For decades, scientists and engineers have searched for ways to merge the properties of superconductors with semiconductors, aiming to enhance the efficiency and performance of electronic devices. Conventional materials like silicon and germanium have proven challenging when it comes to achieving superconductivity due to inherent limitations in maintaining a stable crystal structure while ensuring optimal conductivity. The new findings regarding germanium may resolve some of these long-standing issues, representing an important step toward the realization of efficient quantum technologies.

The researchers, led by New York University’s Javad Shabani, have focused on harnessing the unique properties of germanium to realize superconductivity. Previously regarded as a difficult task, this achievement involved an innovative approach to manipulating the atomic structure of germanium through a process known as doping. By introducing gallium, a softer element commonly found in the electronics sector, into the germanium matrix, the scientists were able to alter the electronic properties to foster superconductivity.

The methodology employed by the researchers is particularly noteworthy. Traditional doping techniques often lead to instability at high levels, resulting in the breakdown of crystal integrity, which is detrimental to achieving superconductivity. However, this new research employed precision techniques to incorporate gallium atoms into the germanium crystal lattice in a controlled manner, enabling the material to maintain structural stability while gaining superconducting properties.

This precise incorporation is achieved through a process known as molecular beam epitaxy, allowing for the growth of thin layers of crystals with a high level of control. By adjusting conditions during the epitaxy, the researchers managed to substitute germanium atoms with gallium at levels that typically would destabilize the crystal structure. Despite the inherent challenges, the researchers successfully demonstrated superconductivity at an astonishingly low temperature of 3.5 Kelvin, equivalent to approximately -453 degrees Fahrenheit.

The implications of these findings extend far beyond theoretical interest. Germanium, already a vital component in many advanced semiconductor devices, holds promise for future technological applications, particularly in the development of low-power cryogenic electronics and quantum circuits. As the demand for faster and more efficient electronic devices grows, integrating superconducting materials within established semiconductor frameworks could lead to rapid advancements in both consumer technology and industrial applications.

The research team also highlights the significance of maintaining clean interfaces between superconductors and semiconductors, essential for the successful integration of these materials into practical devices. This breakthrough could usher in a new era of high-performance electronic systems, where the advantages of both superconductivity and semiconducting materials are harmoniously combined.

In the larger context, the advancement of superconducting germanium is a pivotal moment for the field of condensed matter physics and materials science. The ability to create a functional superconducting material from a substance already prevalent in the semiconductor industry addresses many of the existing barriers to implementing quantum technologies in real-world applications. This discovery showcases the potential of controlled atomic manipulation to change conventional understanding of material properties.

Collaborating institutions, including ETH Zurich and Ohio State University, played a vital role in the research, contributing expertise in experimental techniques and analysis. This multifaceted collaboration underscores the importance of interdisciplinary approaches in addressing complex scientific problems. Furthermore, the funding support from the US Air Force’s Office of Scientific Research signifies the strategic importance of such advancements for national interests in technology development.

Ultimately, this study challenges previously held beliefs about the limitations of semiconductor materials regarding superconductivity. As researchers continue to explore the inextricable link between structure and electrical properties, the potential for unlocking new materials with tailor-made functions becomes increasingly feasible. The possibility of widespread implementation of superconductive materials in mainstream application could revolutionize numerous sectors, creating efficiency gains and enhancing usability across a range of technologies.

This research raises important questions concerning the systematic nature of superconductivity and the parameters that influence the emergence of zero-resistance states. As the scientific community digs deeper into these findings, further explorations may reveal additional routes to achieving superconductivity in other elemental semiconductors, fostering a new wave of innovation across industry sectors.

In summary, the pursuit of superconductivity in germanium represents an exciting intersection of material science and quantum physics, where innovative thinking and precise experimental techniques converge to unveil new capabilities. This achievement not only broadens the potential applications of germanium in technology but also sets the stage for future exploration of superconducting materials, emphasizing the role of controlled atomic interactions in driving modern scientific breakthroughs.

Subject of Research: Superconductivity in germanium
Article Title: Superconductivity in substitutional Ga-hyperdoped Ge epitaxial thin films
News Publication Date: 30-Oct-2025
Web References: http://dx.doi.org/10.1038/s41565-025-02042-8
References: N/A
Image Credits: Patrick Strohbeen/NYU

Keywords

Superconductivity, Semiconductors, Quantum technology, Germanium, Gallium, Molecular beam epitaxy, Material science, Condensed matter physics.

