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This work proposes a new design strategy for overcoming the strength-ductility trade-off of γ′′-strengthened multi-component alloys at intermediate-temperatures. By increasing the V content, the alloy’s ductility is enhanced accordingly (650°C tensile-elongation, 9.85%→15.97%) without compromising strength. This enhancement is attributed to the improved stability of γ′′, which results in a suppression of brittle ε lamellae forming at grain boundaries after deformation and a reduction in γ/γ′′ lattice misfit. Concurrently, the increasing V content increases the γ′′ volume fraction, offsetting potential strength loss due to the lattice misfit reduction.

A novel FeCoNiCrCu high-entropy alloy (HEA) coating and ultrasonic vibration composite treatment method were developed to prepare the Al/Mg bimetal to enhance the Al/Mg interface. Elemental interdiffusion at the Al/HEA interface led to the formation of AlxFeCoNiCrCu. The HEA/Mg interface melted due to friction heat and cavitation effect by ultrasonic vibration and then cooled at an extremely high cooling rate to form nanocrystals. The presence of the nanocrystals hindered dislocation movement, resulting in difficulty in crack propagation, significantly improving the shear strength of the Al/Mg bimetal, which increased from 34.52 MPa without treatment to 77.86 MPa, increasing by 125.55%.

Abstract New methods for producing surfaces with suitable biocompatible properties are desirable due to increasing demands for biomedical devices. Stainless steel 316 L and cp- titanium specimens were coated with thin films of alumina and hafnia deposited using the atomic layer deposition method at two temperatures, 180 and 260 °C. The morphology of the films was analysed using scanning electron microscopy, and their surface energies were determined based on drop contact angle measurements. Biocompatibility assays performed using mesenchymal stem cells were evaluated by incubating the specimens and then exposing their extracts to the cells or directly seeding cells on the specimen surfaces. No detrimental effect was noticed for any of the specimens. Antibacterial properties were tested by directly incubating the specimens with the bacteria Staphylococcus aureus. Overall, our data show that all prepared films were biocompatible. Alumina films deposited on cp-titanium at 260 °C outperform the other prepared and tested surfaces regarding antiadhesive properties, which could be related to their low surface energy.

Abstract We explore the optical properties of La3Ni2O7 bilayer nickelates by using density functional theory including a Coulomb repulsion term. Convincing agreement with recent experimental ambient-pressure spectra is achieved for U ~ 3 eV, which permits tracing the microscopic origin of the characteristic features. Simultaneous consistency with angle-resolved photoemission spectroscopy and x-ray diffraction suggests the notion of rather moderate electronic correlations in this novel high-T c superconductor. Oxygen vacancies form predominantly at the inner apical sites and renormalize the optical spectrum quantitatively, while the released electrons are largely accommodated by a defect state. We show that the structural transition occurring under high pressure coincides with a significant enhancement of the Drude weight and a reduction of the out-of-plane interband contribution that acts as a fingerprint of the emerging hole pocket. We further calculate the optical spectra for various possible magnetic phases including spin-density waves and discuss the results in the context of experiment. Finally, we investigate the role of the 2–2 versus 1–3 layer stacking and compare the bilayer nickelate to La4Ni3O10, La3Ni2O6, and NdNiO2, unveiling general trends in the optical spectrum as a function of the formal Ni valence in Ruddlesden–Popper versus reduced Ruddlesden–Popper nickelates.

Abstract Light-matter interactions have emerged as a new research focus recently offering promises of unveiling novel physics and leading to applications under nonequilibrium conditions. The quantized Hall conductivities predicted by Floquet theory assuming a Fermi-Dirac distribution however deviate from experimental observations. To resolve these puzzles, we consider the effect of nonequilibrium electron occupation to study the anomalous, valley, and spin Hall effects of a prototype monolayer transition metal dichalcogenide MoS2. We find that spin Hall conductivity can be effectively suppressed approaching zero value by linearly polarized light under near resonant excitations. In contrast, it is substantially enhanced by circularly polarized light, originating from optical selection rules and topological phase transitions. Besides, the quantized anomalous Hall conductivity is much reduced if nonequilibrium occupations of Floquet bands are considered. Our study provides a novel avenue for engineering various Hall effects in two-dimensional materials using light, holding great promises for future device applications.

Abstract Uranium ditelluride (UTe2) is the strongest contender to date for a p-wave superconductor in bulk form. Here we perform a spectroscopic study of the ambient pressure superconducting phase of UTe2, measuring conductance through point-contact junctions formed by metallic contacts on different crystalline facets down to 250 mK and up to 18 T. Fitting a range of qualitatively varying spectra with a Blonder-Tinkham-Klapwijk (BTK) model for p-wave pairing, we can extract gap amplitude and interface barrier strength for each junction. We find good agreement with the data for a dominant p y -wave gap function with amplitude 0.26 ± 0.06 meV. Our work provides spectroscopic evidence for a gap structure consistent with the proposed spin-triplet pairing in the superconducting state of UTe2.

Abstract Solution-processable 2D materials (2DMs) are gaining attention for applications in logic, memory, and sensing devices. This review surveys recent advancements in memristors, transistors, and sensors using 2DMs, focusing on their charge transport mechanisms and integration into silicon CMOS platforms. We highlight key challenges posed by the material’s nanosheet morphology and defect dynamics and discuss future potential for monolithic 3D integration with CMOS technology.

Abstract Two-dimensional materials-based ultrafast spintronics are expected to surpass conventional data storage and manipulation technologies, that are now reaching their fundamental limits. The newly discovered van der Waals (VdW) magnets provide a new platform for ultrafast spintronics since their magnetic and electrical properties can be tuned by many external factors, such as strain, voltage, magnetic field, or light absorption for instance. Here, we report on the direct relationship between magnetic order and Terahertz (THz) electrodynamics in Fe n GeTe2 (n = 3, 5) (FGT) films after being illuminated by a femtosecond optical pulse, studying their ultrafast THz response as a function of the optical pump-THz probe temporal delay. In Fe5GeTe2, we find clear evidence that light-induced electronic excitations directly influence THz electrodynamics similarly to a demagnetization process, contrasting with the effects observed in Fe3GeTe2, which are characterized by a thermal energy transfer among electrons, magnons, and phonons. We address these effects as a function of the pump fluence and pump-probe delay, and by tuning the temperature across the magnetic ordering Curie temperature, highlighting the microscopic mechanisms describing the out-of-equilibrium evolution of the THz conductivity. Finally, we find evidence for the incoherent-coherent crossover predicted by the Kondo-Ising scenario in Fe3GeTe2 and successfully simulate its light-driven electrodynamics through a three-temperature model. As indicated by these results, FGT surpasses conventional metals in terms of modulating their properties using an optical lever.

