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The dry reforming of methane (DRM) is a recognized technique for transforming carbon dioxide (CO₂) and methane (CH₄) into synthesis gas (syngas), an important blend of hydrogen (H₂) and carbon monoxide (CO). This procedure is generally carried out with a feed ratio of CO₂ to CH₄ near one. Nevertheless, upcoming methane sources like carbon dioxide-laden natural gas are anticipated to have significantly elevated concentrations of CO₂. These high concentrations frequently necessitate expensive separation methods to achieve the desired methane level. 

 

In research featured in Nature Chemistry, a team led by Professors Guoxiong Wang, Jianping Xiao, and Xinhe Bao from the Dalian Institute of Chemical Physics at the Chinese Academy of Sciences presented a novel approach for the direct production of syngas. This method, known as super dry reforming of methane, functions with a CO₂ to CH₄ ratio of two or higher. It allows for the direct transformation of CO₂-laden natural gas via high-temperature tandem electro-thermocatalysis employing solid oxide electrolysis cells (SOECs). 

 

These electrolysis cells operate at elevated temperatures between 600 and 850 degrees Celsius, enabling them to transform carbon dioxide and water into carbon monoxide and hydrogen. Their benefits encompass rapid reaction rates, excellent energy efficiency, and comparatively minimal operating expenses. Consequently, they present considerable opportunities for carbon dioxide utilization, hydrogen generation, and storage of renewable energy. 

It appears that trees can alert us when a volcano is about to erupt. Researchers have long been aware that alterations in tree foliage, such as appearing greener or more vibrant, can indicate nearby volcanic activity. Thanks to an exhilarating partnership between NASA and the Smithsonian Institution, researchers now think they can detect these changes from orbit. 

 

As magma rises through the Earth’s crust, it emits gases such as carbon dioxide. Trees take in this carbon dioxide, and as a result, their leaves frequently appear more vibrant and healthy. By employing advanced instruments such as NASA’s Landsat 8 satellite and airborne devices utilized in the Airborne Validation Unified Experiment: Land to Ocean (AVUELO), researchers can now identify these subtle indicators from the sky. 

Approximately 10 percent of the world's population resides in regions that are vulnerable to volcanic threats. For individuals residing near volcanoes, an eruption can pose severe threats—such as airborne debris, heavy ash, and currents of burning gases. Even individuals located at a greater distance can encounter significant hazards such as mudslides, ash fall, or even tsunamis caused by volcanic eruptions. 

 

As there is no method to prevent a volcanic eruption, detecting the initial signs of activity is crucial for ensuring public safety. The U.S. Geological Survey states that the United States is one of the most volcanically active nations globally. 

 

Prior to a volcanic eruption, ascending magma emits a combination of gases, such as carbon dioxide and sulfur dioxide. Although sulfur dioxide can be more readily detected from space, the carbon dioxide released earlier in the process is significantly more challenging to identify. Nevertheless, this initial emission of carbon dioxide may provide one of the most distinct early indicators that a volcano is becoming active. 

The remote sensing of carbon dioxide plant greening possibly provides researchers an additional method—together with seismic waves and alterations in ground elevation—to gain clarity about the happenings beneath the volcano. "Systems for early volcano warning are in place," stated volcanologist Florian Schwandner, head of the Earth Science Division at NASA’s Ames Research Center in California’s Silicon Valley, who collaborated with climate scientist Josh Fisher from Chapman University in Orange, California, and volcanologist Robert Bogue from McGill University in Montreal ten years prior. "The goal here is to improve them and expedite their arrival." 

 

“Volcanoes release significant amounts of carbon dioxide,” stated Bogue, yet the sheer volume of carbon dioxide already in the atmosphere makes it challenging to isolate and quantify the carbon dioxide from volcanoes specifically. Although significant eruptions can release sufficient carbon dioxide to be detectable from space using instruments such as NASA’s Orbiting Carbon Observatory 2, identifying these subtler, early warning signals has proven challenging. “A volcano releasing the small quantities of carbon dioxide that could indicate an impending eruption won’t appear in satellite images,” he mentioned. 

 

As a result, researchers need to journey to volcanoes to directly assess carbon dioxide levels. Nevertheless, a significant number of the approximately 1,350 possibly active volcanoes globally are situated in isolated areas or difficult mountainous landscapes. This renders monitoring carbon dioxide at these locations demanding, costly, and occasionally hazardous. 

Volcanologists such as Bogue have collaborated with botanists and climate scientists to examine trees for tracking volcanic activity. “The main concept is to identify a factor that we can quantify rather than measuring carbon dioxide directly,” Bogue stated, “to provide us a substitute for monitoring variations in volcanic emissions.” 

 

"Numerous satellites are available for conducting this type of analysis," stated volcanologist Nicole Guinn from the University of Houston. She has analyzed images obtained from Landsat 8, NASA's Terra satellite, ESA’s Sentinel-2, and other Earth-observing satellites to track trees near the Mount Etna volcano on Sicily's coast. Guinn's research is the first to demonstrate a significant connection between the color of tree leaves and carbon dioxide produced by magma. 

