Newswise — Each year, the Physical Sciences and Engineering (PSE) directorate at the U.S. Department of Energy’s (DOE) Argonne National Laboratory recognizes exceptional early-career researchers breaking into their fields with the PSE Early Investigator Named Awards. In 2025, the lab announced that six awardees would be receiving support in the form of funding and mentorship to conduct groundbreaking research aligned with Argonne’s strategic mission.

One member of the 2025 cohort is Maria Żurek, an assistant physicist in Argonne’s Physics (PHY) division, who studies the fundamental structure of protons and neutrons using the Continuous Electron Beam Accelerator Facility (CEBAF) at the DOE’s Thomas Jefferson National Accelerator Facility. For the PSE Early Investigator Named Award, Żurek will work under the guidance of Sylvester Joosten, interim leader of the Medium Energy group at Argonne, on a proposal titled, ​“Seeing the Unseen: Precision Calorimetry for 3D Nucleon Imaging.” In particle physics experiments, calorimetry refers to detection and analysis methods used to calculate particle energy.

“The national lab environment allows me to lead large projects and collaborate with fantastic scientists and engineers across divisions and institutions.” — Maria Żurek, Argonne assistant physicist

Here, Żurek discusses her research and other work she supports at Argonne.

Q: What role do you play at the lab?
A: I am an experimental nuclear physicist in the Physics division’s Medium Energy group, and I am working to understand the fundamental structure of the visible matter that makes up our world.

Q: What initiatives or projects are you most excited about being involved in at Argonne?
A: The national lab environment allows me to lead large projects and collaborate with fantastic scientists and engineers across divisions and institutions. I have the opportunity to work with talented postdocs on uncovering the inner workings of protons and neutrons using data from the CLAS12 experiment at Jefferson Lab, and I co-lead the development of electromagnetic calorimetry for the ePIC detector at the future Electron-Ion Collider (EIC) at the DOE’s Brookhaven National Laboratory. I am a team player, and doing great science with great people is the best job in the world.

Q: Can you talk a bit about the research you’re conducting for your proposal for which you received the 2025 PSE Early Investigator Named Award?
A: My PSE Early Investigator Named Award project tackles a hard problem: improving calorimetry for hadrons — protons, neutrons and other similar subatomic particles — in the medium-energy range typical of experiments at Jefferson Lab. Neutral particles, like neutrons, and another subatomic particle called muons are notoriously difficult to measure in this range. I will run preliminary simulations to test a practical dual-readout approach that separates light generated by different types of subatomic interactions, with the aim of getting cleaner, more precise energy and position measurements. The goal is to open new opportunities for 3D studies of proton and neutron structure and to provide evidence that can guide the next generation of detector designs.

Q: What do you like most about your job?
A: The people I work with, the diversity of problems I get to solve and the fact that I am always learning something new.

Q: How does your work support the lab’s mission? 
A: In my work I analyze data from world-class DOE user facilities, using measurements to sharpen our most fundamental understanding of how the universe is put together. I design and test modern detector technologies that let us see proton and neutron structure with greater clarity. This work uses Argonne’s strengths in hands-on experimentation and computation, and it delivers practical capability, validated hardware, documented procedures and reconstruction tools, for national research facilities today and for the EIC tomorrow. I work with engineers, scientists and trainees across Argonne to get from concept to instrument to reliable results. That is my piece of the mission.

Q: What do you enjoy doing outside of work?
A: I love hunting for hole-in-the-wall restaurants in Chicago’s neighborhoods and suburbs with my husband, and I never tire of admiring the city’s architecture, always walking with my head up. I love going to ballet, opera, musicals, sports games and concerts. A year ago, I started aerial gymnastics, and I even appreciate the bruises because they mean I am getting better. I enjoy leaf peeping in local parks and running our annual ​“fat squirrel contest” with friends. As someone who moved here, I still carry a newcomer’s curiosity — and ope! — I’m always ready to explore one more corner of American and Midwestern culture.

Q: What other sorts of career or professional development opportunities has Argonne provided?
A: I’ve gotten a lot from Argonne’s Mentorship Program, on both sides. As a mentee, the conversations with my mentors pushed me to set clear goals and get honest feedback; they also gave me a better view of how the lab works across divisions. As a mentor, I’ve learned to give useful feedback and to connect postdocs with the right people and resources. It’s simple, but it works because it creates time for focused conversations. Beyond mentoring, I’ve benefited from proposal workshops, science communication sessions and serving on several internal review committees.

Q: What encouraged you to get involved in the scientific discipline you are in?
A: I have always been drawn to big questions. In school I loved math, physics and chemistry, but I also loved literature for the way a good story pulls you in. A great high school physics teacher showed me that science can do the same thing: It tells a story about how the world works. I thought I might become a teacher, but during university I spent undergraduate internships at Fermilab (another DOE national laboratory), where I saw how national labs ​“zoom in” on particles to understand the building blocks of matter. That experience shifted my path. I wanted to be part of that discovery process.

Since then, I have followed the thread from curiosity to experiment — first, learning how to measure, then learning how to ask better questions, until it became a career in nuclear physics.