Abstract Dynamical and structural heterogeneities play an important role in glass transition phenomena. However, the relation between these heterogeneities is not fully revealed. In this study, we simultaneously observed these heterogeneities near the glass transition temperature in Zr50Cu40Al10 using five-dimensional scanning transmission electron microscopy, which can record the spatiotemporal distribution of diffraction patterns. The heterogeneities were visualized with sub-nanometer resolution, and a correlation between them was measured up to the glass transition temperature. We verified that ordered structures had slow dynamics, and the order decreased as the temperature increased.

Abstract Recent advances in deep learning generative models (GMs) have created high capabilities in accessing and assessing complex high-dimensional data, allowing superior efficiency in navigating vast material configuration space in search of viable structures. Coupling such capabilities with physically significant data to construct trained models for materials discovery is crucial to moving this emerging field forward. Here, we present a universal GM for crystal structure prediction (CSP) via a conditional crystal diffusion variational autoencoder (Cond-CDVAE) approach, which is tailored to allow user-defined material and physical parameters such as composition and pressure. This model is trained on an expansive dataset containing over 670,000 local minimum structures, including a rich spectrum of high-pressure structures, along with ambient-pressure structures in Materials Project database. We demonstrate that the Cond-CDVAE model can generate physically plausible structures with high fidelity under diverse pressure conditions without necessitating local optimization, accurately predicting 59.3% of the 3547 unseen ambient-pressure experimental structures within 800 structure samplings, with the accuracy rate climbing to 83.2% for structures comprising fewer than 20 atoms per unit cell. These results meet or exceed those achieved via conventional CSP methods based on global optimization. The present findings showcase substantial potential of GMs in the realm of CSP.

Abstract Superalloys are indispensable materials for the fabrication of high-temperature components in aircraft engines. The discovery of a novel class of γ/γ′ Co-Al-W alloys has ignited a surge of interest in Co-based superalloys, with the aspiration to transcend the inherent constraints of their Ni-based counterparts. However, the conventional methodologies utilized in the design and advancement of new γ/γ′ Co-based superalloys are frequently characterized by their laborious and resource-intensive nature. In this study, we employed a coupled Density Functional Theory (DFT) and machine learning (ML) approach to predict and analyze the stability of the crucial γ′ phase, which is instrumental in expediting the discovery of γ/γ′ Co-based alloys. A dataset comprised of thousands of reliable formation (H f) and decomposition (H d) energies was obtained through high-throughput DFT calculations. Through regression model selection and feature engineering, our trained Random Forest (RF) model achieved prediction accuracies of 98.07% for H f and 97.05% for H d. Utilizing the well-trained RF model, we predicted the energies of over 150,000 ternary and quaternary γ′ phases within the Co-Ni-Fe-Cr-Al-W-Ti-Ta-V-Mo-Nb system. The energy analyses revealed that the presence of Ni, Nb, Ta, Ti, and V significantly reduced the H f and the H d of γ′, while Mo and W deteriorate the stability by increasing both energy values. Interestingly, although Al reduces the H f, it increases H d, thereby adversely affecting the stability of γ′. Applying domain-specific screening based on our knowledge, we identified 1049 out of >150,000 compositions likely to form stable γ′ phases, predominantly distributed across 11 Al-containing systems and 25 Al-free systems. Combining the analysis of CALPHAD method, we experimentally synthesized two new Co-based alloys with γ/γ′ dual-phase microstructures, corroborating the reliability of our theoretical prediction model.

Abstract Quantum algorithms are emerging tools in the design of functional materials due to their powerful solution space search capability. How to balance the high price of quantum computing resources and the growing computing needs has become an urgent problem to be solved. We propose a novel optimization strategy based on an active learning scheme that combines the Quantum-inspired Genetic Algorithm (QGA) with machine learning surrogate model regression. Using Random Forests as the surrogate model circumvents the time-consuming physical modeling or experiments, thereby improving the optimization efficiency. QGA, a genetic algorithm embedded with quantum mechanics, combines the advantages of quantum computing and genetic algorithms, enabling faster and more robust convergence to the optimum. Using the design of planar multilayer photonic structures for transparent radiative cooling as a testbed, we show superiority of our algorithm over the classical genetic algorithm (CGA). Additionally, we show the precision advantage of the Random Forest (RF) model as a flexible surrogate model, which relaxes the constraints on the type of surrogate model that can be used in other quantum computing optimization algorithms (e.g., quantum annealing needs Ising model as a surrogate).

Abstract Lightweight refractory high-entropy alloys (LW-RHEAs) hold significant potential in the fields of aviation, aerospace, and nuclear energy due to their low density, high strength, high hardness, and corrosion resistance. However, the enormous composition space has severely hindered the development of novel LW-RHEAs with excellent comprehensive performance. In this paper, an machine learning (ML)-based alloy design strategy combined with a multi-objective optimization method was proposed and applied for a rational design of Al-Nb-Ti-V-Zr-Cr-Mo-Hf LW-RHEAs. The quantitative relation of “composition-structure-property” was first established by ML modeling. Then, feature analysis reveals that Cr content greater than 12 at.% is a key criterion for alloys with high corrosion resistance. The phase structure, density, melting point, hardness and corrosion resistance of the alloys were screened layer by layer, and finally, three LW-RHEAs with superb hard and corrosion resistance were successfully designed. Key experimental validation indicates that three target alloys have densities around 6.5 g/cm3, and all alloys are disordered bcc_A2 single-phase with the highest hardness of 593 HV and the largest pitting potential of 2.5 VSCE, which far exceeds all the literature reports. The successful demonstration in this paper clearly demonstrates that the present design strategy driven by the ML technique should be generally applicable to other RHEA systems.

