MRI is one of the most important imaging tools employed in medical diagnostics. But for deep-lying tissues or complex anatomic features, MRI can struggle to create clear images in a reasonable scan time. A research team led by Thoralf Niendorf at the Max Delbrück Center in Germany is using metamaterials to create a compact radiofrequency (RF) antenna that enhances image quality and enables faster MRI scanning.

Imaging the subtle structures of the eye and orbit (the surrounding eye socket) is a particular challenge for MRI, due to the high spatial resolution and small fields-of-view required, which standard MRI systems struggle to achieve. These limitations are generally due to the antennas (or RF coils) that transmit and receive the RF signals. Increasing the sensitivity of these antennas will increase signal strength and improve the resolution of the resulting MR images.

To achieve this, Niendorf and colleagues turned to electromagnetic metamaterials – artificially manufactured, regularly arranged structures made of periodic subwavelength unit cells (UCs) that interact with electromagnetic waves in ways that natural materials do not. They designed the metamaterial UCs based on a double-square split-ring resonator design, tailored for operation at a high magnetic field strength of 7.0 T.

Metamaterials improve transmit–receive performance

In their latest study, led by doctoral student Nandita Saha and reported in Advanced Materials, the researchers created a metamaterial-integrated RF antenna (MTMA) by fabricating the UCs into a 5 x 8 array. They built two configurations: a planar antenna (planar-MTMA); and a version with a 90° bend in the centre (bend-MTMA) to conform to the human face. For comparison, they also built conventional counterparts without the metamaterial (planar-loop and bend-loop).

The researchers simulated the MRI performances of the four antennas and validated their findings via measurements at 7.0 T. Tests in a rectangular phantom showed that the planar-MTMA demonstrated between 14% and 20% higher transmit efficiency than the planar-loop (assessed via B₁⁺ mapping).

They next imaged a head phantom, placing planar antennas behind the head to image the occipital lobe (the part of the brain involved in visual processing) and bend antennas over the eyes for ocular imaging. For the planar antennas, B₁⁺ mapping revealed that the planar-MTMA generated around 21% (axial), 19% (sagittal) and 13% (coronal) higher intensity than the planar-loop. Gradient-echo imaging showed that planar-MTMA also improved the receive sensitivity, by 106% (axial), 94% (sagittal) and 132% (coronal).

The bend antennas exhibited similar trends, with B₁⁺ maps showing transmit gains of roughly 20% for the bend-MTMA over the bend-loop. The bend-MTMA also outperformed the bend-loop in terms of receive signal intensity, by approximately 30%.

“With the metamaterials we developed, we were able to guide and modulate the RF fields generated in MRI more efficiently,” says Niendorf. “By integrating metamaterials into MRI antennas, we created a new type of transmitter and detector hardware that increases signal strength from the target tissue, improves image sharpness and enables faster data acquisition.”

In vivo imaging

Importantly, the new MRI antenna design is compatible with existing MRI scanners, meaning that no new infrastructure is needed for use in the clinic. The researchers validated their technology in a group of volunteers, working closely with partners at Rostock University Medical Center.

Before use on human subjects, the researchers evaluated the MRI safety of the four antennas. All configurations remained well below the IEC’s specific absorption rate (SAR) limit. They also assessed the bend-MTMA (which showed the highest SAR) using MR thermometry and fibre optic sensors. After 30 min at 10 W input power, the temperature increased by about 1.5°C. At 5 W, the increase was below 0.5°C, well within IEC safety thresholds and thus used for the in vivo MRI exams.

The team first performed MRI of the eye and orbit in three healthy adults, using the bend-loop and bend-MTMA antennas positioned over the eyes. Across all volunteers, the bend-MTMA exhibited better transmit performance in the ocular region that the bend-loop.

The bend-MTMA antenna also generated larger intraocular signals than the bend-loop (assessed via T₂-weighted turbo spin-echo imaging), with signal increases of 51%, 28% and 25% in the left eyes, for volunteers 1, 2 and 3, respectively, and corresponding gains of 27%, 26% and 29% for their right eyes. Overall, the bend-MTMA provided more uniform and higher-intensity signal coverage of the ocular region at 7.0 T than the bend-loop.