Central to this revelation is the concept known as nonequilibrium chemical short-range order (SRO), where atoms within metals do not achieve a fully randomized distribution despite intense deformation and thermal processing. Instead, atoms organize into intricate, stable configurations that deviate from the thermodynamic equilibrium predicted by classical metallurgy. Utilizing state-of-the-art machine learning techniques coupled with molecular dynamics simulations, the researchers meticulously tracked millions of atomic movements under conditions mimicking industrial metal processing. Surprisingly, rather than eradicating chemical order, these processes revealed persistent, non-random atomic motifs maintained even at high temperatures.

A critical discovery was that dislocations—line defects or three-dimensional “scribbles” in the metal’s crystal lattice—play a pivotal role in catalyzing this enduring chemical arrangement. Traditionally, such defects were thought merely to disrupt atomic bonds randomly, fostering homogeneity within the material. However, the new MIT study demonstrates that dislocations possess chemical preferences in the bonds they break. Specifically, they selectively sever weaker bonds, restructuring atomic neighborhoods in a non-random pattern that supports the persistence of short-range order. This dislocation-guided atomic shuffling fosters unique atomic patterns far from equilibrium, akin to the dynamic steady states vital for living systems, where constant energy exchanges prevent complete disorder.

This discovery challenged the prevailing dogma within materials engineering that mechanical deformation and thermal treatments inherently erase all atomic order, leaving a chemically randomized alloy microstructure. Instead, the MIT research presents a nuanced narrative, showing that the metallurgical processes leave an indelible imprint on atomic arrangements. Such nonequilibrium states manifest as complex, previously unseen chemical motifs, which materialize exclusively under realistic manufacturing conditions rather than idealized laboratory scenarios. The research underscores that atoms never achieve total randomness, holding out the tantalizing possibility that these chemical patterns could be deliberately manipulated to tune material properties.

The implications of this work extend across numerous technologically pivotal domains. Aerospace engineering, for instance, often requires materials optimized for exceptional strength-to-weight ratios. The ability to influence chemical short-range order through controlled dislocation dynamics during metal forging and rolling could enable the creation of alloys with bespoke performance characteristics, balancing low density with formidable mechanical strength. Similarly, in the semiconductor and nuclear sectors, understanding and harnessing these nonequilibrium chemical states could improve the reliability and efficiency of components exposed to extreme environments, such as radiation exposure inside reactors or the delicate interfaces within microelectronic devices.

Technically, unlocking this phenomenon required the development of computational frameworks capable of capturing the subtle interplay between atomic interactions and material deformation. The MIT team deployed advanced machine-learning interatomic potentials, which provide rapid, highly accurate predictions of atomic behavior by learning directly from quantum mechanical calculations. This enabled simulation of millions of atoms over timescales sufficient to observe the emergence and evolution of chemical patterns during thermal cycles and mechanical deformation that closely emulate real manufacturing processes. Complementing these simulations were statistical tools to quantify how short-range order evolves spatially and temporally, validating the computational predictions with experimental data.

The researchers further distilled their findings into a simplified theoretical model—one that encapsulates the essential physics underpinning the persistence of nonequilibrium SRO in metals. This model explicates how dislocations act as chemical order modulators rather than mere disorder agents. By showing that dislocations preferentially shuffle atoms to form low-energy atomic configurations, the model offers a predictive handle to anticipate chemical patterns across a range of alloy compositions and manufacturing parameters. This capability is transformative, offering material scientists a predictive blueprint for alloy design where processing-induced atomic order can be an engineerable feature rather than an overlooked artifact.

Intriguingly, the discovery of these nonequilibrium chemical orders does more than advance metallurgy; it broadens our fundamental understanding of out-of-equilibrium states in solid-state systems. These findings resonate with concepts from statistical mechanics and complex systems, where energy fluxes through a system maintain organized structures far from thermodynamic equilibrium. Metals undergoing deformation can, therefore, be viewed as dynamic adaptative systems where defect chemistry and mechanical work collectively imprint and sustain atomic-scale order, analogous in spirit to biological systems that harness nonequilibrium states for function and survival.

Beyond the material-specific insights, the innovative methodology championed by the MIT team highlights the growing importance of machine learning in physical sciences. By overcoming the computational limitations of traditional approaches, the team’s hybrid simulation and modeling framework could be applied to explore similar nonequilibrium phenomena in other materials, such as ceramics or composite systems. The integration of high-fidelity data-driven potentials with large-scale atomistic simulations sets a new benchmark for studying processing-microstructure-property relationships in materials engineering.