Abstract Directed atomic fabrication using an aberration-corrected scanning transmission electron microscope (STEM) opens new pathways for atomic engineering of functional materials. In this approach, the electron beam is used to actively alter the atomic structure through electron beam induced irradiation processes. One of the impediments that has limited widespread use thus far has been the ability to understand the fundamental mechanisms of atomic transformation pathways at high spatiotemporal resolution. Here, we develop a workflow for obtaining and analyzing high-speed spiral scan STEM data, up to 100 fps, to track the atomic fabrication process during nanopore milling in monolayer MoS2. An automated feedback-controlled electron beam positioning system combined with deep convolution neural network (DCNN) was used to decipher fast but low signal-to-noise datasets and classify time-resolved atom positions and nature of their evolving atomic defect configurations. Through this automated decoding, the initial atomic disordering and reordering processes leading to nanopore formation was able to be studied across various timescales. Using these experimental workflows a greater degree of speed and information can be extracted from small datasets without compromising spatial resolution. This approach can be adapted to other 2D materials systems to gain further insights into the defect formation necessary to inform future automated fabrication techniques utilizing the STEM electron beam.

Abstract To advance the development of high-strength polycrystalline metallic materials towards achieving carbon neutrality, it is essential to design materials in which the atomic level control of general grain boundaries (GGBs), which govern the material properties, is achieved. However, owing to the complex and diverse structures of GGBs, there have been no reports on interatomic potentials capable of reproducing them. This accuracy is essential for conducting molecular dynamics analyses to derive material design guidelines. In this study, we constructed a machine learning interatomic potential (MLIP) with density functional theory (DFT) accuracy to model the energy, atomic structure, and dynamics of arbitrary grain boundaries (GBs), including GGBs, in α-Fe. Specifically, we employed a training dataset comprising diverse atomic structures generated based on crystal space groups. The GGB accuracy was evaluated by directly comparing with DFT calculations performed on cells cut near GBs from nano-polycrystals, and extrapolation grades of the local atomic environment based on active learning methods for the entire nano-polycrystal. Furthermore, we analyzed the GB energy and atomic structure in α-Fe polycrystals through large-scale molecular dynamics analysis using the constructed MLIP. The average GB energy of α-Fe polycrystals calculated by the constructed MLIP is 1.57 J/m2, exhibiting good agreement with experimental predictions. Our findings demonstrate the methodology for constructing an MLIP capable of representing GGBs with high accuracy, thereby paving the way for materials design based on computational materials science for polycrystalline materials.

Abstract We present a comprehensive and user-friendly framework built upon the pyiron integrated development environment (IDE), enabling researchers to perform the entire Machine Learning Potential (MLP) development cycle consisting of (i) creating systematic DFT databases, (ii) fitting the Density Functional Theory (DFT) data to empirical potentials or MLPs, and (iii) validating the potentials in a largely automatic approach. The power and performance of this framework are demonstrated for three conceptually very different classes of interatomic potentials: an empirical potential (embedded atom method - EAM), neural networks (high-dimensional neural network potentials - HDNNP) and expansions in basis sets (atomic cluster expansion - ACE). As an advanced example for validation and application, we show the computation of a binary composition-temperature phase diagram for Al-Li, a technologically important lightweight alloy system with applications in the aerospace industry.

Abstract We develop an automated high-throughput workflow for calculating lattice dynamical properties from first principles including those dictated by anharmonicity. The pipeline automatically computes interatomic force constants (IFCs) up to 4th order from perturbed training supercells, and uses the IFCs to calculate lattice thermal conductivity, coefficient of thermal expansion, and vibrational free energy and entropy. It performs phonon renormalization for dynamically unstable compounds to obtain real effective phonon spectra at finite temperatures and calculates the associated free energy corrections. The methods and parameters are chosen to balance computational efficiency and result accuracy, assessed through convergence testing and comparisons with experimental measurements. Deployment of this workflow at a large scale would facilitate materials discovery efforts toward functionalities including thermoelectrics, contact materials, ferroelectrics, aerospace components, as well as general phase diagram construction.

Abstract Phase-changing materials have been a cornerstone of condensed matter physics for decades. A quintessential example is iron-rhodium (FeRh), which undergoes a first-order phase transition from antiferromagnetic to ferromagnetic states near room temperature. The pivotal aspect of this transition is a marked alteration in electrical conductivity. However, its underlying origin still remains elusive, largely due to the difficulties of directly probing fundamental transport during this phase transition. In this study, we investigate the fundamentals of FeRh’s electrical transport employing terahertz time-domain spectroscopy (THz-TDS). Leveraging the Drude model, we discerned the distinct contributions of extrinsic (momentum scattering time, τ) and intrinsic (charge density, n, and effective mass, m*) factors to electrical conductivity independently. Notably, our investigation unveiled a sharp alteration in n and m* during the phase transition, contrasting with the gradual monotonic decrease of τ with rising temperature. Consequently, our findings provide compelling evidence that the conductivity change in FeRh during the phase transition originates from a restructuring of its band structure. This work provides a crucial step towards a comprehensive understanding of the electrical transport changes occurring during the phase transition, offering valuable insights into the behaviour of phase changing materials.

Abstract Highly sensitive and selective gas-sensing materials are critical for applications ranging from environmental monitoring to breath analysis. A rational approach at the nanoscale is urgent to design next-generation sensing devices. In previous work, we unveiled interesting charge transfer channels at the interface between p-type doped graphene and a layer of nickel phthalocyanine (NiPc) molecules, which we believe could be successfully exploited in gas sensing devices. Here, we have investigated the graphene-NiPc interface’s response to adsorbed gas molecules via first-principles calculations. We focused on NH3 and NO2 as test molecules, representing electron donors and acceptors, respectively. Notably, we identified the Ni d z 2 orbital as a key player in mediating the charge transfer and affecting the charge carrier density in graphene. As a proof-of-concept, we then prepared the graphene-NiPc system as a chemiresistor device and exposed it to NH3 and NO2 at room temperature. The sensing tests revealed excellent sensitivity and selectivity, along with a rapid recovery time and a remarkably low detection limit.