To further demonstrate clinical application of the bend-MTMA, the team used it to image a volunteer with a retinal haemangioma in their left eye. A 7.0 T MRI scan performed 16 days after treatment revealed two distinct clusters of structural change due to the therapy. In addition, one of the volunteer’s ocular scans revealed a sinus cyst, an unexpected finding that showed the diagnostic benefit of the bend-MTMA being able to image beyond the orbit and into the paranasal sinuses and inferior frontal lobe.

The team used the planar antennas to image the occipital lobe, a clinically relevant target for neuro-ophthalmic examinations. The planar-MTMA exhibited significantly higher transmit efficiency than the planar-loop, as well as higher signal intensity and wider coverage, enhancing the anatomical depiction of posterior brain regions.

“Clearer signals and better images could open new doors in diagnostic imaging,” says Niendorf. “Early ophthalmology applications could include diagnostic confirmation of ambiguous ophthalmoscopic findings, visualization and local staging of ocular masses, 3D MRI, fusion with colour Doppler ultrasound, and physio-metabolic imaging to probe iron concentration or water diffusion in the eye.”

He notes that with slight modifications, the new antennas could enable MRI scans depicting the release and transport of drugs within the body. Their geometry and design could also be tuned to image organs such as the heart, kidneys or brain. “Another pioneering clinical application involves thermal magnetic resonance, which adds a thermal intervention dimension to an MRI device and integrates diagnostic guidance, thermal treatment and therapy monitoring facilitated by metamaterial RF antenna arrays,” he tells Physics World.

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Todd McNutt is a radiation oncology physicist at Johns Hopkins University in the US and the co-founder of Oncospace, where he led the development of an artificial intelligence (AI)-powered tool that simultaneously accelerates radiation planning and elevates plan quality and consistency. The software, now rebranded as Plan AI and available from US manufacturer Sun Nuclear, draws upon data from thousands of previous radiotherapy treatments to predict the lowest possible dose to healthy tissues for each new patient. Treatment planners then use this information to define goals that streamline and automate the creation of a best achievable plan.

Physics World’s Tami Freeman spoke with McNutt about the evolution of Oncospace and the benefits that Plan AI brings to radiotherapy patients and cancer treatment centres.

Can you describe how the Oncospace project began?

Back in 2007, several groups were discussing how we could better use clinical data for discovery and knowledge generation. I had several meetings with folks at Johns Hopkins, including Alex Szalay who helped develop the Sloan Digital Sky Survey. He built a large database of galaxies and stars and it became a huge research platform for both amateur and professional astronomers.

From that discussion, and other initiatives, we looked at moving towards structured data collection for patients in the clinical environment. By marrying these data with radiation treatment plans we could study how dose distributions across the anatomy affect patient outcomes. And we took that opportunity to build a database for radiotherapy.

What inspired the transition from academic research to founding the company Oncospace Inc in 2019?

After populating the database with data from many patients, we could examine which anatomic features impact our ability to generate a plan that minimizes radiation dose to normal tissues while treating target volumes as best as possible. We came up with a feature set that characterized the relationships between normal anatomy and targets, as well as target complexity.

This early work allowed us to predict expected doses from these shape-relationship features, and it worked well. At that point, we knew we could tap into this database and generate a prediction that could help create treatment plans for new patients. We thought of this as personalized medicine: for the first time, we could see the level of treatment plan quality that we could achieve for a specific patient.

I thought that this was useful commercially and that we should get it out to other clinics. Praveen Sinha, who I’d known from my previous work at Philips and now leads Sun Nuclear’s software business line, asked if I wanted to create a startup. The timing was right for both of us and I had a team here ready to go, so we went ahead and did it. With his knowledge of startups and my knowledge of what we wanted to achieve, we had perfect timing and a perfect group to work with.

Plan AI enables both predictive planning and peer review, how do these functions work?

The idea behind predictive planning is that, for a given patient, I can predict the expected dose that I should be able to achieve for them.

