Scientists crack a decades-old CO2 problem and triple fuel production

Converting carbon dioxide (CO₂) into methanol is widely viewed as a promising way to recycle carbon resources. However, scientists have long faced a difficult challenge when trying to improve the process.

At lower temperatures, converting CO2 into methanol is thermodynamically favorable. The problem is that CO2 becomes difficult to activate under these conditions, resulting in weak catalytic performance. Raising the temperature speeds up the reaction, but it also encourages a competing process known as the reverse water-gas shift reaction, which produces unwanted byproducts and lowers methanol selectivity. This persistent trade-off between catalytic activity and selectivity has limited progress in increasing methanol yields.

New Catalyst Design Overcomes Long-Standing Trade-Off

In a study published in Chem, researchers led by Prof. Jian Sun and Prof. Jiafeng Yu of the Dalian Institute of Chemical Physics (DICP) at the Chinese Academy of Sciences (CAS) developed a new catalyst design aimed at addressing this challenge.

Their approach uses a strong metal-support interaction (SMSI)-driven overlayer structure to spatially separate active sites within the catalyst. This design allows different reaction steps to occur in different locations, improving the efficiency of methanol production from CO₂.

By restructuring the catalyst surface and changing how reactants adsorb, dissociate, and move through the reaction pathway, the team achieved a space-time yield of 1.2 g·g_cat^-1·h^-1 at 300 ℃ and 3 MPa. That performance is approximately three times higher than that of conventional commercial Cu/Zn/Al catalysts.

Redirecting CO₂ Toward Methanol

The researchers found that their catalyst encourages CO₂ to adsorb and activate primarily on zirconia (ZrO₂) sites. This steers the reaction toward methanol production through the formate pathway.

In conventional Cu-based catalysts, activation typically begins by breaking the C=O bond before hydrogenation occurs. The new strategy follows a different sequence. Hydrogenation takes place first on ZrO₂ sites, and C=O bond cleavage occurs afterward.

According to the researchers, this change in reaction mechanism significantly reduces the formation of carbon monoxide (CO) byproducts while preserving the strong ability of Cu sites to dissociate H₂ efficiently.

"Our study may provide a new pathway to addressing the long-standing trade-off between activity and selectivity in methanol synthesis from CO₂," said Prof. Sun.

Journal Reference:

1. Habib Zada, Jiafeng Yu, Chuanyan Fang, Jian Sun. Disentangling the activity-selectivity trade-off in CO2 hydrogenation to methanol. Chem, 2026; 102942 DOI: 10.1016/j.chempr.2026.102942

Courtesy:

Dalian Institute of Chemical Physics, Chinese Academy Sciences. "Scientists crack a decades-old CO2 problem and triple fuel production." ScienceDaily. ScienceDaily, 14 June 2026. <www.sciencedaily.com/releases/2026/06/260613034234.htm>. 

 

 

New AI body map reveals obesity’s hidden attack on facial nerves

 

Researchers at Helmholtz Munich, Ludwig Maximilians University Munich (LMU), and several partner institutions have created an artificial intelligence (AI) system capable of mapping disease-related changes throughout an entire mouse body at cellular-level detail. Using the new platform, known as MouseMapper, the team discovered widespread inflammation and previously unknown nerve damage linked to obesity.

The study also identified similar molecular patterns in human tissue, suggesting that important aspects of obesity-related nerve damage may occur in both mice and people. The findings were published in the journal Nature.

Obesity is known to affect much more than body weight and metabolism. It can alter immune activity, disrupt nerve structures, and reshape tissues throughout the body, increasing the risk of conditions such as type 2 diabetes, cardiovascular disease, stroke, neuropathy, and cancer. Despite these widespread effects, scientists have lacked tools capable of studying disease-related changes across an entire intact body in high detail.

To address that challenge, a research team led by Prof. Ali Ertürk, Director of the Institute for Biological Intelligence (iBIO) at Helmholtz Munich and Professor at LMU, developed MouseMapper. The AI framework uses foundation-model-based deep learning algorithms to analyze massive whole-body imaging datasets.

The system can automatically identify and segment 31 organs and tissue types while also mapping nerves and immune cells throughout the body. This allows researchers to examine how diseases affect multiple organ systems at the same time in intact mice.

"MouseMapper is built on a foundation model, which means it generalizes far beyond the data it was originally trained on," says Ying Chen, co-first author of the study.

Transparent Mice and Whole-Body Imaging

To build the body maps, researchers first tagged nerves and immune cells in mice using fluorescent markers that glow under a microscope. They then used tissue-clearing methods to make the mice transparent while preserving the fluorescent signals, allowing scientists to see deep inside the body without cutting tissues apart.

Next, the team used advanced light-sheet microscopy to capture detailed three-dimensional images of entire mice. The process generated enormous datasets containing tens of millions of cellular structures from organs and tissues across the body.

