Science
How a Record-Breaking Copper Catalyst Converts CO2 Into Liquid Fuels
Researchers at Berkeley Lab have made real-time movies of copper nanoparticles as they evolve to convert carbon dioxide and water into renewable fuels and chemicals. Their new insights could help advance the next generation of solar fuels
Video of a 4D-STEM experiment: Berkeley Lab researchers used a new electrochemical liquid cell to observe copper nanoparticles (ranging in size from 7 nanometers to 18 nanometers) evolve into active nanograins during CO2 electrolysis – a process that uses electricity to drive a reaction on the surface of an electrocatalyst. The new electrochemical liquid cell allows researchers to resolve images of objects smaller than 10 nanometers.
Previous
Newswise — Since the 1970s, scientists have known that copper has a special ability to transform carbon dioxide into valuable chemicals and fuels. But for many years, scientists have struggled to understand how this common metal works as an electrocatalyst, a mechanism that uses energy from electrons to chemically transform molecules into different products.
Now, a research team led by Lawrence Berkeley National Laboratory (Berkeley Lab) has gained new insight by capturing real-time movies of copper nanoparticles (copper particles engineered at the scale of a billionth of a meter) as they convert CO2 and water into renewable fuels and chemicals: ethylene, ethanol, and propanol, among others. The work was reported in the journal Nature last week.
“This is very exciting. After decades of work, we’re finally able to show – with undeniable proof – how copper electrocatalysts excel in CO2 reduction,” said Peidong Yang, a senior faculty scientist in Berkeley Lab’s Materials Sciences and Chemical Sciences Divisions who led the study. Yang is also a professor of chemistry and materials science and engineering at UC Berkeley. “Knowing how copper is such an excellent electrocatalyst brings us steps closer to turning CO2 into new, renewable solar fuels through artificial photosynthesis.”
The work was made possible by combining a new imaging technique called operando 4D electrochemical liquid-cell STEM (scanning transmission electron microscopy) with a soft X-ray probe to investigate the same sample environment: copper nanoparticles in liquid. First author Yao Yang, a UC Berkeley Miller postdoctoral fellow, conceived the groundbreaking approach under the guidance of Peidong Yang while working toward his Ph.D. in chemistry at Cornell University.
Credit: Thor Swift/Berkeley Lab
(From left to right): Julian Feijoo, Jianbo Jin, Cheng Wang, Peidong Yang, Yao Yang, Inwhan Roh, and Maria Fonseca Guzman at the Advanced Light Source.
Previous
Artist’s rendering of a copper nanoparticle as it evolves during CO2 electrolysis: Copper nanoparticles (left) combine into larger metallic copper “nanograins” (right) within seconds of the electrochemical reaction, reducing CO2 into new multicarbon products.
Previous
Credit: Thor Swift/Berkeley Lab
Yao Yang (center) loads a sample into the soft X-ray scattering chamber as Cheng Wang (left) and Peidong Yang (right) observe at the RSoXS Beamline (Beamline 11.0.1.2) at the Advanced Light Source.
Previous
Scientists who study artificial photosynthesis materials and reactions have wanted to combine the power of an electron probe with X-rays, but the two techniques typically can’t be performed by the same instrument.
Electron microscopes (such as STEM or TEM) use beams of electrons and excel at characterizing the atomic structure in parts of a material. In recent years, 4D STEM (or “2D raster of 2D diffraction patterns using scanning transmission electron microscopy”) instruments, such as those at Berkeley Lab’s Molecular Foundry, have pushed the boundaries of electron microscopy even further, enabling scientists to map out atomic or molecular regions in a variety of materials, from hard metallic glass to soft, flexible films.
On the other hand, soft (or lower-energy) X-rays are useful for identifying and tracking chemical reactions in real time in an operando, or real-world, environment.
But now, scientists can have the best of both worlds. At the heart of the new technique is an electrochemical “liquid cell” sample holder with remarkable versatility. A thousand times thinner than a human hair, the device is compatible with both STEM and X-ray instruments.
