Science and Technology

Hard-to-abate industries such as cement, steel, chemicals, refining, and heavy transport account for a large share of global industrial emissions. These sectors rely on high-temperature heat, chemical reactions, or fossil-based feedstocks that are difficult to replace with electrification alone. As net-zero targets tighten, carbon capture has emerged as a central tool to manage emissions that cannot be eliminated through efficiency or fuel switching.Recent years have seen a shift from pilot-scale experimentation toward commercially oriented deployment. The trends below highlight how carbon capture is evolving to meet industrial, economic, and regulatory realities.From Single-Plant Projects to Industrial HubsOne of the primary movements…

Perovskite solar cells are a class of photovoltaic devices that employ a perovskite-structured compound as their light-harvesting layer. Throughout the past ten years, these systems have evolved from experimental laboratory novelties into major players within the worldwide solar industry. Their attractiveness stems from a blend of high power conversion efficiency, low manufacturing cost, and design flexibility. Laboratory efficiencies have surged swiftly, moving from under 4 percent during the early 2010s to exceeding 26 percent for single-junction cells, and climbing past 33 percent in tandem setups paired with silicon.This pace of improvement is unprecedented in solar technology and has triggered intense…

Electrolyzers split water into hydrogen and oxygen using electricity. Their economics determine whether clean hydrogen can compete with fossil-based alternatives across industry, transport, and power storage. Historically, high capital cost and modest efficiency limited deployment. Over the past decade, *materials science* has become the main lever for change, reshaping catalysts, membranes, electrodes, and structural components. The result is a steady drop in system cost per kilowatt and a rise in efficiency and lifetime.The main cost drivers in electrolyzersThe cost of an electrolyzer is not driven by any single component, so progress emerges from a range of material enhancements rather than…

Methane detection technology is advancing at an exceptionally swift rate, surpassing the progress seen in many other environmental monitoring systems, and this surge is far from random as it stems from a mix of climate pressures, regulatory demands, technological leaps, and economic motivations that position methane as both a major challenge and a manageable one, making it clear that these combined factors illuminate why innovation in this sector is accelerating more rapidly than ever.The Climate Impact of Methane Has Become Impossible to IgnoreMethane has driven about 30 percent of global warming since the preindustrial era, and although it remains in…

Neural Processing Units, commonly referred to as NPUs, are purpose-built processors engineered to boost artificial intelligence and machine learning tasks. While CPUs manage broad, general operations and GPUs deliver exceptional parallel graphics and computation, NPUs concentrate on matrix calculations, inference processes, and highly efficient on-device training. In addition to NPUs, the wider family of AI chips encompasses dedicated accelerators embedded within system-on-a-chip architectures or supplied as standalone components for PCs and servers.Over the last half decade, AI workloads have steadily moved away from cloud‑first computation and toward on-device intelligence. This transition is fueled by calls for reduced latency, heightened privacy,…

Neural Processing Units, commonly called NPUs, are specialized processors designed to accelerate artificial intelligence and machine learning workloads. Unlike CPUs, which handle general-purpose tasks, or GPUs, which excel at parallel graphics and compute operations, NPUs focus on matrix math, inference, and on-device learning with extreme efficiency. Alongside NPUs, the broader category of AI chips includes dedicated accelerators integrated into system-on-a-chip designs or offered as discrete components in PCs and servers.Over the past five years, AI workloads have shifted from cloud-centric processing toward on-device intelligence. This shift is driven by latency requirements, privacy concerns, and the need for energy efficiency. As…