A Coating that Combines Lotus‐Effect and Contact‐Active Antimicrobial Properties on Silicone

The antimicrobial equipment of materials is of great importance in medicine but also in daily life. A challenge is the antimicrobial modification of hydrophobic surfaces without increasing their low surface energy. This is particularly important for silicone‐based materials. Because most antimicrobial surface modifications render the materials more hydrophilic, methods are needed to achieve antimicrobial activity without changing the high water‐contact‐angle. This is achieved in the present work, where SiO₂ nanoparticles are prepared and functionalized with 3‐(trimethoxysilyl)‐propyldimethyloctadecyl ammonium chloride (QAS) in a one‐pot synthesis. The modified nanoparticles are applied onto a silicone surface from suspension with no need of elaborate pretreatment. The resulting surface exhibits a Lotus‐Effect combined with contact‐active antimicrobial properties. The particle surfaces show self‐organizing micro‐ and nanostructures that afford a water‐contact angle of 144° and a hysteresis below 10°. The particles are self‐adhering on the silicone after solvent evaporation and resistant against immersion into and washing with water for at least 5 d. Thereby, the adhesion of the bacterial strain Staphylococcus aureus to these surfaces is reduced and the remaining bacterial cells are killed within 16 h. This is the first example of a Lotus‐Effect surface with intrinsic contact‐active antimicrobial properties.     

Inverted Organic Solar Cells with Sol–Gel Processed High Work‐Function Vanadium Oxide Hole‐Extraction Layers

For large‐scale and high‐throughput production of organic solar cells (OSCs), liquid processing of the functional layers is desired. We demonstrate inverted bulk‐heterojunction organic solar cells (OSCs) with a sol–gel derived V₂O₅ hole‐extraction‐layer on top of the active organic layer. The V₂O₅ layers are prepared in ambient air using Vanadium(V)‐oxitriisopropoxide as precursor. Without any post‐annealing or plasma treatment, a high work function of the V₂O₅ layers is confirmed by both Kelvin probe analysis and ultraviolet photoelectron spectroscopy (UPS). Using UPS and inverse photoelectron spectroscopy (IPES), we show that the electronic structure of the solution processed V₂O₅ layers is similar to that of thermally evaporated V₂O₅ layers which have been exposed to ambient air. Optimization of the sol gel process leads to inverted OSCs with solution based V₂O₅ layers that show power conversion efficiencies similar to that of control devices with V₂O₅ layers prepared in high‐vacuum.     

On‐the‐Spot Immobilization of Quantum Dots,Graphene Oxide,and Proteins via Hydrophobins

Class I hydrophobin Vmh2, a peculiar surface active and versatile fungal protein, is known to self‐assemble into chemically stable amphiphilic films, to be able to change wettability of surfaces, and to strongly adsorb other proteins. Herein, a fast, highly homogeneous and efficient glass functionalization by spontaneous self‐assembling of Vmh2 at liquid–solid interfaces is achieved (in 2 min). The Vmh2‐coated glass slides are proven to immobilize not only proteins but also nanomaterials such as graphene oxide (GO) and quantum dots (QDs). As models, bovine serum albumin labeled with Alexa 555 fluorophore, anti‐immunoglobulin G antibodies, and cadmium telluride QDs are patterned in a microarray fashion in order to demonstrate functionality, reproducibility, and versatility of the proposed substrate. Additionally, a GO layer is effectively and homogeneously self‐assembled onto the studied functionalized surface. This approach offers a quick and simple alternative to immobilize nanomaterials and proteins, which is appealing for new bioanalytical and nanobioenabled applications.     

Tough Transient Ionic Junctions Printed with Ionic Microgels

Emerging soft ionotronics better match the human body mechanically and electrically compared to conventional rigid electronics. They hold great potential for human-machine interfaces, wearable and implantable devices, and soft machines. Among various ionotronic devices, ionic junctions play critical roles in rectifying currents as electrical p–n junctions. Existing ionic junctions, however, are limited in electrical and mechanical performance, and are difficult to fabricate and degrade. Herein, the design, fabrication, and characterization of tough transient ionic junctions fabricated via 3D ionic microgel printing is reported. The 3D printing method demonstrates excellent printability and allows one to fabricate ionic junctions of various configurations with high fidelity. By combining ionic microgels, degradable networks, and highly charged biopolymers, the ionic junctions feature high stretchability (stretch limit 27), high fracture energy (>1000 Jm⁻²), excellent electrical performance (current rectification ratio >100), and transient stability (degrade in 1 week). A variety of ionotronic devices, including ionic diodes, ionic bipolar junction transistors, ionic full-wave rectifiers, and ionic touchpads are further demonstrated. This study merges ionotronics, 3D printing, and degradable hydrogels, and will motivate the future development of high-performance transient ionotronics.     

Using anisotropic diffusion equations in pixon domain for image de-noising

Image enhancement is an essential phase in many image processing algorithms. In any image de-noising algorithm, it is a major concern to keep the interesting structures of the image. Such interesting structures in an image often correspond to the discontinuities in the image (edges). In this paper, we propose a new algorithm for image de-noising using anisotropic diffusion equations in pixon domain. In this approach, diffusion equations are applied on the pixonal model of the image. The algorithm has been examined on a variety of standard images and the performance has been compared with algorithms known from the literature. The experimental results show that in comparison with the other existing methods, the proposed algorithm has a better performance in de-noising and preserving image edges.     

Plasmonic Nanoparticles as a Physically Unclonable Function for Responsive Anti‐Counterfeit Nanofingerprints

Far‐field scattering of randomly deposited Au nanoparticles (NPs) is demonstrated as a physically unclonable optical function for anti‐counterfeit applications in which the scattering patterns are easily produced yet impractical to replicate. Colloidal metal NPs are superb components for nanoscale labels owing to their small dimensions and intense far‐field scattering visible at wavelengths that depend on colloidal size, shape, composition, and their local environment. The feasibility of Au NP depositions as nanofingerprints is presented using a simple pattern matching algorithm. These NPs offer extended functionality as environmental sensors. Taking advantage of the local refractive index dependent scattering wavelengths of metal NPs, a detectable color change is also demonstrated from a nanofingerprint comprised of Au and Ag NPs when placed in media with different refractive index. The facile deposition method coupled with the intense scattering and optical response of metal NPs provides physically unclonable tags (nanofingerprints) with the ability to serve as tamper‐evident and aging labels.     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.     

Isoflavone determination in spontaneous legumes identified by DNA barcodes

Isoflavones have been associated with several health protective effects. In this work spontaneous legume plants were screened as putative sources of dietary isoflavones. A molecular identification of the collected species was performed throughout DNA barcoding using ITS, rbcL, rpoC1 and matK sequences. The use of a multi-locus barcoding system complemented with basic morphological information allowed the unequivocal identification at the species level of 90% of the samples. The determination of isoflavone content was performed by high-performance liquid chromatography with diode-array detection. Total average contents in the studied species were significantly different, Ononis natrix and Cytisus scoparius possessing the highest total isoflavones content (396 and 273 mg kg⁻¹, respectively) and Lotus creticus, the lowest (20 mg kg⁻¹). The correlation of total isoflavone content with the phylogeny of this set of plants as determined by the rpoC1 sequences was evaluated for the first time.