Abstract Topological semimetals, known for their intriguing properties arising from band degeneracies, have garnered significant attention. However, the discovery of a material realization and the detailed characterization of spinless Dirac semimetals have not yet been accomplished. Here, we propose from first-principles calculations that the RE 8CoX 3 group (RE = rare earth elements, X = Al, Ga, or In) contains ideal spinless Dirac semimetals whose Fermi surfaces are fourfold degenerate band-crossing points (without including spin degeneracy). Despite the lack of space inversion symmetry in these materials, Dirac points are formed on the rotation-symmetry axis due to accidental degeneracies of two bands corresponding to different 2-dimensional irreducible representations of the C 6v group. We also investigate, through first-principles calculations and effective model analysis, various phase transitions caused by lattice distortion or elemental substitutions from the Dirac semimetal phase to distinct topological semimetallic phases such as nonmagnetic linked-nodal-line and Weyl semimetals (characterized by the second Stiefel–Whitney class) and ferromagnetic Weyl semimetals.

Abstract Doping of luminescent materials by rare-earth ions is common practice to achieve desired emission properties for a large variety of applications. As several rare-earths ions are frequently combined, it is subsequently difficult to effectively detect and control their homogeneous distribution within the host material. Here, we present a simple, rapid, large scale and precise method of rare-earth mapping using a commercial UV-Vis scanner. We discuss the influence of rare-earth distribution on the physical, optical and luminescent properties with no observable qualitative effect on photoluminescent properties and optical anisotropy. On the contrary, rare-earth-rich areas exhibit significantly higher values of refractive index and optical absorption, which allowed for their identification by the commercial scanner device. The presented method thus provides fast and accurate information about the rare-earth distribution in the material volume with high resolution (≈2.7 µm) and low limit of concentration difference detection (≈0.014 at.%) compared to other techniques, which makes it a promising candidate for high throughput measurements.

Abstract Recycled aggregate concrete (RAC) is recognized as an environmentally friendly construction material derived from reclaimed concrete components. This paper aims to conduct a comprehensive scientometric analysis of RAC research published between 2000 and 2023 in the Web of Science core database. The study includes analyses of publication trends over time, contributions and collaborations among authors, productivity of institutions and countries, co-citation networks, and keyword co-occurrence patterns. Additionally, the research identifies emerging frontiers in RAC studies. The results are visually presented to provide a holistic overview of the current state of RAC research and future developmental trajectories. The study analyzes publication trends over time, with over 80% of the papers published after 2017, reflecting the growing interest in sustainable construction. Key trends identified include the increasing focus on improving the mechanical properties and durability of RAC, microstructural analysis, and innovative manufacturing techniques. While the field has advanced significantly, challenges remain in areas such as the integration of nanoparticles, biomineralization techniques, carbon capture and utilization, and 3D printing technologies. These challenges underscore the need for continued innovation and exploration. With these advancements, RAC has the potential to play a pivotal role in promoting sustainable construction practices in the future.

Abstract Due to their excellent properties, blue CsPbBr3 quantum dots show great promise for full-colour display and lighting applications. This study used acetonitrile, a polar solvent, to post-treat CsPbBr3 quantum dots, resulting in a blue shift to 453 nm. To enhance stability, these quantum dots were encapsulated within the pore structure of mesoporous silica. A flexible luminescent fiber material was prepared using poly (lactic acid) (PLA) as the substrate, demonstrating improved hydrophobicity and stable optical properties. The material exhibited a contact angle of 99.7° and maintained 82.2% of its fluorescence intensity after 30 days at room temperature. These findings highlight its significant potential for optical applications.

Abstract Brain cancer pose significant life-threats by destructively invading normal brain tissues, causing dysneuria, disability and death, and its therapeutics is limited by underdosage and toxicity lying in conventional drug delivery that relied on passive delivery. The application of nanorobots-based drug delivery systems is an emerging field that holds great potential for brain cancer active targeting and controllable treatment. The ability of nanorobots to encapsulate, transport, and supply therapies directly to the lesion site through blood–brain barriers makes it possible to deliver drugs to hard-to-reach areas. In order to improve the efficiency of drug delivery and problems such as precision and sustained release, nanorobots are effectively realized by converting other forms of energy into propulsion and motion, which are considered as high-efficiency methods for drug delivery. In this article, we described recent advances in the treatment of brain cancer with nanorobots mainly from three aspects: firstly, the development history and characteristics of nanorobots are reviewed; secondly, recent research progress of nanorobots in brain cancer is comprehensively investigated, like the driving mode and mechanism of nanorobots are described; thirdly, the potential translation of nanorobotics for brain diseases is discussed and the challenges and opportunities for future research are outlined.

Abstract Upconversion nanoparticles (UCNPs), capable of converting near-infrared (NIR) light into high-energy emission, hold significant promise for bioimaging applications. However, the presence of tissue barriers poses a challenge to the effective delivery of nanoparticles (NPs) to target organs. In this study, we demonstrate the core–shell UCNPs modified with cationic biopolymer, i.e., N, N-trimethyl chitosan (TMC), can overcome endothelial barriers. The core–shell UCNP is composed of NaGdF4: Yb3+,Tm3+ (16.7 ± 2.7 nm) as core materials and silica (SiO2) shell. The average particle size of UCNPs@SiO2 is estimated at 26.1 ± 3.7 nm. X-ray diffraction (XRD), transmission electron microscopy (TEM) and element mapping shows the formation of hexagonal crystal structure of β-NaGdF4 and elements doping. The surface of UCNPs@SiO2 has been modified with poly(ethylene glycol) (PEG) to enhance water dispersibility and colloidal stability, and further modified with TMC with the zeta potential increasing from -2.1 ± 0.96 mV to 26.9 ± 12.6 mV. No significant toxic effect is imposed to HUVECs when the cells are treated with core–shell UCNPs with surface modification up to 250 µg/mL. The transport ability of the core–shell UCNPs has been evaluated by using the in vitro endothelial barrier model. Transepithelial electrical resistance (TEER) and immunofluorescence staining of tight junction proteins have been employed to verify the integrity of the in vitro endothelial barrier model. The results indicate that the transport percentage of the UCNPs@SiO2 with PEG and TMC through the model is up to 4.56%, which is twice higher than that of the UCNPs@SiO2 with PEG but without TMC and six times that of the UCNPs@SiO2.

