Monday, April 28, 2025

 

‘Wood you believe it?’ FAU engineers fortify wood with eco-friendly nano-iron



Research breakthrough using nano-iron technology results in stronger wood for sustainable materials



Florida Atlantic University

MicroCT Wood Cell Wall 

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A microCT image that shows the distribution of the iron mineral in the wood cell wall (in turquoise).

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Credit: Florida Atlantic University





Scientists and engineers are developing high-performance materials from eco-friendly sources like plant waste. A key component, lignocellulose – found in wood and many plants – can be easily collected and chemically modified to improve its properties.

By using these kinds of chemical changes, researchers are creating advanced materials and new ways to design and build sustainably. With about 181.5 billion tons of wood produced globally each year, it’s one of the largest renewable material sources.

Researchers from the College of Engineering and Computer Science at Florida Atlantic University, and collaborators from the University of Miami and Oak Ridge National Laboratory, wanted to find out if adding extremely hard minerals at the nanoscale could make the walls of wood cells stronger – without making the wood heavy, expensive or bad for the environment. Few studies have investigated how treated wood performs at different scales, and none have successfully strengthened entire pieces of wood by incorporating inorganic minerals directly into the cell walls.

The research team focused on a special type of hardwood known as ring-porous wood, which comes from broad-leaf trees like oak, maple, cherry and walnut. These trees feature large, ring-shaped vessels in the wood that transport water from the roots to the leaves. For the study, researchers used red oak, a common hardwood in North America, and introduced an iron compound into the wood through a simple chemical reaction. By mixing ferric nitrate with potassium hydroxide, they created ferrihydrite, an iron oxide mineral commonly found in soil and water.

Results of the study, published in the journal ACS Applied Materials and Interfaces, revealed that a simple, cost-effective chemical method using a safe mineral called nanocrystalline iron oxyhydroxide can strengthen the tiny cell walls within wood while adding only a small amount of extra weight. Although the internal structure became more durable, the wood’s overall behavior – such as how it bends or breaks – remained largely unchanged. This is likely because the treatment weakened the connections between individual wood cells, affecting how the material holds together on a larger scale.

The findings suggest that, with the right chemical treatment, it’s possible to enhance the strength of wood and other plant-based materials without increasing their weight or harming the environment. These bio-based materials could one day replace traditional construction materials like steel and concrete in applications such as tall buildings, bridges, furniture and flooring.

“Wood, like many natural materials, has a complex structure with different layers and features at varying scales. To truly understand how wood bears loads and eventually fails, it’s essential to examine it across these different levels,” said Vivian Merk, Ph.D., senior author and an assistant professor in the FAU Department of Ocean and Mechanical Engineering, the FAU Department of Biomedical Engineering, and the FAU Department of Chemistry and Biochemistry within the Charles E. Schmidt College of Science. “To test our hypothesis – that adding tiny mineral crystals to the cell walls would strengthen them – we employed several types of mechanical testing at both the nanoscale and the macroscopic scale.”

For the study, researchers used advanced tools like atomic force microscopy (AFM) to examine the wood at a very small scale, allowing them to measure properties such as stiffness and elasticity. Specifically, they employed a technique called AM-FM (Amplitude Modulation – Frequency Modulation), which vibrates the AFM tip at two different frequencies. One frequency generates detailed surface images, while the other measures the material’s elasticity and stickiness. This method gave them a precise view of how the wood’s cell walls were altered after being treated with minerals.

Additionally, the team conducted nanoindentation tests within a scanning electron microscope (SEM), where tiny probes were pressed into the wood to measure its response to force in different areas. To round out their analysis, they performed standard mechanical tests – such as bending both untreated and treated wood samples – to evaluate their overall strength and how they broke under stress.

“By looking at wood at different levels – from the microscopic structures inside the cell walls all the way up to the full piece of wood – we were able to learn more about how to chemically improve natural materials for real-world use,” said Merk.

This combination of small- and large-scale testing helped the researchers understand how the treatment affected both the fine details inside the cell walls and the overall strength of the wood.

