If you’ve ever swum in the ocean and been touched by seaweed, there’s a good chance it was Sargassum! This free-floating seaweed can have population explosions in coastal waters, degrading water quality, ecosystems, fisheries, and tourism. However, despite its drawbacks, scientists think that Sargassum could be useful for extracting valuable metals, known as rare earth elements (REEs). 

REEs are classified by their average masses and atomic weights. Light REEs include cerium, praseodymium, and neodymium, middle REEs include gadolinium, terbium, and dysprosium, and heavy REEs include erbium, thulium, and ytterbium. These metals are used in magnets, electronic motors, wind turbines, spacecraft, and more. Our current methods of extracting REEs, open-pit mining and chemical processing, are invasive, causing habitat loss and producing toxic waste. Additionally, REEs are difficult to import due to restricted access worldwide. But what if these metals could be extracted using natural methods? 

Scientists from Woods Hole and UCLA recently tested whether Sargassum could accumulate REEs, creating a sustainable solution to these problems. They harvested Sargassum at Waterfront Park in Woods Hole, rinsed the wet samples, and stored them in sterilized, filtered seawater, which they aerated with 12-hour cycles of light and dark to keep the Sargassum alive. They dried another set of samples in the sun under controlled outdoor conditions, stored them in sealed containers, and later rehydrated them in seawater for 3 hours before the experiments.

For each experiment, they added 5 grams (about 0.2 ounces) of Sargassum to solutions containing a mixture of 9 REEs – cerium, praseodymium, neodymium, gadolinium, terbium, dysprosium, erbium, thulium, and ytterbium – in concentrations totalling 0.1 micromolar (μM) REEs, 11μM REEs, 100 to 110μM REEs, and 575 to 600μM REEs. They performed each experiment 4 times: twice with fresh seaweed and twice with rehydrated seaweed. 

The researchers removed 1 gram (about 0.04 ounces) of seaweed from each solution after 1, 3, and 7 days, and dissolved it in acid to release the REEs it had absorbed. They analyzed the concentrations of REEs in each seaweed sample and the leftover solutions using 2 different plasma-based methods. They used these REE concentrations to calculate the ratio of REEs in the Sargassum versus in the solution, known as the bioconcentration factor, to determine how effectively the seaweed absorbed REEs. 

They found that after 7 days at lower REE concentrations (0.1μM), the fresh seaweed had bioconcentration factors of over 1,000. At higher REE concentrations (11μM), their bioconcentration factors ranged from about 400 to 700. However, these levels of REEs were eventually toxic to the fresh Sargassum, so they died after 2 weeks. In addition, the higher REE concentrations of 100 to 600μM were immediately toxic to the fresh Sargassum, which died within 1 day in solution. 

The researchers determined that once the seaweed died, it could no longer actively take up REEs into its tissues but could only accumulate them on its surface or adsorb them. Adsorption can be thought of as a powdered donut – when the surface is covered with powdered sugar, no more powder fits on the donut. Similarly, there was a limit to how many REEs could stick to the Sargassum. Due to this limit, the fresh seaweed samples had bioconcentration factors below 250 in the highest REE concentrations. In contrast, the rehydrated Sargassum consumed less REEs at lower concentrations, but didn’t experience toxicity and continued to accumulate REEs at higher concentrations.

Finally, the team compared Sargassum to a common industrial method of accumulating REEs, known as activated carbon. REEs bond easily to activated carbon because of its abundant surface area and high porosity. Both materials accumulated a similar amount of REEs in solutions of 0.1μM REEs. However, at 575 to 600μM REEs, Sargassum accumulated about twice as many REEs as activated carbon, especially the heavier REEs. 

​The researchers concluded that Sargassum is a natural hyper-accumulator, meaning it successfully concentrates REEs. However, they recommended that future researchers test whether Sargassum performs the same in the natural environment as it does in the lab. If so, Sargassum could be used to accumulate REEs from wastewater, like mine drainage, or other water sources with higher concentrations of REEs. Regardless, they suggested that using Sargassum to obtain REEs could simultaneously expand global access to these metals and mitigate toxic blooms.