Argonne National Laboratory seeks solutions to pressing national problems in science and technology by conducting leading-edge basic and applied research in virtually every scientific discipline. Argonne is managed by UChicago Argonne, LLC for the U.S. Department of Energy’s Office of Science.

The U.S. Department of Energy’s Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit https://​ener​gy​.gov/​s​c​ience.

Newswise — With the global shift toward renewable energy, solid oxide–based electrochemical devices have become essential for hydrogen production, energy storage, and fuel-to-electricity conversion. Traditional oxygen-ion–conducting cells require high operating temperatures, creating cost, durability, and material compatibility challenges. Protonic ceramic cells (PCCs) offer an alternative, operating efficiently at 300–600 °C and allowing the use of cheaper components, improved thermal cycling, and enhanced stability. Despite rapid progress in materials engineering, the fundamental mechanisms governing hydration, proton conduction, and electrode reactions remain insufficiently understood. These gaps hinder rational catalyst design and slow the translation of new materials into practical PCC devices. Based on these challenges, there is a critical need to deeply investigate proton behavior, interfacial chemistry, and catalytic mechanisms.

Researchers from Idaho National Laboratory and collaborating universities published (DOI: 10.1016/j.esci.2025.100437) a comprehensive review on August 2025, in eScience, detailing how diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) is transforming protonic ceramic cell research. The article summarizes recent breakthroughs in applying DRIFTS to oxygen electrodes, proton-conducting electrolytes, and hydrogen electrodes under realistic operating conditions. By capturing surface intermediates and hydration signatures, the review highlights DRIFTS as an essential technique for understanding reaction pathways, improving proton uptake, and guiding next-generation materials design for high-performance PCC systems. This platform was recently reported in a research article by the same group in Energy Environmental Science, providing the substantial evidence on how it is powerful in electrochemical system at elevated temperatures, specifically for PCC.

The review outlines how DRIFTS enables direct observation of surface species and dynamic reactions across PCC components. For oxygen electrodes, DRIFTS detects hydroxyl stretching bands associated with proton uptake, providing insights into triple-conducting materials such as PrNi₀.₅Co₀.₅O₃–δ, PrBaCo₂O₅+δ, and high-entropy perovskites. Doping-induced enhancements—such as Zn-stabilized hydration sites or Cs-driven oxygen vacancy formation—are revealed through stronger –OH peaks and temperature-dependent hydration behavior. DRIFTS also verifies steam-induced structural transformations, including monoclinic-to-cubic transitions and the emergence of multi-phase composites that improve catalytic performance.

For protonic electrolytes, DRIFTS distinguishes Zr–OH–Zr and Zr–OH–X environments, enabling researchers to identify proton trapping, dehydration kinetics, and dopant-dependent hydrogen-bonding effects in materials like Sc- and Y-doped BaZrO₃. The technique further detects carbonate residues that impair sintering, guiding optimized fabrication routes.

In catalytic applications, DRIFTS captures intermediates during CO₂ hydrogenation, methane reforming, and chemical-fuel co-conversion, identifying formates, carbonates, and CO adsorption species crucial to mechanistic understanding. Emerging operando DRIFTS configurations with applied voltage demonstrate the movement of surface protons during real electrochemical reactions, validating proton migration and reaction coupling at electrode interfaces. Collectively, the review shows how DRIFTS bridges fundamental chemistry with practical PCC engineering.

According to the authors, DRIFTS provides a uniquely powerful lens for understanding how PCC materials behave under realistic conditions. They emphasize that the ability to monitor hydration, proton uptake, and catalytic intermediates in real time offers insights unavailable from traditional characterization tools. The authors note that integrating DRIFTS with complementary methods—such as synchrotron-based IR, X-ray spectroscopy, and computational modeling—will further expand its impact. They conclude that establishing operando DRIFTS systems capable of applying electrical load represents a critical next step for unraveling the complex, surface-driven processes that dictate PCC performance.

The review underscores that advancing DRIFTS techniques will accelerate the rational design of PCC materials for clean-energy technologies. Improved understanding of hydration behavior and proton migration can guide the development of durable oxygen electrodes, CO₂-tolerant electrolytes, and carbon-resistant hydrogen electrodes. Insights into reaction intermediates also support catalyst optimization for hydrogen production, CO₂ reduction, methane reforming, and value-added chemical synthesis. As energy systems evolve toward efficiency and sustainability, DRIFTS-enabled mechanistic knowledge will help bridge laboratory discoveries and scalable PCC devices. Ultimately, the authors note that expanding operando DRIFTS capabilities will be essential for building the next generation of robust, high-performance ceramic energy systems.

###

References

DOI

10.1016/j.esci.2025.100437

Original Source URL

https://doi.org/10.1016/j.esci.2025.100437

Funding information

This work is supported by the HydroGEN Advanced Water Splitting Materials Consortium, established as part of the Energy Materials Network under the U.S. Department of Energy (USDOE); the Office of Energy Efficiency and Renewable Energy (EERE); and the Hydrogen and Fuel Cell Technologies Office (HFTO), under DOE Idaho Operations Office, under contract no. DE-AC07-05ID14517.