“This research marks a significant advancement in sustainable materials science and a meaningful stride toward eco-friendly construction and design,” said Stella Batalama, Ph.D., the dean of the College of Engineering and Computer Science. “By reinforcing natural wood through environmentally conscious and cost-effective methods, our researchers are laying the groundwork for a new generation of bio-based materials that have the potential to replace traditional materials like steel and concrete in structural applications. The impact of this work reaches far beyond the field of engineering – it contributes to global efforts to reduce carbon emissions, cut down on waste, and embrace sustainable, nature-inspired solutions for everything from buildings to large-scale infrastructure.”

Study co-authors are Steven A. Soini, a Ph.D. graduate from the FAU College of Engineering and Computer Science and FAU Charles E. Schmidt College of Science; Inam Lalani, a Ph.D. student at the University of Miami; Matthew L. Maron, Ph.D., a doctoral researcher at the University of Miami; David Gonzalez, a graduate student in the FAU College of Engineering and Computer Science; Hassan Mahfuz, Ph.D., a professor in the FAU Department of Ocean and Mechanical Engineering; and Neus Domingo-Marimon, Ph.D., senior R&D staff scientist, group leader for the Functional Atomic Force Microscopy Group, Oak Ridge National Laboratory.

- FAU -

A microCT image that shows the distribution of the iron mineral in the wood cell wall (in turquoise).


A biomodal atomic force microscopy (AFM) instrument used by the researchers at the Oak Ridge National Laboratory. 

Credit

Steven A. Soini, Florida Atlantic University

About FAU’s College of Engineering and Computer Science:

The FAU College of Engineering and Computer Science is internationally recognized for cutting-edge research and education in the areas of computer science and artificial intelligence (AI), computer engineering, electrical engineering, biomedical engineering, civil, environmental and geomatics engineering, mechanical engineering, and ocean engineering. Research conducted by the faculty and their teams expose students to technology innovations that push the current state-of-the art of the disciplines. The College research efforts are supported by the National Science Foundation (NSF), the National Institutes of Health (NIH), the Department of Defense (DOD), the Department of Transportation (DOT), the Department of Education (DOEd), the State of Florida, and industry. The FAU College of Engineering and Computer Science offers degrees with a modern twist that bear specializations in areas of national priority such as AI, cybersecurity, internet-of-things, transportation and supply chain management, and data science. New degree programs include Master of Science in AI (first in Florida), Master of Science and Bachelor in Data Science and Analytics, and the new Professional Master of Science and Ph.D. in computer science for working professionals. For more information about the College, please visit eng.fau.edu

 

About Florida Atlantic University:
Florida Atlantic University, established in 1961, officially opened its doors in 1964 as the fifth public university in Florida. Today, Florida Atlantic serves more than 30,000 undergraduate and graduate students across six campuses located along the Southeast Florida coast. In recent years, the University has doubled its research expenditures and outpaced its peers in student achievement rates. Through the coexistence of access and excellence, Florida Atlantic embodies an innovative model where traditional achievement gaps vanish. Florida Atlantic is designated as a Hispanic-serving institution, ranked as a top public university by U.S. News & World Report, and holds the designation of “R1: Very High Research Spending and Doctorate Production” by the Carnegie Classification of Institutions of Higher Education. Florida Atlantic shares this status with less than 5% of the nearly 4,000 universities in the United States. For more information, visit www.fau.edu.

 

 

Don’t resent your robot vacuum cleaner for its idle hours – work it harder!



Computer scientists have reprogrammed a Roomba to perform four new tasks, showcasing how domestic robots can be harnessed during their downtime to make our lives easier.



University of Bath

Busy robots 

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Domestic robots, such as robot vacuum cleaners, spend most of their day idle - researchers propose ways to work them harder to make our own lives easier.

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Credit: Adwait Sharma & Yoshiaki Shiokawa, University of Bath





At a time when we run ourselves ragged to meet society’s expectations of productivity, performance and time optimisation, is it right that our robot vacuum cleaners and other smart appliances should sit idle for most of the day?

