Metamaterials, by definition, derive their unique properties not from their chemical composition but from their engineered structures. In this study, the scientists began by cutting T-shaped patterns into polymer sheets, effectively creating elastic metamaterials whose mechanical responses differ significantly from unpatterned polymers. Traditionally, when tension is applied to such a patterned sheet, the cuts simultaneously “pop” open, creating mesh-like deformations that extend the material’s length. This synchronous snapping, while fascinating, leaves little room for controlled actuation or sequential responses critical for advanced engineering purposes.

The research team, led by Haoze Sun and Jie Yin, hypothesized that integrating magnetic materials into the polymer matrix and subsequently magnetizing the sheets could influence the snapping sequence. Upon experimentation, they observed a striking shift from simultaneous to sequential opening events within the patterned rows. Rather than all cuts opening en masse, the rows snapped open one at a time, revealing an intricate interplay between gravitational forces attempting to pull the sheet apart and magnetic forces striving to hold it together.

What sets this discovery apart is its reproducibility and specificity. While individual magnetized sheets snapped open their rows in seemingly random orders, these sequences were consistent for each sheet. In other words, Sheet A might open rows in the order 1-2-3 consistently, whereas Sheet B would always follow a 3-1-2 pattern. This led the researchers to investigate the role of micro-scale imperfections inherent in the manufacturing process. They found that these small, unavoidable defects serve as “fingerprints” dictating the precise order of snapping events, effectively encoding a mechanical memory into each sheet.

Building on this insight, the team explored how arrays of these magnetized sheets interact when stacked and clamped together. By aligning two sheets back-to-back with opposing magnetic fields that repel each other, they achieved an orderly and predictable snapping sequence from top to bottom in 90% of trials. This magnetic coupling suppressed randomness and introduced a level of control essential for practical applications. This behavior underscores the potential of engineered magnetic interactions to precisely modulate mechanical responses in soft materials.

One of the most compelling outcomes of this work relates to kinetic energy absorption. Soft materials that can reliably absorb and dissipate energy find vital roles in impact mitigation, protective gear, and vibration damping. The researchers demonstrated that magnetized elastic metamaterials could absorb up to 30% more kinetic energy than their unmagnetized counterparts. They substantiated this by dropping a ball onto the sheets: the ball bounced off the unmagnetized material, whereas it was effectively trapped by the magnetized sheet, coming to rest as its energy was absorbed. Crucially, the degree of energy absorption was tunable by adjusting the magnetic attraction strength among the material’s components.

The implications of these findings extend beyond passive energy absorption. The ability to engineer a controllable snapping sequence heralds possibilities in wave-guiding devices that direct mechanical waves through materials in precise patterns. Similarly, reconfigurable robotics could employ magnetically coupled metamaterials to create soft robotic components that change shape and function predictably under magnetic stimuli. Biomedical engineering may also benefit, potentially utilizing these materials to design dynamic implants or devices that respond to physiological forces in highly controlled ways.

This research marks a significant advance in the field of soft metamaterials, bridging the gap between random, unpredictable mechanical behavior and ordered, programmable actuation. The study reveals how magnetic forces embedded at the microscale can transform fundamental mechanical properties and provide a new dimension of control over material behavior. Such control, intricately linked to material structure and magnetic coupling, could usher in an era of soft materials with customizable and repeatable mechanical responses.

The experimental methodology involved meticulous fabrication of patterned polymer sheets with embedded magnets, followed by systematic mechanical testing with variations in magnet strength and sheet configuration. High-speed imaging was employed to capture the snapping sequences, enabling detailed analysis of the dynamics involved. Through this rigorous approach, the researchers validated their hypothesis and illuminated the subtle yet powerful effects of magnetic coupling on material mechanics.

Future research directions will likely focus on enhancing the scalability and robustness of these magnetized metamaterials. Integrating more sophisticated magnetic architectures or combining multiple types of stimuli-responsive materials could expand the capabilities and functionalities of such systems. The discovery also prompts theoretical inquiries into the underlying physics governing coupled mechanical and magnetic interactions in complex soft materials, potentially inspiring new models and simulations.

Published open-access in the journal Science Advances, this study entitled “Magnetic coupling transforms random snapping into ordered sequences in soft metamaterials” brings a fresh perspective to the manipulation of soft matter. The research team, including collaborators from institutions such as Syracuse University and Germany’s Helmholtz-Zentrum Dresden-Rossendorf, emphasizes the collaborative and multidisciplinary nature of this endeavor, combining mechanical engineering, materials science, and applied physics.

As the landscape of smart materials expands, this magnetically controlled snapping phenomenon represents a tangible step towards engineering responsive, programmable soft materials that can adapt their mechanical behavior to external cues. The potential technological impacts are vast, spanning protective equipment, adaptable architectural materials, soft robotics, and beyond. This work exemplifies how fundamental research into material behavior at the microscale can inspire innovations with real-world significance.

Subject of Research:
Soft elastic metamaterials with magnetically controlled mechanical behavior.

Article Title:
Magnetic coupling transforms random snapping into ordered sequences in soft metamaterials

News Publication Date:
20-Mar-2026

Web References:
10.1126/sciadv.aec3182

Image Credits:
Haoze Sun, NC State University

Keywords

Metamaterials, Magnetic coupling, Elastic polymers, Snapping sequence, Kinetic energy absorption, Soft robotics, Mechanical metamaterials, Programmable materials, Magnetized materials, Material behavior control, Energy dissipation, Smart materials

The research categorizes mills based on two pivotal characteristics: the type of fibers they utilize—virgin or recycled—and whether they operate as integrated units. Virgin mills derive their raw materials from fresh wood, whereas recycled mills focus on reprocessing existing fibers. An integrated mill possesses the capability to convert raw wood into pulp and paper on-site. Conversely, non-integrated mills depend on externally sourced pulp that has already been processed, highlighting a significant operational difference. This distinction is crucial for understanding the varying degrees of emissions output and energy consumption across the industry.

Lokendra Pal, a professor at NC State and co-author of the study, emphasized the importance of this multifaceted approach. “Most previous research has narrowed the focus to single products or specific mill types,” he stated. “By simulating entire mill operations across multiple configurations, we can better understand the intricate relationships and trade-offs that dictate efficiency and emissions.”