About eScience

eScience – a Diamond Open Access journal cooperated with KeAi and published online at ScienceDirect. eScience is founded by Nankai University (China) in 2021 and aims to publish high quality academic papers on the latest and finest scientific and technological research in interdisciplinary fields related to energy, electrochemistry, electronics, and environment. eScience provides insights, innovation and imagination for these fields by built consecutive discovery and invention. Now eScience has been indexed by SCIE, CAS, Scopus and DOAJ. Its impact factor is 36.6, which is ranked first in the field of electrochemistry.

Newswise — RICHLAND, Wash.—In a historic event decades in the making, the Hanford Site recently began immobilizing low-activity, radioactive waste by converting it into glass: a process known as vitrification. The event marked the successful start-up of Hanford’s Waste Treatment and Immobilization Plant, or “Vit Plant,” which will render millions of gallons of waste—generated by plutonium production during the Manhattan Project and Cold War—into glass for safe storage for thousands of years. The milestone also represents nearly 60 years of scientific contributions made by scientists and engineers at the Department of Energy’s Pacific Northwest National Laboratory.

“PNNL is proud to have played a pivotal role in advancing modern vitrification technology,” said Deb Gracio, PNNL director. “This milestone underscores the importance of innovation, collaboration, and scientific excellence in solving some of the world’s most pressing problems. It wouldn’t have been possible without a strong partnership among PNNL, DOE’s Hanford Field Office, Bechtel National Inc., the Office of Environmental Management, Hanford Tank Waste Operations & Closure, and of course our local community and stakeholders.”

Persistent and intense efforts by PNNL researchers—chemical engineers, computational scientists, materials scientists and chemists, among others—have advanced the science of vitrification since the 1960s, making this pursuit of materials science a defining element of PNNL’s history and impact. Not only have their innovations and collaborations with staff at the Vit Plant led to this historic achievement—their work has also informed vitrification operations around the world.

Birthplace of the melter

In the 1960s, researchers at PNNL engineered a technology that even today is among the most widely used tools for nuclear waste vitrification: the liquid-fed ceramic melter, which can be found amid vitrification operations on nearly every continent. Inside a melter, where temperatures can reach 2,100°F, low-activity waste is immobilized after being mixed with glass-forming chemicals—using formulas determined by a PNNL algorithm—then fed on top of a pool of molten glass. After the mixture is efficiently converted into glass, it is poured into containers and cooled to yield solid glass with radionuclides “locked” into the atomic structure of the glass. Simple at its core, carrying this process out in the real world can be anything but.

Each of the Hanford Site’s 177 one-million-gallon-capacity tanks contained a chemically unique and nonuniform waste. The composition of these wastes dictates both the behavior of the waste and which glass-forming chemicals are needed to make an acceptable glass. The “right” glass must not only incorporate and immobilize as much waste as possible—it also needs to be durable and avoid pitfalls like being difficult to transport through the plant, producing gas products in quantities that are challenging to treat or damaging to the plant’s infrastructure. Historically, designing a glass that strikes a balance among these goals meant spending a great deal of time fine-tuning the recipes.

For years, this process was carried out in a methodical, back-and-forth approach between glass design and performance testing. Scientists would consider the composition of a target waste, design a type of glass for the task, test its properties and adjust its composition until successful. In most facilities, this process can take months or even years.

“For the Vit Plant here in the Tri-Cities to operate successfully, we had to make it so that process happened on the order of minutes,” said John Vienna, PNNL materials scientist and lab fellow.

The challenge of vitrifying Hanford Site waste is made profoundly more challenging by the waste’s chemical complexity, according to Vienna, who has led a wide variety of research efforts in waste management, including the design of glasses used at the Hanford Site today. The Hanford Site’s waste is not only the most complex waste in the world but also the largest quantity ever to be targeted for vitrification.

From conventional to computational

Vienna, alongside his fellow scientists and colleagues from the Waste Treatment and Immobilization Plant with support from DOE glass scientist Albert Kruger and the Hanford Field Office, helped to solve the timeline challenge by innovating an entirely new approach to glass design. Instead of relying on the conventional approach carried out in a laboratory, they created a computational approach that utilizes modeling. Computer models are trained on hundreds of historical testing results and then prompted to make predictions by taking in the chemical makeup of waste to generate corresponding “recipes” that yield processable, economic and incredibly long-lasting glass.

Incorporating a partially computational approach has saved many years of effort and many millions of dollars for vitrification operations like those underway at DOE’s Savannah River Site in South Carolina. In the desert of southeastern Washington state, pretreated waste arrives at the beginning of the vitrification process in roughly 9,000-gallon batches. The waste is analyzed, and that information is fed into an algorithm that generates the corresponding glass design.

By comparison, a similar traditional approach was used at New York’s West Valley Demonstration Project site in the late 1990s, where glass design took roughly a decade. At the Hanford Site, this process now takes less than 120 minutes, and PNNL’s glass algorithm app is getting faster with each update.

Many current and former staff at PNNL have contributed to the design of the melters and other key equipment at the Vit Plant. The submerged bed scrubber, the air displacement slurry pump and melter technologies were all initiated and developed at PNNL. Will Eaton, a melter specialist, led portions of the Vit Plant melter designs and continues to lead research to improve melter materials and optimize melter processing. These innovations, along with PNNL contributions to designs led by other Vit Plant partners, make it possible for each melter to produce up to 15 metric tons of glass per day when operating at full capacity.

