A fully automated warehouse system is changing the way we shop for groceries. GROCERIES IN 5 MIN: Imagine a grocery store where your entire order is picked, packed and ready for delivery in just five minutes without a single human hand touching your food. BRAVE NEW WORLD: Anthropic – the company behind the artificial intelligence platform Claude – anticipates that digital AI employees will appear on corporate networks in the next year, the organization's top security leader informed Axios. THESE FUELS ARE OUT: Imagine powering your boat not with gasoline but with clean hydrogen fuel. That's exactly what Yamaha, together with Roush Industries and Regulator Marine, is working on right now.

Kawasaki's CORLEO is a hydrogen-powered, AI-driven rideable robot. Kawasaki Heavy Industries has introduced something that feels straight out of a video game: CORLEO, a hydrogen-powered, four-legged robot prototype designed to be ridden by humans. Unveiled at the Osaka-Kansai Expo 2025, this futuristic machine is built to handle rugged terrain with ease, combining cutting-edge robotics and sustainable energy. Let's take a closer look at what makes CORLEO so cutting-edge. GET SECURITY ALERTS & EXPERT TECH TIPS – SIGN UP FOR KURT'S'THE CYBERGUY REPORT' NOW Instead of wheels, it has four robotic legs that move independently, allowing it to handle uneven ground like rocks, grass and steep inclines.

If you thought robot dogs were the coolest animatronic animals out there, prepare to think again. Kawasaki Heavy Industries, a company better known for its high-end motorcycles, has unveiled a hydrogen-powered, ride-on robo-horse. The bizarre device was unveiled at the Osaka Kansai Expo on April 4 as part of Kawasaki's'Impulse to Move' project. Dubbed the CORLEO, this two-seater quadruped is capable of galloping over almost any terrain. The company calls it a'revolutionary off-road personal mobility vehicle' which swaps out the familiar wheels for four robotic legs. To steer, all you need to do is move your body and the machine's AI vision will pick out the best route to take.

In this research, renewable energy expansion in South America up to 2050 is predicted based on machine learning models that are trained on past energy data. The research employs gradient boosting regression and Prophet time series forecasting to make predictions of future generation capacities for solar, wind, hydroelectric, geothermal, biomass, and other renewable sources in South American nations. Model output analysis indicates staggering future expansion in the generation of renewable energy, with solar and wind energy registering the highest expansion rates. Geospatial visualization methods were applied to illustrate regional disparities in the utilization of renewable energy. The results forecast South America to record nearly 3-fold growth in the generation of renewable energy by the year 2050, with Brazil and Chile spearheading regional development. Such projections help design energy policy, investment strategy, and climate change mitigation throughout the region, in helping the developing economies to transition to sustainable energy.

The transition to a low-carbon economy demands efficient and sustainable energy-storage solutions, with hydrogen emerging as a promising clean-energy carrier and with metal hydrides recognized for their hydrogen-storage capacity. Here, we leverage machine learning (ML) to predict hydrogen-to-metal (H/M) ratios and solution energy by incorporating thermodynamic parameters and local lattice distortion (LLD) as key features. Our best-performing ML model provides improvements to H/M ratios and solution energies over a broad class of ternary alloys (easily extendable to multi-principal-element alloys), such as Ti-Nb-X (X = Mo, Cr, Hf, Ta, V, Zr) and Co-Ni-X (X = Al, Mg, V). Ti-Nb-Mo alloys reveal compositional effects in H-storage behavior, in particular Ti, Nb, and V enhance H-storage capacity, while Mo reduces H/M and hydrogen weight percent by 40-50%. We attributed to slow hydrogen kinetics in molybdenum rich alloys, which is validated by our pressure-composition isotherm (PCT) experiments on pure Ti and Ti5Mo95 alloys. Density functional theory (DFT) and molecular simulations also confirm that Ti and Nb promote H diffusion, whereas Mo hinders it, highlighting the interplay between electronic structure, lattice distortions, and hydrogen uptake. Notably, our Gradient Boosting Regression model identifies LLD as a critical factor in H/M predictions. To aid material selection, we present two periodic tables illustrating elemental effects on (a) H2 wt% and (b) solution energy, derived from ML, and provide a reference for identifying alloying elements that enhance hydrogen solubility and storage.