The study also encourages a reevaluation of catalysis and surface chemistry in metals, where local atomic arrangements significantly influence activity and selectivity. These nonequilibrium chemical orders could explain unexpected catalytic behaviors observed in industrial alloys and help design catalysts with unprecedented efficiency by tuning the short-range order via processing conditions. Similarly, radiation damage resistance—crucial for materials in nuclear reactors and space applications—might be enhanced by exploiting these persistent atomic motifs that alter defect evolution dynamics under irradiation.

Looking ahead, the research team intends to expand their investigation across a broader spectrum of metals and processing regimes, constructing comprehensive maps correlating fabrication parameters with emergent chemical short-range orders. Such maps will empower engineers with the means to predict and control atomic order in real-world manufacturing settings, ushering in an era where atomic-scale design is as integral to metals engineering as macroscopic shape and composition. The transition from fundamental discovery to applied innovation promises to be swift, given the increasing industry appetite for lightweight, high-performance metals tailored for specialized functions.

In sum, the MIT study marks a paradigm shift in our understanding of metal alloy microstructures, revealing that chemical order endures the rigors of manufacturing in far-from-equilibrium states orchestrated by dislocation dynamics. This insight upends traditional assumptions and equips materials scientists and engineers with novel theoretical and computational tools to harness these hidden atomic orders. Such progress heralds transformative potential across aerospace, nuclear, catalytic, and electronic materials, redefining how metals are designed, manufactured, and optimized for the future.

Subject of Research: Nonequilibrium chemical short-range order in metallic alloys

Article Title: “Nonequilibrium chemical short-range order in metallic alloys”

Web References:

• DOI: 10.1038/s41467-025-64733-z

Image Credits: Courtesy of Rodrigo Freitas

Keywords

Metals, Alloys, Materials Science, Materials Engineering, Alloy Behavior, Machine Learning, Computer Modeling

At the heart of this discovery lies the challenge of continuously shrinking the features etched onto silicon wafers, the foundational substrates for modern microchips. Traditional photolithography—the primary method of imprinting electrical circuits onto these wafers—reaches physical and material limits as engineers strive for features smaller than 10 nanometers. The problem is compounded by the inadequacy of standard resists, the radiation-sensitive coatings used to expose circuit patterns, which do not absorb higher energy radiation efficiently. To address this, the Johns Hopkins team has introduced a groundbreaking approach leveraging “beyond extreme ultraviolet radiation” or B-EUV, a sophisticated radiation source with shorter wavelengths capable of defining finer details.

The crux of their innovation stems from engineering novel metal-organic resists that absorb this high-energy radiation effectively, enabling ultra-fine patterning below the current technological limits. By incorporating metals such as zinc, the researchers created materials that strongly interact with B-EUV light, triggering electron emissions that initiate chemical transformations within an organic framework. This process etches intricate circuitry into the resist with unprecedented resolution, opening pathways to next-generation microchip manufacturing. The organic component they utilized is based on imidazole, a versatile molecule capable of forming robust bonds with metal atoms, thus creating stable but reactive coatings ideal for lithography protocols.

A significant hurdle overcome by the team was developing a method to reliably deposit these metal-organic resists at the wafer scale, maintaining nanometer-level control over the thickness. They pioneered a chemical liquid deposition (CLD) technique that allows these materials to be spin-coated in precisely calibrated layers. This solution-based deposition method is advantageous for scalability and consistency, vital factors for integration into existing semiconductor fabrication lines. Using a synergy of experimental investigations and computational modeling, the multidisciplinary team encompassing Johns Hopkins University, East China University of Science and Technology, and other leading labs, refined the chemistry and process parameters to optimize resist performance on standard 10 cm silicon wafers.

This breakthrough not only advances materials science but also revolutionizes lithography engineering by unlocking a spectrum of new metal-organic pairings. The team demonstrated that by manipulating both the metallic element and the organic ligand, they could fine-tune the absorbance efficiency and subsequent chemical reactivity post-irradiation. This versatility is crucial because different metal atoms respond distinctly to various radiation wavelengths, allowing tailored solutions for specific lithography applications. For instance, zinc emerged as a particularly effective metal for B-EUV, despite its relatively poor performance under traditional EUV radiation, highlighting the nuanced interplay between material composition and photonic excitation.

The implications of this discovery extend far beyond laboratory settings. As semiconductor manufacturers race to meet Moore’s Law’s demands, the ability to incorporate B-EUV lithography with these novel resists promises to significantly reduce feature sizes and enhance chip densities. The researchers anticipate that production lines utilizing this technology could enter commercial use within the next decade, spearheading a new era of microelectronics characterized by unparalleled device miniaturization and energy efficiency. This aligns seamlessly with the strategic roadmaps companies have set for their product development timelines, targeting breakthroughs in 10 to 20-year horizons.