Treatment planning involves specifying dosimetric objectives to the planning system and asking it to optimize radiation delivery to meet these. But nobody really knows what the right objectives even are – it is just a trial-and-error process. Plan AI’s prediction provides a rational set of objectives for plan optimization, allowing the planning system’s algorithm to move towards a good solution and making treatment planning an easier problem to solve.

Peer review involves a peer physician looking at every treatment plan to evaluate it for quality and safety. But again, people don’t really know the level of quality you can generate, it depends on the patient’s anatomy. Providing a predicted dose with clinical dose goals enables a rapid review to see whether it is a high-quality plan or not.

In the past we looked at simple things like whether a contour is missing slices or contains discontinuities and Plan AI checks for this, but you can do far more with AI. For example, you could look at all the contoured rectums in the system and predict if your contour goes too far into the sigmoid colon, then it may be mis-contoured. We have research software that can flag such potential anomalies so they don’t get overlooked.

The Plan AI models are developed using Oncospace’s database of previous treatments; can you describe this data lake?

When we first started, we developed a large SQL database containing all the shape-relationship features and dosimetry features. The SQL language is ideal for being able to query and sift through the data, but when the company was formed, we recognized that there was some age to that technology.

So for the Plan AI data lake, we extracted all the different shape-relationship and shape-complexity features and put them into a Parquet database in the cloud. This made the data lake much more amenable to applying machine learning algorithms to it. The SQL data lake at Johns Hopkins is maintained separately and primarily used to investigate toxicity predictions and spatial dose patterns. But for Plan AI, the models are fixed and streamlined for the specific task of dose prediction.

What does the model training process entail?

One of the first tasks was to curate the data, using the AAPM’s standardized structure-naming model. Our data scientist Julie Shade wrote some tools for automatic name mapping and target identification; that helped us process much larger amounts of data for the model.

Once we had all the shape-relationship and shape-complexity features and all the doses, we trained the models by anatomical region. We have FDA-approved models for the male and female pelvis, thorax, abdomen and head-and-neck. For each of these, we predict the doses for every organ-at-risk. Then we used a five-fold validation model to make sure that the predictions were good on an internal data set.

We also performed external validation at institutions including Johns Hopkins and Montefiore hospitals. We created predicted plans from recent treatment plans that had been evaluated by physicians. For almost all cases, both plan quality and plan efficiency were improved with Plan AI.

One aspect of this training is that whenever we drive optimization via predictive planning we want to push towards the best achievable dose. Regular machine learning predicts an expected, or average, dose across all patients. But you never want to drive a treatment plan towards the average dose, because then every plan you generate will be happy being average. Our model predicts both the average and the best achievable dose, and drives plan optimization towards the best achievable.

When implementing new technology in the clinic, it’s important to fit into the existing treatment workflow. How clinic-ready are these AI tools?

Radiation therapy is protocol-driven: we know what technique we’re going to use to treat and what our clinical dose goals are for different structures. What we don’t know is the patient-specific part of that. So for each anatomical region, we built models out of a wide range of treatment protocols, with many different types of patients, to ensure that the same prediction model works for any protocol. This means a user can use any protocol for treatment and the predictions will work, they don’t have to retrain anything. It’s ready to go out of the box, there’s a library of protocols to start with, and you can change protocols as you need for your own clinic.

The other part of being clinic-ready is aligning with the way that planning is currently performed, which is using dose–volume histograms. Treatment plans are optimized by manipulating these dose objectives, and that’s exactly what we predict. So users aren’t changing the whole paradigm of how planners operate. They still use their treatment planning system (TPS) – we just put the objectives in there. Basically, a TPS script sends the patient’s CT and contours to the cloud, where Plan AI makes the predictions. The TPS then pulls back in the objectives built from the models, based on this specific patient’s anatomy. The TPS runs the optimization and, as a last step, can send the plan back to Plan AI to check that it fits within the best achievable predictions.

Did you encounter any challenges bringing AI into a clinical setting?

Interestingly, the challenges aren’t technical, they are more human related. One of the more systemic challenges is data security when using medical data for training. A nice thing about our system is that the features we generate from treatment plans are just mathematical shape-relationship features and don’t involve a lot of identifiable information.