The post Using seaweed to harvest valuable metals appeared first on Sciworthy.

In the realm of advanced manufacturing and materials science, nickel-based alloys have long stood as a cornerstone for various high-performance applications, especially within nuclear reactors where exposure to neutron irradiation is inevitable. A recently corrected study by Roy, Mondal, Clement, and colleagues published in npj Advanced Manufacturing delves deep into how these materials respond to such extreme conditions. This comparative analysis between powder metallurgy-hot isostatic pressing (PM-HIP) and forging manufacturing methods unravels critical insights that could revolutionize the future selection and engineering of nuclear materials.

Neutron irradiation, a primary stressor in nuclear environments, can dramatically change the microstructural and mechanical properties of alloys. The study foregrounds two predominant fabrication techniques for Ni-based superalloys—PM-HIP and forging—and investigates how each method governs the alloys’ resilience against such irradiation. Given that any degradation in these materials could undermine reactor safety and efficiency, understanding these effects is paramount.

The PM-HIP process, an innovatively additive approach, involves pressing powdered alloy under high temperature and pressure to densify the structure. This technique inherently produces fine-grained microstructures with minimal defects. Conversely, forging, a traditional approach, shape-controls metals through plastic deformation at elevated temperatures, often yielding distinct grain structures and phase distributions. Each method imparts unique starting conditions affecting how the materials evolve under neutron bombardment.

Roy and team meticulously subjected both PM-HIP and forged Ni-based alloys to neutron irradiation, closely mimicking operational reactor environments. Through advanced characterization methods—such as transmission electron microscopy (TEM), atom probe tomography (APT), and nanoindentation—they captured the nuanced transformations occurring within the alloys on an atomic scale. These analyses provided a granular view of irradiation-induced embrittlement, swelling, and phase stability.

One striking revelation from their findings was how PM-HIP produced alloys exhibited superior radiation tolerance compared to their forged counterparts. The uniform and refined microstructure generated by PM-HIP appeared to restrict defect cluster growth, subsequently limiting radiation-induced hardening and swelling. This effect can be attributed to a higher density of grain boundaries acting as defect sinks—a critical factor in mitigating radiation damage.

In contrast, forged alloys manifested more pronounced irradiation-induced phase segregation and precipitate coarsening. The larger grain sizes and residual stresses inherent to forging seemingly amplified defect accumulation. This phenomenon has deleterious effects on mechanical performance, increasing susceptibility to crack initiation and propagation under operational stressors.

Another fascinating aspect explored was the comparative evolution of the gamma-prime (γ’) phase, a key strengthening phase in Ni-based superalloys. The study detailed how PM-HIP processing results in a more homogenous γ’ distribution, facilitating a stable microstructure even as irradiation introduces energetic defects. The forged alloys, however, showed more significant γ’ coarsening and dissolution, jeopardizing their long-term strength.

The authors pointed out the role of irradiation temperature in modulating damage severity. While neutron flux intensity was kept consistent, variations in testing temperatures revealed differing diffusion kinetics in the alloys. At elevated temperatures, PM-HIP samples maintained microstructural integrity longer, hinting at a critical thermal activation component in defect annealing processes.

In terms of mechanical properties, nanoindentation experiments post-irradiation revealed that PM-HIP alloys retained a higher fraction of their original hardness and modulus values. The forging counterparts suffered more drastic declines, underscoring the enhanced radiation resilience provided by processing route optimization. This directly correlates to potential improvements in structural components exposed to neutron-rich environments.

Perhaps one of the most compelling implications of this research is its potential impact on reactor lifespan and maintenance costs. Using PM-HIP fabricated Ni-based alloys could meaningfully delay the onset of irradiation-induced failure mechanisms. This, in turn, translates into longer service intervals, reducing downtime and enhancing overall operational safety margins.

