Open Access

Herein, we report the design and synthesis of a layered redox-active, antiferromagnetic metal organic semiconductor crystals with the chemical formula [Cu(H2O)2V(µ-O)(PPA)2] (where PPA is phenylphosphonate). The crystal structure of [Cu(H2O)2V(µ-O)(PPA)2] shows that the metal phosphonate layers are separated by phenyl groups of the phenyl phosphonate linker. Tauc plotting of diffuse reflectance spectra indicates that [Cu(H2O)2V(µ-O)(PPA)2] has an indirect band gap of 2.19 eV. Photoluminescence (PL) spectra indicate a complex landscape of energy states with PL peaks at 1.8 and 2.2 eV. [Cu(H2O)2V(µ-O)(PPA)2] has estimated hybrid ionic and electronic conductivity values between 0.13 and 0.6 S m−1. Temperature-dependent magnetization measurements show that [Cu(H2O)2V(µ-O)(PPA)2] exhibits short range antiferromagnetic order between Cu(II) and V(IV) ions. [Cu(H2O)2V(µ-O)(PPA)2] is also photoluminescent with photoluminescence quantum yield of 0.02%. [Cu(H2O)2V(µ-O)(PPA)2] shows high electrochemical, and thermal stability.

We report the synthesis of AgNP@PEDOT:PSS hybrid conductive particles with silver cores and polythiophene shells that can be used to formulate sinter-free inks for printing electronics. First, Ag nanocrystals capped with the weakly bound ligand aminohexanoic acid (ε-Ahx) are prepared. The ligand shell is exchanged by reacting the dispersion with the polymer ionomer mixture poly(3,4-ethylene dioxythiophene):polystyrene sulfonate (PEDOT:PSS). The particles are characterized by electron microscopy, dynamic light scattering, Z potential, and Raman spectroscopy, confirming the replacement of the ligands on the metal particle surface. The resulting dispersion is colloidally stable as confirmed by DLS. Inks with a solid content of the hybrid particles of 300 mg mL−1 were prepared and deposited on different substrates. The new particles are components for hybrid inks that become electrically conductive without any chemical or thermal post-deposition treatment. We show that silver-based hybrid inks can be deposited on different substrates and possess an average conductivity after 24 h of drying at room temperature of 1.726 × 106 S m−1 ± 0.326 × 106 S m−1, only one order of magnitude lower than elemental silver and within the same order of magnitude as their gold ink counterpart.

In this study, polyetheretherketone composites were compounded using a two-screw extruder followed by injection moulding. The effects of multi-fillers on the mechanical properties and crystallization performances were investigated. Differential scanning calorimetry results indicate that the addition of fillers slightly increases the crystallization temperature and crystallinity. Compared to neat polyetheretherketone, the incorporation of inorganic filler leads to a significant improvement in matrix hardness, matrix stiffness and a slight increase in tensile strength. However, the material ductility, the impact strength and the fracture toughness of polyetheretherketone composites decrease. Fractography analyses show that the addition of fillers restraints the ductile deformation of polymers, which is responsible for the reduction of material ductility, impact strength as well as fracture toughness of polyetheretherketone composites.

Purpose: A powerful principle in nature is the presence of surface patterns to improve specific characteristics or to enable completely new functions. Here, we present two case studies where bioinspired surface patterns based on the adhesive system of geckos may be applied for biomedical applications: residue-free adhesion to skin and gecko-inspired suture threads for knot-free wound closure. Methods: Gecko-inspired skin adhesives were fabricated by soft lithography of polydimethylsiloxane with successive inking and dipping steps. Their adhesion was measured using a home built adhesion tester designed for patterned surfaces. Preliminary lap shear tests on the back of a human hand were also performed. Commercial suture threads from different materials were patterned in the group of A. del Campo at the Max-Planck-Institute for Polymer Research (Mainz, Germany) using oxygen plasma. The treated threads were pulled through artificial skin in both directions measuring the peak force and the pull through force. Results and Conclusions: Unpatterned reference samples of the skin adhesive did not stick to human skin, while the patterned samples all showed notable adhesion up to 1.2 Newton for a sample size of approximately 3 cm². First results with the patterned suture threads indicated that the surface patterning of the thread has only a minor effect on the pull-through forces. To achieve knot-free sewing the surface geometry of the suture threads needs to be optimized and more realistic testing procedures, e.g. testing on human skin, are necessary.

An unresolved issue in contemporary biomedicine is the overwhelming number and diversity of complex images that require annotation, analysis and interpretation. Recent advances in Deep Learning have revolutionized the field of computer vision, creating algorithms that compete with human experts in image segmentation tasks. However, these frameworks require large human-annotated datasets for training and the resulting “black box” models are difficult to interpret. In this study, we introduce Kartezio, a modular Cartesian Genetic Programming-based computational strategy that generates fully transparent and easily interpretable image processing pipelines by iteratively assembling and parameterizing computer vision functions. The pipelines thus generated exhibit comparable precision to state-of-the-art Deep Learning approaches on instance segmentation tasks, while requiring drastically smaller training datasets. This Few-Shot Learning method confers tremendous flexibility, speed, and functionality to this approach. We then deploy Kartezio to solve a series of semantic and instance segmentation problems, and demonstrate its utility across diverse images ranging from multiplexed tissue histo-pathology images to high resolution microscopy images. While the flexibility, robustness and practical utility of Kartezio make this fully explicable evolutionary designer a potential game-changer in the field of biomedical image processing, Kartezio remains complementary and potentially auxiliary to mainstream Deep Learning approaches.