The electrochemical liquid cell’s ultrathin design allows reliable imaging of delicate samples while protecting them from electron beam damage. A special electrode custom-designed by co-author Cheng Wang, a staff scientist at Berkeley Lab’s Advanced Light Source, enabled the team to conduct X-ray experiments with the electrochemical liquid cell. Combining the two allows researchers to comprehensively characterize electrochemical reactions in real time and at the nanoscale.
Getting granular
During 4D-STEM experiments, Yao Yang and team used the new electrochemical liquid cell to observe copper nanoparticles (ranging in size from 7 nanometers to 18 nanometers) evolve into active nanograins during CO2 electrolysis – a process that uses electricity to drive a reaction on the surface of an electrocatalyst.
The experiments revealed a surprise: copper nanoparticles combined into larger metallic copper “nanograins” within seconds of the electrochemical reaction.
To learn more, the team turned to Wang, who pioneered a technique known as “resonant soft X-ray scattering (RSoXS) for soft materials,” at the Advanced Light Source more than 10 years ago.
With help from Wang, the research team used the same electrochemical liquid cell, but this time during RSoXS experiments, to determine whether copper nanograins facilitate CO2 reduction. Soft X-rays are ideal for studying how copper electrocatalysts evolve during CO2 reduction, Wang explained. By using RSoXS, researchers can monitor multiple reactions between thousands of nanoparticles in real time, and accurately identify chemical reactants and products.
The RSoXS experiments at the Advanced Light Source – along with additional evidence gathered at Cornell High Energy Synchrotron Source (CHESS) – proved that metallic copper nanograins serve as active sites for CO2 reduction. (Metallic copper, also known as copper(0), is a form of the element copper.)
During CO2 electrolysis, the copper nanoparticles change their structure during a process called “electrochemical scrambling.” The copper nanoparticles’ surface layer of oxide degrades, creating open sites on the copper surface for CO2 molecules to attach, explained Peidong Yang. And as CO2 “docks” or binds to the copper nanograin surface, electrons are then transferred to CO2, causing a reaction that simultaneously produces ethylene, ethanol, and propanol along with other multicarbon products.
“The copper nanograins essentially turn into little chemical manufacturing factories,” Yao Yang said.
Further experiments at the Molecular Foundry, the Advanced Light Source, and CHESS revealed that size matters. All of the 7-nanometer copper nanoparticles participated in CO2 reduction, whereas the larger nanoparticles did not. In addition, the team learned that only metallic copper can efficiently reduce CO2 into multicarbon products. The findings have implications for “rationally designing efficient CO2 electrocatalysts,” Peidong Yang said.
The new study also validated Peidong Yang’s findings from 2017: That the 7-nanometer-sized copper nanoparticles require low inputs of energy to start CO2 reduction. As an electrocatalyst, the 7-nanometer copper nanoparticles required a record-low driving force that is about 300 millivolts less than typical bulk copper electrocatalysts. The best-performing catalysts that produce multicarbon products from CO2 typically operate at high driving force of 1 volt.
The copper nanograins could potentially boost the energy efficiency and productivity of some catalysts designed for artificial photosynthesis, a field of research that aims to produce solar fuels from sunlight, water, and CO2. Currently, researchers within the Department of Energy-funded Liquid Sunlight Alliance (LiSA) plan to use the copper nanograin catalysts in the design of future solar fuel devices.
“The technique’s ability to record real-time movies of a chemical process opens up exciting opportunities to study many other electrochemical energy conversion processes. It’s a huge breakthrough, and it would not have been possible without Yao and his pioneering work,” Peidong Yang said.