“PNNL has been an integral part of the Hanford Waste Treatment and Immobilization Plant. They have assisted in solving technical challenges and developed the vitrification glass recipes that are currently being processed in the Low-Activity Waste Facility,” said Chris Musick, general manager of the Bechtel-led Waste Treatment Completion Company LLC. “We look forward to growing our partnership with PNNL in the future as we move forward with treating tank waste and completing the high-level waste scope.”

The next generation

Today, PNNL scientists continue to support Hanford’s Waste Treatment and Immobilization Plant by analyzing pretreated and vitrified waste, as well as providing fast answers during the facility’s start-up. Seeing the first vitrified waste marked an especially satisfying career moment, said materials scientist José Marcial.

“It’s extremely exciting,” said Marcial, whose scientific career began with a vitrification-focused internship at PNNL as a high school student while studying at Kiona-Benton City High School then Columbia Basin College. “This shows that this isn’t just an academic exercise. It’s all of our effort being put to real use to benefit the country and our community. It’s truly an amazing time to be a part of this work.”

Similarly, Vienna, a mentor to Marcial, is enjoying the chance to witness the culmination of scientific effort spanning dozens of careers and thousands of scientific manuscripts and reports. “We’ve got three generations of researchers that have dedicated their careers to Hanford tank waste,” said Vienna. “Since the 1960s, there has always been a vitrification presence here at PNNL.”

Though vitrification at Hanford has begun, the work is far from over. Marcial and others are now focused on continuing near- and long-term support for the Waste Treatment and Immobilization Plant by contributing to improvements in overall efficiency, fine-tuning the glass algorithm performance and being part of the team addressing any emerging operational challenges. Additional PNNL researchers are applying their expertise to the broader cleanup mission, including grout waste form development, tank waste treatment, tank waste solids, the high-level waste facility and environmental remediation of subsurface soil and groundwater. As they look toward the future, Marcial looks toward the next generation of scientists.

BYLINE: Shannon Brescher Shea: Social media manager and senior writer/editor in the Office of Science’s Office of Communications and Public Affairs

Newswise — Scientists recognized by the Department of Energy Office of Science Distinguished Scientists Fellows Award are pursuing answers to science’s biggest questions. Mary Bishai is a senior physicist at DOE’s Brookhaven National Laboratory.

If it wasn’t for a magazine, I may have become a completely different type of scientist. 

In 1985, my uncle – who was a prominent marine biology professor – was tutoring me in high school biology. As a science lover, he had copies of National Geographic lying around. Intrigued, I convinced my parents to get me a subscription. One article caught my eye – “Worlds Within the Atom.” It described how physicists used massive particle accelerators to study the tiniest things in existence. 

Even though I was born in and living in Egypt, I was enthralled by the research in Europe and the United States. I decided I would one day work at CERN in Switzerland or the Tevatron collider at the Department of Energy’s (DOE) Fermilab.

Although my engineer parents wanted me to follow in their footsteps, I entered the American University of Cairo as a physics major instead. An exchange program later brought me to the United States.

Then nearly 13 years after I first read about the Tevatron at Fermilab, I was there. Fulfilling my dream, I delved into the interactions between the Standard Model of Particle Physics fundamental particles called Quarks and Gluons.

But that’s not the end of the story. Along the way, another type of physics caught my eye – neutrino physics. Since then, I’ve pursued the question – how can neutrinos help us answer some of the biggest questions about how our universe evolved?

The little neutral one

Neutrinos are a type of fundamental particle. They’re in a group called the leptons, which also includes electrons. However, neutrinos are much smaller than their familiar cousins.

Neutrinos are incredibly abundant. On the tip of your tongue right now, there are 300 neutrinos left over from the Big Bang. The sun, Supernovae, cosmic rays interacting with the atmosphere, and nuclear reactors also produce neutrinos. They’re the second most abundant particle in the universe, after photons (particles of light). Neutrinos are everywhere. 

Despite them being so common, neutrinos interact very little with other matter. Every second, 100 billion neutrinos produced by the sun move through your thumbnail and never leave a mark. A neutrino would have to travel 1.6 light years through lead – or 100,000 times the distance from the Earth to the sun – to interact with a single atom. Or as writer John Updike declared in the poem “Cosmic Gall,” “The earth is just a silly ball / To them, through which they simply pass, / Like dustmaids through a drafty hall / Or photons through a sheet of glass.” This lack of interaction inspired the nickname of “ghost particles.” 

Scientists are interested in neutrinos because of their ubiquity and the fact that they could hold the answers to some of physics’ biggest questions. One of those questions is the issue of why there is something in our universe rather than nothing. 

But none of that drew me to neutrino research. Wave-particle duality – or the idea that all matter can act like waves or particles – is a key concept in quantum mechanics. Scientists in the 1960’s stipulated that if neutrinos have non-zero mass, one type of neutrino could convert to another then back again. This would be a direct signature of quantum interference and wave-particle duality. In the late 1990s and early 2000s, experimental results confirmed the observation of neutrino “oscillations.” Hearing about one of the experiments, I said, “Oh my God, this is wave particle duality. It’s quantum mechanics and it’s just there. That’s cool, that’s what I want to do.” 

