of environmental sustainability, researchers at Zhaoqing University, China, have uncovered groundbreaking insights into the carbon dynamics of waterlogged pond fields. Led by Dr. Guodong Yuan from the Guangdong Provincial Key Laboratory of Eco-Environmental Studies and Low-Carbon Agriculture in Peri-Urban Areas and the Guangdong Technology and Equipment Research Center for Soil and Water Pollution Control, this study, titled "Unveiling Carbon Dynamics in Year-Round Waterlogged Pond Fields: Insights into Soil Organic Carbon Accumulation and Sustainable Management," offers a fresh perspective on how these unique ecosystems can contribute to carbon sequestration and sustainable land management.

Over the last few years, systems and applications that help visually impaired people navigate their environment have undergone rapid development, but still have room to grow, according to a team of researchers at Penn State. The team recently combined recommendations from the visually impaired community and artificial intelligence (AI) to develop a new tool that offers support specifically tailored to the needs of people who are visually impaired. 

Researchers have demonstrated a new approach to building quantum convolutional neural networks (QCNNs) using photonic circuits, paving the way for more efficient quantum machine learning. The method, reported in Advanced Photonics, introduces an adaptive step called “state injection,” allowing the circuit to adjust its behavior based on real-time measurements. Using single photons and integrated quantum photonic processors, the team achieved over 92 percent classification accuracy on simple image patterns, closely matching theoretical predictions. This proof-of-concept shows that QCNNs can be implemented with existing photonic technology and highlights a path toward scalable quantum processors for future applications in AI and data processing.