Researchers from Berkeley Lab, UC Berkeley, and Cornell University contributed to the work. Other authors on the paper include co-first authors Sheena Louisa and Sunmoon Yu, former UC Berkeley Ph.D. students in Peidong Yang’s group, along with Jianbo Jin, Inwhan Roh, Chubai Chen, Maria V. Fonseca Guzman, Julian Feijóo, Peng-Cheng Chen, Hongsen Wang, Christopher Pollock, Xin Huang, Yu-Tsuan Shao, Cheng Wang, David A. Muller, and Héctor D. Abruña.
Parts of the experiments were performed by Yao Yang at Cornell under the supervision of Héctor Abruña, professor of chemistry and chemical biology, and David A. Muller, professor of engineering.
This work was supported by the DOE Office of Science.
The Molecular Foundry and Advanced Light Source are user facilities at Berkeley Lab.
Source: Lawrence Berkeley National Laboratory
Author
Discover more from Daily News
Subscribe to get the latest posts sent to your email.
Last Updated on February 10, 2026 by Daily News Staff
Valerie Thomas is a true pioneer in the world of science and technology. A NASA engineer and physicist, she is best known for inventing the illusion transmitter, a groundbreaking device that creates 3D images using concave mirrors. This invention laid the foundation for modern 3D imaging and virtual reality technologies.
Beyond her inventions, Thomas broke barriers as an African American woman in STEM, mentoring countless young scientists and advocating for diversity in science and engineering. Her work at NASA’s Goddard Space Flight Center helped advance satellite technology and data visualization, making her contributions both innovative and enduring.
In our latest short video, we highlight Valerie Thomas’ remarkable journey—from her early passion for science to her groundbreaking work at NASA. Watch and be inspired by a true STEM pioneer whose legacy continues to shape the future of space and technology.
🎥 Watch the video here: https://youtu.be/P5XTgpcAoHw
Dive into “The Knowledge,” where curiosity meets clarity. This playlist, in collaboration with STMDailyNews.com, is designed for viewers who value historical accuracy and insightful learning. Our short videos, ranging from 30 seconds to a minute and a half, make complex subjects easy to grasp in no time. Covering everything from historical events to contemporary processes and entertainment, “The Knowledge” bridges the past with the present. In a world where information is abundant yet often misused, our series aims to guide you through the noise, preserving vital knowledge and truths that shape our lives today. Perfect for curious minds eager to discover the ‘why’ and ‘how’ of everything around us. Subscribe and join in as we explore the facts that matter. https://stmdailynews.com/the-knowledge/
Forgotten Genius Fridays
https://stmdailynews.com/the-knowledge-2/forgotten-genius-fridays/
🧠 Forgotten Genius Fridays
A Short-Form Series from The Knowledge by STM Daily News
Every Friday, STM Daily News shines a light on brilliant minds history overlooked.
Forgotten Genius Fridays is a weekly collection of short videos and articles dedicated to inventors, innovators, scientists, and creators whose impact changed the world—but whose names were often left out of the textbooks.
From life-saving inventions and cultural breakthroughs to game-changing ideas buried by bias, our series digs up the truth behind the minds that mattered.
Each episode of The Knowledge runs 30–90 seconds, designed for curious minds on the go—perfect for YouTube Shorts, TikTok, Reels, and quick reads.
Because remembering these stories isn’t just about the past—it’s about restoring credit where it’s long overdue.
🔔 New episodes every Friday
📺 Watch now at: stmdailynews.com/the-knowledge
🧠 Now you know.
Author
Discover more from Daily News
Subscribe to get the latest posts sent to your email.
The Knowledge
Beneath the Waves: The Global Push to Build Undersea Railways
Undersea railways are transforming transportation, turning oceans from barriers into gateways. Proven by tunnels like the Channel and Seikan, these innovations offer cleaner, reliable connections for passengers and freight. Ongoing projects in China and Europe, alongside future proposals, signal a new era of global mobility beneath the waves.
For most of modern history, oceans have acted as natural barriers—dividing nations, slowing trade, and shaping how cities grow. But beneath the waves, a quiet transportation revolution is underway. Infrastructure once limited by geography is now being reimagined through undersea railways.