The study outlines that virgin integrated mills are responsible for 30% of the United States’ annual paper production, simultaneously contributing to 33% of greenhouse gas emissions from the sector. Despite having the capability to generate between 80% and 90% of their energy from on-site waste wood, these mills still rely on natural gas boilers for the remainder of their energy needs. In contrast, non-integrated mills are heavily reliant on gas boilers and purchased electricity, with no on-site energy production methods, placing them at a significant emissions disadvantage.

One of the most compelling findings of the research revealed that electric-powered boilers could enable these mills to diminish their emissions by up to 61%. However, Pal cautions that merely transitioning to electric power is not sufficient. The efficacy of this transition hinges on the energy sources powering the electrical grid. If fossil fuels still dominate electricity generation, emissions reductions may be rendered ineffective. “The shift to a cleaner electrical grid by 2050 is crucial,” Pal explained. “It could create a scenario where electric boilers become significantly more environmentally friendly than continuing to depend on natural gas.”

In addition to exploring shifts to electric power, researchers also investigated the potential benefits of utilizing more efficient dewatering processes. Traditional thermal drying methods consume extensive amounts of energy, making it the most energy-intensive segment of the paper manufacturing process. The study suggests that employing dewatering technologies, such as enzymatic treatments and mechanical presses, could significantly alleviate the water content in pulp before it enters the thermal drying phase, leading to substantial energy savings.

Maria E. Gonzalez, the lead author and a Ph.D. student in the Department of Forest Biomaterials at NC State, underscored the importance of this energy-efficient approach. “Our analysis found that for each 1% of water removed during pressing, there was a corresponding increase of 3% in energy efficiency for the overall papermaking process,” she shared. However, these advancements are not without trade-offs, as enhanced dewatering could result in a decrease in steam production, which is essential for powering turbines used in electricity generation.

The researchers also examined the use of low-carbon alternatives, specifically the incorporation of biomass fuels. The study revealed that substituting fossil fuels with waste wood in boilers can lead to considerable reductions in emissions. The effectiveness of this biomass approach varied by mill type, with integrated mills benefitting significantly, especially when electricity prices surpassed those of natural gas.

Overall, the study concluded that a combined approach utilizing electric power, efficient dewatering, and sustainable biomass fuel could considerably reduce emissions across various mill configurations. This triad of strategies presents a viable roadmap for the pulp and paper industry, showcasing a path toward a sustainable future.

However, Pal noted the complexities involved, stating, “While there are significant opportunities for emissions reductions, the interdependencies of mill operations require a careful, customized strategy for each facility.” He advocates for a balanced, case-specific approach toward decarbonization, acknowledging the unique operational parameters and challenges of each mill.

The comprehensive study has been published in the Journal of Cleaner Production, setting the stage for future research aimed at further reducing the environmental impact of the pulp and paper sector while ensuring operational viability. This pivotal work not only sheds light on the pressing challenges within the industry but also highlights the potential pathways toward achieving long-term sustainability.

As the world grapples with the pressing need to reduce carbon emissions and tackle climate change substantially, the pulp and paper industry appears poised to play a critical role in this effort. By leveraging new technologies and embracing renewable energy sources, these facilities can transform into leaders in environmental stewardship, thereby contributing positively to a greener planet.

Subject of Research: Decarbonization strategies within the pulp and paper industry
Article Title: Electric power could bring paper mills to net zero emissions
News Publication Date: 1-Sep-2025
Web References: Access the study
References: Journal of Cleaner Production
Image Credits: North Carolina State University

Keywords

Decarbonization, Pulp and Paper Industry, Renewable Energy, Electric Boilers, Energy Efficiency, Biomass, Greenhouse Gas Emissions, Sustainability, NC State University

Coughing is more than just a nuisance; it serves as a vital indicator of health. Monitoring cough frequency can provide early warnings about the exacerbation of respiratory diseases and help in timely interventions, such as the use of inhalers in asthma patients. Edgar Lobaton, a professor of electrical and computer engineering and the lead author of the study, emphasizes cough detection’s potential to transform patient care by offering real-time health insights through continuous monitoring using wearable technology.

Wearable health devices capture data in real-world environments that are complex and noisy, posing a significant challenge for cough-detection algorithms. While prior machine learning models have been trained to identify cough sounds, they often falter when confronted with everyday noises that mimic coughing, such as sneezing, throat clearing, or even the sounds of speech. This limitation largely stems from models encountering unfamiliar sounds — sounds they were not exposed to during initial training phases.

To overcome this, Lobaton and his team devised an innovative approach involving multimodal data inputs collected from chest-worn wearable monitors. These devices gather not only audio signals but also accelerometer data that registers subtle chest movements associated with coughing. The synergy of sound data with motion data offers a richer, more nuanced understanding of cough events, as movement patterns serve as corroborative evidence supporting the acoustic signals.

While movement tracking alone is insufficient due to overlaps with non-cough actions like laughing or groaning, combining it with audio input significantly sharpens detection accuracy. Yuhan Chen, the study’s first author, elaborates that this integrated data fusion approach empowers the model to distinguish cough events with higher confidence. It reduces false alarms—instances where the device mistakenly identifies non-cough sounds as coughs—thus improving reliability.

Building upon previous machine learning advancements, the researchers refined their algorithms to optimize what they term “out-of-distribution detection.” This refers to the model’s enhanced ability to recognize when it encounters unfamiliar sounds and adjust its confidence level accordingly, reducing erroneous cough classifications. Their approach marks a pivotal advancement in wearable biosensing, allowing devices to better generalize across the diverse acoustic environments they experience in daily life.

When subjected to rigorous laboratory testing, the new multimodal cough detection model outperformed existing technologies with a measurable reduction in false positives. This improvement indicates the model’s proficiency in accurately filtering out speech and other nonverbal sounds that historically plagued cough-detection efforts. Such robustness is essential for clinical applications where high specificity and sensitivity directly impact patient health management decisions.

This enhanced cough detection capability opens up transformative possibilities for continuous health monitoring. Wearable devices equipped with these refined models can more effectively track the progression of respiratory diseases, aiding both patients and healthcare providers in managing conditions proactively. The technology’s potential extends to predicting acute health events, such as asthma attacks, enabling timely interventions that could prevent hospitalizations.

The team’s work dovetails with broader efforts to integrate artificial intelligence into personalized medicine, using real-time sensory data to paint a clearer picture of patient health. As wearable health technologies mature, innovations like this multimodal model will be foundational in delivering actionable health insights outside clinical settings, empowering individuals to take charge of their respiratory health.

