In the relentless quest to mimic the extraordinary efficiency and precision of biological molecular machines, chemists have long sought to create synthetic molecular motors capable of directed, unidirectional motion. These artificial constructs promise revolutionary advances in nanotechnology, potentially transforming everything from targeted drug delivery to energy conversion at the smallest scales. Yet, despite these strides, achieving complex functionalities akin to biological machinery remains a formidable challenge. The recent breakthrough presented by van Beek, Sidler, and Feringa introduces a novel class of molecular motors with two distinct rotors operating simultaneously at different rotational frequencies. This pioneering design echoes the advanced control found in natural molecular assemblies and hints at unprecedented levels of mechanical complexity in synthetic nanoscale devices.

Traditional molecular motors have predominantly featured a single rotor unit, which undergoes conformational changes driven by light irradiation or thermal energy to induce continuous rotation. While impressive on its own, the single-rotor model imposes limits on the diversity and complexity of mechanical outputs that these molecules can generate. The innovation introduced by this research lies in the integration of two structurally distinct rotors within a single molecule, each capable of independent, actively powered rotation. This dual-rotor configuration effectively operates like a molecular steering system, a concept previously unrealized in synthetic chemistry.

A key challenge addressed by the authors is the control of rotor activation preferences without relying solely on thermal processes, which typically govern isomerization rates in molecular motors. Instead, they harness differences in photochemical behavior—how each rotor responds to specific wavelengths of light—to selectively activate one rotor over the other. This photochemical bias allows each rotor to turn at its intrinsic frequency, unaffected by the constraints of thermal equilibration, thus imparting a finely tunable dynamic to the system.

The design strategy involves careful selection and modification of rotor structures to exploit their unique absorption spectra and photochemical reaction pathways. By tuning these molecular features, the researchers demonstrated that the rotational frequencies could be modulated through variations in the rotor’s electronic and steric environments. Moreover, solvent effects were shown to influence the photochemical behavior, providing an additional parameter to fine-tune the relative activity of each rotor within the same molecular framework.

The practical implications of this work extend beyond fundamental chemistry into the realm of molecular machinery design. By proving the feasibility of dual, independently driven rotors, this study opens avenues for creating nanoscale devices capable of complex mechanical outputs—such as synchronized or coupled rotational motions, directional switching, and multi-step reaction sequences powered by light. Such capabilities mirror the intricate, multi-component systems observed in biological motors like ATP synthase and flagellar motors.

Furthermore, this research underscores the versatility of photochemical control in molecular machines. Photons offer a non-invasive, highly controllable energy input, allowing spatial and temporal precision in motor activation. By establishing a protocol for biasing rotor activity photochemically, the authors have laid the groundwork for future systems where multiple rotors or motor components can be selectively engaged or inhibited simply by altering the wavelength or intensity of incident light.

Another compelling aspect of this dual rotor system is its potential adaptability. The approach could be extended to other rotor architectures or combinations thereof, including different classes of molecular motors. This modularity suggests a general blueprint for engineering synthetic systems with multi-functional and multi-frequency components, akin to the modular design principles seen in biological nano-machines, where distinct parts perform specialized roles coordinated to achieve complex outcomes.

The team’s experiments were complemented by detailed photochemical analyses and kinetic studies revealing how the energy landscape of the molecule facilitates selective rotor activation. Advanced spectroscopic techniques and computational models helped elucidate the mechanistic basis underlying the asymmetric light-driven activation pathways. This mechanistic insight not only reinforces the robustness of the dual rotor concept but also guides future molecular designs aimed at refining rotor selectivity and performance.

In practical terms, the ability to drive two rotors simultaneously but asynchronously offers the potential to develop molecular-level “gearboxes” or “steering systems,” conceptually similar to mechanical systems in macroscopic machinery. Such systems could allow precise control of molecular orientation and movement, a prerequisite for constructing more sophisticated nanoscale machines capable of performing intricate tasks with timing and sequence control.

Importantly, the work provides a novel approach to tackle a long-standing hurdle in synthetic molecular machine development: the interplay and coordination of multiple active components within the same system. By establishing photochemical rotor bias as a tunable parameter, the authors effectively demonstrate a path forward where multi-component interactions can be controlled predictably, a crucial step towards integrating molecular motors into complex functional assemblies.

The research, appearing in Nature Chemistry, comes from the laboratories of renowned molecular scientist Ben Feringa, who famously contributed to the development of the first light-driven molecular motors. This latest advance not only cements his legacy but also paves the way for a new era where molecular machines achieve unprecedented dynamism, complexity, and autonomy, all powered by light.

One of the most exciting prospects emerging from this work is its potential to inspire future applications beyond fundamental science, including the assembly of nanoscale robotic devices capable of performing useful work or information processing at the molecular level. By harnessing the responsive behavior of each rotor to specific light stimuli, molecular systems can be engineered for programmability—turning on or off mechanical functions with exquisite control.

However, challenges remain in scaling and integrating these dual rotor systems into larger networks and ensuring sustained operation under biologically or technologically relevant conditions. Nonetheless, this pioneering study solidly advances the frontier of molecular machines, showing that complex, multi-rotor systems are no longer aspirational but firmly within reach, thanks to innovative photochemical engineering.