Fundamental to this research was the collaboration across international scientific communities and national laboratories, combining expertise and state-of-the-art facilities. Institutions such as Brookhaven National Laboratory and Lawrence Berkeley National Laboratory contributed instrumental resources and knowledge, facilitating advanced characterization techniques essential for validating the resist materials’ properties. Likewise, partners like École Polytechnique Fédérale de Lausanne and Soochow University played pivotal roles in theoretical modeling and experimental verification, respectively, underscoring the collaborative nature of this cutting-edge research.

The team’s publication, appearing in the prestigious journal Nature Chemical Engineering, details the spin-on deposition approach of amorphous zeolitic imidazolate framework films for lithography applications. This work elucidates the fundamental chemistry enabling the formation of homogeneous, ultra-thin films that serve as precise masks during radiation exposure. By controlling the film formation down to nanometer-level variations, the technique ensures reproducible patterning critical for semiconductor fabrication standards, paving the way for widescale adoption in industrial processes.

The novel resist materials offer more than just patterning precision; they boast increased chemical robustness and environmental stability compared to traditional photoresists. This resilience is paramount as manufacturers integrate higher intensity radiation sources, which can degrade or damage conventional resists, leading to defects and yield loss. In contrast, the metal-organic frameworks developed provide stability under extreme processing conditions, minimizing degradation and enhancing throughput – a vital economic factor.

Moreover, the adaptability of this chemistry is noteworthy. With a palette of over ten metals and hundreds of potential organic ligands, the research opens an expansive design space enabling lithographers to customize resist properties for specific wavelengths and manufacturing needs. This modular approach empowers semiconductor fabrication engineers to fine-tune absorption characteristics and chemical response profiles tailored to emerging lithography technologies beyond B-EUV, future-proofing the industry against rapid evolution in photonic sources and fabrication demands.

As the semiconductor industry faces mounting pressure to reduce costs while pushing the limits of miniaturization, this research arrives as a potential game-changer. The use of solution-processed metal-organic resists combined with advanced deposition techniques promises to streamline production and enhance feature resolution simultaneously. The approach not only promises to drive innovation in consumer electronics with faster, more energy-efficient chips but also holds promise for broader technological domains including artificial intelligence hardware, quantum computing elements, and next-generation sensors in aerospace and automotive systems.

In sum, Johns Hopkins researchers have unveiled a multidisciplinary, collaborative solution that marries cutting-edge materials science with revolutionary lithography methods. Their work charts a new course for semiconductor manufacturing, offering a tangible pathway to surpass existing technological limits with an economically viable process. As the world increasingly demands smarter, smaller, and faster devices, this innovation stands poised to catalyze the next major leap in microelectronics, fundamentally altering how circuits are built and enabling unprecedented capabilities across multiple industries.

Subject of Research: Development of novel metal-organic resists for B-EUV lithography enabling sub-10 nanometer microchip features.

Article Title: Spin-on deposition of amorphous zeolitic imidazolate framework films for lithography applications

News Publication Date: 11-Sep-2025

Web References: https://www.nature.com/articles/s44286-025-00273-z

Image Credits: Xinpei Zhou, Johns Hopkins University

Keywords: Semiconductors, Microelectronics, Manufacturing, Electrical engineering, Chemical engineering

A recent multi-institutional research collaboration involving Drexel University, Purdue University, Vanderbilt University, the University of Pennsylvania, Argonne National Laboratory, and the Institute of Microelectronics and Photonics in Warsaw has unveiled groundbreaking insights into the atomic thermodynamics of MXenes. Led by renowned researchers Yury Gogotsi and Babak Anasori, this team has decoded the atomic-level interplay of energy and disorder within MXenes, illuminating the forces that dictate their structural formation and stability. Their landmark study, published in the journal Science, is poised to revolutionize how AI-driven tools can accelerate the discovery and design of new MXene materials with tailor-made functionalities.

MXenes derive their fascinating properties from the precise organization of atom-thick layers, where subtle alterations in the types of metals and their sequence dramatically influence electrical conductivity, thermal characteristics, and chemical reactivity. Yet, this structural complexity makes experimental synthesis an iterative and painstaking process. Until now, much of MXene research has centered on empirical methods, synthesizing and characterizing thousands of variants in search of promising candidates. The collaborative research effort shifts focus towards a fundamental thermodynamic understanding of how atomic arrangements transition from order to disorder, governed by competing enthalpic (energy) and entropic (disorder) forces.

