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Auricular reconstruction in children with microtia is one of the more complex procedures in plastic surgery. Obtaining sufficient native material to build an ear requires harvesting large fragments of rib cartilage in children. Herein, we investigated how to optimize autologous chondrocyte isolation, expansion and re-implantation using polyglycolic acid (PGA) scaffolds for generating enough cartilage to recapitulate a whole ear starting from a small ear biopsy. Ear chondrocytes isolated from human microtia subjects grew slower than microtia rib or healthy ear chondrocytes and displayed a phenotypic shift due to the passage number. Rabbit ear chondrocytes co-cultured with mesenchymal stem cells (MSC) at a 50 : 50 ratio recapitulated the cartilage biological properties in vitro. However, PGA scaffolds with different proportions of rabbit chondrocytes and MSC did not grow substantially in two months when subcutaneously implanted in immunosuppressed mice. In contrast, rabbit chondrocyte-seeded PGA scaffolds implanted in immunocompetent rabbits formed a cartilage 10 times larger than the original PGA scaffold. This cartilage mimicked the biofunctional and mechanical properties of an ear cartilage. These results indicate that autologous chondrocyte-seeded PGA scaffolds fabricated following our optimized procedure have immense potential as a solution for obtaining enough cartilage for auricular reconstruction and opens new avenues to redefine autologous cartilage replacement., European Regional Development Fund; Instituto de Salud Carlos III; Ministerio de Ciencia e Innovación; National Institutes of Health

A planar two-dimensional computational analysis is presented to qualify traditional and fractal vane-in-cup geometries for accurate rheometry of simple viscoplastic fluids with and without slip. Numerical simulations based on an adaptive augmented Lagrangian scheme are used to study the two-dimensional flow field of yield-stress fluids within and around vane tools with N=3 to 24 arms for a wide range of Bingham numbers, B (i.e. the ratio of the yield stress over the characteristic viscous stress). This allows for accurate calculations of the velocity and stress fields around vanes with various geometries, as well as direct comparison to experimental observations of the output torque measured by a rheometer, revealing sources of variation and error. We describe the impact of the vane structure on the fluid velocity field, from few-arm cruciform vanes (N < 6) that significantly perturb the flow away from ideal azimuthal kinematics, to many-arm fractal vanes (N > 12) in which the internal structural features are successfully ``cloaked" by a yield surface. This results in the shearing of an almost-circular ring of viscoplastic fluid that is indistinguishable from the annular ring of fluid deformed around a slip-free rotating cylindrical bob and leads to more accurate rheometric measurements of the material flow curve. Moreover, in direct comparison with data from previous literature, we show that slip conditions on the vane surface do not impact the velocity field or measured overall torque T, whereas slip conditions on the smooth outer wall have significant impact on data, even when using a vane geometry. Finally, we describe the impact of vane topography and Bingham number, B, on the measured torque and rheometric accuracy of vane-in-cup geometries for ``simple" (inelastic) yield-stress fluids described by either the Bingham plastic or Herschel-Bulkley constitutive model.

Crystal nucleation and growth is a fundamental pillar of materials design. To advance our understanding of the underlying mechanisms, in situ visual observation plays an important role by providing dynamic information unavailable through conventional postgrowth analysis. Such information includes nucleation and growth rates, diffusion phenomena, phase transformation kinetics, strain relaxation mechanisms, and defect formation. Here, we review the contributions of ultra-high vacuum transmission electron microscopy (UHV-TEM) to our = understanding of dynamic crystal growth phenomena. We describe the vacuum, sample handling, and deposition capabilities essential for quantitative studies of reactive metals and semiconductors, and discuss how these capabilities are achieved while preserving the imaging performance of the microscope. We then show examples of growth processes explored using UHV-TEM, where the high spatial and temporal resolution provides unique insights into nanocrystal nucleation, thin film microstructure evolution, and oxidation in controlled environments. We assess these past accomplishments in the context of recent advances in transmission electron microscopy, discussing how aberration correction, modified sample environments, fast and sensitive detectors, and data science are unlocking powerful opportunities for atomic and temporal resolution measurements using UHV-TEM. We conclude by discussing the challenges and future perspectives for scientific advances using this technique., MIT Energy Initiative, Independent Research Fund Denmark