As this exciting field continues to evolve, the marriage of photochemistry and molecular motor design promises to unlock deeper control over motion and function at the nanoscale, bringing us ever closer to realizing artificial molecular machinery with capabilities rivaling those honed by nature over billions of years.

Subject of Research: Molecular machines; dual molecular motors; photochemical rotor control; nanoscale mechanical motion

Article Title: A photochemical rotor bias in dual molecular motors

Article References:
van Beek, C.L.F., Sidler, E. & Feringa, B.L. A photochemical rotor bias in dual molecular motors.
Nat. Chem. (2026). https://doi.org/10.1038/s41557-026-02142-5

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s41557-026-02142-5

In the evolving landscape of digital education, the integration of artificial intelligence (AI) has opened new frontiers for enhancing both teaching and assessment methodologies. A pioneering study published recently in Frontiers of Digital Education introduces an innovative framework—PEG-Prompt—that harnesses the power of large language models (LLMs) to evaluate student course project reports (CPRs) with unprecedented depth and precision. Unlike conventional automated essay scoring systems primarily focused on writing proficiency, PEG-Prompt goes beyond, embedding the sophisticated Paul-Elder critical thinking model to offer a multifaceted appraisal of student output.

The necessity for such an advanced framework arises from the inherent limitations of manual CPR assessment. Educators often face labor-intensive processes and subjective evaluation inconsistencies. Automated solutions have attempted to alleviate these challenges but typically emphasize rhetorical and grammatical aspects alone. The PEG-Prompt framework, however, acknowledges the multidimensionality of academic projects by rigorously assessing six critical dimensions: structure, logic, coherence, originality, citation, and knowledge proficiency. This holistic approach ensures a thorough appraisal aligned with real-world academic standards.

Central to PEG-Prompt’s design is the innovative application of the Paul-Elder critical thinking framework—a well-established pedagogical model that underscores essential intellectual traits such as clarity, accuracy, relevance, and logic. By embedding these principles into the prompting mechanism used by LLMs, PEG-Prompt guides AI to dissect course reports not only for linguistic quality but also for the depth and rigor of argumentation. This enables a nuanced evaluation that mirrors human critical analysis, fostering higher-order thinking skills in students.

To further refine the evaluation process, PEG-Prompt employs an advanced technique of extracting key report content before scoring. This step effectively filters essential information, ensuring that LLM evaluations focus accurately on pertinent components of the project. Additionally, the framework implements few-shot learning strategies by incorporating exemplary scoring cases within the prompts. This method fine-tunes the response of language models, enhancing their ability to replicate human grading standards and minimize discrepancies.

The empirical strength of PEG-Prompt is demonstrated through a rigorously constructed dataset comprising 110 anonymized CPRs, which served as the validation ground. Experiments conducted across four mainstream large language models reveal that PEG-Prompt not only consistently reduces scoring errors but also significantly improves alignment with human evaluations. Quantitative metrics combined with visualization analyses confirm the model’s enhanced performance, solidifying its practical viability.

Beyond mere numerical scoring improvements, PEG-Prompt’s value lies in generating rich, human-like feedback that supports both formative and summative educational objectives. Students receive targeted insights that illuminate their strengths and areas needing improvement, encouraging reflective learning and intellectual growth. Such feedback aligns with modern educational paradigms emphasizing continuous improvement and metacognitive awareness.

The broader implications of PEG-Prompt extend into cultivating vital intellectual habits in students. By systematically integrating dimensions like originality and citation, the framework nurtures academic integrity and creativity. Its emphasis on logical coherence and knowledge proficiency equips learners with analytical reasoning acumen, essential for success in an information-rich and complex world.

Moreover, this breakthrough emphasizes the potential of AI to transcend conventional limitations, embodying critical teaching philosophies within algorithmic constructs. PEG-Prompt illustrates how prompt engineering, when thoughtfully designed, can transcend mechanical scoring, offering a pathway to elevate educational evaluation through sophisticated reasoning frameworks.

The publication of this work marks a significant milestone in AI-powered educational assessment, potentially redefining how academic outputs are evaluated in digital domains. It paves the way for future innovations that harmonize human pedagogical wisdom with the computational power of large-scale language models, promising more equitable, insightful, and instructive evaluation mechanisms.

As digital education continues expanding globally, frameworks like PEG-Prompt serve as vital tools for educators aiming to balance scalability with qualitative depth. This synergistic approach ensures technology amplifies—not replaces—the critical human elements central to effective pedagogy.

Ultimately, the PEG-Prompt framework exemplifies a harmonious fusion of classical critical thinking models and cutting-edge AI technology, charting a path toward more comprehensive, transparent, and supportive educational assessments. Its successful implementation underscores the transformative capacity of interdisciplinary innovation at the nexus of cognitive science and artificial intelligence.