AI has been used in radiation therapy for image contouring and auto-segmentation, and early efforts were not so good. So, there’s always a good, healthy scepticism. But once you show people that it works and works well, this can be overcome. I have seen some people worried about job security and AI taking over. We are medical professionals designing a treatment plan to care for a patient and there’s a lot of pride and art in that – if you automate that, it takes away some of this pride and art.

I tell people that if we automate the easier things, then they can spend their quality time on the more difficult and challenging cases, because that’s where their talent might be needed more.

Do you have any advice for clinics looking to adopt AI-driven planning?

Introduce it as an assistant, not as a solution. You want people that already know what they’re doing to be able to use their knowledge more efficiently. We want to make their jobs easier and show them that it also improves quality.

With dosimetrists, for example, they create a plan and work hard getting the dose down – and then the physician looks at it and suggests that they can do better. Predictive planning gives them confidence that they are right and takes the uncertainty out of the physician review process. And once you’ve gained that level of confidence, you can start using it for adaptive planning or other technologies.

Where do you see predictive modelling and AI in oncology in five years from now?

Right now, there’s been a lot of data collected, but we want that data to advance and learn. Having multiple centres adding to this pool of knowledge and being able to continually update those models from new, broader data sets could be of huge value.

In terms of patient outcomes, we’ve done a lot of the work looking at how the spatial pattern of dose impacts toxicity and outcomes. This is part of the research being performed at Johns Hopkins and still in discovery mode. But down the road, some of these predictions of normal tissue outcomes could be fed into the planning process to help reduce toxicity at the patient level.

Finally, what’s been the most rewarding part of this journey for you?

During my prior experience building treatment planning systems, the biggest problem was always that nobody knew what the objective was. Nobody knew how to tell the system: “this is the dose I expect to receive, now optimize to get it for me”, because you didn’t know what you could do. For any given patient, you could ask for too much or too little. Now, for the first time, I argue that we actually know what our objective is in our treatment planning.

This levels the playing field between different environments, different countries, or even different dosimetrists with different levels of experience. The Plan AI tool brings all this to a consistent state and enables high quality, efficient planning everywhere. We can provide this predictive planning tool to clinics around the world. Now we just have to get everybody using it.

• You can listen to the full interview with Todd McNutt in the Physics World Weekly podcast.

 

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Personalized health – the use of individualized measurements to address each patient’s specific needs – is a research field that’s evolving at pace. Bringing this level of personalization into the clinic is an interdisciplinary challenge, requiring the development of sensors that generate clinically meaningful data outside the hospital, new imaging modalities and analysis techniques, and computational tools that address the uncertainties of dealing with just one individual.

Much of the most impactful work in this field sits in the spaces between established disciplines. And for researchers looking to publish their findings or read about the latest breakthroughs, this work is often scattered across discipline-specific journals. A new open access journal from IOP Publishing – Medical Sensors & Imaging (MSI) – aims to remedy this shortfall, providing a dedicated home for authors working across sensing, imaging, modelling and data-driven healthcare.

“We want a journal where physicists, engineers, computer scientists, biomedical researchers and clinicians can publish and read work that advances personalized health, without confinement into traditional silos,” explains founding editor-in-chief Marco Palombo from Cardiff University. “MSI also aims to play an important role in strengthening interdisciplinary exchange.”

“The community needs a specialized forum that doesn’t just report on new materials or a clinical trial, but validates innovations that can specifically solve complex biomedical challenges,” adds deputy editor Xiliang Luo from Qingdao University of Science and Technology. “I think this journal is a perfect fit for that gap.”

Connecting communities

Published by IOP Publishing on behalf of the Institute of Physics and Engineering in Medicine (IPEM), MSI aims to dismantle the barriers between engineering innovation and clinical application by creating a community of experts that work together to translate innovative technology into clinical settings.

MSI sits within IPEM’s journal portfolio that includes Physics in Medicine & Biology, Physiological Measurement and Medical Engineering & Physics. Its aims and scope were designed to complement, rather than overlap with, these existing journals and provide a dedicated venue for translational work and practical applied research that may otherwise struggle to fit a traditional scope.