Abstract We propose a novel design for multi-junction photonic crystal surface emitting lasers (PCSELs) operating in higher-order waveguide modes to minimize internal losses. This paper details the design and simulation of a 2-junction PCSEL, including calculations of confinement factors, coupling coefficients, radiation loss, and threshold currents. We compare the performance of 2 J-PCSELs and 1 J-PCSELs, demonstrating the potential for highly efficient multi-junction PCSELs with improved power conversion efficiency and output power.

Abstract Background Malaria, a tropical neglected disease, imposes a significant burden on global health, leading to the loss of thousands of lives annually. Its gold standard treatment is a combination therapy of lumefantrine (LUM) and artemether (ART). Nanotechnology holds significant potential for improving drug bioavailability and potency while reducing adverse effects. Objectives This study aimed to develop lipid-core nanocapsules containing ART and LUM and evaluate their effects in an experimental cerebral malaria model (ECM). Methods The polymeric interfacial deposition method was used to develop lipid-core nanocapsules (LNCs) containing ART and LUM (LNCARTLUM) and were characterized using micrometric and nanometric scales. Male C57BL/6 mice were infected with Plasmodium (P.) berghei ANKA (PbA, 1 × 105 PbA-parasitized red blood cells, intraperitoneally). On day 5 post-infection, PbA-infected mice were orally administered with ART + LUM, LNCARTLUM, blank nanocapsules (LNCBL), or ethanol as a control. Parasitemia, clinical scores, and survival rates were monitored throughout the experiment. Organ-to-body weight ratios, cytokine quantification, and intravital microscopy analyses were conducted on day 7 post-infection. Results LNCs were successfully developed and characterized. The treatment with LNCARTLUM in ECM resulted in complete clearance of parasitemia at 10 dpi, decreased clinical scores, and maintained 100% survival rates. Thereated mice exhibited splenomegaly and reduced TNF-α, IL-1β, and MCP1 levels in the brain. Furthermore, the LNCARTLUM treatment protected the brain microvasculature, reducing the number of cells in the rolling process and adherent to the microvasculature endothelium. Conclusion Nanoformulations can potentially improve the efficacy of antimalarial drugs and be considered a promising approach to treat malaria.

Abstract Nanomaterials play a pivotal role in food preservation and its safety, offering ingenious solutions for sustainable food packaging. Nanomaterials enable the creation of packaging materials having unique functional properties. It not only extends the shelf life of the foods by releasing preservatives but also enhances food safety by preventing microbial contamination or food spoilage. In this review, we aim to provide an overview of the various applications of nanotechnology in food packaging, highlighting its key advantages. We also delve into the safety considerations and regulatory issues involved in developing nanotechnology-based food packaging materials. Additionally, advancements in the field of nanotechnology-based packaging have the potential to create safer, more sustainable, and high-quality packaging with greater functionality that delivers essential benefits to manufacturers and consumers.

Abstract Breast cancer (BC) bone metastasis poses a significant clinical challenge due to its impact on patient prognosis and quality of life. Curcumin (CUR), a natural polyphenol compound found in turmeric, has shown potential in cancer therapy due to its anti-inflammatory, antioxidant, and anticancer properties. However, its metabolic instability and hydrophobicity have hindered its clinical applications, leading to a short plasma half-life, poor absorption, and low bioavailability. To enhance the drug-like properties of CUR, nanotechnology-based delivery strategies have been employed, utilizing polymeric, lipidic, and inorganic nanoparticles (NPs). These approaches have effectively overcome CUR’s inherent limitations by enhancing its stability and cellular bioavailability both in vitro and in vivo. Moreover, targeting molecules with high selectivity towards bone metastasized breast cancer cells can be used for site specific delivery of curcumin. Alendronate (ALN), a bone-seeking bisphosphonate, is one such moiety with high selectivity towards bone and thus can be effectively used for targeted delivery of curcumin loaded nanocarriers. This review will detail the process of bone metastasis in BC, elucidate the mechanism of action of CUR, and assess the efficacy of nanotechnology-based strategies for CUR delivery. Specifically, it will focus on how these strategies enhance CUR’s stability and improve targeted delivery approaches in the treatment of BC bone metastasis. Graphical abstract

Abstract Among the numerous driving forces that cause the atherosclerotic cardiovascular disease (ASCVD), pathogenic bacterial extracellular membrane nanovesicles (BEMNs) containing toxins and virulence factors appear to be the key trigger of inflammation and atherogenesis, the major processes involved in the pathogenesis of ASCVD. Since BEMNs are the carriers of nanosized biomolecules to distant sites, they are now being considered as a novel drug delivery system. Nowadays, many therapeutic strategies are used to treat ASCVD. However, the conventional anti-atherosclerotic therapies are not effective enough. This primarily due to the inefficiency of non-targeted drug delivery systems to tissue affected areas, which, in turn, leads to numerous side effects, as well as faulty pharmacokinetics. In this regard, nanomedicine methods using nanoparticles (NPs) as targeted drug delivery vehicles proved to be extremely useful. Bioengineered BEMNs equipped with disease-specific ligand moieties and loaded with corresponding drugs represent a promising tool in nanomedicine, which can be used as a novel drug delivery system for a successful therapy of ASCVD. In this review, we outline the involvement of pathogenic BEMNs in the triggering of ASCVD, the conventional therapeutic strategies for the treatment of ASCVD, and the recent trends in nanomedicine using BEMNs and NPs as a vehicle for targeted drug delivery.

Abstract We explored safer, superior energy storage solutions by investigating all-solid-state electrolytes with high theoretical energy densities of 3860 mAh g−1, corresponding to the Li-metal anode. Despite challenges like dendrite growth, we synthesized ceramic-based electrolytes using green chemistry. These non-doped and doped electrolytes with F-, Ce-, and Mo demonstrated notable ionic conductivity (0.15–0.54 S cm−1) and durability. By customizing nanostructured materials, we improved battery performance, surpassing the conductivity of commercial electrolytes.

