More than a decade after the advent of the 3D-printed gun as an icon of libertarianism and a gun control nightmare, police say one of those homemade plastic weapons has now been found in the hands of perhaps the world’s most high-profile alleged killer. For the community of DIY gunsmiths who have spent years honing those printable firearms models, in fact, the handgun police claim Luigi Mangione used to fatally shoot United Healthcare CEO Brian Thomson is as recognizable as the now-famous alleged shooter himself—and shows just how practical and lethal those weapons have become.
In the 24 hours since police released a photo of what they say is Mangione’s gun following the 26-year-old’s arrest Monday, the online community devoted to 3D-printed firearms has been quick to identify the suspected murder weapon as a particular model of printable “ghost gun”—a homemade weapon with no serial number, created by assembling a mix of commercial and DIY parts. The gun appears to be a Chairmanwon V1, a tweak of a popular partially 3D-printed glock design known as the FMDA 19.2—an acronym that stands for the libertarian slogan, “Free Men Don’t Ask.”
The FMDA 19.2, released in 2021, is a relatively old model by 3D-printed gun standards, says one gunsmith who goes by the first name John and the online handle Mr. Snow Makes. But it’s one of the most well-known and well-tested printable ghost gun designs, he says. The Chairmanwon V1 remix that police say Mangione had in his possession when he was arrested in a Altoona, Pennsylvania McDonald’s varies from that original FMDA 19.2 design only in that another amateur gunsmith, who goes by the pseudonym Chairmanwon, added a different texture to the gun’s grip.
“For someone who has been building firearms like this for five years, it’s a bit of an odd choice. We’ve been building nicer models,” says says Mr. Snow Makes, who hosts an annual ghost gun shooting competition. But he adds that “this is one of the earliest 3D print glock styles that was widely tested and successful at creating a reliably functional firearm.”
Authorities in New York charged Mangione on Monday in the December 4 murder of Thompson, alongside weapons charges and other alleged offenses in Pennsylvania. A handwritten “manifesto” police say they found on Mangione’s person upon his arrest laments United Healthcare’s practices and the US health insurance industry more broadly. Bullet casings discovered at the scene of the shooting outside the New York Hilton Midtown hotel in Manhattan were reportedly emblazoned with the words “deny,” “defend,” “depose”—likely criticisms of health care industry practices.
The fact that even a relatively old model of 3D-printed firearm allegedly allowed Mangione to shoot Thomson repeatedly on a Manhattan street—certainly the most high-profile shooting ever committed with a ghost gun or a 3D-printed weapon—shows how far DIY weapons tech has come, says Cody Wilson, the founder of the gun rights group Defense Distributed. Unlike the earliest 3D-printed gun models, the FDMA 19.2 can be fired hundreds or even thousands of times without its plastic components breaking.
Nature, Published online: 11 December 2024; doi:10.1038/d41586-024-03853-w
Pre-eclampsia is a common and dangerous pregnancy-related illness. Using tiny lipid particles to deliver messenger RNA directly to the placenta to boost blood-vessel function might prove to be an effective treatment.
Mitochondrial DNA (mtDNA) is the part of the genome found in mitochondria, the ‘powerhouses’ of the cell. An analysis of mitochondrial genomes from nearly 60,000 people shows where selection has removed deleterious genetic variants from the population, revealing which mtDNA sites are most important for human health and disease.
Fertilized crocodile eggs (imported from Seronera Crocodile Farm) were transported to the University of Geneva and incubated at 31 °C in moist vermiculite. All treated and non-treated crocodile embryos were fixed and stored in 10% neutral buffered formalin (NBF). All non-fluorescence imaging of embryonic crocodile samples was undertaken using the Keyence VHX 7000 digital microscope. Imaging of hatched crocodile specimens was undertaken using an overhead-mounted Nikon D800 camera. Maintenance of, and experiments with, crocodile embryos and juveniles were approved by the Geneva Canton ethical regulation authority (authorization GE10619B) and performed according to Swiss law. The sample sizes are specified in figure legends and the Supplementary Information. Randomization and blinding was not required.
Nanoindentation
A Piuma nanoindenter (Optics11) was used to acquire stiffness measurements (effective Young’s modulus, Pa) of embryonic crocodile skin surface. When comparing measurements in two skin samples, a change in epidermal keratinization will produce a change in surface stiffness, which is very likely to be correlated with a change of the same sign in the effective overall Young’s modulus of the whole multilayered epidermis. In other words, an increase in epidermal surface stiffness is very likely accompanied by an increased stiffness of the whole epidermis. Freshly dissected upper jaws were positioned lateral side upwards, submerged in PBS. A probe with a tip radius of 99 µm and stiffness rating of 0.48 N m−1 was used to indent at a depth of 2,000 nm. Only measurements from load-displacement curves with a Hertzian contact model fit of ≥95% were subsequently analysed. Each biological replicate for the embryonic nanoindentation series was indented 10 times (Fig. 1c) or 5 times (Fig. 2f). These indentation points were positioned in a single row with each point separated by 120 µm. Plots showing the mean effective Young’s modulus values for each biological replicate with s.d. are presented. Statistical analysis was undertaken in Prism 9 (GraphPad).
