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Plasmodium sporozoites actively migrate in the dermis and enter blood vessels to infect the liver. Despite their importance for malaria infection, little is known about these cutaneous processes. We combine intravital imaging in a rodent malaria model and statistical methods to unveil the parasite strategy to reach the bloodstream. We determine that sporozoites display a high-motility mode with a superdiffusive Lévy-like pattern known to optimize the location of scarce targets. When encountering blood vessels, sporozoites frequently switch to a subdiffusive low-motility behavior associated with probing for intravasation hotspots, marked by the presence of pericytes. Hence, sporozoites present anomalous diffusive motility, alternating between superdiffusive tissue exploration and subdiffusive local vessel exploitation, thus optimizing the sequential tasks of seeking blood vessels and pericyte-associated sites of privileged intravasation. Plasmodium sporozoites actively migrate in the dermis and enter blood vessels to induce infection. Here, Formaglio et al. show that Plasmodium sporozoites alternate global superdiffusive skin exploration and local subdiffusive blood vessel exploitation to find intravasation hotspots associated with pericytes, enter the blood circulation and start malaria infection.

Thermoelectric materials play a crucial role in energy conversion technologies by enabling the direct transformation of heat into electricity, offering a promising route for sustainable energy harvesting. This study investigates the thermoelectric properties of the two-dimensional monolayer using first-principles calculations within the density functional theory (DFT) framework, combined with Boltzmann transport theory. We systematically compute the electrical conductivity, Seebeck coefficient, and electronic contribution to thermal conductivity using the BoltzTraP code. At the same time, the relaxation time is determined via an Arrhenius approach, enabling the estimation of absolute transport coefficients. The lattice thermal conductivity is also obtained through a semi-empirical method that accounts for phonon contributions. The thermoelectric figure of merit ( ZT ) is evaluated across various chemical potentials and temperatures, revealing strong anisotropy between the x and y transport directions. At room temperature ( K), the maximum ZT values reach 0.5 for electrons and 0.35 for holes. As the temperature increases, the thermoelectric efficiency improves, with the highest ZT reaching 0.81 for electrons along the x direction at K. At the same time, hole transport becomes nearly isotropic with in both directions. These results establish as a strong candidate for thermoelectric applications, demonstrating significant efficiency from ambient to high temperatures. The material’s favorable transport properties and high ZT values make it an excellent prospect for energy conversion technologies. Future research should focus on experimental validation and potential optimizations through strain engineering and doping strategies to enhance performance.

We present a combined semiempirical and machine learning approach to predict oxygen interaction barriers in 4036 two-dimensional (2D) materials from the C2DB database. Using the Extended Hückel Method (EHM), calibrated to reproduce the known oxygen barrier on graphene, we computed barrier energies along multiple adsorption paths. These values served as targets for supervised learning models based on descriptors from C2DB and Matminer. Among the tested models, XGBoost delivered the best performance, with SHAP analysis revealing that electronic features, such as electronegativity and valence electron count, are key predictors of barrier height, highlighting the underlying nonlinear relationships between material features and adsorption behavior. This framework enables efficient and interpretable screening of oxygen reactivity in 2D systems, supporting the design of oxidation-resistant and functional surfaces. These findings underscore the role of nonlinear science in materials discovery and highlight how combining semiempirical modeling with interpretable machine learning can efficiently capture complex surface interactions in 2D materials. Graphic abstract

Laser ablation synthesis in solution (LASiS) was used to synthesize quantum dots (QDs) of manganese telluride (MnTe). Size-tuneable QDs exhibit physicochemical property variation in the bandgap, optical, electrical, and magnetic properties. The size of QDs was fine-tuned with varying power and time duration of laser ablation. The characteristics of MnTe QDs were investigated using basic structural and morphological characterizations. The observed bandgap opening in the material is due to the quantum confinement effect, which led to increased energy band separation, as predicted from DFT simulations. The magnetic property of the synthesized MnTe QD catalysts influences the degradation process, with the process following pseudo-first-order kinetics. The photocatalytic dye degradation was studied using UV–visible spectroscopy. MnTe QDs were able to photodegrade methylene blue reagent up to 93.4% in 60 min under an external magnetic field. The magnetic field-induced MnTe QDs showed enhanced photocatalytic degradation efficiency with increased apparent rate kinetics up to ten times (0.0453 min −1 ) compared to just sunlight exposure (0.00456 min −1 ). Graphical Abstract (a) HAADF-STEM of the synthesized MnTe QD dispersed, (b) SEM image of the same, (c) relative concentration of the samples with and without magnetic fields, inset: photo degraded samples on exposure of magnetic fields and (d) schematic representation of the photocatalytic dye-degradation.

ZnO and doped ZnO films with non-ferromagnetic metal have been widely used as biosensor elements. In these studies, the electrochemical measurements are explored, though the electrical impedance of the system. In this sense, the ferromagnetic properties of the material can be used for multifunctionalization of the sensor element using external magnetic fields during the measurements. Within this context, we investigate the room-temperature ferromagnetism in pure ZnO and Ag-doped ZnO films presenting zigzag-like columnar geometry. Specifically, we focus on the films’ structural and quasi-static magnetic properties and disclose that they evolve with the doping of low-Ag concentrations and the columnar geometry employed during the deposition. The magnetic characterization reveals ferromagnetic behavior at room temperature for all studied samples, including the pure ZnO one. By considering computational simulations, we address the origin of ferromagnetism in ZnO and Ag-doped ZnO and interpret our results in terms of the Zn vacancy dynamics, its substitution by an Ag atom in the site, and the influence of the columnar geometry on the magnetic properties of the films. Our findings bring to light an exciting way to induce/explore the room-temperature ferromagnetism of a non-ferromagnetic metal-doped semiconductor as a promising candidate for biosensor applications.

