A study published in the Journal of Bioresources and Bioproducts presents a novel method to improve the anti-fibrillation and multifunctional properties of lyocell fabric through electrostatic adsorption and discontinuous membrane formation. This sustainable approach enhances fabric durability and dyeability without compromising mechanical properties.

Researchers from RISE PFI AS in Norway have successfully utilized Kraft lignin as a wet-strength agent in molded pulp materials, achieving significant improvements in both dry and wet strength. The study, published in the Journal of Bioresources and Bioproducts, highlights the potential of lignin to replace plastic packaging while maintaining recyclability and biodegradability.

The researchers extracted 10 principal regulatory modules from the whole-genome data and ranked them in descending order of binding energy.

The automotive industry is undergoing a transformative shift toward sustainable propulsion systems to meet stringent environmental regulations, such as the European Union’s push for near-zero-emission vehicles. Among the promising alternatives, proton exchange membrane fuel cells (PEMFCs) stand out due to their high efficiency and rapid response times. However, optimizing PEMFC performance requires advanced turbocharging systems to supply compressed air to the fuel cell stack. Centrifugal compressors, a key component in these systems, face challenges in maintaining stable operation across varying conditions, particularly near surge limits—a phenomenon causing disruptive flow fluctuations. To address this, researchers have explored passive flow control methods, with the ported shroud emerging as a critical innovation for extending compressor operability.

The global push toward sustainable transportation has made electric buses (E-buses) a key solution for reducing greenhouse gas emissions. However, their widespread adoption faces challenges, particularly in cold climates where battery efficiency drops significantly. Canada, with its harsh winters, presents a unique case study for optimizing E-bus operations, yet existing energy consumption models perform poorly in such conditions. A new study addresses this gap by developing advanced machine learning models tailored to Canadian data, offering precise predictions for E-bus energy use under varying climates and heating systems.