Confocal microscopy
Confocal microscopy was used for embryonic crocodile samples stained with the Fast Green FCF dye (Sigma-Aldrich) according to a protocol of whole-mount collagen staining25. Image acquisition was undertaken as previously described25, using the SP8 microscope (Leica Microsystems), with a ×63 oil-immersion objective (numerical aperture, 1.4). Fast Green was excited at 627 nm and the signal was detected in the range of 630–730 nm. Image reconstruction was undertaken using Imaris (Oxford Instruments).
H&E staining
Fixed embryonic crocodile samples were dissected and embedded in paraffin as previously described24. Paraffin sections were cut at 10 µm with a RM2255 microtome (Leica Microsystems) before staining with haematoxylin and eosin (H&E). Slides were imaged using an automated slide scanner (3DHISTECH).
In ovo intravenous EGF injections in crocodiles
The injection of crocodile eggs was undertaken in accordance with our previously published work20,21 (https://youtu.be/qCYWSgbffnY). Crocodile eggs were incubated until the appropriate developmental stage and then cleaned with 70% ethanol. Eggs were candled to identify a suitable vein for injection, and a detailing saw (Micromot 50/E, Proxxon) was used to remove the shell while keeping the underlying membrane intact. The eggshell was then removed using fine forceps, and mineral oil was applied to the membrane with a cotton bud, thereby increasing membrane transparency to allow clear visualization of the underlying veins. The samples were injected with either 30 µl of PBS as a control or 30 µl of PBS containing recombinant murine EGF (PeproTech). Different doses of EGF were injected (0.625 µg, 1.5 µg or 2 µg). Patent Blue was also added to the solution to enable visualization of the solution entering the vein during injection. Injections were undertaken using a Hamilton syringe attached to a micromanipulator (MM33 right, Marzhauser). Once injected, the eggs were cleaned to remove excess mineral oil, and the eggshell window was covered with adhesive tape. Treated embryos were then returned to their incubator. The samples were each injected three times over the course of 10 days for each experiment (Fig. 2a). At collection, the embryos were treated with an intravenous injection of EdU to label proliferating cells (Baseclick); embryo collection and fixation were undertaken 3 h after EdU injection. Some EGF-treated embryos were used for nanoindentation at the end of the experiment, and some others were incubated until hatching. Embryos were subsequently fixed in 10% NBF at 4 °C and imaged with a Keyence VHX 7000 digital microscope. Every embryo injected with EGF exhibited modified head-scale patterning. All of the replicates from these experiments are shown in Supplementary Fig. 4 and are summarized in Supplementary Table 1.
The drug that we use here (EGF) has the remarkable property of specifically promoting epidermal growth and differentiation without exhibiting strong deleterious effects in other aspects of in vivo embryonic development. Further validation of the parameters involved in the compression-folding process of crocodile head-scale patterning will require the identification of other drugs that would specifically affect one parameter at the time. For example, it would be particularly interesting to pharmacologically perturb the 3D architecture of collagen in developing crocodile embryos to investigate the corresponding effects on skin folding of the dorsal versus lateral upper jaw surface. Unfortunately, drugs currently known to effect collagen organization (such as β-aminoproprionitrile, BAPN) are highly toxic in vivo as they strongly affect the development of multiple connective tissues such as skin, bones and blood vessels. Given the great difficulties of experimentation with crocodile embryos, the screening of drugs that could, in vivo, specifically affect one mechanical parameter at a time in the skin, could be initially performed in a more classical model (such as the chicken) with more reliable source of embryos.
LSFM
The upper and lower jaws of fixed embryonic crocodile samples were dissected, dehydrated into methanol, and bleached with hydrogen peroxide, before rehydration and permeabilization in PBS with Triton X-100 (Sigma-Aldrich) (PBST). For nuclear staining, the samples were incubated in either TO-PRO-3 iodide or YO-PRO-1 iodide (3:1,000, Thermo Fisher Scientific) for 6 h. EdU-positive cells (EdU+) were detected using the EdU detection kit manufacturer’s guidelines (Baseclick). The samples were then dehydrated into methanol and collagen staining was undertaken in anhydrous conditions with the same Fast Green protocol25 as for confocal microscopy (see above). Samples were then cleared according to the iDISCO+ protocol37. Upper and lower jaw samples were imaged separately using a light-sheet microscope (Ultramicroscope Blaze, Miltenyi Biotec). Selected specimens were restained with Alizarin Red in potassium hydroxide (KOH) and re-imaged to visualize the developing calcified bone matrix (Extended Data Fig. 1b). Image stacks were processed using ImageJ38, before rendering with the Redshift engine of Houdini (SideFX) and the Unreal Engine (Epic Games). A summary of replicates used for LSFM is shown in Supplementary Table 5. Each sample includes both upper and lower jaws, which we scanned separately.