By delving into the “order to disorder transition” in layered 2D carbides, the researchers established foundational principles that quantify how these thermodynamic forces influence MXene stability. This approach combines theoretical atomic modeling with advanced experimental imaging methodologies such as dynamic secondary ion mass spectrometry (SIMS) to observe atomic distributions layer-by-layer. Such high-resolution analyses revealed that MAX phases—the parent materials of MXenes, made of layers of multiple metallic elements—exhibit discernible ordering patterns when containing up to six different metals. In contrast, beyond six elements, the MXenes tend toward entropically stabilized, random atomic mixing.

This enthalpy versus entropy playbook is more than an academic insight; it unlocks a predictive framework for synthesizing MXenes with custom atomic architectures. These findings directly impact the strategic selection of metal constituents and layered arrangements to engineer MXenes with optimized properties, from electrical resistivity to infrared radiation permeability. Notably, the research team correlated increasing metallic diversity within layers to changes in these critical functional parameters, offering new avenues for material design in fields ranging from energy storage to aerospace engineering.

Significantly, the integration of these thermodynamic insights with artificial intelligence (AI) and machine learning technologies heralds a new era in material discovery. Historically, AI approaches in materials science have been handicapped by insufficient foundational data on complex chemical interactions and underlying physical forces. This study bridges that gap by providing a robust dataset and governing principles to train AI models capable of predicting stable MXene configurations before physical synthesis. Such AI-augmented design can rapidly breach previously insurmountable experimental bottlenecks, enabling exploration of the vast compositional space of MXenes—effectively an infinite sea of potential materials.

Lead researcher Babak Anasori envisions a future where AI-guided strategies streamline not only the discovery but also the atomistic design of materials with extraordinary capabilities. The ultimate ambition lies in developing MXenes that outperform existing materials under extreme environmental conditions—whether in harsh outer space or demanding deep-sea environments. Applications could include longer-lasting electric vehicle batteries operating efficiently across temperature extremes or materials enabling clean energy technologies that rely on unprecedented durability and conductivity.

The study’s findings also contribute valuable knowledge to the broader field of high-entropy materials—complex alloys and ceramics composed of multiple principal elements. Their demonstration that short-range atomic ordering governs the balance of enthalpy and entropy paves the way for engineering layered ceramics with finely tuned disorder, offering enhanced performance and stability. This bridges the gap between traditional alloy design paradigms and the emergent domain of 2D nanomaterials, amplifying the potential applications beyond MXenes alone.

Utilizing a methodical approach, the researchers synthesized 40 unique MXene variations—30 of which were novel—integrating up to nine different metallic elements within layered lattices. Such compositional complexity required precise atomic characterization, backed by dynamic SIMS, which enabled direct observations of atomic distributions down to several atomic diameters. These experimental observations not only corroborated theoretical predictions but also provided essential parameters for future modeling and AI training datasets.

As artificial intelligence continues to evolve, this synergy between foundational thermodynamic principles and computational power could fundamentally accelerate the timeline from material conception to real-world application. Machine learning algorithms, trained with empirical data from these novel MXenes, can intelligently predict the most promising candidates, drastically reducing the cost and time required to explore uncharted compositional territories. This paradigm shift offers hope for breakthrough solutions in sustainable energy, electronics, and beyond.

In summary, the collaborative work represents a milestone in understanding how atomic-level enthalpy and entropy dictate the formation and properties of layered 2D carbides. By merging experimental atomic-scale insights with sophisticated AI frameworks, researchers stand on the brink of a revolution in materials science—a revolution that promises to unlock MXenes’ full potential and empower next-generation technologies with unprecedented performance in extreme environments. As the scientific community embraces these tools and principles, the frontiers of what materials can achieve will expand dramatically, charting a promising path for both fundamental research and industrial innovation.

Subject of Research: Not applicable

Article Title: Order to disorder transition due to entropy in layered 2D carbides

News Publication Date: 4-Sep-2025

Web References:
https://www.science.org/doi/10.1126/science.adv4415

References:
Gogotsi, Y., Anasori, B., Wyatt, B. C., et al. (2025). Order to disorder transition due to entropy in layered 2D carbides. Science. DOI: 10.1126/science.adv4415

Image Credits: Devynn Leatherman-May, Brian C. Wyatt, and Babak Anasori, Purdue University.

Keywords

Materials science, Artificial intelligence, Machine learning, Chemistry