Consolidating published research on aluminum alloys into insights about microstructure–property relationships can simplify and reduce the costs involved in alloy design. One critical design consideration for many heat-treatable alloys deriving superior properties from precipitation are phases as key microstructure constituents because they can have a decisive impact on the engineering properties of alloys. Here, we present a computational framework for high-throughput extraction of phases and their impact on properties from scientific papers. Our framework includes transformer-based and large language models to identify sentences with phase-property information in papers, recognize phase and property entities, and extract phase-property relationships and their “sentiment.” We demonstrate the application of our framework on aluminum alloys, for which we build a database of 7,675 phase–property relationships extracted from a corpus of almost 5000 full-text papers. We comment on the extracted relationships based on common metallurgical knowledge.

This groundbreaking research delves into the intricate molecular interactions between MXene and trihalomethanes (THs) through a comprehensive theoretical study employing density-functional theory (DFT). Trihalomethanes are common carcinogenic chlorination byproducts found in water sanitation systems. This study focuses on a pristine MXene [Mn+1·Xn] monolayer and its various terminal [Tx] functional groups [Mn+1·XnTx], strategically placed on the surface for enhanced performance. Our investigation involves a detailed analysis of the adsorption energies of THs on different MXene types, with the MXene-Cl layer emerging as the most compatible variant. This specific MXene-Cl layer exhibits remarkable properties, including a total dipole moment (TDM) of 12.443 Debye and a bandgap of 0.570 eV, achieved through meticulous geometry optimization and computational techniques. Notably, THs such as trichloromethane (CHCl3), bromide-chloromethane (CHBrCl2), and dibromochloromethane (CHBr2Cl) demonstrate the highest TDM values, indicating substantial changes in electronic and optical parameters, with TDM values of 16.363, 15.998, and 16.017 Debye, respectively. These findings highlight the potential of the MXene-Cl layer as an effective adsorbent and detector for CHF3, CHClF2, CHCl3, CHBrCl2, and CHBr2Cl. Additionally, we observe a proportional increase in the TDM and bandgap energy, indicative of conductivity, for various termination atom combinations, such as Mxene-O-OH, Mxene-O-F, Mxene-O-Cl, Mxene-OH-F, Mxene-F-Cl, and Mxene-OH-Cl, with bandgap energies measured at 0.734, 0.940, 1.120, 0.835, and 0.927 eV, respectively. Utilizing DFT, we elucidate the adsorption energies of THs on different MXene surfaces. Our results conclusively demonstrate the significant influence of the termination atom nature and quantity

Understanding lattice deformations is crucial in determining the properties of nanomaterials, which can become more prominent in future applications ranging from energy harvesting to electronic devices. However, it remains challenging to reveal unexpected deformations that crucially affect material properties across a large sample area. Here, we demonstrate a rapid and semi-automated unsupervised machine learning approach to uncover lattice deformations in materials. Our method utilizes divisive hierarchical clustering to automatically unveil multi-scale deformations in the entire sample flake from the diffraction data using four-dimensional scanning transmission electron microscopy (4D-STEM). Our approach overcomes the current barriers of large 4D data analysis without a priori knowledge of the sample. Using this purely data-driven analysis, we have uncovered different types of material deformations, such as strain, lattice distortion, bending contour, etc., which can significantly impact the band structure and subsequent performance of nanomaterials-based devices. We envision that this data-driven procedure will provide insight into materials’ intrinsic structures and accelerate the discovery of materials.

Granular hydrogel matrices have emerged as promising candidates for cell encapsulation, bioprinting, and tissue engineering. However, it remains challenging to design and optimize these materials given their broad compositional and processing parameter space. Here, we combine experimentation and computation to create granular matrices composed of alginate-based bioblocks with controlled structure, rheological properties, and injectability profiles. A custom machine learning pipeline is applied after each phase of experimentation to automatically map the multidimensional input-output patterns into condensed data-driven models. These models are used to assess generalizable predictability and define high-level design rules to guide subsequent phases of development and characterization. Our integrated, modular approach opens new avenues to understanding and controlling the behavior of complex soft materials.

