Glioblastoma (GBM) is one of the most aggressive types of cancer which currently does not have a cure. Its invasive nature and heterogeneity makes its complete surgical removal impossible. Hence, a targeted treatment is critically needed to effectively eradicate this cancer. In this work, the authors report the synthesis of hollow TiO2 nanospheres (HTiO2NS) and their functionalization with folic acid (FA) and zinc (II) tetranitrophthalocyanine (ZnPc) to achieve cell selectivity and light absorption in the visible range. In vitro cytotoxicity of the functionalized HTiO2NS against M059K cell line (Human GBM cancer cells) was tested. In vitro generation of reactive oxygen species by HTiO2NS–FA–ZnPc nanostructures under UV irradiation was detected by fluorescence probing. To identify HTiO2NS–FA–ZnPc cell localization, the nanoparticles were labeled with fluorescein isothiocyanate dye and visualized by fluorescence microscopy. Results illustrate that HTiO2NS–FA–ZnPc nanostructures have the potential to be used for targeted photodynamic therapy for the treatment of GBM cancer.

The processes of singlet fission and triplet fusion could allow state-of-the-art photovoltaic devices to surpass the Shockley–Queisser limit by optimizing the utilized solar spectrum by reducing thermal relaxation and inaccessible sub-bandgap photons, respectively. Triplet fusion demands precise control of the spin-triplet state population, and requires a sensitizer to efficiently populate the triplet state of an acceptor molecule. In this perspective, we highlight the established field of sensitized upconversion and further examine alternative triplet sensitization routes, including the possibility of bulk solid-state semiconductors as triplet sensitizers, which provide a new avenue for charge transfer-based triplet sensitization rather than excitonic triplet energy transfer.

By electron-beam (e-beam) melting, we prepared 0.4 wt% carbon-infused copper (CuCv4), and a copper control without carbon addition (CuCv0). Scanning electron microscopy and helium ion microscopy (HIM) were performed on the as-solidified surface, fracture surface, and ion-polished surface of the CuCv4 sample. The results revealed that graphitic carbon flakes cover the as-solidified surface, and carbon nanoparticles and clusters exist in the fracture and ion-polished surfaces. HIM on the ion-polished surface revealed a unique ripple-shaped feature, which is possibly associated with the infusion of carbon nanoribbons in the copper matrix. The bulk densities were measured to be 8.86 and 8.53 g/cm3, which correspond to relative densities of 98.9% and 96.4% for the CuCv0 and CuCv4 samples, respectively. In addition, apparent electrical conductivities were measured to be 56.9 and 57.5 MS/m, respectively, for the e-beam melted CuCv0 and CuCv4 samples. These values correspond to true electrical conductivities of 100.5% IACS (International Annealed Copper Standard) and 107.4% IACS after correction for the porosity. Our results reveal remarkable promise of using covetic copper for the next generation conductors in energy applications from microelectronic devices to high-power transmission cables.

Nano-structured thin films have a variety of applications from waveguides, gaseous sensors to piezoelectric devices. Grazing Incidence Small Angle x-ray Scattering images enable classification of such materials. One challenge is to determine structure information from scattering patterns alone. This paper highlights the design of multiple Convolutional Neural Networks (CNN) to classify nanoparticle orientation in a thin film by learning scattering patterns. The network was trained on several thin films with a success rate of 94%. We demonstrate CNN robustness under different noises as well as demonstrate the potential of our proposed approach as a strategy to decrease scattering pattern analysis time.

The authors have shown recently that the neurite extension by neuronal PC12 cells is greatly impacted by aerogel topography. Indeed, the average neurite length of PC-12 cells grown on aerogels is greater than that in cells cultured on control substrates. Here, the authors report on the first experimental study focused on the design and development of a plasmonic photo-patterning technique for collagen-coated mesoporous aerogel biomaterials. Herein, the authors have produced specific patterns on silica aerogels by performing precise plasmonic photo-patterning on liquid crystal-coated aerogels. The authors report the methodology employed to create a collagen–liquid crystal gel mixture imprinted with precise plasmonic photo-patterns. PC12 cells plated on these patterns did attach and survive and followed the spatial cues of the pattern to align themselves in a similar pattern.