Undersea rail tunnels—like the Channel Tunnel and Japan’s Seikan Tunnel—proved decades ago that trains could reliably travel beneath the ocean floor. Today, new projects are expanding that vision even further.
Around the world, engineers and governments are investing in undersea railways—tunnels that allow high-speed trains to travel beneath oceans and seas. Once considered science fiction, these projects are now operational, under construction, or actively being planned.
Undersea Rail Is Already a Reality
Japan’s Seikan Tunnel and the Channel Tunnel between the United Kingdom and France proved decades ago that undersea railways are not only possible, but reliable. These tunnels carry passengers and freight beneath the sea every day, reshaping regional connectivity.
Undersea railways are cleaner than short-haul flights, more resilient than bridges, and capable of lasting more than a century. As climate pressures and congestion increase, rail beneath the sea is emerging as a practical solution for future mobility.
What’s Being Built Right Now
China is currently constructing the Jintang Undersea Railway Tunnel as part of the Ningbo–Zhoushan high-speed rail line, while Europe’s Fehmarnbelt Fixed Link will soon connect Denmark and Germany beneath the Baltic Sea. These projects highlight how transportation and technology are converging to solve modern mobility challenges.
The Mega-Projects Still on the Drawing Board
Looking ahead, proposals such as the Helsinki–Tallinn Tunnel and the long-studied Strait of Gibraltar rail tunnel could reshape global affairs by linking regions—and even continents—once separated by water.
Why Undersea Rail Matters
The future of transportation may not rise above the ocean—but run quietly beneath it.
Discover more from Daily News
Subscribe to get the latest posts sent to your email.
child education
Special Education Is Turning to AI to Fill Staffing Gaps—But Privacy and Bias Risks Remain
With special education staffing shortages worsening, schools are using AI to draft IEPs, support training, and assist assessments. Experts warn the benefits come with major risks—privacy, bias, and trust.
Seth King, University of Iowa
In special education in the U.S., funding is scarce and personnel shortages are pervasive, leaving many school districts struggling to hire qualified and willing practitioners.
Amid these long-standing challenges, there is rising interest in using artificial intelligence tools to help close some of the gaps that districts currently face and lower labor costs.
Over 7 million children receive federally funded entitlements under the Individuals with Disabilities Education Act, which guarantees students access to instruction tailored to their unique physical and psychological needs, as well as legal processes that allow families to negotiate support. Special education involves a range of professionals, including rehabilitation specialists, speech-language pathologists and classroom teaching assistants. But these specialists are in short supply, despite the proven need for their services.
As an associate professor in special education who works with AI, I see its potential and its pitfalls. While AI systems may be able to reduce administrative burdens, deliver expert guidance and help overwhelmed professionals manage their caseloads, they can also present ethical challenges – ranging from machine bias to broader issues of trust in automated systems. They also risk amplifying existing problems with how special ed services are delivered.
Yet some in the field are opting to test out AI tools, rather than waiting for a perfect solution.
A faster IEP, but how individualized?
AI is already shaping special education planning, personnel preparation and assessment.
One example is the individualized education program, or IEP, the primary instrument for guiding which services a child receives. An IEP draws on a range of assessments and other data to describe a child’s strengths, determine their needs and set measurable goals. Every part of this process depends on trained professionals.
But persistent workforce shortages mean districts often struggle to complete assessments, update plans and integrate input from parents. Most districts develop IEPs using software that requires practitioners to choose from a generalized set of rote responses or options, leading to a level of standardization that can fail to meet a child’s true individual needs.
Preliminary research has shown that large language models such as ChatGPT can be adept at generating key special education documents such as IEPs by drawing on multiple data sources, including information from students and families. Chatbots that can quickly craft IEPs could potentially help special education practitioners better meet the needs of individual children and their families. Some professional organizations in special education have even encouraged educators to use AI for documents such as lesson plans.