Being part of this established family of journals brings with it strong editorial standards, an established readership base and a commitment to scientific integrity. The journal also offers rapid, high-quality peer review, with feedback that’s constructive, rigorous and fair. MSI is fully open access, which maximizes the visibility, reach and impact of its published papers.

“For a new journal in a dynamic field, ensuring content is discoverable and barrier-free is essential for building an audience quickly and establishing credibility,” says Palombo. “We also wanted MSI to support global participation. Many excellent groups operate with limited budgets but make major scientific contributions. Open access reduces inequities in who can read and build on published work.”

“For the authors, we can provide a specialized platform for scientists whose work transcends traditional boundaries, offering visibility to a broad audience that’s eager for translational solutions,” says Luo. “And for the readers, I think we will be the go-to resource for academic researchers, industry R&D leaders, and healthcare innovators seeking the latest breakthroughs in personalized health monitoring and advanced diagnostics.”

Hot topics

Palombo contributed to the strategic development of the journal at an early stage, drawing upon his experience in healthcare and medical imaging research and engaging with the research community to identify the scientific niche that MSI could fill. Working with IOP Publishing, he helped shape the journal’s aims and scope and assembled a diverse, internationally recognized editorial board with knowledge aligned with the journal’s mission – including Lui, who brings specialist expertise in wearable technologies and biosensors.

The journal will publish high-quality research on novel biomedical sensing and imaging techniques, along with the algorithms, validation frameworks and translational studies that demonstrate their application in real-world medicine. MSI also provides a platform to showcase research on hot topics such as wearable and implantable sensors for continuous physiological monitoring, for example, or microneedle-based sensing technologies and breath analysis.

The development of flexible and biocompatible materials will be key for the growth of bio-integrated devices and biodegradable or transient electronics, as will anti-fouling strategies that enable use of sensors in complex biological environments. On the imaging side, the journal scope encompasses mainstay medical imaging techniques such as MRI, CT, ultrasound, PET and SPECT, as well as emerging multimodal and hybrid approaches, with a focus on technical innovation and translational relevance.

“Given my own background, I’m particularly keen to see strong submissions in the area of MRI, including advanced quantitative biomarkers and approaches that probe tissue microstructure,” notes Palombo. “I also see huge potential in connecting imaging to computational modelling – particularly digital twins – and in building imaging pipelines that enable personalized diagnosis and prognosis.”

“Other exciting areas include combining sensing and imaging technologies into one system, and closed-loop ‘sense then act’ systems, which sense something and can then release medicine to treat the disease,” says Luo.

The rise of AI

Artificial intelligence (AI) is becoming increasingly central to both sensing and imaging, and will likely play a major role in the evolution of personalized health, enabling a shift towards multimodal fusion of sensor streams, imaging and clinical data. AI could also facilitate the introduction of integrated sensor systems that collect data and interpret signals in real time, and digital twins that link patient-specific data with computational models to simulate disease progression or treatment response.

Palombo emphasizes the importance of trustworthy AI: methods that don’t just provide an output, but are explainable, robust and explicitly handle uncertainty. This is a direction seen in the general field of AI, but is especially important within healthcare. He also cites the increasing momentum around green healthcare and green AI, with personalized health technologies designed to reduce waste and minimize energy consumption, and clinical models developed with far greater computational efficiency.

“It would be fantastic to have an AI model running directly on the sensor, for example, and this ties in with the environmental impact of AI,” he explains. “If we keep AI small and manageable, then it pollutes less, is more affordable for everybody and can be deployed on small, lightweight devices.”

A community focal point

Looking ahead, Palombo hopes that MSI will becomes a leading platform for interdisciplinary innovation in personalized health, and the routine home for publishing major advances in sensing, imaging, modelling and trustworthy AI. “Over time, I’d like the journal to build depth in core areas, while also actively shaping emerging directions such as digital twins, uncertainty-aware and explainable AI, multimodal integration and technologies that are genuinely deployable in clinical workflows.”

“Currently, the fields of sensor engineering and clinical medicine often run on parallel tracks. My hope is that this journal will force these tracks to converge over time,” adds Luo. “I see the journal fostering a new language where chemists, physicists, engineers and doctors can understand each other by publishing papers in MSI.”