Abstract Clayey soils present challenges in engineering applications due to their inherent properties, such as low shear strength, which usually limits their use in engineering applications. Stabilization of clayey soils is crucial to enhance their performance and suitability for various purposes. Utilizing waste materials like discarded tyres and tile powder as soil stabilizers presents a sustainable solution to mitigate environmental concerns while enhancing soil properties. While previous studies have explored using either crumbled rubber or tile powder independently for soil improvement, their combined effect remained largely unexplored. This study addresses this gap by investigating the synergistic potential of blending crumbled rubber and tile powder to enhance the strength and ductility of clayey soils. A series of laboratory tests were conducted to investigate the combined effect of crumbled rubber and tile powder. First, varying percentages of crumbled rubber (2.5%, 5.0%, 7.5%, and 10%) were mixed with the soil, and standard proctor tests, California bearing ratio (CBR) tests, and unconfined compressive strength (UCS) tests were performed. Results showed optimal performance at 5.0% crumbled rubber, exhibiting the highest values for maximum dry density, CBR, and UCS. Subsequently, different percentages of tile powder (5%, 10%, 15%, and 20%) were added to the soil-rubber mixture (with 5% crumbled rubber). The addition of 15% tile powder to the 5% crumbled rubber mixture yielded the most significant improvements as maximum dry density (MDD) increased from 1.842 g/cm3 (raw soil) to 1.963 g/cm3, UCS increased from 0.5176 kg/cm2 (raw soil) to 2.606 kg/cm2, and CBR increased from 1.757% (raw soil) to 7.84%. The addition of crumbled rubber was found to shift the failure behaviour of the clayey soil from brittle to more ductile, indicating an enhanced ability to undergo deformation before failure. This study’s findings highlight the potential of combining crumbled rubber and tile powder as a sustainable solution for enhancing clayey soil properties, paving the way for further research into optimized mixture designs and expanded applications in geotechnical engineering.

Abstract Single-crystal samples of the 3R-CuCrO2 phase (R3m, a = 2.9613(2) Å, c = 17.098(2) Å) with a delafossite structure were grown by the flux method. Structural parameters were refined by X-ray structural analysis. The elemental composition of the grown crystals was confirmed by Auger electron spectroscopy. A threshold electric field switching effect from a high-resistance to a low-resistance state was discovered. The effect is characterized by a jump in electrical resistance (up to 5 orders of magnitude at E = 4.7 kV/cm, T = 120 K) and S-shaped current–voltage characteristics with a region of negative differential resistance. The temperature dependences of dielectric permittivity and dielectric loss tangent exhibit a Debye-type relaxation process with an activation energy of 0.51(3) eV. The observed features of dc conductivity, current–voltage characteristics, and dielectric properties interpreted on the basis of the existence of charge carriers in a small polaron state and the destruction of this state by the electric field.

Abstract This study investigates stress-deformation behavior in Himalayan tunnels, focusing on how geological features impact stability. The objective is to enhance the understanding of displacement phenomena, particularly in tunnels traversing jointed rocks. A modified support system, to specific rock mass classifications, is employed to address the unique challenges posed by geological discontinuities. Kinematic analysis reveals a 20% probability of wedge failure due to these discontinuities. Numerical analysis using Hoek–Brown parameters identifies significant stress concentrations at the tunnel crown, especially in jointed sections, where increased convergence and displacement (1.2 mm at the crown compared to 0.25 mm at the walls) highlight the susceptibility to deformation. The study indicates the critical need for specialized support in jointed regions to mitigate stability risks.

Abstract Electrical stimulation is effective for various therapeutic applications; however, to increase convenience, it is crucial to eliminate generators and batteries for wireless power transmission. This paper presents a full textile-based body-coupled electrical stimulation (BCES) system designed for wireless electrical stimulation using energy loss from electronic devices and static electricity from physical activity. We developed the BCES socks by knitting conductive threads to ensure stability and comfort. BCES socks generate electric fields ranging from tens to hundreds of millivolts per millimeter, which are sufficient to activate muscle fibers. Experimental and computational analyses confirmed the effective concentration of the electric fields. Human trials demonstrated significant improvements in exercise performance, with a 21.47% increase in calf raise frequency, an 11.97% increase in repetition count, and a 6.25% reduction in muscle fatigue. These results indicate the potential of BCES socks as a practical battery-free solution for enhancing muscle activity and reducing fatigue.

In multi-principal element alloys, dislocation motion and lattice diffusion are two key plasticity carriers, which are strongly interacted with the local chemical order. While in creep deformation, the difference between the impediment of the local chemical order on dislocation motion and lattice diffusion, along with the temperature dependency of such difference, is an intriguing topic that remains unsolved. By atomistic simulations on a typical NiCoCr system and rationalization with classic constitutive models, we found that the lattice diffusion receives less impediment compared to dislocation glide so that transition of creep mechanisms presents in a chemically ordered system. The critical stress of transition is tuned by temperature.

The mechanical properties of negative stiffness (NS) metamaterial could be customized and tuned in a wide range for various requirements, achieving programmable performances by the design from the aspect of structure and material. This work investigates the viscoelastic NS metamaterial based on double curved beams using a combined approach of experiments, simulations, and analytical modeling with an emphasis on multistage loading bearing and programmable energy absorption ability. Numerical simulations are first implemented based on the finite element models of three types of metamaterial cells, which provide comparisons of load bearing and energy absorption properties. Further, the effects of geometric parameters of multistage metamaterial element and the mechanisms are analyzed. An analytical discrete model is then innovatively developed to provide straightforward understandings of the geometric effects and reveal the role of viscoelasticity by examining the instantaneous loading responses and rate-dependent behaviors. Experimentally, we fabricate the metamaterial samples using 3D-printing technique and perform compression tests to validate the properties based on different boundary conditions, loading rates and cyclic loading and unloading. Results of this work show the potential of wide programmable room for mechanical properties through structure and functional material design, such as multistage load bearing capacity and energy absorption ability.

Designing new alloys with high performance is challenging due to the large search space for composition and process parameters. We propose an alloy design strategy based on machine learning algorithms for navigating the enormous search space. Specifically, feature engineering was applied to screen the major features, and a three-step alloy design strategy was employed to extract the required composition. The material design strategy for the multi-performance optimization of Cu-Ni-Si alloy through Bayesian optimization was proposed. This work provides novel insights into the comprehensive properties of Cu-Ni-Si alloys using machine learning with small data, with potential applicability to other materials systems.