Computer scientists at the University of Bath in the UK think not. In a new paper, they propose over 100 ways to tap into the latent potential of our robotic devices. The researchers say these devices could be reprogrammed to perform helpful tasks around the home beyond their primary functions, keeping them physically active during their regular downtime.

New functions could include playing with the cat, watering plants, carrying groceries from car to kitchen, delivering breakfast in bed and closing windows when it rains.

For their study – presented today at the CHI Conference on Human Factors in Computing Systems, the premier international conference of Human-Computer Interaction (HCI) – the researchers identified 100 functions that domestic cleaning robots could, with some fine tuning, perform during idle periods. They then demonstrated the technical feasibility of working robots harder by reprogramming a Roomba (a popular robot vacuum cleaner) to perform the following four functions:

  1. Mobile wireless charger: The robot, fitted with a holder, charged a phone, navigating the home to find the phone user when mobile charging was needed.
  2. Workout projector: Equipped with a projector, the robot displayed workout videos on a wall. When it was time for floor exercises, it seamlessly shifted the projection to the ceiling, ensuring uninterrupted viewing.
  3. Home monitor: The robot monitored the home remotely, providing live video and task control, such as observing the oven while the user watched and controlled it.
  4. Work-status signpost: Fitted with a screen signalling 'meeting in progress,' the robot could be sent to a specific location (such as outside a room) to deter disturbances.

Yoshiaki Shiokawa, first author of the study and a PhD student in the Department of Computer Science at Bath, said: “Mobile domestic robots, like robot vacuum cleaners and lawnmowers, are perceived as limited, single-task devices but there is a strong argument that they are under-used for practical tasks. For most of the day, they sit idle.

“We should be extending their utility beyond their primary tasks by programming them to physically navigate the home to perform a range of additional functions. Just think how much more efficiently households would run if Roombas could be converted into household assistants.

“Our study proved that after making minimal adjustments, a Roomba can serve multiple roles around the home.”

Untapped potential

Prior work has investigated how stationary smart devices (such as smart speakers, thermostats, or security cameras) can perform additional tasks when idle, like updating software or processing information.

Researchers have also explored how robots can signal they are powered on and ready for action through subtle cues, such as having lights that fade in and out or gentle movement, even when they are not actively performing a task.

But the new study is believed to be the first where scientists have investigated the untapped potential of domestic robot’s mobility, systematically exploring how a device's idle time can be repurposed for diverse, value-adding interactions that cover home maintenance, on-demand assistance and pet care.

The range of tasks proposed for future mobile robots would be made possible by developing a series of robot-compatible bases (for instance of different heights), extendable arms and attachable cart.

With the right extensions and attachments, the researchers suggest that robots could immediately undertake some of the proposed new tasks, such as delivering mobile light therapy for individuals with seasonal affective disorder (SAD) or reminding users to take their medication and schedule medical appointments. Other concepts, like using robots to predict users' needs based on behavioural patterns, are more aspirational.

Robots on the rise

Domestic robots such as vacuum cleaners and lawnmowers are growing in popularity and expected to see annual market growth of 18.8% by 2028. The authors of the new study found that on average, a robot vacuum cleaner cleans for just one hour and 47 minutes every day.

Study co-author and supervisor Dr Adwait Sharma said: “Idle time presents unique opportunities for value-adding interactions and it aligns with the growing need for adaptable robots and integrated systems that can seamlessly fit into our daily lives. A robot vacuum could, for instance, use its idle time to monitor home security, water the plants or assist an older person to stand from a sitting position. These tasks tap into the robot’s advanced sensors, as well as its mobility.

Addition function proposed by the study’s authors for tomorrow’s robotic devices – in consultation with 12 global experts with extensive knowledge of robots and AI – include: searching for lost items; managing smart devices, for instance by changing a TV channel; assisting a user in taking a family photo; scanning the fridge and suggesting items to purchase; entertaining children; playing a card game; interacting with a pet; cleaning pet litter boxes and bowls; detecting unusual sounds and navigating within the home to inspect the situation; checking if doors are locked; cooking in parallel with a user; wiping a table; clearing and sorting food waste while a meal is being prepared; notifying family members when a meal is ready by knocking on doors; organising items and tidying up, for instance toys; moving plants for sunlight; clearing paths and alerting users to tripping hazards; receiving and delivering packages when the user is busy.