Heterogeneous integration of single-crystal materials offers great opportunities for advanced device platforms and functional systems1. Although substantial efforts have been made to co-integrate active device layers by heteroepitaxy, the mismatch in lattice polarity and lattice constants has been limiting the quality of the grown materials2. Layer transfer methods as an alternative approach, on the other hand, suffer from the limited availability of transferrable materials and transfer-process-related obstacles3. Here, we introduce graphene nanopatterns as an advanced heterointegration platform that allows the creation of a broad spectrum of freestanding single-crystalline membranes with substantially reduced defects, ranging from non-polar materials to polar materials and from low-bandgap to high-bandgap semiconductors. Additionally, we unveil unique mechanisms to substantially reduce crystallographic defects such as misfit dislocations, threading dislocations and antiphase boundaries in lattice- and polarity-mismatched heteroepitaxial systems, owing to the flexibility and chemical inertness of graphene nanopatterns. More importantly, we develop a comprehensive mechanics theory to precisely guide cracks through the graphene layer, and demonstrate the successful exfoliation of any epitaxial overlayers grown on the graphene nanopatterns. Thus, this approach has the potential to revolutionize the heterogeneous integration of dissimilar materials by widening the choice of materials and offering flexibility in designing heterointegrated systems.

Sr(Ti,Fe)O3−y perovskite solid solutions are relevant functional materials for energy conversion and electronic devices such as solid oxide fuel and photoelectrochemical cells, electrolyzers, oxygen sensors, resistive random access memories or synaptic transistors. The Raman spectra and vibrational characteristics of the Sr(Ti,Fe)O3−y materials class are suitable for describing their defect chemistry and the iron valence state, which governs a multitude of its mixed ionic–electronic transport and other characteristics. We synthesize a standard range of compositions containing 1–75 mol% of iron including the end members in the form of macrocrystalline bulk pellets, nanocrystalline poly- and single crystalline thin films. Through the change in both iron substitution level and microstructure, we directly see the effect of defect chemistry such as its phase, transition metal ion valence and oxygen nonstoichiometry on the Raman spectra. These are discussed in terms of in and ex situ experiments under oxidizing/reducing atmosphere. In contrast to long range structural X-ray diffraction measurements, Raman spectroscopy provides valuable insights into oxygen vacancy ordering and oxygen nonstoichiometry for the Sr(Ti,Fe)O3−y material class., Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

The design of soft magnetic hydrogels with high concentrations of magnetic particles is complicated by weak retention of the iron oxide particles in the hydrogel scaffold. Here, we propose a design strategy that circumvents this problem through the in situ mineralization of iron oxide nanoparticles within polymer hydrogels functionalized with strongly iron-coordinating nitrocatechol groups. The mineralization process facilitates the synthesis of a high concentration of large iron oxide nanoparticles (up to 57 wt % dry mass per single cycle) in a simple one-step process under ambient conditions. The resulting hydrogels are soft (kPa range) and viscoelastic and exhibit strong magnetic actuation. This strategy offers a pathway for the energy-efficient design of soft, mechanically robust, and magneto-responsive hydrogels for biomedical applications.

Atomic vibrations influence a variety of phenomena in solids and molecules, ranging from thermal transport to chemical reactions. These vibrations can be decomposed into normal modes, often known as phonons, which are collective motions of atoms vibrating at certain frequencies; this provides a rigorous basis for understanding atomic motion and its effects on material phenomena, since phonons can be detected and excited experimentally. Unfortunately, traditional theories such as the phonon gas model do not allow for the general study of vibrational modes since they only apply to ideal crystals where modes have a wave-like characteristic. Traditional computational methods based on molecular dynamics (MD) simulations allow for the study of phonons in more general systems with disorder, where the modes are less wave-like, but traditional methods do not simulate mode interactions and energy transfer between modes. Here we present, for the first time, a theory and massively parallel open-source software for modeling vibrational modes and simulating their interactions, or energy transfers, in large systems (>103 atoms) using MD. This is achieved by rewriting the atomic equations of motion in mode coordinates, from which analytical expressions for anharmonic mode coupling constants arise. Hamiltonian mechanics then provides a simple expression for calculating power transfer between modes. As a simple application of this theory, we perform MD simulations of phonon-interface scattering in a silicon–germanium superlattice and show the various pathways of energy transfer that occur. We also highlight that while many interaction pathways exist, only a tiny fraction of these pathways transfer significant amounts of energy, which is surprising. The approach allows for the prediction and simulation of mode/phonon interactions, thus unveiling the real-time dynamics of phonon behavior and energy transport.