Despite this progress, the researchers acknowledge ongoing challenges. Future work aims to enhance detection capabilities further, particularly in distinguishing coughs amidst even more variable sounds and environments. There is a clear trajectory toward refining sensor technology, algorithm robustness, and real-world deployment, making cough detection a dependable tool in the health-monitoring arsenal.

This research was supported by the National Science Foundation and NC State’s Center for Advanced Self-Powered Systems of Integrated Sensors and Technologies (ASSIST), underscoring the critical role of interdisciplinary collaboration in advancing wearable biosensor technologies. The study titled “Robust Multimodal Cough Detection with Optimized Out-of-Distribution Detection for Wearables” was published in the IEEE Journal of Biomedical and Health Informatics, offering a landmark reference point for future developments in this area.

In conclusion, the integration of multimodal data from wearable devices marks a vital advancement in cough detection technology. By harnessing both audio and motion inputs and optimizing algorithmic responses to new sound environments, this research paves the way for more reliable, real-world health monitoring solutions that could revolutionize respiratory disease management and chronic care.

Subject of Research: People
Article Title: Robust Multimodal Cough Detection with Optimized Out-of-Distribution Detection for Wearables
News Publication Date: 2-Oct-2025
Web References: IEEE Journal of Biomedical and Health Informatics DOI
Image Credits: Edgar Lobaton, NC State University
Keywords: cough detection, wearable devices, respiratory health, machine learning, multimodal data, accelerometer, audio processing, chronic disease monitoring, asthma prediction, biosensors, artificial intelligence, health technology

The central revelation from the study is that increased soil temperatures alone do not cause a sustained spike in CO2 release from soil. Instead, it is the confluence of warming alongside the availability of accessible carbon and vital nutrients—such as nitrogen and phosphorus—that commands a marked increase in microbial respiration and subsequent carbon release. This complex synergy underscores a more intricate picture whereby soil microbes, the primary drivers of soil respiration, require both energy sources and essential nutrients to amplify their metabolic activities under warming conditions.

Microbes inhabiting the soil, including bacteria, fungi, and viruses, share striking similarities with other living organisms in their metabolic requirements. These microorganisms essentially “breathe” out CO2 as they degrade organic matter to fuel their growth and survival. When soil temperatures rise, it catalyzes plant photosynthesis, which in turn can produce more organic matter and provide substrates for microbial metabolism. However, as the study highlights, without sufficient carbon substrates and nutrient inputs, microbial communities remain constrained, and warming alone fails to induce notable CO2 emissions.

This empirical research was situated in an often-overlooked ecosystem: nutrient-poor, subtropical forest soils derived from former cotton fields in Athens, Georgia. Unlike the fertile soils of native forests or colder temperate and boreal zones where most previous warming studies have focused, these soils are characterized by low nutrient density and limited organic carbon reserves. This context is critical, as it presents a natural laboratory for isolating the substrate limitations constraining microbial activity under climate warming scenarios.

The researchers executed a sophisticated experimental design involving soil samples collected from the long-term field-warming experiment site. These samples underwent controlled laboratory incubations simulating incremental temperature increases of up to 2.5°C above ambient conditions. Alongside warming treatments, nutrient and labile carbon amendments were applied to disentangle the relative contributions of substrate and nutrient availability from temperature effects alone. Detailed measurements were taken to track changes in microbial biomass, respiration rates, enzyme activities, and diverse soil organic carbon pools over several weeks.

One of the study’s pivotal technical findings is the clear identification of substrate limitation as a bottleneck in microbial carbon cycling under warming. Microbial respiration and biomass did not exhibit sustained increases when soil was warmed in isolation, confirming that temperature alone does not overcome the scarcity of bioavailable carbon in such depleted soils. Enzymatic assays further confirmed that the reduction in microbial activity was not due to enzyme denaturation at elevated temperatures but rather due to insufficient substrates to fuel microbial metabolism.

When researchers introduced labile carbon, either alone or combined with nitrogen and phosphorus, microbial respiration accelerated significantly, highlighting a co-limitation framework. This framework posits that nutrient availability becomes consequential only after microbes’ carbon demand is met. Essentially, microbes require an energy-rich diet, composed of accessible carbon sources to sustain their metabolic machinery, alongside nutrients to build biomass and produce enzymes capable of decomposing complex organic matter.

The implications of this study resonate far beyond the confines of subtropical forest soils. It challenges Earth system models that often extrapolate from nutrient-rich, temperate ecosystems and underscores the necessity of incorporating substrate availability and nutrient co-limitation into predictive frameworks of soil carbon feedbacks under climate change. Such advances are crucial for refining projections of soil carbon storage and atmospheric CO2 fluxes in the vast, nutrient-poor terrestrial environments that span tropical and subtropical regions globally.

Moreover, this research emphasizes the intricate balance between carbon sequestration and carbon release in soil ecosystems. Nature’s dual role as both a sink and source of atmospheric carbon hinges precariously on microbial responses to environmental drivers. An accurate understanding of the thresholds and controls governing microbial metabolism is paramount for devising effective strategies to mitigate anthropogenic carbon emissions and feedback loops associated with climate warming.

Further reinforcing the study’s broader ecological relevance, ongoing investigations led by the research team include comparative warming experiments in tropical forests in Puerto Rico and Panama. These complementary studies aim to unravel how variations in ecosystem type, soil fertility, and climatic conditions modulate microbial sensitivities to climate perturbations, thereby refining our grasp of global carbon cycling processes.

The study’s collaborative effort, involving graduate and undergraduate researchers alongside principal investigators, utilized an integrative approach fusing field experiments with controlled laboratory incubations. Such methods allowed for precision in assessing individual variables—temperature, carbon, and nutrient availability—without confounding interactions often inherent in complex field environments.

Funding provided by the U.S. Department of Energy’s Environmental System Science Program facilitated this vital contribution to biogeochemistry. The resulting publication in the journal Biogeochemistry offers a detailed mechanistic exploration of soil carbon cycling in substrate-limited forest ecosystems, a previously underrepresented ecosystem type in soil warming literature.

In conclusion, these findings present a paradigm shift in understanding soil carbon dynamics under climate change. They reveal that the microbial response to warming is fundamentally constrained by the availability of resources necessary for metabolism, not just the temperature increase itself. This intricate dependence dictates whether soils act as carbon sources or sinks in a warming world, underscoring the importance of substrate quality and nutrient inputs in shaping global carbon feedback loops.