The research team also included Dr. Aditya Shekhar Nittala, Asst. Prof. at the University of Calgary (Canada), alongside master’s student Winnie Chen and Professor Jason Alexander from the Department of Computer Science at the University of Bath.

 

AI suggestions make writing more generic, Western



Cornell University




ITHACA, N.Y. – A new study from Cornell University finds AI-based writing assistants have the potential to function poorly for billions of users in the Global South by generating generic language that makes them sound more like Americans.

The study showed that when Indians and Americans used an AI writing assistant, their writing became more similar, mainly at the expense of Indian writing styles. While the assistant helped both groups write faster, Indians got a smaller productivity boost, because they frequently had to correct the AI’s suggestions.

“This is one of the first studies, if not the first, to show that the use of AI in writing could lead to cultural stereotyping and language homogenization,” said senior author Aditya Vashistha, assistant professor of information science. “People start writing similarly to others, and that’s not what we want. One of the beautiful things about the world is the diversity that we have.”

The study, “AI Suggestions Homogenize Writing Toward Western Styles and Diminish Cultural Nuances,” will be presented by first author Dhruv Agarwal, a doctoral student in the field of information science, at the Association of Computing Machinery’s conference on Human Factors in Computing Systems.

ChatGPT and other popular AI tools powered by large language models, are primarily developed by U.S. tech companies, but are increasingly used worldwide, including by the 85% of the world’s population that live in the Global South.

To investigate how these tools may be impacting people in nonWestern cultures, the research team recruited 118 people, about half from the U.S. and half from India, and asked them to write about cultural topics. Half of the participants from each country completed the writing assignments independently, while half had an AI writing assistant that provided short autocomplete suggestions. The researchers logged the participants’ keystrokes and whether they accepted or rejected each suggestion.

A comparison of the writing samples showed that Indians were more likely to accept the AI’s help, keeping 25% of the suggestions compared to 19% kept by Americans. However, Indians were also significantly more likely to modify the suggestions to fit their topic and writing style, making each suggestion less helpful, on average.

For example, when participants were asked to write about their favorite food or holiday, AI consistently suggested American favorites, pizza and Christmas, respectively. When writing about a public figure, if an Indian entered “S” in an attempt to type Shah Rukh Khan, a famous Bollywood actor, AI would suggest Shaquille O’Neil or Scarlett Johansson.

“When Indian users use writing suggestions from an AI model, they start mimicking American writing styles to the point that they start describing their own festivals, their own food, their own cultural artifacts from a Western lens,” Agarwal said.

This need for Indian users to continually push back against the AI’s Western suggestions is evidence of AI colonialism, researchers said. By suppressing Indian culture and values, the AI presents Western culture as superior, and may not only shift what people write, but also what they think.

“These technologies obviously bring a lot of value into people’s lives,” Agarwal said, “but for that value to be equitable and for these products to do well in these markets, tech companies need to focus on cultural aspects, rather than just language aspects.”

For additional information, see this Cornell Chronicle story.

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Carnegie Mellon launches Human-Centered AI Research Center with Seoul National University


Work at SNU-CMU HCAI Center will enhance lives and society


Carnegie Mellon University

SNU-CMU HCAI 

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Seoul National University and Carnegie Mellon University faculty stand in front of a banner during the Opening Ceremony for the new Human-Centered AI Research Center.

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Credit: Carnegie Mellon University





Carnegie Mellon University and Seoul National University (SNU) have announced a new collaboration to advance human-centered artificial intelligence research that prioritizes human well-being, accessibility and social responsibility.

The SNU-CMU Human-Centered AI Research Center (HCAI) aims to pioneer innovative AI solutions by combining interdisciplinary expertise in human-centered design.