3D reconstructions of hatched crocodiles
Using our custom-built imaging system39, combining a robotic arm, high-resolution camera and illumination basket of light-emitting diodes, we combine ‘photometric stereo’ and ‘structure from motion’ to reconstruct the precise 3D surface mesh and colour-texture of hatched crocodile heads (Fig. 5b–e and Extended Data Fig. 6a–d). To compare the polygonal scale sizes among individuals, we first compute the minimum principle curvature of the meshes. Then, the folding network of each sample is computed by applying a skeletonization algorithm40, followed by graph simplification (using MATLAB R2021a), on the negative curvature regions of the mesh. Using the colour texture of meshes, the folding networks were manually completed and cleaned using Houdini (SideFX).
Segmentation of LSFM data
Using TO-PRO-3, YO-PRO-1, EdU, Alizarin Red and Fast Green staining (see above), we segmented the light-sheet microscopy data to extract (in both the upper and lower jaws) the geometry of the epidermis, dermis and bone tissues (Supplementary Video 6), as well as the dominant orientations of the dermal collagen fibres, and the distribution of proliferating cells in the dermis and epidermis. The segmented data were used to build a finite element model (FEM, see below) of the crocodile head.
Cell nuclei staining signal enables precise segmentation of the epidermis from the dermis because the former exhibits a higher cell density (Fig. 3a). More specifically, the 3D image generated by LSFM on the basis of the TO-PRO-3/YO-PRO-1 fluorescence signal was subjected to 3D Canny’s edge detection41 in MATLAB-R2021a, generating a 3D binary image in which non-zero voxels form point clouds corresponding to two 3D surfaces: the surface of the epidermis and the epidermis–dermis boundary. For each of these two surfaces, we compute at each point the surface normal vector from the intensity gradient. The position of points and their corresponding normal vectors are then fed to a screened Poisson surface reconstruction algorithm42 in Meshlab43 to reconstruct triangular surface meshes, which effectively represent the initial point clouds in a much lighter format: 3D meshes are much easier to manipulate, for example, with the Laplacian smoothing algorithm to filter out the artifactual stair-step patterns in the original voxelized data format. The epidermis surface and the epidermis–dermis boundaries allow for computing the epidermis thickness across each control and treated sample at different developmental stages.
Collagen network 3D architecture is likely to become instrumental in biomechanical modelling25,26 because it endows tissues with distinctive mechanical properties such as anisotropic response to homogeneous stress. Thus, we assess the orientation(s) of collagen fibres in the dermis across the face and jaws of developing crocodile embryos (Fig. 3b). To this end, we use our recently published whole-mount Fast Green staining method, which provides unmatched visualization of 3D collagen network architecture via confocal or light-sheet microscopy25. In brief, (1) the two most dominant orientation(s) of populations of collagen fibres were identified by determining the dominant 3D Fast Fourier transform coefficients in each of 13,000 homogeneously distributed dermal samples (cubic patches of 50 × 50 × 50 voxels) of 3D light-sheet images (Supplementary Note 1); (2) smoothing of the spatial variation of fibres orientations was achieved with an exact optimization procedure using a fibre axis mismatch energy functional (Supplementary Note 2); and (3) the two dominant fibre orientations, both tangential to the dermis mid-plane, were interpolated using spectral least-squares approximation (Supplementary Note 3).
After standard EdU labelling and detection (Supplementary Video 3), we used a 3D principal curvatures approach36 (on the fluorescence signal) to segment proliferating cells in the jaws of an embryonic crocodile at E51, that is, at the onset of head-scale emergence (Fig. 3c). This approach is highly efficient for individually segmenting cells when they are grouped (that is, in contact). As the signal intensity is embedded in a 3D domain, three signal principal curvatures k1,2,3 are computed (in MATLAB) for each voxel, and voxels characterized by ks > kthreshold, where \({k}_{s}={({k}_{1}^{+}{k}_{2}^{+}{k}_{3}^{+})}^{\frac{1}{3}}\) and \({k}_{i}^{+}=\max ({k}_{i},0)\) are stored. The centroid of the connected voxels is considered as the location of an EdU+ cell. We then compute the density of EdU+ cells, separately for the dermis and the epidermis, by choosing sampling points in the corresponding segmented tissue layers. The space surrounding each sampling point is limited to a box of 80 × 80 × 80 voxels clipped by the layer boundaries. The density of EdU+ cells at a sampling point is computed as the number of cells inside the clipped box divided by its volume. In our numerical model, densities of proliferating cells are represented as a space-dependent growth function. We transfer this information to the 3D model using a spectral least-squares approximation approach to interpolate data on the spatial modes of the target mesh (details are provided in Supplementary Note 3).
For segmenting bone tissue, we use either the 3D Canny’s edge detection of the (very strong) Alizarin Red signal or a semi-automatic procedure for samples with (weaker) Fast Green or EdU signals. In the latter case, we (1) choose several sections in the x, y and z directions and manually mark the separation between the dermis and the bone, (2) store the coordinates of all profile points as a 3D point cloud and compute their normal with Variational Implicit Point Set Surface44 and (3) use screened Poisson surface reconstruction42 from Meshlab43 to generate the mesh corresponding to the bone surface.