Subject of Research: Soil carbon cycling and microbial responses to warming in nutrient-poor subtropical forest soils

Article Title: Decoding the hidden mechanisms of soil carbon cycling in response to climate change in a substrate-limited forested ecosystem

News Publication Date: September 12, 2025

Web References:
https://link.springer.com/article/10.1007/s10533-025-01265-0
http://dx.doi.org/10.1007/s10533-025-01265-0

References:
Du, Y., Franke, G., Chen, Z., Mohan, J., Frankson, P., & Sihi, D. (2025). Decoding the hidden mechanisms of soil carbon cycling in response to climate change in a substrate-limited forested ecosystem. Biogeochemistry. https://doi.org/10.1007/s10533-025-01265-0

Image Credits: Photo courtesy of Debjani Sihi, NC State University

Keywords: soil warming, microbial respiration, carbon cycling, substrate limitation, nutrient co-limitation, subtropical forests, soil organic carbon, climate change, microbial metabolism, enzyme kinetics, biogeochemistry, soil carbon feedback

Permanent magnets constitute the backbone of numerous emerging technologies, from electric vehicles and wind turbines to robotics and consumer electronics. The escalating demand for these powerful magnets has strained conventional manufacturing methods, which struggle to reconcile the need for superior magnetic properties with efficiency and sustainability. Traditional approaches rely heavily on sintering metal alloy powders under intense heat and pressure, a method beset by complexity, energy consumption, and material imperfections.

The lead author and assistant professor of materials science and engineering, Bharat Gwalani, highlights the inherent limitations of standard sintering techniques. “Conventional magnet manufacturing involves compressing and heating alloy powders into solid forms at high temperatures and pressures exceeding 100 megapascal,” Gwalani explains. “This process is not only complicated and energy-intensive but frequently results in magnets with uneven porosity and suboptimal magnetic properties, particularly with increased porosity concentrated at the core.”

Porosity — the presence of microscopic voids within the magnet material — undermines magnetic uniformity and strength. Unequal distribution of these pores compromises magnet performance, introducing weaknesses that can impact reliability and lifespan. Moreover, the reliance on rare earth metals in these magnets—which are highly reactive to oxygen—adds further complications. Exposure to oxygen during the high-heat sintering accelerates oxidation, deteriorating the magnetic performance.

Addressing these challenges, the research team pioneered a new approach rooted in friction stir consolidation (FSC), a technique originally developed for metallurgical joining and shaping. FSC involves placing alloy powders into a chamber where a rotating tool applies pressure while stirring the powder bed. The mechanical energy generated during rotation consolidates the powder into solid bulk without reaching melting temperatures, a key distinction from traditional sintering.

“The beauty of friction stir consolidation lies in its ability to circumvent the thermal exposure that triggers oxidation and undesired phase transformations,” Gwalani remarks. “Our process applies pressures below one megapascal—substantially less than the norms—while frictional heating at the exact interface of powder particles fuses them together, preserving the alloy’s integrity and yielding a uniform, dense magnet.”

Unlike unidirectional pressure in traditional methods which concentrates force at the top and bottom faces, FSC induces an omnidirectional pressure distribution owing to the rotational stirring. This dynamic pressure environment effectively eliminates porosity by preventing pore entrapment at the center of the material. The result is a dense, flaw-free magnetic composite with homogenous microstructure and superior magnetic properties throughout.

Furthermore, because FSC generates heat internally through friction between powder particles, the bulk material does not endure harsh external heating. This localized thermal input means oxidation rates are significantly reduced compared to ovens or furnaces used in sintering. Preserving the delicate rare earth elements in their optimum state yields a magnet with enhanced coercivity and remanence—crucial parameters for high-performance applications.

This newly developed technique offers several transformative benefits: faster production cycles that could accelerate scaling to industrial levels, reduced energy usage contributing to sustainability goals, and cost savings by minimizing material waste and simplifying manufacturing requirements. These factors together create a compelling case for FSC as the future standard for permanent magnet fabrication.

Having successfully demonstrated the FSC process with samarium-cobalt (Sm-Co) powders—materials renowned for their exceptional magnetic strength and thermal stability—the team is already venturing into exploratory research for next-generation magnetic materials. They aim to incorporate non-magnetic binders to fabricate magnets that are lighter, tougher, and less dependent on scarce rare earth elements, potentially redefining the landscape of magnet technology altogether.

The implications of such advances reach far and wide. As electric vehicles strive for lighter, more efficient motors, wind turbines demand reliably strong magnets to optimize energy conversion, and robotics require durable compact actuators, the ability to manufacture superior magnets rapidly and cost-effectively is a critical bottleneck. FSC holds the promise to break through this barrier, unlocking new possibilities in performance and innovation.

Publication of these findings in the esteemed journal Nature Communications marks a milestone in the field. The paper, titled “In-Situ Thermo-Mechano-Chemical Transformation and Consolidation of Sm-Co Powders via a Single-Step Route for Bulk Magnet Fabrication,” details the intricate mechanisms of friction stir consolidation and its benefits over traditional sintering. The collaboration involves a robust team of experts from North Carolina State University, the Pacific Northwest National Laboratory, Stevens Institute of Technology, and Bruker Nano, reflecting the interdisciplinary effort behind this breakthrough.

Supported by grants from the Office of Naval Research, the National Science Foundation, and the Department of Energy’s Office of Science, the study underscores the strategic importance of advancing magnet technology for defense, industry, and sustainability sectors. Continuous improvements and scaling of FSC could usher in a new era of magnetic materials that are not just stronger and more reliable but also greener and more accessible.

In essence, friction stir consolidation represents a paradigm shift in permanent magnet manufacturing—bridging the gap between material science innovation and practical industrial application. The combination of pressure, rotation, and frictional heat into a single-step process alleviates historical flaws and inefficiencies, providing an elegant solution to a complex problem. As the global community accelerates towards electrification and renewable energy, such advances in magnet fabrication will play a pivotal role in powering the technologies of tomorrow.

Subject of Research: Not applicable

Article Title: In-situ thermo-mechano-chemical transformation and consolidation of Sm-Co powders via a single-step route for bulk magnet fabrication

News Publication Date: 13-Aug-2025

Web References:
https://www.nature.com/articles/s41467-025-62804-9
http://dx.doi.org/10.1038/s41467-025-62804-9

References:
Malakar, A., Martin, A., Ishrak, F., Schenck, C., Lastovich, M., Tracy, J., Thuo, M., Yu, A., Pole, M., Darsell, J., Wang, T., Kovarik, L., Grant, G., Efe, M., Helsing, J., Thornton, J., & Gwalani, B. (2025). In-situ thermo-mechano-chemical transformation and consolidation of Sm-Co powders via a single-step route for bulk magnet fabrication. Nature Communications. https://doi.org/10.1038/s41467-025-62804-9

Image Credits: Not provided

Keywords

Permanent magnets, friction stir consolidation, samarium-cobalt, rare earth metals, magnet manufacturing, porosity elimination, oxidation reduction, solid-state sintering alternative, magnetic materials engineering, energy-efficient production, advanced magnet fabrication, electric vehicle magnets, renewable energy technology.