“We’re excited to officially launch this partnership with our colleagues at Seoul National University,” said Laura Dabbish, professor in the Human-Computer Interaction Institute at Carnegie Mellon University’s School of Computer Science. “The groundwork for the center began two years ago with workshops that brought together students and faculty from both universities. We’re now building on that foundation to reimagine how AI can support human connection, empower individuals and enhance everyday life. Together, we’re creating a global model for AI innovation that’s rooted in human needs and values.”

So far, the researchers have hosted on-site workshops at both campuses, and met again while attending the same conference. HCII faculty David Lindlbauer and John Stamper participated in the HCAI Center’s official opening ceremony on February 13, 2025, at the SNU campus in Seoul, South Korea. Attendees discussed the vision for the center, upcoming research initiatives and opportunities for joint projects.

"The Human-Centered AI Research Center brings together the best of Seoul National University and Carnegie Mellon University to advance AI that serves humanity,” said Gahgene Gweon, associate professor in the SNU Department of Intelligence and Information and HCII Ph.D. alumna. “By combining SNU’s leading role in AI innovation across Asia with CMU’s excellence in interdisciplinary and human-centered AI, we are pioneering research that makes AI more ethical, intuitive and impactful for society." 

One of the center’s first research collaborations has already achieved major recognition at the Association of Computing Machinery (ACM) Conference on Human Factors in Computing Systems (CHI). The joint SNU-CMU paper, “Letters from Future Self: Augmenting the Letter-Exchange Exercise with LLM-based Future Self Agents to Enhance Young Adults’ Career Exploration,” was accepted to CHI 2025 and honored with a Best Paper award, an accolade reserved for the top 1% of submissions. The project explored how large language models (LLMs) can support young adults in imagining their futures through guided career exploration activities, such as writing reflective letters to themselves or exchanging chats or letters with an LLM-based agent for advice. This work explores the capabilities of LLM-based conversational AI agents to simulate specific characters and provide tailored responses, while responding with personalized interventions in self-guided contexts.

“Researchers at CMU and SNU have a shared interest in how Agentic AI offers a new type of social intelligence that might allow agents to operate in complex interpersonal relationships,” said John Zimmerman, Tang Family Professor of Artificial Intelligence and Human-Computer Interaction at CMU and co-author on the paper. “We want to explore how agents could and should operate between older parents and their adult children, between teens and parents, and between bosses and teams. When does the agent add value, and when has it crossed a social boundary?”

CMU and SNU have four joint research projects in the works for 2025. Each project brings together interdisciplinary teams of faculty and students from both institutions to advance ethical, people-centered AI. These projects explore key challenges in AI, including: 

  • How AI can support teamwork in programming with faculty leads Stamper and Carolyn Rosé of CMU, and Gweon of SNU.
  • Enhancing interactive problem-solving with Nikolas Martelaro and Scott Hudson of CMU and Joonhwan Lee of SNU.
  • Detecting societal bias in vision-language models with Motahhare Eslami, Ken Holstein, Hong Shen, Adam Perer, Jason Hong of CMU and Gunhee Kim and Eunkyu Park of SNU.
  • Assisting older adults through socially intelligent agents with Zimmerman and Jodi Forlizzi of CMU, and Hajin Lim and Eunmee Kim of SNU.

Additional HCAI activities slated for this year will include research workshops, student internships and faculty and student visits between the campuses.

More details about the center are available on its website.

 

With AI, researchers can now identify the smallest crystals



AI solves the century-old puzzle of uncovering the shape of atomic clusters by examining the patterns produced by an X-ray beam refracted through fine powder.



Columbia University School of Engineering and Applied Science

Crystallography 

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Crystallography is the science of analyzing the pattern produced by shining an X-ray beam through a material sample. A powder sample produces a different pattern than solid crystal.

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Credit: Columbia Engineering





One longstanding problem has sidelined life-saving drugs, stalled next-generation batteries, and kept archaeologists from identifying the origins of ancient artifacts. 

For more than 100 years, scientists have used a method called crystallography to determine the atomic structure of materials. The method works by shining an X-ray beam through a material sample and observing the pattern it produces. From this pattern – called a diffraction pattern – it is theoretically possible to calculate the exact arrangement of atoms in the sample. The challenge, however, is that this technique only works well when researchers have large, pure crystals. When they have to settle for a powder of minuscule pieces — called nanocrystals — the method only hints at the unseen structure.