Plasmodium sporozoites actively migrate in the dermis and enter blood vessels to infect the liver. Despite their importance for malaria infection, little is known about these cutaneous processes. We combine intravital imaging in a rodent malaria model and statistical methods to unveil the parasite strategy to reach the bloodstream. We determine that sporozoites display a high-motility mode with a superdiffusive Lévy-like pattern known to optimize the location of scarce targets. When encountering blood vessels, sporozoites frequently switch to a subdiffusive low-motility behavior associated with probing for intravasation hotspots, marked by the presence of pericytes. Hence, sporozoites present anomalous diffusive motility, alternating between superdiffusive tissue exploration and subdiffusive local vessel exploitation, thus optimizing the sequential tasks of seeking blood vessels and pericyte-associated sites of privileged intravasation.

Thermoelectric materials play a crucial role in energy conversion technologies by enabling the direct transformation of heat into electricity, offering a promising route for sustainable energy harvesting. This study investigates the thermoelectric properties of the two-dimensional [Formula: see text][Formula: see text] monolayer using first-principles calculations within the density functional theory (DFT) framework, combined with Boltzmann transport theory. We systematically compute the electrical conductivity, Seebeck coefficient, and electronic contribution to thermal conductivity using the BoltzTraP code. At the same time, the relaxation time is determined via an Arrhenius approach, enabling the estimation of absolute transport coefficients. The lattice thermal conductivity is also obtained through a semi-empirical method that accounts for phonon contributions. The thermoelectric figure of merit (ZT) is evaluated across various chemical potentials and temperatures, revealing strong anisotropy between the x and y transport directions. At room temperature ([Formula: see text] K), the maximum ZT values reach 0.5 for electrons and 0.35 for holes. As the temperature increases, the thermoelectric efficiency improves, with the highest ZT reaching 0.81 for electrons along the x direction at [Formula: see text] K. At the same time, hole transport becomes nearly isotropic with [Formula: see text] in both directions. These results establish [Formula: see text][Formula: see text] as a strong candidate for thermoelectric applications, demonstrating significant efficiency from ambient to high temperatures. The material’s favorable transport properties and high ZT values make it an excellent prospect for energy conversion technologies. Future research should focus on experimental validation and potential optimizations through strain engineering and doping strategies to enhance performance.

Closely packed quasi-hexagonal and quasi-tetragonal crystalline phase of C\\(_60\\) molecules (named qHPC\\(_60\\)) was recently synthesized. Here, we used DFT simulations to investigate the electronic, optical, and mechanical properties of qHPC\\(_60\\) monolayers. qHPC\\(_60\\) has a moderate direct electronic bandgap, with anisotropic mechanical properties. Their elastic modulus ranges between 50 and 62 GPa. The results for optical properties suggest that qHPC\\(_60\\) can act as UV collectors for photon energies until 5.5 eV since they present low reflectivity and refractive index greater than one. The estimated optical bandgap (1.5-1.6 eV) is in very good agreement with the experimental one (1.6 eV).

The stability of chemically complex nanoparticles is governed by an immense configurational space arising from heterogeneous local atomic environments across surface and interior regions. Efficiently identifying low-energy configurations within this space remains a central challenge for first-principles-based materials discovery, particularly when the available reference data are limited. Here, we introduce a data-efficient and physically interpretable machine-learning framework based on a fragmented, layer-resolved descriptor that explicitly decomposes nanoparticles into surface, intermediate, and core environments using a topology-driven definition. This representation preserves a compact and fixed feature dimensionality while retaining spatial resolution, enabling controlled emphasis on different regions of the nanoparticle through physically motivated weighting schemes. Coupled with gradient-boosted decision tree models and a ranking-based learning strategy, the proposed framework enables accurate identification of the most stable nanoparticle configurations using only a few hundred density functional theory reference calculations. Ranking performance metrics demonstrate near-saturation of correlation, high top-k recall, and rapidly vanishing regret at moderate training-set sizes, highlighting the strong data efficiency of the approach. Beyond predictive performance, layer-weighting and SHAP-based interpretability analyses reveal how surface segregation, coordination topology, and local chemical disorder contribute differently to stability across spatial regions of the nanoparticle. These insights provide a transparent physical interpretation of the learned models and establish a natural pathway toward active learning-driven exploration of complex nanoparticle configurational spaces.

The quasi-hexagonal phase of the two-dimensional fullerene network (qHPC\\(_60\\)), recently synthesized, has emerged as a stable carbon-based material with distinct structural and electronic features. In this work, we employed density functional theory (DFT) calculations to investigate the electronic and optical properties of its endohedral derivatives. The encapsulation of nitrogen, cerium, and strontium atoms inside fullerene cages was systematically analyzed at different concentrations. Our results show that encapsulation preserves the semiconducting backbone of pristine qHPC\\(_60\\) while introducing localized electronic states that alter the bandgap and enable new transition channels. Nitrogen encapsulation produces intragap states with potential relevance for discrete optical emission, whereas cerium and strontium generate intraband states near the conduction edge. These modifications induce a red shift of the absorption onset into the visible spectrum, accompanied by enhanced refractive and absorptive responses. The robustness of the electronic structure under reduced concentrations indicates that the fully encapsulated limit adequately represents the system. Overall, the findings highlight impurity-endowed qHPC\\(_60\\) as a promising platform for optoelectronic and light-harvesting applications.