Subject of Research: Not applicable
Article Title: Evaluating the Efficacy of a Multifaceted Prompt for Use with LLMs to Evaluate Course Project Reports
News Publication Date: 23-Apr-2026
Web References: http://dx.doi.org/10.1007/s44366-026-0086-y
Image Credits: Higher Education Press
Keywords: Education, Large Language Models, Critical Thinking, Automated Assessment, Artificial Intelligence, Course Project Reports, Prompt Engineering, Paul-Elder Model

In a groundbreaking advancement for molecular biology and computational chemistry, researchers at the University of Amsterdam’s Van ’t Hoff Institute for Molecular Sciences have unveiled an innovative software suite designed to accurately model DNA structures within biomolecular assemblies. Dubbed MDNA, this state-of-the-art toolkit empowers scientists across multiple disciplines—including biochemistry, molecular biology, bioinformatics, and biophysics—to visualize, analyze, and simulate DNA with unprecedented atomic precision. This development promises to significantly deepen our understanding of DNA behavior in complex biological environments, advancing both fundamental research and applied sciences.

At the heart of MDNA’s innovation is its ability to generate three-dimensional atomic coordinates for double-stranded DNA molecules, regardless of their shape or complexity. Unlike traditional tools that might rely heavily on generalized models or limited structural libraries, MDNA adopts the rigid base formalism originally embodied in the Curves+ code, a well-regarded computational framework for nucleic acid conformation analysis. This approach treats each base pair within the DNA as an individual rigid unit, allowing for a finely tuned representation of the molecule’s structural intricacies.

What sets MDNA apart from many existing molecular modeling tools is its flexibility and adaptability. Users can effortlessly design DNA molecules following virtually any arbitrary spatial curve, making the creation of highly customized and intricate DNA architectures more accessible than ever before. Moreover, the software supports the modification and extension of pre-existing DNA structures, facilitating iterative design and refinement processes crucial for research that explores DNA-protein interactions and biomolecular mechanics.

The software’s user-friendly nature further democratizes molecular modeling. It has been extensively tested by students and researchers from diverse scientific backgrounds—many with minimal prior programming experience—and has proven accessible for both novices and experts. Accompanying the software are comprehensive tutorials and demonstrations, positioning MDNA as not only a research tool but also as an invaluable educational resource suitable for workshops and classroom environments.

A vital component of MDNA’s structural modeling capabilities comes from the collaborative implementation of an advanced energy function, developed in partnership with the group led by Helmut Schiessel at TU Dresden. This energy function facilitates rapid equilibration of DNA structures while accurately modeling essential physical properties such as stiffness, flexibility, and intrinsic mobility. By incorporating physical constraints, it enables the simulation of biologically relevant phenomena like DNA supercoiling without the computational overhead typically associated with all-atom simulations.

In addition to its robust structural generation features, MDNA excels as an analytical tool. It can process DNA configurations derived from molecular dynamics simulations, facilitating a seamless integration between modeling and analysis within a unified workflow. This integration is crucial for researchers investigating the dynamic nature of DNA and its interactions with proteins and other cellular components, as it reduces the barriers between data generation, exploration, and hypothesis testing.

The scope of MDNA extends beyond just double-stranded DNA; the software includes a growing library of sixteen nucleobase types with plans for future expansion, offering an expanding toolkit to model various DNA modifications and analogs. Such versatility is especially pertinent as synthetic biology and epigenetics increasingly demand precise modeling tools capable of representing non-canonical DNA structures and chemical modifications.

MDNA’s efficient computational framework leverages simplifications that avoid simulating every atom explicitly, allowing structures to reach equilibrium within seconds. This significant reduction in computational time without sacrificing accuracy presents substantial advantages for high-throughput DNA modeling tasks, enabling rapid prototyping of DNA-based nanodevices or exploring a vast landscape of theoretical DNA conformations.

The open-source nature of the MDNA suite invites broad usage and collaborative development within the scientific community. Available publicly via repositories like Figshare and Github, it encourages transparency, reproducibility, and community-driven enhancements. This openness not only fosters innovation but also helps establish MDNA as a standard platform for DNA modeling in both academic and industrial research contexts.

By bridging detailed atomic-level resolution with high computational efficiency and an intuitive interface, MDNA fills a critical gap in the current toolbox for molecular simulation. It offers molecular scientists an indispensable means to unravel DNA’s structural complexities, enhancing our grasp on biological mechanisms ranging from gene regulation to chromosome packaging.

As research increasingly focuses on the interplay between DNA and proteins within the crowded cellular environment, tools like MDNA pave the way for more accurate models that can directly inform experimental design and therapeutic development. These models may, in turn, accelerate progress in fields such as drug discovery, gene editing, and synthetic biology, where precise structural understanding is paramount.

The collaboration between experimental insight and computational ingenuity as demonstrated in MDNA exemplifies the future of molecular sciences—where software not only supports but actively shapes research frontiers. With the support of comprehensive documentation and educational outreach, MDNA is poised to become a cornerstone technology for any scientist captivated by the elegance and complexity of DNA.

Subject of Research: Molecular modeling and simulation of DNA in biomolecular assemblies

Article Title: MDNA: A comprehensive molecular modeling toolkit for DNA in biomolecular assemblies

Web References:
DOI link to the published paper

Image Credits: HIMS / University of Amsterdam

Keywords: Computational chemistry, Biochemistry, Molecular biology, Bioinformatics, Biophysics, DNA modeling, Molecular simulation, DNA-protein interactions, Molecular dynamics

In a landmark study conducted at the Icahn School of Medicine at Mount Sinai, researchers have revealed a previously undetected drug-binding pocket within PKMYT1, a kinase intimately involved in cell cycle regulation and cancer progression. This groundbreaking discovery not only challenges current understanding of the protein’s structural dynamics but also underscores both the promise and inherent limitations of contemporary artificial intelligence (AI) methods in the field of drug discovery.