When I joined DOE’s Brookhaven National Laboratory in 2004 to study neutrinos, I joined a history of “ghostbuster” physicists.  

A history of ghostbusters

Our story starts in the 1930s. At that point, scientists were interested in how radioactive particles fall apart. Beta decay is when a nucleus emits an electron or its anti-matter partner, the positron. When a Nuclei nucleus undergoes beta decay, it transforms into another type of nucleus. When scientists looked at this process, they expected it to release a specific amount of energy. But it didn’t. It seemed like this result contradicted the Law of Energy Conservation, where energy can neither be created nor destroyed. 

Enter our first ghostbuster – Wolfgang Pauli. In a letter to fellow physicists attending a workshop, he proposed the idea of a yet-unknown particle that would carry away some of the energy. It would be neutral and have extremely small mass. While he valued his research enough to write the letter, it didn’t win out over a social obligation. In the same letter, he explained that he couldn’t have traveled to the workshop “since I am indispensable here in Zurich because of a ball.” Physicists do like to party. 

Now let’s jump ahead to the 1950s at DOE’s Los Alamos National Laboratory. Determined to track down these mysterious particles, Fred Reines and Clyde Cowan pursued the “poltergeist project.” While they first proposed detecting neutrinos from nuclear bomb testing, that idea was dismissed. Instead, they placed particle detectors near the Hanford and Savannah River nuclear reactors. The detectors sensed a telltale: two flashes of light from ghost-like neutrinos emitted by the reactors interacting with the material in the detectors. By counting these flashes, the scientists could count the neutrinos being captured by the detector. Developing the first neutrino detector netted Reines the Nobel Prize in 1995.

In addition to reactors, scientists realized that they could produce neutrinos in particle accelerators. From early on, Brookhaven was a leader in neutrino research. Physicists Leon Lederman, Melvin Schwartz, and Jack Steinberger used a proton beam from Brookhaven’s Alternating Gradient Synchrotron to slam protons into a target. A type of particle called a pi meson emerged, which then decayed into a neutrino and a Muons (another cousin of the electron). 

The scientists wanted to know if these were the same type of neutrinos as the ones from beta decay. The tracks the neutrinos left in their detector revealed mostly muon neutrinos and not electron neutrinos which are the type of neutrinos from beta decay. Another Nobel Prize-winning discovery. Later experiments at Fermilab confirmed a third type of neutrino called the tau neutrino – the neutral partner of the tau lepton, the heavier sibling of electrons and muons.

But both reactors and accelerators are made by humans. What about neutrinos from the sun? That was Ray Davis’s question. A chemist and physicist from Brookhaven, Davies began a long-standing physics experiment in 1967. He wanted to test the models that predicted how many solar neutrinos Earth receives. 

Davies installed a particle detector with 615 tons of cleaning fluid in the Homestead gold mine in South Dakota. The solar neutrinos interacted with the chlorine in the cleaning fluid to produce a unique isotope – argon-37. To track the interactions, he painstakingly counted the atoms of argon-37. He kept this up for almost 20 years! For demonstrating how to detect solar neutrinos, he also received a Nobel Prize. 

As these experiments revealed different types of neutrinos – called “flavors” – they also brought up new questions. From studying beta decay, scientists knew that neutrinos are extraordinarily light. In fact, they assumed that neutrinos didn’t have mass at all, like photons. But observations suggested that assumption was wrong. 

In the late 1950s to 1960s, scientists suggested that the different flavors of neutrinos were different mixes of quantum states. In highly relativistic particles like neutrinos, mass, energy, and momentum are all closely related. So when neutrinos act like waves and not particles, you can use their speed to understand their mass. If the different flavors had different speeds, neutrinos would have to have mass. One sign of neutrinos having mass would be one flavor of neutrino turning into another. 

While theory supported that idea, no one had observed that behavior – at least not until 1998 at the Super-Kamiokande (Super-K) detector. This experiment studied neutrinos created by cosmic rays smacking into the atmosphere. It identified if they were muon or electron neutrinos, as well as the direction they came from. The number of neutrinos that came from near the experiment matched well with estimates. In contrast, the ones from far away had a major deficit. The “disappearing” neutrinos were the first observations of neutrinos changing flavor, called oscillation. 

Later experiments confirmed the idea of neutrino oscillation. They also gave evidence of at least three different masses. The results won the leaders of the Super-K and Sudbury Neutrino Observatory experiments yet another Nobel Prize.

From not knowing that neutrinos existed to realizing that they change flavors over time, a lot changed in neutrino science in 60 years. But there was so much we still didn’t know.

Becoming a ghostbuster

This is where I come back into the story. The results from the KamLAND experiment following the Super-K project were so intriguing that I wanted to study this bizarre particle. 

One of the earliest projects I worked on was the Daya Bay experiment. This was an extremely difficult project. This experiment measured neutrinos from one of the most powerful nuclear reactors in the world. We had three detectors: one close to the reactor core, one a few hundred meters away, and a last one about a kilometer away. Spreading out the detectors allowed us to study the differences between them. Taking data over the course of 10 years, we detected 5 million anti-neutrino interactions! They were the most precise measurements in the world of antineutrinos from reactors. 