A biomechanical model of head-scale emergence
We use the segmented data to build a 3D finite-element numerical growth model. Triangular meshes were generated, both for upper and lower jaws, at the surface boundaries of the epidermis, dermis and bone of embryos before the onset of head-scale patterning (Fig. 1b and see above). The epidermis surface and the epidermis–dermis interface were smoothed to remove any artificial local deformations associated with sample preparation, including dehydration into methanol. The 3D volume of each of the three layers was represented as a tetrahedral mesh generated with TetGen45 (Extended Data Fig. 8a).
During simulated growth, the deformation of tetrahedral elements is realized through finite-strain theory in which the bulk material configuration at current time t is represented as the spatial coordinates of a collection of points in the form of a vector variable x = x(X,t), where X is the spatial coordinates of these points at a reference configuration, that is, at t = 0 (Extended Data Fig. 8b). The coordinates between the current and the reference configurations are connected by the deformation gradient map, F—that is, a second-order tensor that incorporates the elastic and growth deformations. The elastic energy and the mechanical stress stored in each tetrahedral element is then calculated from the neo-Hookean material model, known to behave appropriately under large deformations30,31,46, and allowing the incorporation of anisotropic material, such as collagen fibres47 (Supplementary Note 4). The direction of fibres, as well as the spatial pattern of cell proliferation density, both inferred from LSFM data (Fig. 3b,c), are fed to the mechanical model. However, the elastic moduli, fibre stiffness and final amount of growth are considered as unknown parameters. Note that the absolute values of stiffness are irrelevant in the numerical simulations as the model key parameters are the fibre stiffness relative to the dermis and epidermis moduli, as well as the ratio of epidermis to dermis stiffnesses (Young’s moduli).
Numerical simulations and parameter optimization
To perform numerical simulations, the mechanical model formulation described above is discretized for tetrahedral elements using the FEM and integrated with contact and viscous forces (Supplementary Note 5). The final model is then implemented in an in-house application that uses NVIDIA GPUs for high-performance computation. For that purpose, we used the CUDA programming language to develop intensive-computation kernels, whereas C++ is used for data management, geometry processing, input/output operations and the graphical user interface. Our application integrates the following open-source libraries: Dear ImGui (https://github.com/ocornut/imgui, MIT licence) for the graphical user interface, CUDA C++ Core Libraries (https://github.com/NVIDIA/cccl, Apache-2.0, FreeBSD, BSD-3-Clause licences) for parallel algorithms, Eigen (https://gitlab.com/libeigen/eigen, MPL-2.0, BSD licences) for linear algebra and libigl (https://github.com/libigl/libigl, GPL-3.0, MPL-2.0 licences) for geometry processing. The simulation input is a tetrahedral mesh that defines the geometry of the crocodile head (epidermis, dermis and bone layers). Moreover, a set of model parameters are used: in addition to the dermal collagen fibres orientation and stiffness, we include, both for epidermis and dermis, the Young’s modulus and Poisson’s ratio, the growth rate functions and the cell proliferation pattern. The deformation of the skin is then computed and the final geometry is generated as a tetrahedral mesh.
The mechanical model is integrated with a Bayesian optimization process (bayesopt library from MATLAB R2021a with parallel sampling), that is, a machine-learning global minimization algorithm. The optimality criterion consists of the distance between the metrics (integrating multiple topological and geometrical features, see below) of the steady-state simulated geometry versus LSFM-acquired meshes. To compute the metrics of a folding network, we first compute the minimum principle curvature of the corresponding surface mesh representing the epidermis boundary. We then segment the skin folds by applying a skeletonization algorithm40, followed by graph simplification (using MATLAB R2021a), on the negative curvature regions of the mesh. Next, we compute the following geometrical and topological features of the network: number of domains (cycles), perimeters of domains, lengths of edges, curvatures of edges and lengths of incomplete edges. The final metrics is a vector of which the components are the means of these features, normalized to the diagonal length of its bounding box. Given that components within a metrics vector may differ significantly among each other, we need to normalize them properly. For this purpose, we use LSFM data to compute the metrics of controls at E64 and treated individuals (2 μg EGF) at E64. We then compute the interindividual (that is, among all individuals) mean and s.d. of each component (Fig. 2e). We finally normalize the components of any newly computed metrics by subtracting the interindividual mean and dividing by the interindividual s.d.