Kinases like PKMYT1 orchestrate critical cellular processes such as growth and division, rendering them prime candidates for therapeutic targeting in oncology. Traditionally, drug development strategies against kinases have centered on the ATP-binding site, which is essential for their catalytic function. However, the ATP-binding motifs among kinases exhibit high degrees of conservation, complicating efforts to engineer drugs with high specificity. This often results in off-target effects that can diminish clinical effectiveness and elevate toxicity risks.

By leveraging a synergistic approach that combined AI-based protein modeling with experimental validation, the researchers uncovered a novel allosteric pocket on PKMYT1. Notably, this binding site escaped detection by leading AI platforms, including the widely acclaimed AlphaFold2. This hidden pocket presents a unique avenue for more selective drug design, diverging from the conventional ATP-competitive strategies and heralding a new paradigm in kinase inhibition.

The research unveiled that PKMYT1 exhibits pronounced conformational flexibility, oscillating between distinct shapes rather than maintaining a static structure. Such dynamic behavior implicates the existence of transient binding pockets that evade prediction by current computational models. These transient pockets might serve as ‘Achilles’ heels’ for selective inhibitor binding, a concept with profound implications for drug discovery beyond this single protein.

Experimentally, the team employed X-ray crystallography and biochemical assays to corroborate binding interactions and validate the biological implications of their findings. Complementing these traditional methods, molecular dynamics simulations and advanced AI models like AlphaFold3 and Boltz-2 were utilized to explore whether computational tools could retrospectively predict the discovered binding modes, exposing gaps in present-day AI predictive capability.

A particularly striking revelation was the sensitivity of the protein-ligand interaction to minuscule chemical modifications. Slight changes in the molecular structure of candidate compounds dramatically altered their binding site preference, toggling between the newfound hidden pocket and more canonical sites. This sensitivity reflects the intricate nature of protein-ligand recognition and underscores the necessity for meticulous experimental validation alongside in silico predictions.

The dual leadership of the study, Professors Avner Schlessinger and Michael Lazarus, highlights a balanced perspective on AI’s role. While AI tools excel at confirming known structural patterns, they may falter in uncovering novel or cryptic sites, especially in proteins that are inherently flexible. This work emphasizes that experimental inquiry remains indispensable, even as AI transforms biomedical research.

From a translational perspective, the discovery of this new druggable site opens exciting therapeutic possibilities. By designing inhibitors that selectively target this unique allosteric pocket, drug developers may circumvent the specificity and toxicity challenges endemic to existing kinase inhibitors. This could potentially accelerate the development of next-generation cancer therapies with improved efficacy and safety profiles.

Moreover, these findings serve as a wake-up call for the AI drug discovery community. The inability of cutting-edge AI platforms to predict the full spectrum of protein conformations spotlights areas for computational innovation, particularly in modeling protein plasticity and allostery. Enhanced algorithms, informed by experimental data like this study’s insights, may soon enable more comprehensive structural predictions with direct impacts on drug development strategies.

Looking ahead, the research team plans to advance the chemical optimization of lead compounds that engage the hidden PKMYT1 pocket with greater potency and selectivity. Concurrently, they aim to survey a broader array of cancer-associated kinases for similar cryptic sites, potentially revealing a wider landscape of novel therapeutic targets across the kinome.

This study represents a significant stride in precision oncology, where the nuanced understanding of protein structure and dynamics can lead to highly selective molecular interventions. It epitomizes the evolving interplay between AI and experiment—where computational hypotheses must be rigorously tested in the laboratory to unlock biomedical breakthroughs.

The work, published recently in the Journal of the American Chemical Society, titled “Allosteric Inhibition of PKMYT1 Induces a Unique, Inactive ATP Binding Site Conformation,” showcases the power of integrating modern AI tools with classical experimental techniques. It exemplifies a model for future drug discovery endeavors aiming to outpace cancer’s complexity through technological and scientific synergy.

As the scientific community digests these revelations, the broader implications are clear: protein targets once deemed structurally intractable may hide exploitable vulnerabilities, awaiting discovery through combined AI and experimental approaches. This challenges researchers to rethink strategies in drug design, moving toward a more dynamic and flexible framework to combat diseases with precision.

In summary, the Icahn School of Medicine’s team has not only unearthed a novel therapeutic target on a cancer-relevant kinase but also illuminated the frontiers and limitations of AI-driven drug discovery. Their pioneering work reinforces that while algorithms can guide drug development, the enduring rigor of experimental science remains critical to truly transformative medical advances.

Subject of Research: Cells

Article Title: Allosteric Inhibition of PKMYT1 Induces a Unique, Inactive ATP Binding Site Conformation

News Publication Date: June 3, 2026

Web References: http://dx.doi.org/10.1021/jacs.6c05178

References: Herrington, N. B., Khamrui, S., Zhao, Y., Lansiquot, C., Wu, R., Pandey, G., Lazarus, M. B., & Schlessinger, A. (2026). Allosteric Inhibition of PKMYT1 Induces a Unique, Inactive ATP Binding Site Conformation. Journal of the American Chemical Society. DOI: 10.1021/jacs.6c05178

Image Credits: Herrington, et al., Journal of the American Chemical Society

Keywords: Drug development, kinase inhibition, cancer therapy, AI drug discovery, protein dynamics, allosteric pocket, PKMYT1, molecular dynamics, AlphaFold, X-ray crystallography

The director defends investment in and use of AI-generated storyboards, saying the immediacy of communicating his vision to cast and crew is ‘creatively freeing’

Martin Scorsese’s announcement that he has invested in an AI company and uses the technology to create storyboards has triggered a backlash from fellow members of the film industry.