Disruptions in energy imports, backlash in social acceptance, and novel technologies failing to develop are unexpected events that are often overlooked in energy planning, despite their ability to jeopardize the energy transition. We propose a method to explore unexpected events and assess their impact on the transition pathway of a large-scale whole-energy system. First, we evaluate unexpected events assuming "perfect foresight", where decision-makers can anticipate such events in advance. This allows us to identify dealbreakers, i.e., conditions that make the transition infeasible. Then, we assess the events under "limited foresight" to evaluate the robustness of early-stage decisions against unforeseen unexpected events and the costs associated with managing them. A case study for Belgium demonstrates that a lack of electrofuel imports in 2050 is the main dealbreaker, while accelerating the deployment of renewables is the most robust policy. Our transferable method can help policymakers identify key dealbreakers and devise robust energy transition policies.

While the Water-Gas Shift (WGS) reaction plays a crucial role in hydrogen production for fuel cells, finding suitable catalysts to achieve high yields for low-temperature WGS reactions remains a persistent challenge. Artificial Intelligence (AI) has shown promise in accelerating catalyst design by exploring vast candidate spaces, however, two key gaps limit its effectiveness. First, AI models primarily train on numerical data, which fail to capture essential text-based information, such as catalyst synthesis methods. Second, the cross-disciplinary nature of catalyst design requires seamless collaboration between AI, theory, experiments, and numerical simulations, often leading to communication barriers. To address these gaps, we present AceWGS, a Large Language Models (LLMs)-aided framework to streamline WGS catalyst design. AceWGS interacts with researchers through natural language, answering queries based on four features: (i) answering general queries, (ii) extracting information about the database comprising WGS-related journal articles, (iii) comprehending the context described in these articles, and (iv) identifying catalyst candidates using our proposed AI inverse model. We presented a practical case study demonstrating how AceWGS can accelerate the catalyst design process. AceWGS, built with open-source tools, offers an adjustable framework that researchers can readily adapt for a range of AI-accelerated catalyst design applications, supporting seamless integration across cross-disciplinary studies.

The vehicle traveled 1,806 miles on a single tank of hydrogen. Hydrogen-powered trucks are making waves in the world of clean transportation, and the H2Rescue truck just set a new Guinness World Record to prove it. This impressive vehicle, developed by Cummins Accelera in collaboration with the U.S. Department of Energy and Department of Defense, traveled an astounding 1,806 miles on a single tank of hydrogen. The H2Rescue truck embarked on its record-setting trip in California, carrying 386 pounds of hydrogen fuel. Throughout the journey, it navigated rush hour traffic, maintained speeds between 50 and 55 mph and operated in temperatures ranging from 60 to 80 degrees Fahrenheit.

Recently, many studies have increasingly explored the use of large language models (LLMs) to generate research ideas and scientific hypotheses. However, real-world research and development often require solving complex, interdisciplinary challenges where solutions may not be readily found through existing knowledge related to the problem. Therefore, it is desirable to leverage the vast, comprehensive knowledge of LLMs to generate effective, breakthrough solutions by integrating various perspectives from other disciplines. Here, we propose SELLM (Solution Enumeration via comprehensive List and LLM), a framework leveraging LLMs and structured guidance using MECE (Mutually Exclusive, Collectively Exhaustive) principles, such as International Patent Classification (IPC) and the periodic table of elements. SELLM systematically constructs comprehensive expert agents from the list to generate cross-disciplinary and effective solutions. To evaluate SELLM's practicality, we applied it to two challenges: improving light extraction in organic light-emitting diode (OLED) lighting and developing electrodes for next-generation memory materials. The results demonstrate that SELLM significantly facilitates the generation of effective solutions compared to cases without specific customization or effort, showcasing the potential of SELLM to enable LLMs to generate effective solutions even for challenging problems.

Panasonic has started a test run of facilities installed at a microwave oven plant in the United Kingdom to allow the plant to run solely on renewable energy. Panasonic installed a system to generate power using green hydrogen, produced without causing carbon dioxide emissions. It is the world's first power generation system using pure hydrogen fuel cells that run on green hydrogen, according to the company. At the plant in Cardiff, Wales, operations powered solely by renewable energy will begin in March next year. Using hydrogen sourced in Wales, the power generation system combines 21 fuel cell generators, two lithium storage batteries and existing solar panels.