• Medical Sensors & Imaging is fully open for submissions, with the first issue expected to publish in Q2/Q3 of this year. During the launch phase, IOPP is covering the article processing charge (APC) for all accepted papers, enabling early contributors to publish at no cost while helping the journal establish a strong foundation of high-quality inaugural content. Beyond this period, many authors will benefit from support through IOPP’s transformative agreements, while others may be eligible for APC waivers and discounts.

 

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A team headed up at TU Wien in Austria has set the Guinness World Record for creating the world’s smallest QR code. Working with industry partner Cerabyte, the researchers produced a stable and repeatedly readable QR code with an area of just 1.977 µm². When read out – using an electron microscope, as its structure is too fine to be seen with a standard optical microscope – the QR code links to a scientific webpage at TU Wien.

But this wasn’t just a ploy to get into the record books, the QR code was created as part of the team’s research into ceramic data storage materials. Unlike conventional magnetic or electronic data storage media, which degrade within decades, ceramic-based storage is designed to withstand extreme temperatures, radiation, chemical corrosion and mechanical damage.

As such, information stored in ceramic materials could endure for centuries, or even millennia. And in contrast to today’s data centres, ceramics preserve stored information without any energy input and without requiring cooling.

To create these ultralong-life data storage systems, the researchers use focused ion beams to mill the QR code into a thin film of chromium nitride, a durable ceramic often used to coat high-performance cutting tools. As each individual pixel is just 49 nm in size, roughly 10 times smaller than the wavelength of visible light, the code cannot be imaged using visible light. But when examined with an electron microscope, the QR code could indeed be read out reliably.

After the writing process, the entire stack of ceramic films is subjected to extreme conditions, such as high temperatures, corrosive environments and mechanical stress, to evaluate the material’s long-term durability and readout stability.

Pushing storage to its limits

Creating a “tiny QR code” was not the team’s initial goal, but emerged as a natural outcome of pushing this storage technology to its limits, says Paul Mayrhofer from TU Wien’s Institute of Materials Science and Technology.

“During a discussion with one of my PhD students, Erwin Peck, we realised that the writing procedure we had developed already produced features smaller than what had previously been reported for QR codes,” he explains. “This sparked the idea: if we can reliably write structures at that scale, why not intentionally create the smallest QR code possible?”

To claim its place in the record books, the QR code was successfully milled and read out in the presence of witnesses and its size independently verified using calibrated scanning electron microscopy at the University of Vienna. It is now officially recognized by Guinness as the world’s smallest QR code, and is roughly one third the size of the previous record holder.

Mayrhofer points out that the storage capacity of the ceramic data storage technology far surpasses that of a single QR code. “Based on current estimates, a cartridge of 100 x 100 x 20 mm with ceramic storage medium could potentially store on the order of 290 terabytes of raw data,” he says.

As well as offering this impressive raw capacity, for practical applications it’s also crucial that the ceramic storage offers high writing speed, which determines how efficiently large datasets can be stored, and low energy consumption during writing, which will influence the potential for scalability and sustainability. The researchers are currently working to optimize both of these parameters.

“Humanity has preserved information for millennia when carved in stone, yet much of today’s digital information risks being lost within decades,” project leader Alexander Kirnbauer tells Physics World. “Our long-term goal is to create an ultrastable, sustainable data storage technology capable of preserving information for extremely long times – potentially thousands to millions of years. In essence, we want to develop a form of storage that ensures the knowledge of our digital age does not disappear over time.”

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Synthetic materials such as plastics are designed to be durable and water resistant. But the processing required to achieve these properties results in a lack of biodegradability, leading to an accumulation of plastic pollution that affects both the environment and human health. Researchers at the Institute for Bioengineering of Catalonia (IBEC) are developing a possible replacement for plastics: a novel biomaterial based on chitin, the second most abundant natural polymer on Earth.

“Every year, nature produces on the order of 10¹¹ tonnes of chitin, roughly equivalent to more than three centuries of today’s global plastic production,” says study leader Javier G Fernández. “Chitin and [its derivative] chitosan are the ultimate natural engineering polymers. In nature, variations of this material produce stiff insect wings enabling flight, elastic joints enabling extraordinary jumping in grasshoppers, and armour-like protective exoskeletons in lobsters or clams.”