Training and diagnosing disabilities
There is also potential for AI systems to help support professional training and development. My own work on personnel development combines several AI applications with virtual reality to enable practitioners to rehearse instructional routines before working directly with children. Here, AI can function as a practical extension of existing training models, offering repeated practice and structured support in ways that are difficult to sustain with limited personnel.
Some districts have begun using AI for assessments, which can involve a range of academic, cognitive and medical evaluations. AI applications that pair automatic speech recognition and language processing are now being employed in computer-mediated oral reading assessments to score tests of student reading ability.
Practitioners often struggle to make sense of the volume of data that schools collect. AI-driven machine learning tools also can help here, by identifying patterns that may not be immediately visible to educators for evaluation or instructional decision-making. Such support may be especially useful in diagnosing disabilities such as autism or learning disabilities, where masking, variable presentation and incomplete histories can make interpretation difficult. My ongoing research shows that current AI can make predictions based on data likely to be available in some districts.
Privacy and trust concerns
There are serious ethical – and practical – questions about these AI-supported interventions, ranging from risks to students’ privacy to machine bias and deeper issues tied to family trust. Some hinge on the question of whether or not AI systems can deliver services that truly comply with existing law.
The Individuals with Disabilities Education Act requires nondiscriminatory methods of evaluating disabilities to avoid inappropriately identifying students for services or neglecting to serve those who qualify. And the Family Educational Rights and Privacy Act explicitly protects students’ data privacy and the rights of parents to access and hold their children’s data.
What happens if an AI system uses biased data or methods to generate a recommendation for a child? What if a child’s data is misused or leaked by an AI system? Using AI systems to perform some of the functions described above puts families in a position where they are expected to put their faith not only in their school district and its special education personnel, but also in commercial AI systems, the inner workings of which are largely inscrutable.
These ethical qualms are hardly unique to special ed; many have been raised in other fields and addressed by early-adopters. For example, while automatic speech recognition, or ASR, systems have struggled to accurately assess accented English, many vendors now train their systems to accommodate specific ethnic and regional accents.
But ongoing research work suggests that some ASR systems are limited in their capacity to accommodate speech differences associated with disabilities, account for classroom noise, and distinguish between different voices. While these issues may be addressed through technical improvement in the future, they are consequential at present.
Embedded bias
At first glance, machine learning models might appear to improve on traditional clinical decision-making. Yet AI models must be trained on existing data, meaning their decisions may continue to reflect long-standing biases in how disabilities have been identified.
Indeed, research has shown that AI systems are routinely hobbled by biases within both training data and system design. AI models can also introduce new biases, either by missing subtle information revealed during in-person evaluations or by overrepresenting characteristics of groups included in the training data.
Such concerns, defenders might argue, are addressed by safeguards already embedded in federal law. Families have considerable latitude in what they agree to, and can opt for alternatives, provided they are aware they can direct the IEP process.
By a similar token, using AI tools to build IEPs or lessons may seem like an obvious improvement over underdeveloped or perfunctory plans. Yet true individualization would require feeding protected data into large language models, which could violate privacy regulations. And while AI applications can readily produce better-looking IEPs and other paperwork, this does not necessarily result in improved services.
Filling the gap
Indeed, it is not yet clear whether AI provides a standard of care equivalent to the high-quality, conventional treatment to which children with disabilities are entitled under federal law.
The Supreme Court in 2017 rejected the notion that the Individuals with Disabilities Education Act merely entitles students to trivial, “de minimis” progress, which weakens one of the primary rationales for pursuing AI – that it can meet a minimum standard of care and practice. And since AI really has not been empirically evaluated at scale, it has not been proved that it adequately meets the low bar of simply improving beyond the flawed status quo.
But this does not change the reality of limited resources. For better or worse, AI is already being used to fill the gap between what the law requires and what the system actually provides.
Seth King, Associate Profess of Special Education, University of Iowa
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Discover more from Daily News
Subscribe to get the latest posts sent to your email.