Finding optimal parameter values for control and treated targets is performed in two steps. First, we use an E64 control target mesh and perform optimization on the six-dimensional parameter space, including epidermis Young’s modulus, Eepidermis (keeping Edermis = 1); epidermis and dermis Poisson’s ratios, vepidermis/dermis; dermis tangential growth values, \({G}_{T,{\rm{dermis}}}^{+/-}\) (keeping \({G}_{T,{\rm{epidermis}}}^{+/-}\) at 80% of the dermis values); and the fibre stiffness, k1 (k2 being set to 0). Second, using a 2 μg EGF-treated target, we perform another optimization on the three-dimensional parameter space including epidermis-related parameters, that is, Eepidermis, vepidermis and \({\lambda }_{T,{\rm{epidermis}}}^{{\rm{EGF}}}\) (additional epidermal tangential growth induced by EGF). See Supplementary Notes 4 and 6 for the definitions of parameters and Supplementary Table 4 for the complete list of parameter values. To minimize the distance between the metrics vectors of the simulated versus LSFM target geometry (control or treated), we use a Gaussian process (that is, a generalization of the multivariate normal distribution to infinite dimensions) in the optimization loop to approximate posterior mean and variance functions from which the objective function is sampled (Extended Data Fig. 8d). The posterior functions are updated at each iteration according to Bayesian inference and this information is then used to compute the expectation of the improvement function, which measures the chance of observing an objective (that is, the distance between simulation and observation) smaller than the minimum objective observed so far (Supplementary Note 7). The optimization process, which typically takes a few thousand iterations, continues until no more improvement is observed in the last 500 iterations.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
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Global issues, such as climate change and improving sustainability in manufacturing, and technological opportunities, including artificial intelligence and quantum computing, are driving forward the frontiers of research in materials science. These four scientists are among a new generation of researchers helping to push forward these boundaries while also bringing diverse skills to the field, ensuring a broader range of views are included in tomorrow’s solutions.
GRACE GU: Composite creator
Grace Gu is using techniques such as machine learning to design composite materials.Credit: Paddy Mills
Grace Gu is taking inspiration from a wide range of places when she comes up with designs for composite materials that are more robust, adaptable and cheaper to produce than current forms. Turning to “the hidden gems of the mathematical world” to inform her designs has been especially rewarding, says Gu, who works as a mechanical engineer at the University of California, Berkeley (UC Berkeley).
Earlier this year, she co-authored a paper1 on composite material designs based on aperiodic monotiles — unique shapes, discovered just last year2 — that can cover a surface without ever repeating pattern. The materials were shown to be stronger, stiffer and tougher than conventional honeycomb tile structures because the non-repeating pattern creates a tightly packed network with a high tolerance of defects due to how the patterns distribute stress throughout the material. Lightweight composite materials with these characteristics are highly sought after in spacecraft and satellite manufacturing.
“Our experiments show that the designs not only absorb energy efficiently, but also exhibit unique interlocking behaviours where the tiles actually interact with and reinforce each other,” says Gu.
Nature Index 2024 Materials science
She says that she is excited by the simplicity of these designs that use a single shape because they have “immense potential for engineering applications, as they could reduce manufacturing complexity and costs”.
“There’s actually an entire family of monotiles, which gives us a wide range of design possibilities, so far more flexibility than traditional honeycomb structures,” Gu says of the potential for these designs in creating stronger and more efficient materials.
Gu’s ability to recognize patterns has been key to her success as a researcher. She recalls a lightbulb moment she had in 2016 when AlphaGo, an artificial-intelligence (AI) system created by London-based company Google DeepMind, defeated the world’s best player at the board game Go. Gu noticed that the grid used in Go — on which two players move stones to control territory — was similar to the pixelated 2D composite-design problem she was working on at the time.
“We have different types of materials that can occupy different positions” on a grid, and “design strategies that are like boardgame strategies”, she says. Gu considered that if machine learning could be used to train a system to play Go — known for its immense complexity and astronomical number of possible moves — it could help her find new composite designs more efficiently.
Gu found that AI could predict the properties of materials at a vastly accelerated pace3, and it changed how she approached this kind of work in the future. Initially applying machine learning to pixelated designs inspired by AlphaGo, Gu and colleagues have since explored graph-based and Bezier curve approaches to this work, which can capture other types of structures and geometries more effectively.
As a woman in a male-dominated field, Gu is passionate about mentoring young women in research. She says she realized the importance of representation when, during the first class she taught at UC Berkeley, a female student raised her hand to say that she was excited for the semester because Gu was the first female professor she had been taught by at the university. “I think back at these moments and remind myself that this is the best part about being a professor; mentoring and teaching the next generation to fulfil their potential and beyond,” she says.
Gu has received numerous accolades for her work, including a 2020 Outstanding Young Manufacturing Engineer Award from US industry body SME and The American Society of Mechanical Engineers Early Career Award in 2023. — Esme Hedley
MARCILEIA ZANATTA: Decarbonization designer
Marcileia Zanatta’s research harnesses decarbonization to create sustainable materials.Credit: Paddy Mills
Marcileia Zanatta’s desire to design new products to overcome challenges began as early as age eight, when she dreamed of inventing something that could dissolve the hair trapped in her shower drain. “I used to observe everyday problems and say: ‘When I grow up, I’m going to create something that solves this’,” she recalls.
She went on to study industrial chemistry at university in her native Brazil and, while doing a master’s at the Federal University of Rio Grande do Sul in Porto Alegre in 2012, she began working on decarbonization — a field she finds “incredibly rewarding because of its direct impact on people’s lives”.