Furthermore, the study highlights the versatility of PM-HIP in producing complex geometries while retaining superior radiation resistance. This opens avenues not just for power generation but also for aerospace and defense sectors where neutron exposure is a concern. The ability to tailor microstructures and properties via powder metallurgy could redefine the boundaries of material engineering.

Roy and colleagues also addressed the necessity for future investigations into the synergistic effects of thermal aging and irradiation, as well as the influence of alloying elements on radiation tolerance. Their research sets a benchmark, encouraging interdisciplinary efforts to merge computational modeling with empirical work for predictive material design.

The meticulous correction appended to the original publication underscores the authors’ commitment to scientific rigor and transparency, reinforcing the reliability of these groundbreaking insights. The study’s implications resonate beyond academic circles, paving the way for industrial adaptations in manufacturing irradiated-resistant components.

In conclusion, this comparative study underscores how manufacturing choices at the microstructural level profoundly sway the performance of Ni-based alloys under neutron irradiation. The findings advocate for a preferential adoption of PM-HIP techniques to bolster material longevity and safety in the nuclear sector, marking a pivotal advance in materials science.

As the energy landscape evolves, and the demand for robust, radiation-resistant materials escalates, such research not only provides a technical beacon but also paves the pathway towards safer, more efficient nuclear technology. The intersection of advanced manufacturing and irradiation science, as exemplified by this study, embodies the future of resilient engineering materials.

---

Subject of Research: Effects of neutron irradiation on Ni-based alloys fabricated by powder metallurgy-hot isostatic pressing (PM-HIP) and forging manufacturing methods.

Article Title: Author Correction: Effects of neutron irradiation on Ni-based alloys: a comparative study between PM-HIP and forging.

Article References:
Roy, R., Mondal, S., Clement, C.D. et al. Author Correction: Effects of neutron irradiation on Ni-based alloys: a comparative study between PM-HIP and forging. npj Adv. Manuf. 3, 25 (2026). https://doi.org/10.1038/s44334-026-00096-7

Image Credits: AI Generated

At the cutting edge of aerospace, energy, and computing, material innovation stands as a critical frontier. Traditionally, companies seeking to enhance the performance and resilience of metals encounter formidable challenges: understanding the intricate behavior of novel materials under real-world conditions often necessitates actual physical synthesis and testing. This step has remained indispensable because even the most sophisticated computational simulations struggle to capture the full complexity of atomic arrangements inherent in many solid materials, particularly metallic alloys. The result is a costly and time-consuming cycle that slows progress in material science and engineering.

A breakthrough has now emerged from a team of researchers at the Massachusetts Institute of Technology (MIT), who have devised a computational framework that promises to transform the way metals are modeled across diverse compositions. Central to this innovation are advanced machine-learning algorithms designed to predict the properties of chemically complex metal alloys with unprecedented accuracy and efficiency. By refining the training datasets used to develop these models, the team has succeeded in encapsulating the vast heterogeneity of atomic environments that characterize disordered metallic materials, thereby enhancing predictive precision.

The research, detailed in a recent publication in the journal Science Advances, offers compelling evidence that machine-learning potentials, when constructed with carefully curated training data, can reliably simulate material properties spanning a broad spectrum of metal alloys and operational conditions. This methodological advancement stands to significantly reduce reliance on expensive experimental validation, enabling faster development cycles and more cost-effective exploration of alloy design spaces.

Rodrigo Freitas, MIT’s TDK Career Development Professor of Materials Science and Engineering and senior author on the paper, emphasizes the versatility of this approach. While the study focuses primarily on metallic alloys—a domain fraught with chemical disorder—the underlying principles and techniques have the potential to be adapted for other classes of materials, including semiconductors and sustainable steels. Freitas envisions wide-ranging applications, from aerospace materials engineered for extreme environments to novel components in energy systems and beyond.

The crux of the challenge in modeling metals lies in capturing the influence of intrinsic chemical disorder on material properties. Two alloys with identical elemental compositions can exhibit starkly different mechanical behaviors depending on their atomic-scale structure; one might be brittle, while another offers impressive ductility. Computational models must therefore operate at the atomic level, simulating interactions between individual atoms to predict macroscopic properties. Over the past twenty years, machine learning has emerged as an indispensable tool for constructing these atomic interaction potentials. However, existing models conventionally assume ordered or near-ordered atomic arrangements, limiting their efficacy when faced with the irregular, heterogeneous atomic environments typical of real-world alloys.