With these results, we knew there were three mass states and three flavors of neutrino. Each mass state is a different mix of flavors. The first mass state is dominated by the electron neutrino flavor. The second mass state has almost equal amounts of all three types. The third mass state is almost all muon and tao neutrino with a tiny amount of electron neutrinos. While we knew the second mass state was heavier than the first one, we didn’t know if the first mass state was heavier or lighter than the third one.

These flavors and mass states brought up a new question – could neutrinos explain why there is something rather than nothing? There is a fundamental principle called charge-parity symmetry. It states that if a particle is swapped with its anti-particle and left and right are swapped, the laws of physics will act in an identical way. However, if this law was universally true, there would have been equal amounts of matter and anti-matter at the beginning of the universe. As matter and anti-matter completely destroy each other and the universe is dominated by matter, we know there must be an exception. If neutrinos and anti-neutrinos demonstrate different mixing of neutrino flavors, this could be the exception. But to find out, we needed to better understand how neutrinos change flavor. 

The ultimate neutrino experiment

Exploring this issue was why we designed the Deep Underground Neutrino Experiment (DUNE). 

In the early 2000s, a multidisciplinary, multi-institutional team proposed the ultimate neutrino experiment. We picked two facilities with a long history of neutrino research – the former Homestake Mine and Fermilab. Where Ray Davies once studied solar neutrinos is now home to the Sanford Underground Research Facility. Fermilab has a particle accelerator that produces the most powerful neutrino beam in the world. The locations are 1,300 kilometers apart, enough space for us to capture plenty of oscillations.

Besides the sheer distance, DUNE is extremely large and complex. From the beam line to the shielding, everything must be extremely precise. The detectors use 17 kilotons of liquid argon that must be kept at -300 degrees F. Each of the two cryostats that keep the liquid cold is the size of a Boeing 787 plane. To fit the equipment, we had to massively expand the underground space of the former mine.

In addition to detecting neutrino oscillation, DUNE should also provide us with new insights into other issues. It will look for new particles, several types of proton decay, and neutrinos produced by supernovas. 

Recognizing the importance of this experiment, more partners joined the effort. Currently, we have 1,400 scientists from 209 institutions. Our international partners at CERN and elsewhere have made essential contributions to building and testing parts of the detectors.  

I have been involved with DUNE since early in its conception and served as DUNE project scientist from 2012 to 2015, leading the conceptual design of the project. I was also honored to serve as DUNE co-spokesperson from 2023 to 2025. In August 2024, we celebrated our biggest milestone yet – the ribbon cutting of the cavern expansion. The next milestone will be installing the first of four detectors underground. 

Looking forward, I hope that DUNE provides the next generation of scientists and engineers with the same opportunities I had. Working in experimental particle physics at the DOE National Labs has given me the incredible opportunity to study the fundamental science of our universe. I am lucky to study the worlds within the atom that I first read about in a magazine 40 years ago.

BYLINE: Will Ferguson

Newswise — When it comes to powering aircraft, jet engines need dense, energy-packed fuels. Right now, nearly all of that fuel comes from petroleum, as batteries don’t yet deliver enough punch for most flights. Scientists have long dreamed of a synthetic alternative: teaching microbes to ferment plant material into high-performance jet fuels. But designing these microbial “mini-factories” has traditionally been slow and expensive because of the unpredictability of biological systems.

In a pair of recent studies, two teams at the Joint BioEnergy Institute (JBEI), which is managed by Lawrence Berkeley National Laboratory (Berkeley Lab), have demonstrated complementary ways to dramatically speed up this process. One combines artificial intelligence and lab automation to rapidly test and refine the genetic designs of biofuel-producing microbes. The other turns a microbe’s “bad habit” into a powerful sensing tool, uncovering hidden pathways that boost production.

Their shared target is isoprenol — a clear, volatile alcohol that can be converted into DMCO, a next-generation jet fuel with higher energy density than today’s conventional aviation fuels. Producing isoprenol efficiently has been a long-standing challenge in synthetic biology.

The two studies — one published in Nature Communications, the other in Science Advances — tackle different sides of this challenge. The first uses automation and machine learning to engineer Pseudomonas putida strains that produce five times more isoprenol than before. The second approach turns the bacterium’s natural fuel-sensing ability into an advantage. By rewiring that system into a biosensor, the team could rapidly screen millions of variants and identify strains that make up to 36 times more isoprenol.

“These are two powerful complementary strategies,” said senior author of the biosensor study Thomas Eng, JBEI deputy director of Host Engineering and a research scientist in Berkeley Lab’s Biological Systems and Engineering (BSE) Division. “One is data-driven optimization; the other is discovery. Together, they give us a way to move much faster than traditional trial-and-error.”

A new engine for strain design

The AI and automation study was led by Taek Soon Lee, director of Pathway and Metabolic Engineering at JBEI, and Héctor García Martín, director of Data Science and Modeling at JBEI, both staff scientists in Berkeley Lab’s BSE Division. They set out to accelerate one of synthetic biology’s most time-consuming steps: improving microbial production through a series of genetic tweaks to different combinations of genes. Traditionally, scientists alter a few genes at a time and test the results — a painstaking, intuition-driven process that can take months or even years to yield meaningful gains.

