A research team has developed a visible-light photocatalyst that nearly doubled hydrogen production by decorating cadmium sulfide (CdS) nanoparticles with carbon quantum dots (CQDs) derived from cellulose.

Expensive high-resolution spectral cameras hold back precise crop monitoring for farmers. A new unsupervised AI network called UDAMSR Net offers a cheap software fix. It upgrades blurry low-resolution aerial and ground crop images without matching high-quality training data. Tested on wheat across two farm sites, the tool sharpens leaf textures, preserves true spectral signals, and restores accurate chlorophyll prediction results across different sensors and flight heights. Keep reading to learn how this model cuts hardware costs for agricultural remote sensing.

Autonomous vehicles promise greener, smoother urban travel, yet their environmental pros and cons are far more complicated than many assume. This new analysis from Engineering breaks down dual carbon trade-offs: AVs cut traffic emissions via platooning and shared fleets, but heavier electric trucks and repeated vehicle platoons speed up road wear and raise infrastructure carbon costs. It also outlines targeted vehicle, road and policy fixes to balance efficiency gains and infrastructure burdens for sustainable automated mobility.

Want to test soil fertility instantly on farmland without sending samples to labs? This new Engineering commentary breaks down four mainstream field sensors tracking soil nutrients, from portable colorimetric kits to laser and γ-ray detectors. Each tool carries unique flaws: model failure across farmlands, air element interference and heavy calibration needs. Scientists now turn to AI to boost sensor performance, targeting simple, buryable detectors that measure crop-absorbable nitrogen on-site for smarter precision farming.