The microneedle patch, which utilizes microneedles engineered to penetrate the top layers of skin, collects interstitial fluid – the fluid that surrounds cells in the dermal and epidermal layers. This interstitial fluid contains a wealth of biomarkers that correspond to those typically found in blood samples. Michael Daniele, a professor at NC State and a lead author of the study, emphasizes that utilizing interstitial fluid can streamline the biomarker testing process by eliminating the complexities associated with blood sample preparation.

During their proof-of-concept experiments, the researchers tested the patch on synthetic skin, demonstrating its ability to collect significant amounts of biomarkers within just 15 minutes. Moreover, the patch has been shown to store these samples for up to 24 hours, making it a versatile tool for patients and healthcare providers alike. An important biomarker monitored during testing was cortisol, a hormone that fluctuates with stress levels. The convenience of multiple readings without the pain and inconvenience of blood draws could revolutionize how individuals manage their stress and overall health.

The microneedle patch is made up of four distinct layers: a visible polymer housing, a gel layer, a paper layer for absorption, and the microneedles themselves. Designed to be completely passive, the patch harnesses the properties of the materials used to facilitate fluid transfer. The microneedles contain a material that swells upon contact with interstitial fluid, allowing the fluid to be drawn through the needle and into the paper layer. As the paper becomes saturated, it interfaces with the gel on the opposite side, which contains high concentrations of glycerol. This creates an osmotic pressure differential that facilitates further fluid movement, ultimately enhancing sample collection efficiency.

Dr. Daniele explains that the sample collected in the paper strip can be easily accessed for analysis once the patch is removed, further simplifying testing procedures. The researchers are not only leveraging this technology for cortisol tracking but also envision its application for a broader range of biomarkers found in interstitial fluid. The prospect of easy, pain-free monitoring opens significant avenues for conditions that require frequent testing and evaluation.

Additionally, the microneedle patch can be produced using affordable materials that are readily accessible, making the technology potentially cost-effective compared to traditional blood sample collection methods. Daniele notes, “The highest cost of the patches would be manufacturing the microneedles, but we think the price would be competitive with the costs associated with blood testing.” The elimination of needles, vials, and the need for trained professionals to draw blood presents a strong case for the widespread adoption of this innovative testing method.

The current phase of research includes human testing, with researchers ambitiously developing electronic devices capable of analyzing the samples collected by the microneedle patches. Thus far, a device has been successfully created to read cortisol levels directly from the patch’s paper strip, and efforts are underway to develop technologies for evaluating other biomarkers as well. The future holds promising potential for partnerships within the diagnostic industry to broaden the applications of this technology.

This self-powered microneedle patch represents a significant leap forward in health monitoring technology—a field that has often been stifled by reliance on invasive techniques. By providing a non-invasive alternative that is both efficient and accessible, this innovation could cater to an extensive range of health applications including stress management, chronic disease monitoring, and preventive healthcare measures.

While this technology is still in its infancy, the potential impact on personal health management could be profound. As researchers continue to refine the microneedle patch and explore its capabilities, it paves the way for a future where health monitoring is both comfortable and continuous, fostering an era of smarter, patient-centered healthcare solutions. This approach aligns with the future direction of medical technology, which increasingly emphasizes minimally invasive procedures aimed at enhancing patient comfort and accessibility.

The implications of this technology stretch beyond mere convenience; as health literacy and personal health monitoring become increasingly valued in contemporary society, the microneedle patch can empower individuals to take charge of their health by providing them with the ability to track important biometrics in a seamless fashion. This newfound autonomy could help trigger widespread changes in preventive healthcare and enhance overall public health outcomes over time.

As researchers in this field look for industry partners to bring their innovation to market, the global health community is poised to benefit from advancements like this, which can facilitate timely interventions and informed health decisions. The microneedle patch signifies a move toward the integration of technology in personal health, making monitoring easier and more attainable than ever before.

With continued support from funding agencies and a focus on exploration and development, the researchers at NC State are setting the stage for a technological revolution in health monitoring, one that could reshape our understanding of wellness and disease management. As they engage in human trials and refine the technology for broader applications, the microneedle patch holds the promise of a future where health monitoring can be performed effortlessly, delivering insights that can change lives.

This innovative research has been documented in the open-access paper titled “Design and Characterization of a Self-Powered Microneedle Microfluidic System for Interstitial Fluid Sampling,” published in the journal Lab on a Chip. The collaborative efforts of the researchers, combined with their entrepreneurial aspirations, suggest a future rich with potential for transformative health technologies that enhance the way we monitor and manage health.

Subject of Research: The development and testing of a self-powered microneedle patch for biomarker monitoring through interstitial fluid sampling.
Article Title: Design and Characterization of a Self-Powered Microneedle Microfluidic System for Interstitial Fluid Sampling
News Publication Date: August 1, 2025
Web References: Lab on a Chip Article
References: Not applicable
Image Credits: Michael Daniele, NC State University

Keywords

Non-invasive monitoring, microneedle patch, biomarkers, interstitial fluid, healthcare innovation, cortisol monitoring, chronic disease management, patient-centered technology.

Plasmid DNA is distinct from the linear DNA typically found in higher organisms, such as humans. Instead, pDNA forms a circular structure that is more stable and easier to manipulate in the laboratory setting. This structural difference allows researchers to introduce genetic information into cells with greater ease, which has made pDNA highly valuable for various applications in medicine, particularly in developing gene therapies and certain veterinary vaccines.

The current methods of producing pDNA are costly, primarily because they rely on genetically modified bacteria, a process that can result in production costs soaring to as high as $100,000 per gram. This represents a significant barrier to the development of new therapies that depend on pDNA. The researchers’ goal was to engineer E. coli strains that would not only be more efficient at producing pDNA, but ideally do so at a fraction of the cost.