Chemical disorder implies a staggering diversity of local atomic neighborhoods, each subtly unique in terms of bonding patterns and energetic stability. This diversity poses a formidable obstacle for machine learning, which relies heavily on representative training data to generalize effectively. Conventional data-generation approaches involve brute-force sampling, which is computationally prohibitive—often demanding upwards of 100,000 hours of supercomputer time for a single alloy—and lacking in adaptability when alloy compositions shift. Thus, the crux of the innovation lies in constructing training datasets that capture the widest possible range of relevant atomic configurations without redundancy.

Building on prior work where they quantified chemical complexity by analyzing the frequency and distribution of small atomic clusters, Freitas’ group has now developed an information-theoretic approach to optimize training data generation. Through a process of atomic substitutions and iterative refinements, they curate datasets that maximize the diversity of local chemical environments presented to the machine-learning models. This ensures that each training example uniquely contributes to the model’s learning, avoiding the pitfalls of repetitive and uninformative data.

Implemented in this way, the newly trained potentials exhibit marked improvements in predicting metallurgical properties compared to models trained on randomly sampled data or even other sophisticated sampling strategies. The fidelity of these simulations to true chemical bonding dynamics is critical; without it, models risk providing generic insights into material behavior rather than precise predictions applicable to specific alloys and practical conditions. This heightened level of chemical realism opens the door to highly reliable simulations that can stand in place of expensive, time-consuming lab tests.

The team put their methodology through rigorous validation against a variety of metal alloys, assessing the performance of their machine-learning models against industry-standard counterparts developed by tech giants like Google and Microsoft. Remarkably, the MIT-trained models consistently outperformed these much larger, computationally intensive models, demonstrating that thoughtful data curation can eclipse brute-force data volume in enhancing machine learning potentials.

Killian Sheriff, a lead author of the paper and a PhD candidate at MIT, spearheaded extensive testing across alloy systems and a wide array of material properties, supported by complementary efforts from colleagues Daniel Xiao, Yifan Cao, and University of Sheffield’s Lewis R. Owen, who contributed experimental data for benchmarking. This collaborative effort provided comprehensive evidence that the models could predict phase diagrams—a cornerstone of materials science that chart alloy phase stability across temperatures and compositions—with accuracy rivaling direct experimental observations.

Phase diagrams are particularly important because they inform practical metallurgical processes like welding, casting, and heat treatment. Accurately capturing the subtle energetic preferences for different atomic arrangements within alloys is essential to forecasting phase transformations and resultant material properties under various conditions. The models’ ability to reveal these “subtle energetic biases” is a testament to the deep chemical insight that machine-learning potentials can now attain.

Beyond phase stability, the researchers are actively deploying their technique to predict mechanical resilience and radiation damage tolerance in alloys—a critical consideration for materials operating in extreme environments such as nuclear reactors and aerospace applications. Their goal is to design alloys that maintain strength and resist degradation under stressors that include high temperatures, intense radiation, and mechanical loads.

A key element of this initiative is harmonizing these advanced computational tools with existing industrial workflows and engineering software. Freitas underscores the necessity of integrating these innovations seamlessly into current decision-making frameworks if they are to foster widespread adoption in materials development pipelines. This pragmatic orientation aims to ensure that the profound scientific advances translate swiftly and effectively into tangible industrial benefits.

The work has garnered support from the U.S. Air Force Office of Scientific Research, reflecting its strategic significance for advanced materials in defense and aerospace contexts where performance margins are tight and failure costs are high. As industries seek materials that combine unmatched performance, sustainability, and economic viability, the MIT team’s contribution could represent a paradigm shift—linking detailed atomic-level understanding with scalable, robust material design accelerated by intelligent data-driven simulations.