By contrast, the Berkeley Lab researchers built an automated pipeline that uses robotics to create and test hundreds of genetic designs in parallel. After each round, machine learning algorithms analyze the results to systematically suggest the next set of strain genetic designs. The result is a system that moves 10 to 100 times faster than conventional methods.

“Standard metabolic engineering is slow because you’re relying on human intuition and biological knowledge,” said García Martín. “Our goal was to make strain improvement systematic and fast.”

Lead author David Carruthers, a scientific engineering associate with JBEI and BSE, developed a robotic workflow that connects key lab steps into one automated system. Working with collaborators at Lawrence Livermore National Laboratory, the team introduced a custom microfluidic electroporation device that can insert genetic material into 384 Pseudomonas putida strains in under a minute — a task that typically takes hours by hand.

At the core of the system is CRISPR interference (CRISPRi), a tool that lets researchers “turn down” gene activity rather than switching genes off completely. This fine-tuning makes it possible to test subtle gene combinations that shape the cell’s metabolism and track the effects through detailed protein measurements. After each round, the machine learning model analyzes the results and recommends the next set of genes that are most likely to boost performance when dialed down.

“Traditionally, optimizing production is a kind of guess-and-check process,” Carruthers said. “You make one change, test it, and hope you’re climbing toward a higher peak. By combining automation and machine learning, we were able to climb that landscape systematically — in weeks, not years.”

Lee, who led the metabolic engineering work, emphasized why this level of automation is so transformative for biology.

“We have been engineering Pseudomonas by hand for years, but biological experiments always come with small variations that are hard to control,” he said. “Automation gives us the ability to generate the same high-quality data every time, which is essential for machine learning to work well.”

Patrick Kinnunen, a former Berkeley Lab JBEI postdoctoral researcher who co-developed the data strategy, highlighted how crucial that reproducibility was for the algorithms. “Automation didn’t just make the experiments faster — it made the data cleaner,” he said. “That clarity is what lets it uncover non-intuitive genetic combinations that we probably would have missed by hand.”

Using their automated learning loop, the team completed six engineering cycles, each lasting just a few weeks instead of the months typical of manual workflows. They boosted isoprenol titers (the concentration of product in the culture) five-fold compared to their starting strain.

Turning a bug into a feature

Meanwhile, a second team led by Eng tackled a different but equally stubborn hurdle: how to select target genes that, when dialed down, improve isoprenol production significantly. The team’s microbe, Pseudomonas putida, posed a peculiar problem. It didn’t just make isoprenol, it also consumed the fuel molecule almost as soon as it produced it, undermining production efforts. Initially, this looked like a flaw. But during the COVID-19 pandemic, Eng and colleagues realized it might be a clue: if the microbe could sense and eat isoprenol, it likely had a built-in molecular sensor.

“There was a real ‘Aha!’ moment,” Eng said. “We had spent more than a year trying to figure out why the cells were consuming the product. One day we thought, ‘Wait, if they can sense it, there has to be a protein that detects it. Maybe we can turn that from a problem into a tool.’”

The team discovered the molecular system the microbe uses to sense isoprenol: two proteins that work together to detect the fuel and send signals inside the cell. They then rewired this system into a biosensor — a kind of biological “engine light” that turns on in proportion to how much fuel the cell produces.

Then came the clever twist: They linked the sensor to genes essential for survival, creating a system where only the microbes that make the most fuel can grow. Instead of measuring thousands of samples by hand, they let natural selection do the screening. This approach rapidly surfaced “champion” strains, including variants that produced up to 36 times more isoprenol than the original.

“What started as a frustrating bug became our biggest asset,” Eng said. “We turned the microbe’s fuel-eating behavior into a sensor that reports and selects for the best producers automatically.”

The approach also revealed surprising biology; high-producing strains switched to feed on their own amino acids once glucose ran out, sustaining production by rewiring their metabolism in unexpected ways. Just as importantly, the workflow can be applied to other molecules, offering a flexible new tool for rapidly engineering microbes — not just for isoprenol, but for a wide range of bio-based products.

Scaling up to industry-ready

Although developed independently, the two approaches fit together well. The AI-driven pipeline excels at rapidly optimizing combinations of a known set of gene targets, while the biosensor method is best for discovering novel gene targets, revealing genetic levers that would be difficult to predict.

“One is depth-first; the other is breadth-first,” Eng said. “Machine learning systematically optimizes combinations of annotated targets, while the biosensor approach starts fresh and lets the cells tell us which gene targets matter.”

Both teams are now working to scale their methods from lab experiments to industrially relevant fermentation systems — a critical step for producing synthetic aviation fuel at commercial levels. They’re also adapting their approaches to other microbes and target molecules, aiming to make them broadly applicable in biomanufacturing.

“If widely adopted, these approaches could reshape the industry,” García Martín said. “Instead of taking a decade and hundreds of people to develop one new bioproduct, small teams could do it in a year or less.”

Aindrila Mukhopadhyay, BSE deputy director for science, director of Host Engineering at JBEI, and a coauthor on the biosensor study, said these kinds of tools are changing how biological research gets done.