The aim of this study is to examine the entropy generation (EG) associated with the transfer of mass and heat in a concentration-dependent fluid with thermal radiation and activation energy, specifically in the context of an unsteady Riga Plate with gyrotactic microorganism. It is important to solve the ordinary differential equations generated from the controlling partial differential equations using Lie symmetry scaling to verify their quality and reliability. The system’s anticipated physical behavior is compared to Mathematica’s Runge–Kutta–Fehlberg numerical solution. Source parameters are essential for validation since they offer accurate results. Methodically change these values as a percentage to determine how they affect the unsteady fluid’s density, mass, and heat transfer over the Riga plate. Velocity, temperature, nanoparticle concentration and microorganism concentration profiles decrease with varying values of the unsteadiness parameter. EG increases with increasing values of concentration difference, thermal radiation, and Reynold number parameters. The Nusselt number experiences a 26.11% rise as a result of radiation when the unsteadiness parameter is A=−0.25A=-0.25, in comparison with the scenario without radiation. Mass transfer upsurges with increasing values of the Brownian motion parameter and reduces with increasing values of thermophoresis parameter. To verify our conclusions, we compare calculated data, specifically the skin friction factor, to theoretical predictions. Tabular and graphical data can show how physical limits affect flow characteristics.

In this article, the sheeting of pseudoplastic material under non-isothermal conditions has been studied. It is an excellent forecasting instrument for sheeting, where the thickness of the sheet is relatively thin in relation to the roll size, according to theoretical study based on the lubrication theory. At the calendering process, the detachment point is determined by taking the material parameter’s impact into account. The governing equations are derived for the constitutive equation of the Carreau fluid with momentum and energy equations. Suitable non-dimensional parameters are used to develop the non-linear partial differential expressions into ordinary differential systems. The maximum pressure, roll separating force, normal stress effect, and power delivered to the fluid by rolls are among the engineering-relevant quantities that are determined. Moreover, a graphic analysis is conducted to examine the impact of different material parameters on the temperature profile, pressure gradient, velocity profile, and pressure distribution. The results specific mechanism is explained in depth.

Ceramic products is one of the important carriers of various civilizations, reflecting the lifestyle, aesthetic concepts, and technological level of society at that time. In order to study the surface treatment design features of ceramic craft products, this article analyzed the ceramic features through computer vision technology and used residual neural networks to detect the surface treatment features of ceramic craft products. The extracted texture features were classified to study and analyze the coupling features of different glazes, colors, and shapes on the formation of different textures. This study used ResNeXt50-SSD, which combined ResNeXt50 and SSD (Single Shot MultiBox Detector) algorithms, to compare feature detection with LeNet-5, VGG-16, and MobileNetV2 network models. From the experimental findings, it can be concluded that ResNeXt50-SSD was the most effective for feature recognition of ceramic craft products, with precision, recall, and mAP of 94.3, 92.1, and 89.5%, respectively. Therefore, the combination of ResNeXt50 and SSD algorithms is an effective method for detecting surface treatment features of ceramic craft products.

The interaction effects of γ-strengthening and γ'-former elements have long troubled the alloying design of superalloys, especially improving γ'-thermostability by Al addition against higher temperatures. Here we establish a thermodynamics-based phase-field model to reveal the γ'-coarsening kinetics at 1200°C for well-designed ternary Ni-based superalloys containing γ'-former element Al and γ-strengthening element Mo. The inhibition effect of Al on coarsening is interestingly enhanced by Mo addition to signify their synergistic effects. The contributions of lattice misfit, diffusion coefficients and elemental partitioning are analysed by decoupling coarsening theories. And an estimation method of the coarsening rate controlled by slow-diffusing element is also established.

Urban traffic congestion, obstacle avoidance, and driving efficiency are the challenges faced by autonomous-vehicle path-planning technology in cities. The traditional artificial potential field (APF) algorithm is insufficient to meet the requirements of efficiency and safety in path planning, as it easily gets trapped in local optima when dealing with complex environments. Therefore, this paper proposes a novel AS-IAPF path-planning algorithm to more efficiently enhance the target reachability of autonomous vehicles in complex traffic environments. Firstly, this paper analyzes and elucidates the macroscopic traffic model, achieving effective modeling of dynamic traffic flow stability based on Lyapunov stability theorem and a classical 1D flow model. Thus, the threshold discriminant formula for traffic element stability is obtained. Secondly, based on the aforementioned threshold discriminant formula, a new AS-IAPF algorithm is proposed. The algorithm mainly includes two aspects: firstly, by pre-generating initial paths and introducing a Gaussian oscillation coefficient of force fields, it avoids the algorithm falling into local optima; secondly, by using the aforementioned driving stability threshold discriminant formula as a dimensional adjustment for adaptively improving and adjusting the strength coefficient of the AS-APF repulsive field, the algorithm further improves the efficiency of path planning. Finally, the algorithm is subjected to joint simulations of 2D and 3D scenarios of different types. The research results show that the AS-IAPF algorithm outperforms other algorithms of the same type with respect to comprehensive performance based on multiple 2D scenario simulation experiments. In the 3D simulation experiments of three different typical traffic scenarios, the proposed algorithm can drive autonomous vehicles to effectively perform corresponding obstacle avoidance actions based on the actual traffic scenarios ahead, ultimately achieving safe obstacle avoidance. The path-planning method proposed in this paper can enhance driving efficiency while considering the safety and stability of vehicles, providing a promising approach and reference for the path planning of autonomous vehicles.

Additive manufacturing processes, such as laser beam melting, are now an integral part of industrial production. However, the development of the powder bed fusion – laser beam (PBF-LB) process is still ongoing in order to fully understand the complex interaction of the different parameters or to avoid process instabilities. As a result, process control and active parameter regulation are increasingly in the focus of current developments. A necessary prerequisite for achieving the goal of a controlled process is reliable defect detection as early as possible during a build job. In this paper a new approach is discussed: An equipment-independent image acquisition system combined with deep learning image processing. image of each layer is acquired after the build platform is recoated. The surface of the entire powder bed is evaluated by a CNN and the defect classification result is returned within seconds. The CNN is trained with a dataset of images from different real build jobs. Evaluating the entire powder bed provides the ability to detect and categorize defects with large extents. The perspective is a real-time process monitoring and control. Correction strategies are to be derived that interrupt the build process at the desired point and intervene in a targeted manner.