By dismantling the bottleneck of excessive computational costs and enabling high-fidelity modeling of chemically complex alloys, this research charts a path toward rapid, informed innovation in materials science. With this toolkit in hand, scientists and engineers can explore uncharted compositional spaces more confidently, accelerating progress toward next-generation metals engineered for the demands of the future.

---

Subject of Research: Machine-learning models for atomic-level simulations of chemically disordered metal alloys

Article Title: Machine learning potentials for modeling alloys across compositions

News Publication Date: 19-Jun-2026

Web References: DOI: 10.1126/sciadv.aea9951

References: Science Advances, 2026

Image Credits: Not provided

Keywords

Materials science, Material properties, Metals, Alloys, Chemical disorder, Machine learning, Atomic simulations, Phase diagrams, Materials engineering, Computational materials science, Artificial intelligence, Chemistry

Physicists report the discovery of new evidence for a mysterious quantum state in an unusual class of materials, in findings that could illuminate a long-running debate over how these systems transition into superconductivity.

In physics, symmetry breaking refers to processes in which a symmetric state collapses from its initial state of disorder into a more ordered one, albeit now possessing less symmetry.

The new research findings reveal evidence that time-reversal symmetry, a fundamental kind of symmetry in nature, appears to be broken at temperatures significantly higher than previously recognized.

The research was detailed in a recent study published in Nature Physics.

Mysterious Kagome Metals

In their study, an international team of researchers led by a team at the Korea Advanced Institute of Science and Technology focused on what are known as kagome metals—a variety of ferromagnetic quantum materials possessing unique qualities.

For their study, the researchers used a specific kagome metal called Cesium Vanadium Antimonide (CsV₃Sb₅), a novel material first discovered in 2021 by researchers at UC Santa Barbara’s NSF Quantum Foundry.

The researchers found that during experiments with the material, which is recognized as a candidate superconductor, CsV₃Sb₅ showed indications that time-reversal symmetry was broken at temperatures significantly higher than previously recognized.

This is significant because it is suggestive of an elusive phenomenon physicists call loop-current order, which describes a state in which the circulation of electrons throughout a material occurs in persistent microscopic loops.

A Signal Emerges

Using an advanced photoemission technique, the team was able to uncover subtle differences in the way electrons respond to circularly polarized light, which is light that is produced using an electric field vector that rotates in a circle as the light wave propagates, equaling one complete rotation for each wavelength.

Based on the team’s measurements, a distinctive signal emerged, which they say is consistent with a time-reversal symmetry-breaking state, meaning that their observations provide what may be the best experimental evidence to-date for an exotic quantum state of matter known as loop-current order, wherein electrons spontaneously assume microscopic wave-like formations.

Loop-current order in kagome metals has been an ongoing area of study for physicists, and the team’s new findings offer what may be the best evidence yet uncovered.

Exotic States and High-Temperature Superconductivity

One reason the team’s findings are so significant is that their results offer some clarity for the otherwise complex sequence of phase transitions that are known to occur within CsV₃Sb₅.

Based on their research, the team now believes loop-current order manifests as the temperature begins to decrease, which is then followed by a periodic rearrangement of electrons known as a charge density wave. Finally, superconductivity then follows, accompanied by the absence of electrical resistance altogether.

Fundamentally, the team’s work could offer new insights into the behavior and characteristics of strongly correlated quantum materials, which could hold promise for future studies involving the mechanisms that produce exotic states of matter.

“These insights deepen our understanding of the phase landscape in kagome metals and highlight connections with a correlated system exhibiting analogous transition hierarchy,” the team reports.

In the future, such studies may also help scientists finally attain the long-sought goal of high-temperature superconductivity, whereby materials capable of conducting electricity with zero resistance, and at temperatures above 30 K (-243°C), helping to reduce the current expenses associated with cooling systems required for superconducting, and ultimately helping to bring these high-power engineering technologies into commercial viability.

The team’s recent study, “Evidence of time-reversal symmetry breaking above the charge density wave order in a kagome metal,” was published in Nature Physics.