The New York Times reported that Scorsese had been appointed in 2025 as a partner and adviser to Black Forest Labs, a German-based venture that specialises in text-to-image generative AI.

In an innovative leap for sustainable architecture, researchers at Chalmers University of Technology in Sweden have engineered a groundbreaking, entirely bio-based material derived from an unconventional source: yeast. This novel material possesses the unique capability to be 3D printed and customized, opening new avenues for ecological design in construction and interior applications. Traditionally, many architectural elements such as plaster, plastics, and synthetic textiles have been heavily reliant on fossil-based resources, which contribute substantially to environmental degradation. The Chalmers team’s yeast-based hydrogel challenges this paradigm by offering a renewable alternative tailored for elements like daylight modulating screens, room partitions, and other interior architectural components.

The construction industry is notoriously resource-intensive and a significant contributor to global greenhouse gas emissions. This demands urgent development of renewable and resource-efficient materials that reduce both the carbon footprint and waste generated in building processes. In response to this challenge, the Chalmers research group investigated the use of industrial residues and natural polymers to create material systems that promote circularity within architecture. Their resulting composite blends baker’s yeast, cellulose fibers extracted from wood, alginate obtained from brown seaweed, glycerol sourced from plants, and water into a cohesive hydrogel matrix suitable for additive manufacturing technologies.

The material is fundamentally a soft, jelly-like substance that maintains malleability and can undergo precise shaping via pressure-based 3D printing at ambient temperature. Unlike conventional manufacturing processes requiring high temperatures or supports, this innovative method allows for energy-saving fabrication and complex geometries without material waste. The researchers have likened the initial phase of preparation to a baker’s process in reverse: the yeast is first heat-deactivated to stabilize it, then blended with other constituents to form a smooth print-ready hydrogel. This technique enables unparalleled design freedom and control over key properties such as texture, shape, and material distribution.

One of the remarkable aspects of this yeast-based system is its tunability. Small modifications in formulation can vary transparency, color, and surface finish, making the material highly adaptable for specific interior environments. The natural hues span from gentle yellows to rich browns, which can be further diversified through the addition of natural pigments or genetically pigmented yeast strains. This versatility promises broad usability, ranging from sunlight-filtering architectural screens to customizable wall panels and partitions. Such attributes position the yeast hydrogel as a potent green substitute for plastics and synthetic textiles in the built environment.

The choice of yeast as a primary biomass component is particularly visionary. Yeast cells proliferate rapidly under non-stringent conditions and are less susceptible to contamination, making production scalable and consistent. Rather than using yeast for its conventional role in fermentation, the research capitalizes on its role as a structural and volumetric agent within the composite. By deactivating the yeast before printing, the material attains physical robustness essential for architectural applications. Additionally, the team highlights the prospect of utilizing by-products from brewing and agricultural industries, which currently often become waste, to strengthen sustainable material cycles.

This research redefines sustainability by embracing the finite lifespan of materials within built systems. Contrary to traditional materials engineered primarily for long-term durability, the yeast-based hydrogel embraces biodegradability and cyclic use. This conceptual shift allows architects and designers to contemplate materials not only in terms of longevity but also their capacity for natural degradation, integrating the aging process as a conscious design element. Such a philosophy aligns closely with principles of circular economy and ecological stewardship.

The fabrication technology employed—3D printing—plays a critical role in actualizing zero-waste production. The additive process enables creation of highly intricate forms at room temperature without generating offcuts or requiring support scaffolds, significantly reducing raw material consumption. Finer control over structural parameters also suggests potential for optimizing thermal properties, light transmission, and mechanical performance. This integration of biomaterials with digital manufacturing marks a significant milestone towards truly sustainable and bespoke architectural solutions.

Despite its promise, the research team acknowledges that additional investigations are necessary before commercial-scale deployment. Future work will explore critical performance metrics including mechanical strength, fire resistance, moisture behavior, and scaling manufacturing techniques. The aspiration is to engineer the yeast composite into a fully certified building material that can withstand practical environmental demands while maintaining its ecological benefits. Addressing these challenges will be pivotal for broader acceptance and utilization of bio-based architectural materials.

Looking forward, the researchers envision a future where Engineered Living Materials (ELMs) transcend current capabilities by incorporating multifunctional properties such as self-healing or air-purifying functions. Such advancements could transform how buildings interact dynamically with their environment, enhancing indoor air quality and reducing maintenance through active material responses. The current yeast-based hydrogel thus represents not just a material innovation but a foundational step towards smart, sustainable architecture.

The multidisciplinary approach behind this innovation combines expertise in biomaterials, architecture, and manufacturing science. The synergy between biology-inspired components and digital fabrication technologies opens new dimensions for creativity and ecological responsibility in design. As awareness about material impact grows globally, solutions like the Chalmers yeast hydrogel position bio-based composites as strategic alternatives within future circular building economies.