To describe a new method for the automatic generation of process parameters for fused filament fabrication (FFF) across varying machines and materials. We use an instrumented extruder to fit a function that maps nozzle pressures across varying flow rates and temperatures for a given machine and material configuration. We then develop a method to extract real parameters for flow rate and temperature using relative pressures and temperature offsets. Our method allows us to successfully find process parameters, using one set of input parameters, across all of the machine and material configurations that we tested, even in materials that we had never printed before. Rather than using direct parameters in FFF printing, which is time-consuming to tune and modify, it is possible to deploy machine-generated data that captures the fundamental phenomenology of FFF to automatically select parameters.

Magic-angle twisted bilayer graphene, consisting of two graphene layers stacked at a special angle, exhibits superconductivity due to the maximized density of states at the energy of the flat band. Generally, experiments on twisted bilayer graphene have been performed using micrometer-scale samples. Here we report the fabrication of twisted bilayer graphene with an area exceeding 3 × 5 mm2 by transferring epitaxial graphene onto another epitaxial graphene, and observation of a flat band and large bandgap using angle-resolved photoemission spectroscopy. Our results suggest that the substrate potential induces both the asymmetrical doping in large angle twisted bilayer graphene and the electron doped nature of the flat band in magic-angle twisted bilayer graphene.

The integration of metallic contacts with two-dimensional (2D) semiconductors is routinely required for the fabrication of nanoscale devices. However, nanometer-scale variations in the 2D/metal interface can drastically alter the local optoelectronic properties. Here, we map local excitonic changes of the 2D semiconductor MoS2 in contact with Au. We utilize a suspended and epitaxially grown 2D/metal platform that allows correlated electron energy-loss spectroscopy (EELS) and angle resolved photoelectron spectroscopy (nanoARPES) mapping. Spatial localization of MoS2 excitons uncovers an additional EELS peak related to the MoS2/Au interface. NanoARPES measurements indicate that Au–S hybridization decreases substantially with distance from the 2D/metal interface, suggesting that the observed EELS peak arises due to dielectric screening of the excitonic Coulomb interaction. Our results suggest that increasing the van der Waals distance could optimize excitonic spectra of mixed-dimensional 2D/3D interfaces and highlight opportunities for Coulomb engineering of exciton energies by the local dielectric environment or moiré engineering.

Epitaxial lift-off techniques, which aim to separate ultrathin single-crystalline epitaxial layers off of the substrate, are becoming increasingly important due to the need of lightweight and flexible devices for heterogeneously integrated ultracompact semiconductor platforms and bioelectronics. Remote epitaxy is a relatively newly discovered epitaxial lift-off technique that allows substrate-seeded epitaxial growth of ultrathin films through few layers of graphene. This universal epitaxial lift-off technique allows freestanding single-crystal membrane fabrication very quickly at low cost. However, the conventional method of remote epitaxy requires transfer of graphene grown on another substrate to the target single-crystalline substrate, which results in organic and metallic residues as well as macroscopic defects such as cracks and wrinkles, significantly reducing the yield of remote epitaxy. Here, we show that direct growth of thick graphene on the target single-crystalline substrate (SrTiO3 for this study) followed by atomic layer etching (ALE) of the graphene layers create a defect- and residue-free graphene surface for high yield remote epitaxy. We find that the ALE efficiently removes one atomic layer of graphene per cycle, while also clearing multi-dots (clumps of carbon atoms) that form during nucleation of the graphene layers. Our results show that direct-grown graphene on the desired substrate accompanied by ALE might potentially be an ideal pathway toward commercialization of remote epitaxy.