“Engineering biology is challenging due to the inherent unpredictability of metabolism and that makes the engineering slow,” Mukhopadhyay said. “By streamlining key steps — as we did through selections — and leveraging automation and AI, we’re making it a faster, more systematic process that is easier to adopt.”

JBEI is a Bioenergy Research Center funded by the Department of Energy Office of Science.

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Newswise — Researchers at Lawrence Livermore National Laboratory (LLNL) have co-developed a new way to precisely control the internal structure of common plastics during 3D printing, allowing a single printed object to seamlessly shift from rigid to flexible using only light.

In a paper published today in Science, the researchers describe a technique called crystallinity regulation in additive fabrication of thermoplastics (CRAFT) that enables microscopic control over how plastic molecules arrange themselves as an object is printed. The work opens new possibilities for advanced manufacturing, soft robotics, national defense, energy damping and information storage, according to the researchers. The team includes collaborators from Sandia National Laboratories (SNL), the University of Texas at Austin, Oregon State University, Arizona State University and Savannah River National Laboratory.

The team demonstrated that by carefully tuning light intensity during printing, they could dictate how crystalline or amorphous a thermoplastic becomes at specific locations within a part. That molecular arrangement determines whether a material behaves more stiff and rigid, or as a softer, more flexible plastic — without changing the base material. CRAFT builds on that principle by allowing researchers to control crystallinity spatially during printing, rather than uniformly throughout a part.

“A classic example of crystallinity is the difference between high-density polyethylene —picture a milk jug — and low-density polyethylene, like squeeze bottles and plastic bags. The bulk property difference in these two forms of polyethylene stems largely from differences in crystallinity,” said LLNL staff scientist Johanna Schwartz. “Our CRAFT effort is exciting in that we are controlling the crystallinity within a thermoplastic spatially with variations in light intensity, making areas of increased and decreased crystallinity to produce parts with control over material properties throughout the whole geometry.”

A key challenge, however, was translating this new materials capability into practical manufacturing instructions that could be used on real 3D printers, according to LLNL engineer Hernán Villanueva. Villanueva joined the project after early discussions with Schwartz and former SNL scientists Samuel Leguizamon and Alex Commisso identified a missing link: a way to convert any three-dimensional computer-aided design (CAD) into the detailed light patterns needed to print parts using the CRAFT method.

Villanueva said he drew on prior work in a multi-institutional team focused on lattice structures and advanced manufacturing workflows. In that effort, he developed software that rapidly converted complex, topology-optimized designs into printing instructions by parallelizing the process on LLNL’s high-performance computing (HPC) systems — reducing turnaround times from days to hours or minutes.

Applying that same computational approach to CRAFT, Villanueva adapted the workflow to encode “changes in light” rather than changes in material. He was soon able to convert 3D CAD geometries directly into CRAFT printing instructions, cutting instruction-generation time from hours — or even a full day — down to seconds, making rapid design iteration and demonstration of the method practical.

“This work is a natural extension of the Lab’s strengths in advanced manufacturing and materials by design,” Villanueva said. “As part of the CRAFT effort, we have evolved a tool that connects materials science with computational workflows and advanced printing, enabling us to move directly from a 3D design to a part with spatially varying properties.”

The team’s method relies on a light-activated polymerization process in which exposure level governs the stereochemistry of growing polymer chains, researchers said. Lower light intensities favor more ordered crystalline regions, while higher intensities suppress crystallization, yielding softer, more transparent material. By projecting grayscale patterns during printing, the team produced parts with smoothly varying mechanical and optical properties.

The demonstrated ability to tune properties by changing a light’s intensity rather than swapping materials could significantly simplify additive manufacturing (3D printing), Schwartz explained.

“If you can get many different properties from one vat of material, printing complex multi-material or multi-modulus structures becomes much easier,” she said.

The researchers demonstrated the CRAFT technique on commercial 3D printers, fabricating objects that combine multiple mechanical behaviors in a single print. Examples included bio-inspired structures that mimic bones, tendons and soft tissue, reproductions of famous paintings, as well as materials designed to absorb or redirect vibrational energy without adding weight or complexity. Among the most striking demonstrations was the ability to encode crystallinity through transparency differences, according to Schwartz.

“Being able to visualize the differences easily spatially, to the point of generating the Mona Lisa out of only one material, was incredibly cool,” Schwartz said.

LLNL’s Villanueva said the work reflects the Lab’s long-standing investments in HPC and in integrating modeling, design tools and novel manufacturing processes. He added that future work could integrate topology optimization directly into the CRAFT framework, enabling researchers to optimize light patterns themselves — rather than material layouts — to achieve desired performance.

Because the process works with thermoplastics — materials that can be melted and reshaped — printed parts remain recyclable and reprocessable, an important advantage for manufacturing sustainability. The findings suggest a future where 3D-printed plastic components can be tailored at the molecular level for specific functions, bridging the gap between material science and digital manufacturing.

From an applications standpoint, Schwartz said the technology could have broad and near-term impact.

“Energy dampening and metamaterial design are the most exciting use cases to me,” she said. “From space to fusion to electronics, there are so many industries that rely on energy and vibrational dampening control. This CRAFT printing process can access all of them.”