Scientists at Columbia Engineering have created a machine learning algorithm that can observe the pattern produced by nanocrystals to infer the material’s atomic structure, as described in a new study published in Nature Materials. In many cases, their algorithm achieves near-perfect reconstruction of the atomic-scale structure from the highly degraded diffraction information — a feat unimaginable just a couple of years ago. 

“The AI solved this problem by learning everything it could from a database of many thousands of known, but unrelated, structures,” says Simon Billinge, professor of materials science and of applied physics and applied mathematics at Columbia Engineering. “Just as ChatGPT learns the patterns of language, the AI model learned the patterns of atomic arrangements that nature allows.”

Crystallography Transformed Science
Crystallography is vital to science because it’s the most effective method for understanding the properties of virtually any material. The method typically relies on a technique called X-ray diffraction, in which scientists shoot energetic beams at a crystal and record the pattern of light and dark spots it produces, sort of like a shadow. When crystallographers use this technique to analyze a large and pure sample, the resulting X-ray patterns contain all the information needed to determine its atomic-level structure. Best known for enabling the discovery of DNA’s double-helix structure, the method has opened important avenues of research in medicine, semiconductors, energy storage, forensic science, archaeology, and dozens of other fields. 

Unfortunately, researchers often only have access to samples of very small crystallites, or atomic clusters, in the form of powder or suspended in solution. In these cases, the X-ray patterns contain much less information, far too little for researchers to determine the sample’s atomic structure using existing methods. 

AI Extends the Method to Nanoparticles
The team trained a generative AI model on 40,000 known atomic structures to develop a system that is able to make sense of these inferior X-ray patterns. The machine learning technique, called diffusion generative modeling, emerged from statistical physics and recently gained notoriety for enabling AI-generated art programs like Midjourney and Sora. 

“From previous work, we knew that diffraction data from nanocrystals doesn’t contain enough information to yield the result,” Billinge said. “The algorithm used its knowledge of thousands of unrelated structures to augment the diffraction data.”

To apply the technique to crystallography, the scientists began with a dataset of 40,000 crystal structures and jumbled the atomic positions until they were indistinguishable from random placement. Then, they trained a deep neural network to connect these almost randomly placed atoms with their associated X-ray diffraction patterns. The net used these observations to reconstruct the crystal. Finally, they put the AI-generated crystals through a procedure called Rietveld refinement, which essentially “jiggles” crystals into the closest optimal state, based on the diffraction pattern.

Although early versions of this algorithm struggled, it eventually learned to reconstruct crystals far more effectively than the researchers had expected. The algorithm was able to determine the atomic structure from nanometer-sized crystals of various shapes, including samples that had proven too difficult for previous experiments to characterize. 

“The powder crystallography challenge is a sister problem to the famous protein folding problem where the shape of a molecule is derived indirectly from a linear data signature,” said Hod Lipson, James and Sally Scapa Professor of Innovation and chair of the Department of Mechanical Engineering at Columbia Engineering, who, with Billinge, co-proposed the study. “What particularly excites me is that with relatively little background knowledge in physics or geometry, AI was able to learn to solve a puzzle that has baffled human researchers for a century. This is a sign of things to come for many other fields facing long-standing challenges.”

The century-old powder crystallography puzzle is particularly meaningful to Lipson, who is the grandson of Henry Lipson CBE FRS (1910–1991) who pioneered computational crystallography methods. In the 1930s, Henry Lipson worked with Bragg and other contemporaries to develop early mathematical techniques that were broadly used to solve the first complex molecules, such as penicillin, leading to the 1964 Nobel prize in Chemistry.

Gabe Guo BS’24, currently a PhD student at Stanford University, who led the project while he was a senior at Columbia, said, “When I was in middle school, the field was struggling to build algorithms that could tell cats from dogs. Now, studies like ours underscore the massive power of AI to augment the power of human scientists and accelerate innovation to new levels.”