Visual question answering (VQA) is a task that generates or predicts an answer to a question in human language about visual images. VQA is an active field combining two AI branches: NLP and computer vision. VQA in the medical field is still at an early stage, and it needs vast efforts and exploration to reach practical usage. This paper proposes two models that are utilized in the latest vision and NLP transformers that outperform the SOTA and have not yet been utilized in medical VQA. The ELECTRA-base transformer is used for textual feature extraction, whereas SWIN is used for visual feature extraction. In the SOTA medical VQA, selecting the model is based on the model that achieves the highest validation accuracy or the last model in training. The first proposed model, the best-value-based model, is selected based on the highest validation accuracy. The second model, the greedy-soup-based model, uses a greedy soup technique based on the fusion of multiple fine-tuned models to set the model parameters. The greedy soup selects the model parameters by fusing the model parameters that have significant performance on the validation accuracy in training. The greedy-soup-based model outperforms the best-value-based model, and both proposed models outperform the SOTA, which has an accuracy of 83.49%. The greedy-soup-based model is optimized with batch size and learning rate. During the optimization, seven extra models exceed the SOTA accuracy. The best model trained with a learning rate of 1.0×10−4 and batch size 16 achieves an accuracy of 87.41%.

In the last decade, robotic-mediated rehabilitation has emerged as a potential solution to improve repetitive task training. Each device in this field has a unique development history shaped by engineers’ expertise in specific programming languages or platforms. In this work we adopt an approach that tries to abstract from the final implementation with the aim to make control logic more shareable and understandable. The authors will present the outcomes of the application of a Rapid Control Prototyping strategy to an upper-limb robotic exoskeleton. A model-based design approach implemented on a real-time target machine is presented. This modern design approach was explored with several control strategies and was used to test the exoskeleton’s performances. The proposed method highlights how it is possible to develop the entire control architecture in a single programming environment.

The concept of a Deborah number is widely used in the study of viscoelastic materials to represent the ratio of a material relaxation time to the time scale of observation and to demarcate transitions between predominantly viscous or elastic material responses. However, this construct does not help quantify the importance of long transients and nonmonotonic stress jumps that are often observed in more complex time-varying systems. Many of these nonintuitive effects are lumped collectively under the term thixotropy; however, no proper nouns are associated with the key phenomena observed in such materials. Thixotropy arises from the ability of a complex structured fluid to remember its prior deformation history, so it is natural to name the dimensionless group representing such behavior with respect to the ability to remember. In Greek mythology, Mnemosyne was the mother of the nine Muses and the goddess of memory. We, thus, propose the definition of a Mnemosyne number as the dimensionless product of the thixotropic time scale and the imposed rate of deformation. The Mnemosyne number is, thus, a measure of the flow strength compared to the thixotropic time scale. Since long transient responses are endemic to thixotropic materials, one also needs to consider the duration of flow. The relevant dimensionless measure of this duration can be represented in terms of a mutation number, which compares the time scale of experiment/observation to the thixotropic time scale. Collating the mutation number and the Mnemosyne number, we can construct a general two-dimensional map that helps understand thixotropic behavior. We quantify these ideas using several of the simplest canonical thixotropic models available in the literature.

Designing efficient catalysts that possess large number of active sites, high catalytic activity and selectivity while also exhibiting strong antimicrobial activity is a challenging task because of poor control over material fabrication. Therefore, developing new and innovative approaches for the synthesis of catalytic materials is crucial for addressing these challenges. Here, we report the controlled fabrication of GQDs/Y-doped MgO nanoparticles achieved by co doping of yttrium (Y) and graphene quantum dots (GQDs) in magnesium oxide (MgO) based nanostructures (NSs) using the co-precipitation method. The co-doping of GQDs and Y was controlled by manipulating the ratio of precursors where introduction of GQD resulted in higher surface area and enhanced conductivity while the doping of Y enhanced the number of active sites in the final product. The GQDs/Y-doped MgO exhibited an average particle size of similar to 50 nm and a bandgap of 3.6 eV. Owing to these excellent characteristics, the GQDs/Y-doped MgO was utilized as a catalyst for treatment of the organic pollutants from water as well as antibacterial activity. The modified GQDs/Y-doped MgO nanostructure exhibited excellent activity of over 99.9 % for dye removal and versatility in a broad range of pH which clearly indicated the application in a range of different environments. Furthermore, the GQDs/Y-doped MgO exhibited excellent antibacterial activity against Escherichia Coli (E. Coli) bacteria. To gain further insights into the origins of this excellent activity response, the molecular docking simulations (MDS) is utilized against DNA gyrase and FabI (two enzymes critical to nucleic acid and fatty acid biosynthesis, respectively), to uncover the mechanism behind the observed antibacterial effects. In summary, the