Abstract The quality of platelet-rich fibrin (PRF) is contingent on the surface characteristics interfacing with blood. Titanium’s superior platelet activation, surpassing silica, has made Titanium-platelet-rich fibrin (T-PRF) a favored autogenous bone graft material due to its extended degradation time. Pioneering a novel approach, this study aims to achieve an enhanced fibrin structure using glass tubes coated with nano-titanium, marking the surface’s debut in our PRF production endeavors. Employing a rapid thermionic vacuum arc (TVA) process under high vacuum, we conducted comprehensive analyses of the tubes. Comprehensive analyses, including X-ray diffraction (XRD), atomic force microscopy (AFM), scanning electron microscopy (SEM), and energy dispersive X-ray spectroscopy (EDS), were conducted on the nano-titanium-coated glass tubes. Three PRF types were formulated: silica-activated leukocyte- and platelet-rich fibrin (L-PRF, control group), machined-surface titanium tubes (T-PRF), and nano-titanium-coated tubes (nanoT-PRF). Analyses unveiled denser fibrin areas in nanoT-PRF than T-PRF, with the least dense areas in L-PRF. Cell distribution paralled between nanoT-PRF and T-PRF groups, while L-PRF cells were embedded in the fibrin border. NanoT-PRF exhibited the densest autogenous fibrin structure, suggesting prolonged in vivo resorption. Additionally, we explore the potential practicality of single-use production for nanoT-PRF tubes, introducing a promising clinical advancement. This study marks a significant stride in innovative biomaterial design, contributing to the progress of regenerative medicine. Graphical Abstract

Abstract The dynamical Franz-Keldysh effect, indicative of the transient light-matter interaction regime between quantum and classical realms, is widely recognized as an essential signature in wide bandgap condensed matter systems such as dielectrics. In this theoretical study, we applied time-resolved transient absorption spectroscopy to investigate ultrafast optical responses in graphene, a zero-bandgap system. We observed in the gate-tuned graphene that the massless Dirac materials notably enhance intraband light-driven transitions, significantly leading to the giant dynamical Franz-Keldysh effect compared to the massive Dirac materials, a wide bandgap system. In addition, employing the angle-resolved spectroscopy, it is found that the unique polarimetry orientation, i.e., perpendicular polarizations for the pump and the probe, further pronounces the optical spectra to exhibit the complete fishbone structure, reflecting quantum pseudospin natures of Dirac cones. Our findings expand the establishment of emergent transient spectroscopy frameworks into not only zero-bandgap systems but also pseudospin-mediated quantum phenomena, moving beyond dielectrics.

Abstract Exploring the ground state properties of many-body quantum systems conventionally involves adiabatic processes, alongside exact diagonalization, in the context of quantum annealing or adiabatic quantum computation. Shortcuts to adiabaticity by counter-diabatic driving serve to accelerate these processes by suppressing energy excitations. Motivated by this, we develop variational quantum algorithms incorporating the auxiliary counter-diabatic interactions, comparing them with digitized adiabatic algorithms. These algorithms are then implemented on gate-based quantum circuits to explore the ground states of the Fermi-Hubbard model on honeycomb lattices, utilizing systems with up to 26 qubits. The comparison reveals that the counter-diabatic inspired ansatz is superior to traditional Hamiltonian variational ansatz. Furthermore, the number and duration of Trotter steps are analyzed to understand and mitigate errors. Given the model’s relevance to materials in condensed matter, our study paves the way for using variational quantum algorithms with counterdiabaticity to explore quantum materials in the noisy intermediate-scale quantum era.

Abstract We present an exploration of the effect of electron-phonon coupling and broken inversion symmetry on the electronic and thermal properties of the semimetal LaRhGe3. Our transport measurements reveal evidence for electron-hole compensation at low temperatures, resulting in a large magnetoresistance of 3000% at 1.8 K and 14 T. The carrier concentration is on the order of 1021/cm3 with high carrier mobilities of 2000 cm2/Vs. When coupled to our theoretical demonstration of symmetry-protected almost movable Weyl nodal lines, we conclude that LaRhGe3 supports a Weyl semimetallic state. We discover superconductivity in this compound with a T c of 0.39(1) K and B c(0) of 2.2(1) mT, with evidence from specific heat and transverse-field muon spin relaxation. We find an exponential dependence in the normal state electrical resistivity below ~50 K, while Seebeck coefficient and thermal conductivity measurements each reveal a prominent peak at low temperatures, indicative of strong electron-phonon interactions. To this end, we examine the temperature-dependent Raman spectra of LaRhGe3 and find that the lifetime of the lowest energy A 1 phonon is dominated by phonon-electron scattering instead of anharmonic decay. We conclude that LaRhGe3 has strong electron-phonon coupling in the normal state, while the superconductivity emerges from weak electron-phonon coupling. These results open up the investigation of electron-phonon interactions in the normal state of superconducting non-centrosymmetric Weyl semimetals.

Abstract Focusing on Rhenium disulfide (ReS2), a group VII transition metal di-chalcogenides (TMDC), being a promising contender system for future nanoelectronics and optoelectronics, here, we present an innovative pathway to experimentally achieve an almost barrier-free contact for the ReS2 field effect transistors (FETs) by using few layered graphene as contact electrodes, further supported by comparative first-principles analysis. Such barrier-free contacts enable the observation of metal-to-insulator transition with enhanced room temperature carrier mobility up to 25 cm2/Vs, linear Ids-Vds characteristic down to 80 K, along with the reduction of 1/f noise by more than two orders of magnitude. We further demonstrate a highly responsive gate- tunable phototransistor (R > 106 A/W) at an illumination wavelength of 633 nm. This work demonstrates a straightforward strategy to unlock the full potential of ReS2 for CMOS compatible future electronic and optoelectronic devices.