In their groundbreaking work, the research team, led by Nathan Crook, an assistant professor in chemical and biomolecular engineering, began with an already enhanced strain of E. coli that had been designed to produce pDNA. Using a method known as genome-wide mutagenesis, they introduced specific mutations into the bacteria, allowing them to observe which genetic alterations led to increased pDNA production. Each mutant strain was meticulously tested in a series of experiments to identify characteristics that contributed positively to the efficiency of pDNA synthesis.

The results were astounding. The researchers reported an increase in pDNA production by factors ranging from 1.44 to as high as 8.7 times compared to the original E. coli strain they used as a baseline. Among the five types of pDNA tested, they found particular success with pAAV, a type commonly used in gene therapies that was notable for its ease of production. By the end of the study, the engineered bacteria proved capable of producing 8.7 times more pAAV than the baseline strain, a remarkable feat that could revolutionize the way pDNA is manufactured.

In contrast, even their least productive strain demonstrated a significant improvement over the original, enhancing p15A pDNA production by 44%. This achievement is particularly noteworthy because p15A is typically more difficult to produce in large quantities, making the 44% increase a remarkable step forward. The broader implications of these findings extend beyond just cost reduction. They could potentially expedite the development of novel therapies that require pDNA, fostering an environment of innovation within the biomedical field.

Crook and his team expressed optimism about the implications of their work, highlighting the potential for collaborating with industry partners to see their research transformed into practical applications. In a field where the cost of production can limit progress, this advancement could provide a much-needed solution. As the demand for gene therapies continues to grow, the ability to produce pDNA more efficiently will be essential in meeting the rigorous requirements of both research and clinical applications.

The findings have been published in an open-access paper in the journal Microbial Cell Factories, emphasizing the researchers’ commitment to sharing their discoveries with the scientific community. The study was co-authored by a team of experts, including Zidan Li, who led the initial experimental phase, and Ibrahim Al’Abri, contributing former graduate students and postdoctoral researchers whose insights and skills were vital to the project’s success.

The challenge of producing pDNA efficiently has long been recognized as a significant barrier in gene therapy research. This new method offers not only a viable solution but also sets a precedent for further innovations in bacterial engineering aimed at harnessing biological systems for industrial and medical purposes. By leveraging the evolutionary potential of E. coli, the research team has opened pathways for enhanced quality and quantity of critical biological materials, thereby reshaping the landscape of gene therapy production.

As the world of biomedical research progresses, innovations like this present a glimmer of hope. The ability to produce crucial components like pDNA more affordably and efficiently means that life-saving treatments could reach the market faster and become accessible to a broader population. With the rapid evolution of biotechnology and genetic engineering, staying at the forefront of these advancements is crucial to foster sustainable and efficient production methods that can truly benefit society.

The research was supported by the North Carolina Biotechnology Center under grant number 2022-TRG-6707, showcasing the importance of collaborative funding in driving innovation within the scientific arena. This support has not only helped propel the project forward but also underscores the necessity of public–private partnerships in advancing healthcare solutions. The authors anticipate that this work will have a lasting impact on the production of genetic materials, setting a new standard for what is achievable in biotechnology.

As the demand for pDNA and related biomolecules increases in tandem with the rise of genetic therapies, researchers are motivated to continue exploring the genetic landscape of E. coli and other microorganisms. The implications of this research extend far beyond pDNA production; they signal a shift toward a more efficient, cost-effective biomedical landscape that could redefine what is possible in therapeutic development.

In conclusion, this groundbreaking research from North Carolina State University presents a pivotal moment in the production of plasmid DNA, with the potential to dramatically affect the landscape of gene therapies. With pDNA being integral to many biomedical applications, the advances made by Crook and his team could lower costs, inspire further innovation, and ultimately change the trajectory of how we approach gene therapy manufacturing.

Subject of Research: Cells
Article Title: Inducible genome-wide mutagenesis for improvement of pDNA production by E. coli
News Publication Date: 13-Aug-2025
Web References: Microbial Cell Factories
References: DOI: 10.1186/s12934-025-02821-x
Image Credits: North Carolina State University

Keywords

Plasmid DNA, E. coli, gene therapy, biotechnology, genome-wide mutagenesis, cost reduction, biomedical applications, bacterial engineering, pDNA production.

Traditional global climate models (GCMs) serve as vital instruments for understanding and projecting Earth’s climate system. Despite their critical role, these models have struggled to accurately represent compound extreme events. Shiqi Fang, the lead author of the study, highlights that current climate datasets and models fall short when it comes to reflecting the intricate correlations between multiple climate variables during these compound events. This inadequacy not only limits the precision of global projections but also reduces the reliability of regional forecasts, thereby impeding effective climate adaptation planning.

The core of the challenge lies in the complex multi-variable interactions inherent in compound events. Standard bias correction techniques employed in climate modeling tend to focus on adjusting single variables independently—correcting biases in rainfall without simultaneously calibrating temperature, for example. Sankar Arumugam, the corresponding author and civil engineering professor at NC State, explains that while these traditional bias corrections improve isolated parameter accuracy, they fall short in capturing the joint distributions and dependencies between variables such as temperature and humidity. This limitation is crucial because compound events inherently involve these multi-parameter dynamics, which pose disproportionate risks to societies and ecosystems worldwide.

In response to this, the research team has introduced a novel approach termed Complete Density Correction using Normalizing Flows (CDC-NF). This machine learning-driven technique leverages the power of normalizing flows—a class of deep generative models capable of learning complex probability distributions—to recalibrate climate model outputs. By modeling the full joint probability distribution of multiple climate variables, CDC-NF provides a robust correction framework that aligns model projections more closely with observed climatic patterns, effectively accounting for the interdependencies that characterize compound events.

The research systematically tested the CDC-NF method across the five most commonly used global climate models within the Coupled Model Intercomparison Project Phase 6 (CMIP6). Evaluations included broad global assessments and focused national-scale analyses over the continental United States. The results indicated consistent improvements in the fidelity of model outputs when corrected using CDC-NF, with marked enhancements in the representation of both isolated and compound extreme weather events. These outcomes signify a substantial step forward in bias correction methodology, improving the granularity and applicability of climate forecasts.

One of the pivotal advantages of CDC-NF lies in its ability to handle multivariate dependencies without compromising the internal physical consistency of climate models. Unlike traditional methods that apply univariate corrections and risk disrupting crucial correlations, CDC-NF simultaneously adjusts multiple variables within a coherent probabilistic framework. This holistic correction ensures that inter-variable relationships—such as the coupling between temperature spikes and humidity levels during heatwaves—are preserved, leading to projections that better mirror nature’s intricacies.