The present paper reports a remote carbon-to-carbon boryl migration via an intramolecular 1,4-B/Cu shift, which establishes an in situ stereospecific electrophilic trap on the alkene moiety. The synthetic application is developed to prepare functionalized cyclopentenes by means of a palladium-catalyzed regioselective intramolecular coupling that completes a strategic cyclopropanation and generates valuable structural bicyclic systems. The mechanism is characterized by DFT (density functional theory) calculations which showed that the 1,4-migration proceeds through an intramolecular, nucleophilic attack of the copper-alkyl moiety on the boron atom bonded to the C(sp2), leading to a 5-membered boracycle structure. The computation of the 1,3- and 1,4-B/Cu shifts is also compared as is the impact of the endo- or exocyclic alkene on the reaction kinetics.The intramolecular 1,4-B/Cu shift justifies the observed remote carbon-to-carbon boryl migration, which makes stereoselective electrophilic trapping on the alkene moiety possible. Subsequent palladium-catalyzed regioselective intramolecular coupling enables functionalized cyclopentenes to be prepared that complete strategic cyclopropanation and deliver structurally valuable bicyclic systems. DFT mechanistic studies show that remote boryl migration occurs efficiently through 5-membered boracycle intermediates. image

Electrohydrodynamic (EHD) jet printing of solvent-based inks or melts allows for the producing of polymeric fiber-based two- and three-dimensional structures with sub-micrometer features, with or without conductive nanoparticles or functional materials. While solvent-based inks possess great material versatility, the stability of the EHD jetting process using such inks remains a major challenge that must be overcome before this technology can be deployed beyond research laboratories. Herein, we study the parameters that affect the stability of the EHD jet printing of polyethylene oxide (PEO) patterns using solvent-based inks. To gain insights into the evolution of the printing process, we simultaneously monitor the drop size, the jet ejection point, and the jet speed, determined by superimposing a periodic electrostatic deflection. We observe printing instabilities to be associated with changes in drop size and composition and in the jet's ejection point and speed, which are related to the evaporation of the solvent and the resulting drying of the drop surface. Thus, stabilizing the printing process and, particularly, the drop size and its surface composition require minimizing or controlling the solvent evaporation rate from the drop surface by using appropriate solvents and by controlling the printing ambient. For stable printing and improved jet stability, it is essential to use polymers with a high molecular weight and select solvents that slow down the surface drying of the droplets. Additionally, adjusting the needle voltages is crucial to prevent instabilities in the jet ejection mode. Although this study primarily utilized PEO, the general trends observed are applicable to other polymers that exhibit similar interactions between solvent and polymer.

Carbon-based nanomaterials are key to developing high-performing electrochemical sensors with improved sensitivity and selectivity. Nonetheless, limitations in their fabrication and integration into devices often constrain their practical applications. Moreover, carbon nanomaterials-based electrochemical devices still face problems such as large background currents, poor stability, and slow kinetics. To advance towards a new class of carbon nanostructured electrochemical transducers, we propose the in-situ polymerization and carbonization of furfuryl alcohol (FA) on porous silicon (pSi) to produce a tailored and highly stable transducer. The thin layer of polyfurfuryl alcohol (PFA) that conformally coats the pSi scaffold transforms into nanoporous carbon when subjected to pyrolysis above 600 °C. The morphological and chemical properties of PFA-pSi were characterized by scanning electron microscopy, and Raman and X-ray photoelectron spectroscopies. Their stability and electrochemical performance were investigated by cyclic voltammetry and electrochemical impedance spectroscopy in [Fe(CN)6]3-/4-, [Ru(NH3)6]2+/3+, and hydroquinone. PFA-pSi showed superior electrochemical performance compared to screen-printed carbon electrodes while also surpassing glassy carbon electrodes in specific aspects. Besides, PFA-pSi has the additional advantage of easy tuning of the electroactive surface area. To prove its potential for biosensing purposes, a DNA sensor based on quantifying the partial pore blockage of the pSi upon target hybridization was built on PFA-pSi. The sensor showed a limit of detection of 1.4 pM, outperforming other sensors based on the same sensing mechanism.