MouseMapper then analyzed the images automatically, identifying anatomical regions, nerve networks, and immune-cell clusters throughout the animals.

This approach allowed the researchers to pinpoint exactly where inflammation and tissue damage appeared in organs such as fat tissue, muscle, liver, and peripheral nerves. Unlike earlier methods, scientists did not need to choose specific regions to study beforehand.

Obesity Linked to Facial Nerve Damage

To explore how obesity changes the body, the researchers fed mice a high-fat diet that produced obesity and metabolic problems similar to those seen in humans.

Using MouseMapper, the team found widespread alterations in immune-cell organization and nerve structures across the body. One of the most surprising discoveries involved the trigeminal nerve, a major facial nerve responsible for facial sensation and certain motor functions.

In obese mice, these sensory nerves showed a major reduction in branches and nerve endings, suggesting impaired nerve function. Behavioral tests supported that conclusion, showing that obese mice were less responsive to sensory stimulation compared to lean mice.

The researchers then focused on the trigeminal ganglion, which contains the cell bodies of facial sensory neurons. Through spatial proteomics analysis, they identified molecular changes linked to inflammation and nerve remodeling.

Importantly, many of the same molecular signatures were also found in trigeminal tissue from people with obesity. This suggests that the nerve-related changes observed in mice may also occur in humans.

"We revealed previously unknown structural and molecular changes in the trigeminal ganglion and its facial branches, and the same molecular signature was conserved in human tissue. This kind of finding simply cannot emerge from studying one organ at a time," says Dr. Doris Kaltenecker, senior scientist at the Institute for Diabetes and Cancer (IDC) at Helmholtz Munich and first author of the study.

A New Tool for Studying Complex Diseases

The researchers believe MouseMapper could become an important tool for studying diseases that affect many organ systems simultaneously, including diabetes, cancer, neurodegenerative diseases, and autoimmune disorders.

Unlike earlier approaches focused on individual tissues or organs, MouseMapper provides an integrated whole-body analysis system that can identify disease hotspots throughout an organism.

The team has also made the whole-body datasets publicly available online so researchers around the world can explore obesity-related changes across organs and tissues.

"Our goal is to create a comprehensive framework for understanding how diseases affect the body as an interconnected system," says Ali Ertürk. "Our long-term vision is to build truly realistic digital twins of mice in health and disease: cell-level atlases that we can query, perturb and screen in silico computationally. That would let us pinpoint the earliest changes a disease causes, design interventions to prevent them, and accelerate the discovery of new treatments while reducing the number of physical experiments we need to run."

The work was supported by the European Research Council (Consolidator Grant CALVARIA to A. Ertürk; grant 949017 to M. Rohm), the German Research Foundation (DFG) under Germany's Excellence Strategy within the Munich Cluster for Systems Neurology (SyNergy, ID 390857198, EXC 2145), DFG SFB 1052 (A9) and TR 296 (P03), the Collaborative Research Centre CRC 1744, the German Federal Ministry of Education and Research (NATON collaboration, 01KX2121, and HIVacToGC), the Vascular Dementia Research Foundation, the Nomis Heart Atlas Project Grant (Nomis Foundation), the Else-Kröner-Fresenius-Stiftung, the Edith-Haberland-Wagner Stiftung, the Helmut Horten Foundation, the EFSD and Novo Nordisk A/S Programme for Diabetes Research in Europe (to D. Kaltenecker), and the China Scholarship Council (to Y. Chen).

Journal Reference:

1. Doris Kaltenecker, Izabela Horvath, Rami Al-Maskari, Ying Chen, Zeynep Ilgin Kolabas, Luciano Hoeher, Mihail Todorov, David-Paul Minde, Saketh Kapoor, Sena Gül Turhan, Louis B. Kuemmerle, Hanno Steinke, Tim Wohlgemuth, Mayar Ali, Florian Kofler, Pauline Morigny, Julia Geppert, Denise Jeridi, Bastian Wittmann, Jie Luo, Suprosanna Shit, Carolina Cigankova, Victor Miro Kolenic, Nilsu Gür, Eren Aydeniz, Alara Yücecan, Melissa Ertürk, Laurent H. A. Simons, Chenchen Pan, Marie Piraud, Daniel Rueckert, Maria Rohm, Farida Hellal, Markus Elsner, Harsharan Singh Bhatia, Ingo Bechmann, Bjoern H. Menze, Stephan Herzig, Johannes Christian Paetzold, Mauricio Berriel Diaz, Ali Ertürk. A deep-learning framework reveals whole-body perturbations at cell level. Nature, 2026; DOI: 10.1038/s41586-026-10535-2

Courtesy:

Helmholtz Munich (GmbH). "New AI body map reveals obesity’s hidden attack on facial nerves." ScienceDaily. ScienceDaily, 23 May 2026. <www.sciencedaily.com/releases/2026/05/260522023308.htm>.