The open-source nature of this innovation furthers its potential impact. The researchers have made both the CDC-NF code and associated datasets publicly available on Figshare, inviting the global scientific community to apply, scrutinize, and extend the methodology in diverse modeling contexts. This transparency encourages collaborative refinement and broader adoption, ensuring that advances in bias correction can proliferate swiftly across climate research institutions worldwide.

Given the increasing prevalence and intensity of compound extreme events—driven by anthropogenic climate change—tools like CDC-NF offer critical improvements in risk assessment frameworks. Enhanced projections enable policymakers and planners to anticipate severe weather sequences with greater confidence, facilitating more resilient infrastructure design, emergency response planning, and resource allocation. These contributions are vital as nations and communities confront escalating climate vulnerabilities amid complex environmental feedbacks.

Technically, normalizing flows represent a powerful class of invertible neural networks that transform simple probability distributions into complex ones by applying a sequence of parametric mappings that are both differentiable and invertible. The CDC-NF framework capitalizes on these mathematical properties to learn the full joint distribution of climate variables conditioned on the output of traditional GCMs. This data-driven approach effectively “corrects” the model biases not through heuristic adjustments but by statistical learning grounded in observed meteorological records, leading to greater reliability in climate simulations.

Moreover, the application of CDC-NF is not limited to temperature and rainfall. The conceptual framework paves the way for future expansions to include additional atmospheric variables such as wind velocity, solar radiation, and soil moisture, amplifying the fidelity of climate projections across multiple dimensions. This scalability positions CDC-NF as a versatile and forward-looking tool in climate analytics.

This research was made possible by funding from the National Science Foundation, demonstrating the importance of sustained investment in climate science and machine learning innovation. The interdisciplinary collaboration, with contributions from experts in civil engineering, statistics, and environmental sciences, exemplifies the holistic approach required to tackle the multifaceted challenges posed by climate change and extreme weather events.

The full details of the study are published in the journal Scientific Data under open access, providing the broader scientific community with in-depth insights and methodologies necessary to integrate CDC-NF into various climate modeling efforts. This transparent dissemination supports reproducibility and accelerates the global endeavor to refine climate projections.

In a climate era marked by volatility and uncertainty, the emergence of sophisticated tools like CDC-NF marks a hopeful stride towards predictive precision. By reinforcing the accuracy of multi-variable climate event forecasting, this innovation empowers decision-makers with better data to safeguard communities, ecosystems, and economies against the accelerating impacts of climate extremes.

Subject of Research: Not applicable
Article Title: A Complete Density Correction using Normalizing Flows (CDC-NF) for CMIP6 GCMs
News Publication Date: 23-Jul-2025
Web References: https://figshare.com/articles/dataset/GCM_biascorrected/27976818, https://www.nature.com/articles/s41597-025-05478-8
References: Arumugam, S., Fang, S., Hector, E., Reich, B., Majumder, R. (2025). A Complete Density Correction using Normalizing Flows (CDC-NF) for CMIP6 GCMs. Scientific Data.
Keywords: Climate modeling, compound extreme events, machine learning, bias correction, normalizing flows, climate projections, CMIP6, global climate models, multi-variable correction, climate adaptation.

The research addresses a long-standing challenge in micropaleontology involving foraminifera, or forams—microscopic protists with calcareous shells that have thrived in Earth’s oceans for over 100 million years. These tiny shells serve as invaluable archives, encoding chemical and environmental information about ancient oceans. For scientists, analyzing thousands of these minuscule and similarly shaped shells by hand is laborious, time-consuming, and prone to error, creating bottlenecks in research that relies on large fossil datasets.

To expedite and automate this painstaking process, the team previously developed Forabot, a robotic sorting system capable of identifying and classifying forams. Although Forabot was functional, optimizing its hardware layout and fine-tuning parameters proved an exhaustive, iterative process plagued by numerous trial-and-error cycles. Recognizing this, the current project sought to integrate high-precision 3D digital models of foram specimens into simulation environments, allowing virtual optimization before physical implementation.

At the heart of this advancement is a computational pipeline that generates synthetic 3D facsimiles of foram shells through a refined mathematical modeling framework. These facsimiles are photorealistic and geometrically faithful reconstructions derived from sparse sets of 2D images, employing state-of-the-art artificial intelligence algorithms capable of inferring shape with remarkable detail. The collaboration with paleontologists ensured that the synthetic models capture essential morphological nuances representative of seven key species, ensuring biological accuracy alongside computational sophistication.

The benefits of this approach are twofold. First, these 3D models enable researchers to simulate and virtually rearrange Forabot’s sorting hardware in a controlled digital environment. This removes the need for repeated physical adjustments that previously consumed vast amounts of time and resources. Second, by accurately replicating the complex geometry of forams, robotic recognition algorithms can be trained on extensive synthetic datasets, enhancing the precision of identification beyond what real-world samples could provide.

Testing this pipeline, the team simulated adaptations to Forabot’s architecture using the synthetic dataset, which led to a notable increase in fossil classification accuracy from 82% to 89%. Such an improvement, achieved without manual hardware reconfigurations, underscores the transformative potential of integrating 3D synthetic modeling with robotics. The researchers emphasize that these simulations streamline iterative design cycles, guiding optimized hardware configurations before physical deployment and sketching a new paradigm for robotic system development.

Sanjana Banerjee, a doctoral candidate and the study’s lead author, explains that the synthetic data enable the evaluation of optimal imaging conditions critical for 3D reconstruction. “Our simulations allowed us to test how current AI models perform with sparse imaging data, guiding the design of next-generation robotic platforms capable of reconstructing micromorphologies autonomously,” Banerjee notes. This 3D reconstruction capability represents a crucial step toward fully automating fossil identification, which traditionally has been constrained by limited datasets and the challenges of shape recovery from 2D observations.

Beyond paleontology, the researchers envision broad applicability for their method. Complex-shaped object sorting appears in numerous fields, from isolating microbes and pathogens in medical diagnostics to automating large-scale agricultural produce sorting. By enabling precise 3D modeling and virtual system optimization, the approach could revolutionize these sectors, improving efficiency and reducing manual intervention.

An important feature of this project is its commitment to open science. The entire codebase supporting the 3D generation pipeline and modeling framework has been made publicly available via an accessible GitHub repository, encouraging researchers worldwide to adopt, adapt, and build upon these tools. This promotes collaborative progress across disciplines reliant on microscopic imaging and sorting technologies.