In order to reveal the quantitative relationship between fatigue crack deflection path and cross-sectional grain boundary (GB) arrangement of metallic nanolayered composites (NLCs), a stochastic model was established based on the interface-dominant fatigue damage for the ultrafine-scale NLCs. The model indicates that the crack deflection length decreases with decreasing GB arrangement deviation and grain size of constituent layers. The observation and quantitative analysis of fatigue cracking behavior of the Cu/W multilayers with a layer thickness of 5 and 20 nm was conducted to verify the model.

The development of reliable, yet computationally efficient interatomic forcefields is key to facilitate the modeling of glasses. However, the parameterization of novel forcefields is challenging as the high number of parameters renders traditional optimization methods inefficient or subject to bias. Here, we present a new parameterization method based on machine learning, which combines ab initio molecular dynamics simulations and Bayesian optimization. By taking the example of glassy silica, we show that our method yields a new interatomic forcefield that offers an unprecedented agreement with ab initio simulations. This method offers a new route to efficiently parameterize new interatomic forcefields for disordered solids in a non-biased fashion.

A specialized dealloying technique called thermal dealloying was developed over 10 years ago for certain biomedical materials. This method is not widely used for synthesizing nanoporous metals. However, it offers advantages over conventional dealloying processes for fabrication of nanoporous structures, and is highly suitable for refractory metals that may be susceptible to oxidation during chemical/electrochemical dealloying and liquid metal dealloying. In this study, nanoporous structures were successfully fabricated from magnesium-based precursor alloys via sublimation of magnesium at elevated temperature under vacuum conditions. Different refractory metal diffusion rates affect the resulting density and amount of retained magnesium in each nanoporous material.

Additive manufacturing used with custom electromyographic sensors has been demonstrated for neuroprosthetic limb manufacturing and is now translating to the clinical environment. These manufacturing methods have dramatically reduced device weight while increasing the capability for multi-finger dexterity. Using wearable electromyography sensors standalone from the prosthetic limb, a new virtual training method has been designed and tested to improve human–machine interaction. This type of training leverages real-time visual feedback to user inputs, supporting improved timing and magnitudes of muscle contractions. The combination of these technologies may provide a stronger affinity between the pediatric patient group and the device.

Research into pyrolysis-based recycling of sheet molding compounds (SMCs) to recover glass fiber for reuse has indicated significant pre-existing tensile strength damage in the shredded recycling input materials. This loss in mechanical durability inherently hurts the value proposition of recycled glass fiber by limiting reuse of the fiber for reinforcement. In this study, the mechanical properties of glass fibers at each step in the first lifecycle of an SMC material are measured to assess the extent of cumulating fiber damage prior to recycling and identify potential causes of this degradation to maximum fiber tensile performance.

We introduce CRYSTAL, a multi-agent AI system for crystal-structure phase mapping. CRYSTAL is the first system that can automatically generate a portfolio of physically meaningful phase diagrams for expert-user exploration and selection. CRYSTAL outperforms previous methods to solve the example Pd-Rh-Ta phase diagram, enabling the discovery of a mixed-intermetallic methanol oxidation electrocatalyst. The integration of multiple data-knowledge sources and learning and reasoning algorithms, combined with the exploitation of problem decompositions, relaxations, and parallelism, empowers AI to supersede human scientific data interpretation capabilities and enable otherwise inaccessible scientific discovery in materials science and beyond.

This paper discusses the unique patterns evolved through phase separation of a bulk liquid crystal (LC) from the self-assembly of lysozyme induced by evaporation of de-ionized water only. Each domain shows a central dark region surrounded by bright regions (randomly oriented LC droplets). The birefringence intensity reveals three regimes (a slow increase, rapid rise, then saturation) not seen without LC droplets. The textural study exhibits a simple exponential behavior that changes as a function of LC concentration. Furthermore, in the presence of LC, the crack patterns are found to be different near the drop edge than those in the central region.

Composites are ubiquitous throughout nature and often display both high strength and toughness, despite the use of simple base constituents. In the hopes of recreating the high-performance of natural composites, numerical methods such as finite element method (FEM) are often used to calculate the mechanical properties of composites. However, the vast design space of composites and computational cost of numerical methods limit the application of high-throughput computing for optimizing composite design, especially when considering the entire failure path. In this work, the authors leverage deep learning (DL) to predict material properties (stiffness, strength, and toughness) calculated by FEM, motivated by DL's significantly faster inference speed. Results of this study demonstrate potential for DL to accelerate composite design optimization.