 

Confirming the precision of satellite imagery through ground validation is a challenge that Fisher is addressing with tree surveys near volcanoes. In March 2025, during the Airborne Validation Unified Experiment: Land to Ocean mission, scientists from NASA and the Smithsonian Institution used a research plane equipped with a spectrometer to study the colors of vegetation in Panama and Costa Rica. 

 

Fisher led a team of researchers who gathered leaf samples from trees close to the active Rincon de la Vieja volcano in Costa Rica and measured carbon dioxide levels. "Our study represents a reciprocal interdisciplinary merger of ecology and volcanology," Fisher stated. “We're keen on understanding both tree reactions to volcanic carbon dioxide as a preliminary signal of an eruption and the extent to which trees can absorb it, offering insights into the future of our planet when all trees encounter elevated carbon dioxide levels.” 

Envision donning a T-shirt that tracks your respiration or gloves that convert your hand gestures into instructions for your computer. Scientists at ETH Zurich, under the guidance of Daniel Ahmed, a Professor of Acoustic Robotics in Life Sciences and Healthcare, have established the groundwork for these advanced textiles. 

 

In contrast to numerous earlier advancements in this field that typically utilize electronics, the ETH researchers depend on acoustic waves transmitted through glass fibers. This enhances the accuracy of the measurements and results in textiles that are lighter, more breathable, and simpler to clean. "They are also affordable since we utilize easily accessible materials, and the energy usage is minimal," states Ahmed. 

 

The researchers refer to their creation as SonoTextiles. They have converted ordinary materials into intelligent sensors that respond to touch, pressure, and motion. "Although studies have been done on acoustic-based smart textiles, we are pioneering the investigation of glass fiber combined with signals that operate at varying frequencies," says Yingqiang Wang. 

 

The scientists have integrated glass fibers into the material at consistent intervals. At one end of every glass fiber, there is a tiny transmitter that releases sound waves. At the opposite end of every glass fiber, there is a receiver that gauges if the waves have altered. 

Within our research team, we have been investigating how adaptable frameworks—thin polymer films—can transform the energy from surrounding currents into electricity by employing piezoelectric substances. These substances produce an electrical signal when physically altered. Consider them as energy interpreters—transforming oscillation and vibration into electrical voltage. 

 

Our project centers on a straightforward concept: affix a pliable plate with a piezoelectric film to the trailing side of a cylinder and subject it to airflow. When the wind moves around the cylinder, it makes the connected plate wave—similar to a flag. 

 

What stands out is the system's dynamic behavior. At reduced speeds, the plate undergoes weak, irregular motion. However, as wind speed rises, the system transitions into a lock-in state—a resonance effect where the oscillation frequency of the plate aligns with the frequency of vortex shedding. In this regime, we witness significant amplitude, regular oscillations that greatly enhance the strain on the piezoelectric material and, as a result, the electrical output. 

 

To provide context, numerous previous devices in this category recorded merely a few microwatts of power at comparable wind speeds. Through adjustments to the plate's thickness, length, and flexibility, combined with accurate alignment of the electrical resistance in the circuit, we successfully increased the power output by two to three orders of magnitude. 

 

To confirm practical viability, we built a rectifier and storage circuit, showing that the collected power could continuously power up to 20 LEDs. Up to 40 LEDs might be briefly illuminated using accumulated charge. These findings indicate a distinct possibility for self-sustaining low-energy gadgets, like environmental monitors or wireless nodes in distant or difficult-to-access locations. That being noted, significant challenges persist—especially in enhancing energy conversion efficiency and refining the design for effective integration. 

 

What thrills us the most is the straightforwardness and expandability of this method. In contrast to conventional turbines, these harvesters lack rotating components, require little maintenance, and can be effortlessly incorporated into urban or natural settings. As the globe searches for more efficient, compact, and eco-friendly methods of energy generation, this flutter-driven harvester could indeed have the wind in its favor. 

 

 

AI chatbots developed by tech firms like OpenAI and Google have received what are known as reasoning enhancements in recent months—intended to improve their reliability in providing trustworthy answers—but recent evaluations indicate that they sometimes perform worse than older versions. The mistakes made by chatbots, referred to as “hallucinations,” have been an issue from the beginning, and it is increasingly evident that we might never eliminate them. 

 

Hallucination is a general term describing specific types of errors made by the large language models (LLMs) that drive systems such as OpenAI’s ChatGPT or Google’s Gemini. It is most recognized as an explanation of how they occasionally portray false information as accurate. However, it may also pertain to an AI-generated response that is factually correct but not pertinent to the question posed or does not adhere to instructions in another manner. 

 

A technical report from OpenAI assessing its newest LLMs revealed that the o3 and o4-mini models, launched in April, exhibited notably greater hallucination rates compared to the earlier o1 model that was released in late 2024. For instance, when condensing publicly accessible information about individuals, o3 made errors 33 percent of the time, whereas o4-mini did so 48 percent of the time. In contrast, o1 exhibited a hallucination rate of 16 percent. 

 

The issue extends beyond just OpenAI. A well-known leaderboard from the company Vectara, which evaluates hallucination rates, shows that certain “reasoning” models – such as the DeepSeek-R1 model created by DeepSeek – experienced double-digit increases in hallucination rates when compared to their earlier models. This kind of model follows several stages to illustrate a line of thought before giving a response.