The research article titled “Foram3D: A Pipeline for 3D Synthetic Data Generation and Rendering of Foraminifera for Image Analysis and Reconstruction” was published in the journal Marine Micropaleontology. The work is the result of a collaboration among experts in electrical and computer engineering, geology, and micropaleontology, underscoring the interdisciplinary nature required to tackle challenges at the micro-scale.

This landmark study was supported by the National Science Foundation, reflecting investment in innovative convergence of computational simulation, AI, and robotic hardware to solve real-world scientific puzzles. By merging theoretical modeling with pragmatic robotic applications, the team has created a powerful blueprint for future automation in analyzing complex organic shapes.

Looking forward, such synergistic advances in synthetic 3D modeling and autonomous robotic systems are poised to accelerate discoveries in climate science through more efficient microfossil analysis. As the accuracy and scalability of these platforms improve, researchers anticipate unprecedented access to high-throughput fossil data, enabling refined reconstructions of Earth’s climatic history and informing projections for future environmental change.

Ultimately, the intersection of mathematics, computer vision, and robotics embodied in this work exemplifies the transformative impact that computational simulation can have on empirical sciences. By turning intricate marine fossils into virtual replicas, scientists and engineers can navigate the challenges of microscopic identification with newfound clarity and efficiency, heralding a new era in micropaleontology and beyond.

Subject of Research: Computational simulation/modeling of 3D synthetic data generation and robotic system optimization for microscopic marine fossil identification.

Article Title: Foram3D: A Pipeline for 3D Synthetic Data Generation and Rendering of Foraminifera for Image Analysis and Reconstruction

News Publication Date: 11-Jul-2025

Web References: https://github.com/ARoS-NCSU/Forams-3DGeneration

References:
Turner Richmond, Michael Daniele, Thomas Marchitto, Sanjana Banerjee, Edgar Lobaton, “Foram3D: A Pipeline for 3D Synthetic Data Generation and Rendering of Foraminifera for Image Analysis and Reconstruction,” Marine Micropaleontology, 2025. DOI: 10.1016/j.marmicro.2025.102486

Image Credits: Sanjana Banerjee, NC State University

Keywords: 3D modeling, Foraminifera, micropaleontology, autonomous robotics, synthetic data generation, AI-based shape reconstruction, simulation optimization, fossil identification, computational imaging, Forabot robot

The fundamentals of these metashells lie in their unique design—spherical shapes created from strands of polyethylene terephthalate (PET) arranged in a complex lattice pattern. This configuration maximizes the material’s inherent capacity to store energy. When weight is applied to the metashell, it deforms, storing potential energy within its structure. Unlike conventional materials that immediately snap back, PET exhibits viscoelastic properties, leading to a slow initial return to its original shape. Following this initial phase, once a critical deformation threshold is reached, the materials undergo a sudden and pivotal transition, restoring their original form rapidly, which results in the spectacular jump.

The research, led by Jie Yin, an associate professor of mechanical engineering, articulates a dual purpose: effectively controlling the jump’s timing while enhancing the dynamics of the mechanical structure. The jump mechanism is meticulously constructed so that the length of time for which the load is applied directly correlates with the timing of the jump. Specifically, if the load remains for an extended duration, the structure will release its potential energy later, resulting in a delayed and potentially lower jump. This novel approach not only reignites interest in materials science but also paves the way for applications in various fields, from robotics to environmental science.

A pivotal element of the research is the visualization of these metashells in action. Image documentation reveals snapshots of a metashell leaping off a snowy surface, demonstrating its versatility across different terrains. During testing, the researchers were able to angle jumps from as brief as three seconds to as long as 58 hours in advance, highlighting the remarkable precision that can be achieved through engineering and material design. The metashells’ jump heights ranged dramatically, allowing them to reach up to nine times their height or a mere half of it, depending on how far in advance the jump was pre-programmed.

The implications of this research extend beyond mere curiosity. By successfully demonstrating that these structures can launch from varied surfaces—from solid ground to sand, snow, and even water—the researchers have opened avenues for practical applications. For instance, the metashells can be employed for purposes ranging from environmental monitoring to precision agriculture. One influential application demonstrated the capacity for the metashells to carry and disperse cargo, such as seeds. This mimics natural processes akin to explosive seed dispersal seen in plants like Impatiens balsamina, which enables the scattering of seeds over significant distances, enriching biodiversity in various ecosystems.

The research also emphasizes the importance of material properties in determining the performance of such programmable structures. The viscoelastic nature of PET combined with intelligent design allows these metashells not only to perform but excel in multifaceted environments, enhancing their functionality and application scope. With the potential to innovate this technology, researchers are keen to explore the use of biodegradable materials that align with the sustainable goals in engineering and apply their findings to the practical world.

Furthermore, this work is underpinned by robust funding from the National Science Foundation, showcasing the value of collaborative research and the transformative potential of new material technologies. Researchers Yang and Yin have filed for a patent related to their invention, signaling robust commercial prospects and innovation pathways for enterprises interested in embedding this technology into their operations.

In communicating these advancements, the researchers advocate for future collaborations. By engaging with both academia and the private sector, they envision expanding the scope and applications of their work, which holds promise for ecological applications, consumer products, and beyond. As the fields of material science and engineering continue to evolve, such collaborations will likely accelerate the translation of research into real-world applications, further amplifying the impact of their discoveries.

The comprehensive nature of this study underscores the intricate relationship between design, material properties, and engineering principles. It sets a precedent for future research, propelling exploration in programmable materials and smart polymers that can be tailored to meet specific operational needs. As the technology progresses, it may well find uses in entirely new domains, expanding the horizons of engineering and innovation.

In conclusion, with their ability to jump on command, the engineered metashells symbolize a new frontier in material science—bridging the gap between theoretical research and practical application. As momentum builds around this technology, the anticipation for its next phases and potential impacts continues to grow, underscoring the role of innovative engineering in shaping our future.

Subject of Research: Metashells with programmable jumping capabilities
Article Title: Programmable seconds-to-days long delayed snapping in jumping metashell
News Publication Date: 2-Jun-2025
Web References: NC State Study
References: Proceedings of the National Academy of Sciences
Image Credits: Haitao Qing, NC State University

Keywords

Metashells, Programmable Materials, Mechanical Engineering, Energy Storage, Seed Dispersal, Viscoelasticity, Polyethylene Terephthalate, Dynamic Structures, Material Science, Innovation.