More than a decade on, Zanatta, a material chemist at Jaume I University in Castelló de la Plana, Spain, is focused on finding energy-efficient ways to convert carbon dioxide into sustainable materials that can be used in chemicals, fuels, and other useful products. “This can lead to a circular economy and a net-zero future,” she says.
In October, the province of Valencia, near where Zanatta lives, experienced one of the deadliest flooding events in Spanish history. There were also devastating floods in May across Rio Grande do Sul, the Brazilian state where she had spent a significant part of her career. “These catastrophes are painful reminders that simply reducing carbon emissions is no longer enough to address the consequences of climate change,” says Zanatta. “This reality underscores the urgency of my work.”
She and her collaborators have invented ways to transform atmospheric carbon into compounds such as formate salts4, which can be used as de-icing agents and fluids to aid drilling, and cyclic carbonates5 — important materials in lithium batteries, cosmetics and industrial solvents.
The latter work, she says, is one of her biggest accomplishments to date. Producing cyclic carbonates from CO2 is typically energy intensive, requiring temperatures above 100 °C, pressures 20 times greater than found in the atmosphere at sea level, and several hours to allow reactions to take place. But in 2023, Zanatta developed a more efficient method — one that takes place under mild conditions using inexpensive, commercially available organic salts such as tetrabutylammonium hydroxide. She’s even used 3D printing to create bespoke reactors that enhance reaction rates by maximizing surface area and improving the distribution of reactants around the catalyst6.
Enabling such reactions in ambient conditions has opened up other avenues, including decarbonization methods that combine both chemical and biological reactions. “Merging the two isn’t so easy, because the second part involves microorganisms, which usually can’t withstand harsh chemical conditions,” Zanatta explains. But harnessing such biological power — some microbes can metabolize simple carbon compounds — is crucial if scientists want to produce biodegradable materials based on biopolymers from CO2. This year, Zanatta and her team successfully demonstrated how a green plastic called poly-3-hydroxybutyrate (PHB) could be produced using such a chemo-biocatalytic process, with formate salts as an intermediary — the first time PHB has been produced from captured atmospheric air4.
Zanatta has received numerous recognitions for her work, including being named a 2023 Rising Star by the materials-science journal ACS Materials Au. But her journey hasn’t always been easy, and Zanatta says female researchers face particular challenges.
“The years when we are close to securing a permanent position are often the same years many women are considering starting a family.” A maternity break “can completely change a woman’s career”, she says.
Working in a male-dominated field also means that Zanatta frequently “hears sexist remarks or encounters ‘mansplaining’”, but she says it’s important to try and speak up because “engagement and awareness are key”. She offers her younger female counterparts the following advice: “Be persistent, resilient, and try not to take things personally. Always demonstrate your value, take initiative, and be confident in your leadership.” — Sandy Ong
CONG XIAO: Quantum explorer
Cong Xiao explores how quantum rules can predict the behaviour of materials.Paddy Mills
Cong Xiao was drawn to the field of condensed-matter physics because he wanted to explore electron wavefunctions: mathematical descriptions of how electrons behave at the quantum-mechanical level.
The fact that it can be used to develop new electronic devices shows the wonder of how “the microscopic quantum-mechanical rules can be connected to the macroscopic devices in our daily life”, says Xiao, a theoretical physicist.
As a PhD student at Peking University in Beijing from 2018 to 2021, for instance, Xiao learnt of the “power and beauty” of the Berry phase — an important unifying theory in the field. He says it cemented his decision to pursue an area of physics that underpins important products in materials science, such as liquid crystals and silicon chips.
Now an assistant professor at the Institute of Applied Physics and Materials Engineering at Macau University, Xiao is exploring new physical effects that can be predicted by quantum rules. For example, in sub-fields such as nonlinear transport and spintronics, he’s looking at how electrons move and interact in unusual ways. Advances in these areas could inform the design of advanced technologies such as quantum computers.
Although his work is theoretical, Xiao says a characteristic of good research in his field is that it “not only reveals some new principle in the microscopy level but can also lead to developments in technology”.
For example, some of Xiao’s current work in nonlinear transport has potential use in rectifiers, electrical devices that convert alternating current into direct current, a common need in communications technology. “Nonlinear transport can be used to achieve such devices, and the underlying principle is truly quantum mechanical,” says Xiao.
An important paper7 in his career — published in 2021 — reported the first-principles calculations of the nonlinear Hall effect in antiferromagnets. The nonlinear Hall effect is the production, upon the application of an electric field, of a transverse voltage that scales nonlinearly with the applied field.
Xiao says the paper gave other researchers the tools to perform more research on nonlinear transport in magnetic systems, as manipulation of these systems has potential applications in information technology.
Xiao says it is sometimes difficult to decide which direction the field of condensed-matter physics is moving in. He says the biggest challenge for a theoretical researcher is “always keeping yourself always at the frontier of the research”, because ideas and topics in condensed-matter physics “move very rapidly”.