This pioneering work underscores an emerging paradigm in which sustainability, functionality, and aesthetics coalesce. It challenges the material conventions of architecture by demonstrating novel pathways to reduce reliance on fossil and synthetic inputs while enhancing design versatility and material lifecycle thinking. As the built environment moves towards more resilient and adaptive frameworks, bio-innovations like those from Chalmers University signal a vibrant direction for future material science in architecture.

Subject of Research: Development of a novel 3D-printable yeast-based architectural material

Article Title: Novel 3D printable yeast-based materials for architectural applications

Web References:
https://doi.org/10.1016/j.foar.2026.01.003

Image Credits: Chalmers University of Technology | Henrik Sandsjö

Keywords

Sustainable Architecture, Bio-based Materials, 3D Printing, Yeast Hydrogel, Circular Design, Engineered Living Materials, Renewable Construction Materials, Biomaterials, Digital Manufacturing, Interior Design, Biodegradability, Environmental Innovation

In a breakthrough that promises to revolutionize the fields of optics and wireless technologies, researchers Xu and Rahmani have introduced an innovative methodology for all-optical and wireless image processing using metasurfaces. This development, presented in their 2026 publication in Light: Science & Applications, unveils the transformative potential of meta-operators—compact, engineered surfaces that manipulate electromagnetic waves with unprecedented precision. By leveraging these ultrathin metasurfaces, the team demonstrated a paradigm shift away from conventional electronic image processing, opening doors to faster, more efficient, and inherently parallel processing systems that can operate at the speed of light.

At the core of this innovation is the concept of metasurfaces, which are artificially structured interfaces composed of subwavelength-scale elements that control wavefronts of light or other electromagnetic signals. Unlike traditional optical components that rely on bulk materials and gradual changes in refractive index, metasurfaces achieve complex wave manipulations via abrupt phase, amplitude, and polarization shifts imposed on impinging waves. Xu and Rahmani’s meta-operators harness these capabilities to perform core image processing tasks, including filtering, edge detection, and spatial frequency analysis—all executed in real time without electronic conversions.

The researchers engineered these metasurfaces with precise nanoscale patterns that implement mathematical operators fundamental to image processing directly in the optical domain. This approach exploits the inherently parallel nature of light propagation, allowing entire two-dimensional images to be processed simultaneously. Not only does this dramatically accelerate processing speeds, but it also reduces the energy consumption and hardware complexity associated with electronic processors. These meta-operators represent a leap forward in green photonics, pushing the envelope for sustainable and high-throughput information processing systems.

Moreover, Xu and Rahmani’s meta-operators are not confined to traditional optical setups. Their design enables wireless image processing, wherein electromagnetic signals are modulated and processed in free space by metasurfaces without the need for wired connections or bulky lenses. This could pave the way for novel wireless imaging applications in various domains, including remote sensing, health diagnostics, and augmented reality. Imagine wearable devices or drones capable of on-the-fly image enhancement and interpretation through invisible metasurface layers, transforming raw capture into actionable data instantaneously.

The theoretical underpinnings of this advancement rest on carefully mapping integral calculus operations onto wavefront transformations enabled by metasurfaces. For example, differentiation and integration operators, commonly used in edge detection and feature extraction, are implemented by designing phase gradients and amplitude masks that mold the incident wave’s spatial profile. Xu and Rahmani utilized a combination of inverse design algorithms and deep learning techniques to optimize meta-atom configurations that realize these operators with minimal signal loss and maximal processing fidelity.

Experimental demonstrations highlighted the remarkable versatility of the meta-operators. In one setup, a metasurface was programmed to perform real-time edge enhancement of input images projected onto it. The processed output, captured via a simple optical detector, showcased sharpness and contrast improvements after one pass through the metasurface—a feat traditionally requiring multiple electronic processing steps. These experimental results validate the massive potential of integrating meta-operators into compact and portable optical devices, which could redefine fields from computer vision to medical imaging diagnostics.

Beyond image enhancement, the meta-operators possess the capacity to conduct complex transformations such as Fourier transforms optically. This realization reduces the latency and hardware footprint of frequency domain analyses, vital for signal processing, holography, and adaptive optics. The ability to seamlessly switch metasurface functionalities through dynamic reconfiguration hints at future devices capable of multifunctional image processing without physical replacement, achieved through externally tunable materials or integrated microelectromechanical systems.

The wireless implications of this research are equally profound. Conventional wireless imaging systems typically rely on electronic demodulation and processing. By embedding metasurfaces into transmitters or receivers, image information can be encoded, transformed, and decoded directly in the electromagnetic wave as it propagates through space. This direct wave processing reduces latency, enhances security by intrinsic encoding, and potentially increases bandwidth utilization. These capabilities are particularly significant for next-generation communication systems, including 6G and beyond, where ultrafast and secure data handling is paramount.

Additionally, this research contributes to the ongoing miniaturization and integration trend in photonics, where entire processing pipelines can be condensed into ultrathin flat devices, removing the bulk and fragility of traditional optical elements. The ultracompact form factor of meta-operators enables their seamless integration with existing hardware such as image sensors, cameras, and wireless communication modules. This paves the way for smart, autonomous devices with embedded intelligence for real-time data interpretation without offloading computation to external processors.