Micah Hanks is the Editor-in-Chief and Co-Founder of The Debrief. A longtime reporter on science, defense, and technology with a focus on space and astronomy, he can be reached at micah@thedebrief.org. Follow him on X @MicahHanks, and at micahhanks.com.

Instead of heating metals, Foundation Alloy beats them into submission. The startup has raised $22 million to scale up production of its alloys.

Industrial-scale mining in Africa to support global supply chains is leading to unprecedented deforestation across the continent, with

Stars are mostly made of 2 elements: hydrogen and helium. While this has always been the case, those 2 elements and lithium were the only elements in existence when the Big Bang occurred around 14 billion years ago. When the first stars exploded, they released those primordial elements, as well as heavier elements produced by nuclear fusion inside them. 

Astronomers call all elements heavier than hydrogen and helium metals, a term chemists use quite differently. Subsequent generations of stars, including the Sun, formed in clouds of gas and dust enriched with these metals, such as carbon, oxygen, magnesium, and silicon. Scientists estimate that modern stars are 1% to 5% metal by mass.

Astronomers claim there is no solid evidence that stars contain exceptionally high amounts of metals, but some, called chemically peculiar stars, appear to. Astronomers study stars by looking at the patterns of light they emit, called spectra. Each element produces a unique light pattern, so astronomers can compare the light patterns in a star’s spectra to determine how much of each element is present, especially in the outer layers of the star. Researchers theorize that chemically peculiar stars don’t actually have more metals than average stars. Instead, they think that metals from their interiors diffuse to their outer layers more than in most stars.

A team of researchers from the American Association of Variable Star Observers and Masaryk University in Czechia recently observed 85 chemically peculiar stars to understand their behavior and better classify them. For their study, they first used the General Catalog of CP Stars, published in 2009 in Astronomy & Astrophysics, to identify targets across the 4 classes of these stars, labeled CP1 through CP4. CP1 stars have strong spectral patterns for iron and other heavy elements, CP2 stars have strong patterns for silicon, chromium, strontium, and europium, CP3 stars have strong patterns for mercury and manganese, and CP4 stars have either unusually weak or usually strong helium patterns. 

The team compiled a list of 85 stars to observe, then used the BRIght Target Explorer (BRITE) Constellation to monitor changes in their brightness. The BRITE Constellation is a set of 5 satellites equipped with telescopes and cameras for either red or blue light. Using the BRITE Constellation, the team monitored each star for several days. 

They found that 74 of these 85 chemically peculiar stars varied in brightness during their survey. They attributed this to the varied abundance of metals on their surfaces, which would form dark patches that go in and out of view from Earth’s perspective as the stars rotate. The team observed that 6 of these 74 stars appeared to change in brightness over multiple periods. They were surprised by this result because a star’s brightness wouldn’t vary over multiple periods if the changes were due to rotation. They compared their findings to data other scientists had collected from these stars with the Transiting Exoplanet Survey Satellite, or TESS, and found that all 6 stars had been misclassified as chemically peculiar stars.

The other 11 chemically peculiar stars appeared to show no periodic changes in their brightness, suggesting that they’re stationary. The team claimed that some CP1 and CP3 stars don’t rotate, but they identified cases in which CP2 and CP4 stars that ought to rotate appeared to be stationary. They suggested 2 potential reasons for this. One is that these CP2 and CP4 stars are misclassified, requiring more thorough analysis of their spectra to confirm their classifications. The other is that the stars rotate slowly, with rotational periods of 50 days or longer, which would be difficult to distinguish from those of totally stationary stars.

The team concluded that more astronomers should revisit the historical classifications of stars, especially as technology advances and more space-based telescopes become available. This strategy would allow future researchers to draw better data from research archives and catalogs. Additionally, they claimed that their method of pairing long-term monitoring via small satellites with TESS data is well-suited for refining classifications, identifying misclassified objects, and further exploring the structure and mechanics of chemically peculiar stars.

The post What makes ‘chemically peculiar stars’ peculiar? appeared first on Sciworthy.