Signal detection limit (SDL), limit of detection (LOD), and limit of quantitation of a portable Raman spectrometer were measured for smokeless gunpowder stabilizers, diphenylamine (DPA) and ethyl centralite (EC), in acetone, acetonitrile, ethanol, and methanol. Acetone yielded the lowest LOD for three of four DPA peaks, and acetonitrile yielded the lowest LOD for two of three EC peaks and the remaining DPA peak. When gold nanoparticles were added to the DPA solutions in acetone and acetonitrile, statistically significant changes were observed (DPA peak position, full width at half maximum, and/or total area) and SDL was improved for the majority of all peaks in both solvents.

Flexible piezoelectric generators (PEGs) present a unique opportunity for renewable and sustainable energy harvesting. Here, we present a low-temperature and low-energy deposition method using solvent evaporation-assisted three-dimensional printing to deposit electroactive poly(vinylidene fluoride) (PVDF)-trifluoroethylene (TrFE) up to 19 structured layers. Visible-wavelength transmittance was above 92%, while ATR-FTIR spectroscopy showed little change in the electroactive phase fraction between layer depositions. Electroactivity from the fabricated PVDF-TrFE PEGs showed that a single structured layer gave the greatest output at 289.3 mV peak-to-peak voltage. This was proposed to be due to shear-induced polarization affording the alignment of the fluoropolymer dipoles without an electric field or high temperature.

The molybdenum disulfide (MoS2) and indium tin oxide (ITO) interface were studied by atom probe tomography (APT). Raman spectroscopy, scanning electron microscopy, and grazing-incidence x-ray diffraction measurements were performed as complementary characterization. Results confirm that nanowires plated shape with the 〈110〉-orientation are aligned perpendicular to the ITO film with principal reflections at (002), (100), (101), (201), and Raman spectroscopy vibrational modes at E12g at 378 cm−1 and A1g at 407 cm−1 correspond to 2H-MoS2. APT reveals MoS+2, MoS+3 as predominant evaporated molecular ions on the sample, indicating no significant diffusion/segregation of Mo or S species within the ITO layer.

The authors report on strong exciton–photon coupling in all-metal microcavities containing functionalized anthradithiophene (ADT) in host poly(methyl methacrylate) matrices for a wide range of ADT concentrations. Angle-resolved reflectance of polycrystalline films revealed Rabi splittings up to 340 meV. Angle-resolved photoluminescence in films with low ADT concentrations (dominated by “isolated” ADT molecules) showed Rabi splittings which scaled with the square root of oscillator strength. When “aggregated” and “isolated” ADT molecules coexisted in film, cavities preferentially coupled to “isolated” molecules due to an anisotropic distribution of aggregates. As a solution-processable high-performance organic semiconductor, ADT shows promise as an (opto)electronic polaritonic material.

This Prospective covers an overview of the injection molding process and the importance of mold design and tooling considerations, important material requirements and thermal properties for molds, polymer material requirements for injection molding, mold flow analysis, and the promise of using the 3D printing process for mold fabrication. The second part demonstrates the injection molding process using 3D-printed polymer molds and its suitability for low-run productions. 3D-printed molds using stereolithography and fused filament fabrication have been injected with polylactic acid, and the quality of the injected parts was assessed in terms of dimensional accuracy and the damage mechanisms during fabrication.

Direct determination of barrier height (ΦBH) value between Ir and single crystal (001) hydrogen-terminated diamond with lightly boron doped has been performed using x-ray photoelectron spectroscopy technique. 70 nm Ir islands were formed on hydrogen-terminated diamond surface using anodic aluminum oxide. The ΦBH value for Ir/hydrogen-terminated diamond was −0.43 ± 0.14 eV, indicating that Ir was a suitable metal for ohmic contact with hydrogen-terminated diamond. The band diagram of Ir/hydrogen-terminated diamond was obtained. The experimental ΦBH was compared with the theoretical ΦBH in this work.

The software package ESPEI has been developed for efficient evaluation of thermodynamic model parameters within the CALPHAD method. ESPEI uses a linear fitting strategy to parameterize Gibbs energy functions of single phases based on their thermochemical data and refines the model parameters using phase equilibrium data through Bayesian parameter estimation within a Markov Chain Monte Carlo machine learning approach. In this paper, the methodologies employed in ESPEI are discussed in detail and demonstrated for the Cu–Mg system down to 0 K using unary descriptions based on segmented regression. The model parameter uncertainties are quantified and propagated to the Gibbs energy functions.