The theoretical and practical significance of meta-operators also stimulates exciting opportunities in artificial intelligence and machine vision. Optical pre-processing via metasurfaces can reduce computational loads on AI models by delivering cleaner, feature-enhanced inputs directly at the hardware level. Such synergy between physical computing and AI algorithms could boost performance in autonomous systems, robotics, and advanced surveillance, where rapid, power-efficient decision-making is critical.

The fabrication techniques behind these metasurfaces rely on state-of-the-art nanolithography and material deposition processes, capable of producing highly reproducible meta-atom arrays on scalable substrates. This suggests that the transition from experimental setups to mass production is feasible, accelerating the adoption of meta-operator based image processing in commercial and industrial domains. Furthermore, the use of versatile materials such as phase-change compounds or tunable dielectrics offers pathways towards dynamically reconfigurable metasurfaces adaptable to variable tasks and environments.

Challenges remain in optimizing the efficiency and signal-to-noise ratio of these devices, particularly as image complexity and processing demands grow. However, ongoing advancements in computational design and fabrication precision promise continuous enhancement in meta-operator performance. The integrated combination of optical physics, materials science, and computational algorithms embodied by this work heralds a new era of multifunctional, compact photonic devices tailored for the ever-expanding demands of modern imaging technologies.

Xu and Rahmani’s landmark study underscores metasurfaces’ potential to transcend passive optical components, transforming them into active computational elements. Their work seamlessly merges fundamental wave physics with practical image processing needs, illustrating a vivid vision for future optical systems where computation and transmission coalesce on the same ultrathin platform. This convergence will likely inspire further interdisciplinary research, culminating in innovative devices that redefine how we capture, process, and interpret visual information.

As society increasingly relies on real-time visual data for myriad applications, from autonomous navigation to medical diagnostics, the meta-operator approach offers a game-changing strategy that combines speed, efficiency, and miniaturization. The prospect of all-optical, wireless image processing compels the scientific community and industry alike to reimagine infrastructure, fostering transformative technologies that operate at the fundamental speed of light.

In conclusion, the introduction of meta-operators as demonstrated by Xu and Rahmani marks a significant milestone in photonics and image processing. By harnessing the tailored resonances and wavefront shaping capabilities of metasurfaces, they have unlocked a versatile toolbox for performing key image manipulations without electronics or bulky optics. This pioneering work sets the stage for future smart optical devices that integrate sensing, processing, and communication in a compact, efficient form factor—ushering in a new era of photonic intelligence that will permeate multiple technological landscapes.

Subject of Research:
New meta-operator-based metasurfaces enabling all-optical and wireless image processing techniques.

Article Title:
Meta-operators: all optical and wireless image processing via metasurfaces.

Article References:
Xu, L., Rahmani, M. Meta-operators: all optical and wireless image processing via metasurfaces. Light Sci Appl 15, 264 (2026). https://doi.org/10.1038/s41377-026-02318-1

Image Credits: AI Generated

In a groundbreaking revelation that challenges long-standing assumptions in the field of frost formation, researchers at the University of Illinois Urbana-Champaign have unveiled a previously unknown mechanism by which frost propagates on surfaces. Led by Professor Nenad Miljkovic from The Grainger College of Engineering, the team’s study introduces the discovery of “suspended ice bridges,” distinct spatial modes of ice bridge formation that occur in stark contrast to the conventional understanding whereby ice bridges grow strictly along the substrate. Their findings, published in the prestigious journal Nature Physics, not only deepen scientific comprehension of frost dynamics but also herald innovative strategies for designing anti-frosting surfaces critical to a wide range of engineering applications.

The formation and propagation of frost is a critical consideration in the design and operation of many technological systems, including but not limited to air-source heat pumps, refrigeration units, and aerospace components. At the microscopic scale, frost spreads primarily through the creation of ice bridges—connective formations that link neighboring supercooled liquid droplets, effectively enabling freezing fronts to advance rapidly across surfaces. For decades, it has been widely accepted, largely based on conventional top-view imaging methods, that these ice bridges advance in two dimensions, traveling along the solid substrate. The Illinois team’s novel research radically revises this view by revealing a three-dimensional aspect to ice bridge growth.

Employing advanced high-resolution optical microscopy complemented by a sophisticated technique known as focal plane shift imaging (FPSI), the researchers were able to visualize frost formation processes in unprecedented detail. This approach enabled them to identify two distinct modes of spatial ice bridge growth that depend heavily on surface wettability. On hydrophilic, or water-attracting, surfaces, ice bridges conform to existing models and propagate along the substrate, consistent with established understanding. Conversely, on superhydrophobic surfaces, which repel water, ice bridges exhibit a unique suspended growth mode, extending above the surface and bridging droplets through the air rather than along the solid interface beneath.

This suspended, or “out-of-plane,” mode of ice bridge formation represents a fundamental departure from previously accepted frost propagation models. Its discovery has been largely overlooked until now due to methodological constraints in prior experimental observations. The significance lies not only in its novelty but also in the profound implications it holds for frost management technologies. According to first author Dr. Siyan Yang, a postdoctoral researcher under Professor Miljkovic, the surface’s wettability is the pivotal parameter that controls the transition between these two ice bridge growth modes.