“We have to keep learning the theoretical skills just to help us to understand the questions in broader contexts, to help to study wider physical questions. I think this is the biggest challenge, to keep exploring wider and wider research” questions.
In the Nature Index, Xiao stands out from other early-career researchers for his relatively high materials science-related output. His Share of 3.53 for the period 2019 to 2023 places him among the leading 20 early-career researchers in the field. — Esme Hedley
CAIO OTONI: Biomolecule magician
Caio Otoni looks at new ways to upcycle biological waste into useful products.Credit: Paddy Mills
At the State University of Campinas in São Paulo, Brazil, Caio Otoni studies the circular economy, where biological waste, such as fruit peels, coffee husks and crustacean shells, is upcycled into new products, materials and energy sources. This approach is particularly relevant to Brazil, a leading producer of sugarcane, coffee and other food crops.
In Otoni’s lab, he and his colleagues break down waste material into its building blocks — cellulose, chitin and other polymers — and pair it with other compounds to create plastics with biodegradable and antibacterial properties.
In a 2019 paper8, for example, Otoni and his colleagues described how they grafted cationic compounds, chemicals that contain positively charged ions, onto upcycled cellulose to create an antibacterial foam material for use in packaging, filtration and hygiene products. The foam’s positively charged compounds adhere to and disrupt the negatively charged surface of bacterial cell membranes, leading to bacterial cell death. In tests, it displayed an 85% higher antimicrobial response to Escherichia coli compared with controls.
Otoni credits his botanist father, whose lab he would visit when he was growing up, for cultivating his appreciation for plants. But it was his time spent as an exchange student at the US Department of Agriculture’s research facility in Berkeley, California, that solidified his passion for sustainably produced materials.
While working on a project with an Alaskan fishing company, Otoni realized how wasteful it was to throw fish skin back into the ocean. The young undergraduate devised a way to isolate collagen from the discarded skin, convert it to gelatin, and produce packaging material.
“That was the very first project I worked on that exploited not only biorenewable resources, but also waste biomass, as a source of polymers,” says Otoni. As a PhD student and postdoc, he went on to create new materials from carrot and peach waste, as well as sugarcane bagasse — the pulpy residue left after sugarcane stalks are crushed to extract their juice.
Securing funding as a young scientist can be tough, he concedes, “because you are competing with the big fish, the established researchers”. Working in Brazil “adds another level of complexity, because in most institutions, staff numbers are limited, meaning we have to deal with paperwork and administrative tasks in addition to our regular teaching, research and outreach duties”, says Otoni.
“Also, in Brazil, almost everything is charged in dollars or euros, and the currency exchange makes it hard to afford some devices that are key to running competitive research.”
It’s also been a steep learning curve to launch his own lab in 2020. “You’re trained to go to the bench and do research; you’re not trained to supervise students and manage a team. That’s something that comes with time and experience,” says Otoni, who in 2023 was the sole researcher based outside Europe and North America to win the Materials Today Rising Star Award in 2023, an annual prize given to six early-career researchers in the field of materials science and engineering.
Otoni is keen to train other young researchers in his lab in how to upcycle waste products, as he sees it as work that can make a real impact.
“I really believe our research on circular plastics can make a difference and help diminish the burden of plastic pollution in the world,” he says. — Sandy Ong
A rendering of a bottlebrush elastomer and carbon-nanotube composite that researchers believe has potential use as a brain electrode.Credit: Xu, P. et al. Nature Commun. 14, 623 (2023)/CC BY 4.0
When Shuai Xu set out to create a wearable biosensor to monitor the vital signs of premature infants and newborns, he faced a major challenge: the skin of these children is so delicate that the adhesive used to attach a sensor could damage it, potentially leading to infection. The stiff device pulling against the skin as the baby moved, and the wires that might pull it in a different direction, added to the problem. The solution was to build a sensor that was soft and stretchable, with flexible circuit boards and thin, 50-millimetre wires, a huge change from the rigid devices that had long been a mainstay of this type of engineering. It was encased in a bendable silicone, transmitted its readings via Bluetooth, and was stuck to the body using a hydrogel, a polymer-based substance made mostly of water. Xu, a dermatologist, helped develop the device as a postdoctoral researcher in the laboratory of John Rogers, an engineer and materials scientist at Northwestern University in Evanston, Illinois, a pioneer in soft materials.
Xu went on to become a founder and chief executive of Sibel Health in Chicago, Illinois, a medical device company that won Nature’s Spinoff Prize in 2020 and sells wearable sensors for monitoring patients. Xu’s challenges are common among researchers trying to develop biosensors and the materials that go into creating them. The devices must be small and lightweight, and must attach to the body with minimum irritation. In some cases, they require long-lasting batteries and circuitry that can handle a growing suite of artificial-intelligence algorithms that make sense of the data they collect.
Nature Index 2024 Materials science
According to one estimate, the global market for health sensors was worth an estimated US$42.6 billion in 2023 and expected to grow to US$142.2 billion by 2030. The wrist-worn or finger-worn devices that were designed to count steps can now measure heartbeat and blood-oxygen levels, and they’ve been joined by patches that allow diabetics to perform continuous monitoring of their glucose levels.