But while biomaterials provide a more environmentally friendly alternative to conventional plastics, most biological materials weaken when exposed to water. In this latest work, Fernández and first author Akshayakumar Kompa took inspiration from nature and developed a new biomaterial that increases its strength when in contact with water, while maintaining its natural biodegradability.

Metal matters

In the exoskeletons of insects and crustaceans, chitin it is secreted in a gel-like form into water and then transitions into a hard structure. Following a chance observation that removing zinc from a sandworm’s fangs caused them to soften in water, Kompa and Fernández investigated whether adding a different transition metal, nickel, to chitosan could have the opposite effect.

By mixing nickel chloride solution (at concentrations from 0.6 to 1.4 M) with dispersions of chitosan extracted from discarded shrimp shells, the researchers entrapped varying amounts of nickel within the chitosan structure. Fourier-transform infrared spectra of resulting chitosan films revealed the presence of nickel ions, which form weak hydrogen bonds with water molecules and increase the biomaterial’s capacity to bond with water.

“In our films, water molecules form reversible bridges between polymer chains through weak interactions that can rapidly break and reform under load,” Fernández explains. “That fast reconfiguration is what gives the material high strength and toughness under wet conditions: essentially a built-in, stress-activated ‘self-rearrangement’ mechanism. Nickel ions act as stabilizing anchors for these water-mediated bridges, enabling more and longer-range connections and making inter-chain connectivity more robust”.

The nickel-doped chitosan samples had tensile strengths of between 30 and 40 MPa, similar to that of standard plastics. Adding low concentrations of nickel did not significantly impact the mechanical properties of the films. Concentrations of 1 M or more, however, preserved the material’s strength while increasing its toughness (the ability to stretch before breaking) – a key goal in the field of structural materials and a feature unique to biological composites.

Upon immersion in water, the nickel-doped films exhibited greater tensile strength, increasing from 36.12±2.21 MPa when dry to 53.01±1.68 MPa, moving into the range of higher-performance engineering plastics. In particular, samples created from an optimal 0.8 M nickel concentration almost doubled in strength when wet (and were used for the remainder of the team’s experiments).

Scaling production

The manufacturing process involves an initial immersion in water, followed by drying for 24 h and then re-wetting. During the first immersion, any nickel ions that are not incorporated into the material’s functional bridging network are released into the water, ensuring that nickel is present only where it is structurally useful.

The researchers developed a zero-waste production cycle in which this water is used as a primary component for fabricating the next object. “The expelled nickel is recovered and used to make the next batch of material, so the process operates at essentially 100% nickel utilization across batches,” says Fernández.

They used this process to produce various nickel-doped chitosan objects, including watertight containers and a 1 m² film that could support a 20 kg weight after 24 h of water immersion. They also created a 244 x 122 cm film with similar mechanical behaviour to the smaller samples, demonstrating the potential for rapid scaling to ecologically relevant scales. A standard half-life test revealed that after approximately four months buried in garden soil, half of the material had biodegraded.

The researchers suggest that the biomaterial’s first real-world use may be in sectors such as agriculture and fishing that require strong, water-compatible and ultimately biodegradable materials, likely for packaging, coatings and other water-exposed applications. Both nickel and chitosan are already employed within biomedicine, making medicine another possible target, although any new medical product will require additional regulatory and performance validation.

The team is currently setting up a 1000 m² lab facility in Barcelona, scheduled to open in 2028, for academia–industry collaborations in sustainable bioengineering research. Fernández suggests that we are moving towards a “biomaterial age”, defined by the ability to “control, integrate, and broadly use biomaterials and biological principles within engineering applications”.

“Over the last 20 years, working on bioinspired manufacturing, we have been able to produce the largest bioprinted objects in the world, demonstrated pathways for resource-secure and sustainable production in urban environments, and even explored how these approaches can support interplanetary colonization,” he tells Physics World. “Now we are achieving material properties that were considered out of reach by designing the material to work with its environment, rather than isolating itself from it.”

The researchers report their findings in Nature Communications.

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