Through systematic experimentation varying the apparent contact angles of water droplets on different surfaces, the research team identified a critical threshold near 105 degrees. Above this value, typical of superhydrophobic surfaces, suspended ice bridges become the dominant frost propagation route. This insight adds a crucial layer to our understanding: wettability influences not just droplet behavior and spacing but fundamentally governs the three-dimensional architecture of ice bridge growth, redirecting freezing pathways and thereby affecting frost dynamics in ways not previously appreciated.

The researchers further elucidated the mechanisms governing the spatial mode of ice bridges by examining the droplet geometries and corresponding vapor diffusion pathways intrinsic to each surface type. On superhydrophobic surfaces, the geometric configuration of droplets alters the shortest path through which vapor diffuses, shifting it away from the substrate and favoring airborne bridge formation. This anatomical shift arises because droplets adopt a more spherical shape, which minimizes the area of contact with the underlying surface and affects vapor transport dynamics, creating conditions favorable for suspended ice bridges.

One of the most striking findings was the markedly slower growth rate of suspended ice bridges compared to their substrate-attached counterparts. This pronounced deceleration stems from the diminished thermal coupling between the suspended ice bridge and the cold substrate below, which effectively reduces the vapor pressure gradients responsible for driving ice accretion. Consequently, frost propagation is substantially impeded on superhydrophobic surfaces displaying suspended ice bridge formation, representing a potent natural defense against frost accumulation.

Experimentally, the Illinois team demonstrated that frost propagation speed can be diminished by more than 80 percent on surfaces promoting the suspended ice bridge mode. This breakthrough has immediate practical relevance, as it directly translates to enhanced operational efficiencies and prolonged performance lifetimes in frost-sensitive systems. To validate this, the researchers extended their experimental framework to encompass commercial finned-tube heat exchangers. These components are ubiquitous in heating, ventilation, air conditioning (HVAC), and refrigeration systems and often suffer from efficiency losses due to frost buildup.

The results obtained from tests on these heat exchangers corroborated the laboratory findings, showcasing that surfaces engineered to support suspended ice bridges can dramatically delay the onset of frost, slow its propagation, and consequently sustain optimal heat transfer performance over extended periods. This represents a crucial advancement in linking microscopic frost structure behavior to macroscopic system-level outcomes. By providing this mechanistic understanding, the research opens the door to the rational design of surfaces that strategically manipulate ice bridge formation to curb frost accumulation and improve energy efficiency.

This discovery also challenges the conventional two-dimensional framework of frost propagation, calling for a re-examination of theoretical models from a three-dimensional perspective. Recognizing that ice bridge growth can extend above the surface plane compels scientists and engineers to reconsider frost formation dynamics and interfacial heat transfer processes in materials and devices exposed to frost conditions. The new paradigm not only reshapes fundamental phase change science but could ripple across disciplines involved in thermal management and surface science.

Professor Miljkovic underscored the transformative potential of these findings by emphasizing how the deeper understanding of ice bridge formation will catalyze innovative surface engineering efforts. These efforts aim to tailor interfacial properties to regulate frost spreading deliberately, fostering more energy-efficient thermal management and phase change systems. The possibility of controlling frost at the microscale through surface wettability and geometry adjustments marks a pivotal step toward technologically advanced, frost-resilient surfaces.

Dr. Siyan Yang’s role as principal experimenter and co-author underscores the multidisciplinary expertise fueling the breakthrough. Her extensive research in frost nucleation, propagation mechanisms, and anti-icing surface design has led to numerous influential publications in high-impact journals and multiple invention patents. The convergence of physics, materials science, and engineering in this study exemplifies the burgeoning field of interface-driven energy transport phenomena.

Together with a diverse team of collaborators, Miljkovic and Yang’s pioneering work redefines the fundamental science of frost formation, presenting suspended ice bridges as a novel, three-dimensional mechanism with profound implications for future research and practical applications. This advancement represents a seminal leap, promising not only enhanced understanding but also transformative technologies for energy and thermal management systems facing the perennial challenge of frost.

Subject of Research: Frost propagation mechanisms and surface-driven ice bridge formation during sessile droplet freezing.

Article Title: Growth and control of suspended ice bridges during sessile droplet freezing

News Publication Date: 28-May-2026

Web References:
https://www.nature.com/articles/s41567-026-03296-2
http://dx.doi.org/10.1038/s41567-026-03296-2

References:
Yang, S., Chu, F., Ganesan, V., Faghihi, P., Ghaddar, D., Zhang, W., Liu, J., Yang, J.B., Huang, A., Boyina, K., Chettiar, K., Dewanjee, S., Aflatounian, S., Khan, R., Braun, P.V., Feng, J., Poulikakos, D., Miljkovic, N. (2026). Growth and control of suspended ice bridges during sessile droplet freezing. Nature Physics.

Image Credits: The Grainger College of Engineering at the University of Illinois Urbana-Champaign

Keywords

Frost propagation, ice bridges, suspended ice bridges, superhydrophobic surfaces, hydrophilic surfaces, sessile droplet freezing, surface wettability, frost mitigation, vapor diffusion pathways, thermal management, phase change phenomena, anti-frost surfaces