“That’s nothing to sneeze at,” Xu says. “But there are so many other things that are out there, biochemical and biophysical, that we still can’t do in a practical, continuous way.” Figuring out how to measure a variety of physical and chemical signals cheaply and non-invasively could provide diagnostic information that could reshape medicine. And this might go beyond sensors that take mechanical measurements, such as heart rate. Researchers are also working on chemical sensors that can detect biomarkers in blood, sweat and tears, as well as in fluids that surround cells.
Aida Ebrahimi, a biosensor engineer at Pennsylvania State University in State College, is working on materials that can detect neurotransmitters in saliva or urine such as dopamine, serotonin, adrenaline and noradrenaline, which change in people with diseases such as Parkinson’s or Alzheimer’s. She’s focused on 2D materials, which are only one atomic layer thick, such as molybdenum disulfide. With a material in which, effectively, the “whole thing is surface, you are going to get high sensitivity in the ability to detect a very low concentration of biomolecules”, says Ebrahimi. The material properties of such atomically thin films are also sensitive to surface modification. For example, attaching molecules of manganese gives the material an affinity for dopamine, creating an ultrasensitive detector1.
A soft and stretchable sensor was developed for a newborn’s sensitive skin.Credit: Northwestern University
Similar materials with different molecules attached could be used as sensors for other chemicals that can provide information about health, says Ebrahimi. Her team built a prototype of the sensor in 2020 that they showed could measure dopamine1, but building it and validating it for use could be several years off.
One measuring challenge is that a lot of signalling, particularly in the brain, is performed by the movement of ions, whereas most monitoring equipment is designed to detect electrical currents carried by the flow of electrons. Sahika Inal, a bioengineer at KAUST in Thuwal, Saudi Arabia, is using organic electrochemical transistors (OECTs)2, devices that can detect signals from biomolecules, cells and lipid layers and turn them into readings that can be measured by electronic equipment. OECTs can be built using organic mixed ionic–electronic conductors (OMIECs), which have been the focus of much interest in the past few years. OMIECs are polymers that both ions and electrons can flow across easily. When part of the transistor experiences a small change in a property it is measuring, the OMIEC amplifies that signal. Because it’s an organic polymer, the material is much more compatible with the wet environment of the body than a standard electronic transistor, which has to be encapsulated to protect it from fluids. As a result, electronics can be developed “that can be integrated directly with the biological system,” Inal says.
OECT’s could be printed directly on the skin’s surface to detect biological signals, for instance, or built on top of threads of fabric to create biosensing garments and wraps that could survive washing. They also have the potential to replace the stiff electrodes used in brain implants to control prosthetic devices and monitor electrical activity in seizure patients. Their flexibility and biocompatibility might cause less irritation to brain tissue, which can render the electrodes less sensitive.
At the University of Toronto, mechanical engineer, Xinyu Liu, and chemical engineer, Helen Tran, have developed another material with the softness and flexibility to be used as a brain electrode3. Dubbed the bottlebrush elastomer, their rubber-like substance is made from a molecule that has a long, stiff spine, which maintains its structure, surrounded by short, flexible bristles, for softness. To give the material electrical conductivity, Liu and Tran add a filler — either carbon nanotubes or a mixture of silver flakes and eutectic gallium indium, a semiconductor in liquid form. They worry, though, that the filler could leech out and have toxic effects, so they’d like to eliminate it. “Ultimately, we would like to design a polymer that is soft and electron-conducting,” Tran says. “These demands are often at odds.”
Liu’s lab is also working on wearable sensors. One, based on a hydrogel, is designed to conform to the skin and measure strain when a body part, a knee, for example — is bent4. Such a device could be useful in monitoring an athlete’s performance or assessing arthritis.
Another sensor they are developing places nanowires of zinc oxide on a cotton thread to create electronic textiles that can measure substances such as lactate and sodium in sweat. The material could be woven into a shirt or a sweatband to monitor an athlete’s health5.
Xu sees a lot of opportunities for new biosensors. “AI is generating new algorithms,” he says, that can then be integrated into sensors to learn from, and react, to measurements they’re recording. That would require developing processors that can work with the limited power available in a sensor. Better batteries might help, as would alternatives such as harvesting power from movement or body heat, he says. Devices that can combine readings — glucose levels with heart rate, for instance — could be transformative, he says. He would also like to be able to detect stress hormones that could be used to monitor fatigue, or drug metabolites to check patients have taken medications.
Biosensors have the potential to collect a lot of useful information, and to do it in everyday settings that might give a more realistic picture of health than a one-time doctor’s test. “Whether you’re ill or not”, says Xu, people do not spend most of their time in a clinic or hospital. The ability to track health “and use the technology yourself, I think is really important”.
Mitochondria are mighty ― especially those of birds that fly thousands of kilometres each year. Investigation into mitochondria, the powerhouses of the cell, shows that they undergo extensive transformation in the muscle tissues of migratory birds in preparation for the long journey1.
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