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In this study, the characteristics of HVOF sprayed WC/Co-Cr and WC/Cr3C2/Ni coatings were investigated in correlation with the variation of the powder feed rate. For this purpose, the mass flow was adjusted to four different levels. The other process parameters were all kept constant. The morphological and mechanical properties as well as the electrochemical corrosion behaviour were investigated and associated with the achieved microstructure.
Both scanning electron microscopy and confocal laser scanning microscopical images of the cross sections demonstrated a good correlation between the selected powder feed rate and the degree of internal porosity produced, which can be attributed to the deposition process. The coatings which fulfilled the requirements of the pre-qualification step were selected for further hardness measurements, tribological tests and electrochemical corrosion measurements in a 3.5 wt% NaCl aqueous solution.
It was found that the powder feed rate strongly influenced the characteristics of the HVOF-sprayed cermet coatings. The tendency to crack formation, especially at the interface coating/substrate, was lower for the samples coated with a lower mass flow rate. These studies have shown that the applied powder feed rates had an important influence on the coatings microstructure and implicitly on the sliding wear behavior respectively on the electrochemical corrosion resistance of the investigated cermet coatings.
Flame-sprayed NiCrBSi/WC-12Co composite coatings were deposited in different ratios on the surface of stainless steel. Oxyacetylene flame remelting treatment was applied to surfaces for refinement of the morphology of the layers and improvement of the coating/substrate adhesion.
The performance of the coated specimens to cavitation erosion and electrochemical corrosion was evaluated by an ultrasonic vibratory method and, respectively, by polarization measurements. The microstructure was investigated by means of scanning electron microscopy (SEM) combined with energy dispersive X-ray analysis (EDX). The obtained results demonstrated that the addition of 15 wt.% WC-12Co to the self-fluxing alloy improves the resistance to cavitation erosion (the terminal erosion rate (Vs) decreased with 15% related to that of the NiCrBSi coating) without influencing the good corrosion resistance in NaCl solution. However, a further increase in WC-Co content led to a deterioration of these coating properties (the Vs has doubled related to that of the NiCrBSi coating).
Moreover, the corrosion behavior of the latter composite coating was negatively influenced, a fact confirmed by increased values for the corrosion current density (icorr). Based on the achieved experimental results, one may summarize that NiCrBSi/WC-Co composite coatings are able to increase the life cycle of expensive, high-performance components exposed to severe cavitation conditions.
To address the question which neocortical layers and cell types are important for the perception of a sensory stimulus, we performed multielectrode recordings in the barrel cortex of head-fixed mice performing a single-whisker go/no-go detection task with vibrotactile stimuli of differing intensities. We found that behavioral detection probability decreased gradually over the course of each session, which was well explained by a signal detection theory-based model that posits stable psychometric sensitivity and a variable decision criterion updated after each reinforcement, reflecting decreasing motivation. Analysis of multiunit activity demonstrated highest neurometric sensitivity in layer 4, which was achieved within only 30 ms after stimulus onset. At the level of single neurons, we observed substantial heterogeneity of neurometric sensitivity within and across layers, ranging from nonresponsiveness to approaching or even exceeding psychometric sensitivity. In all cortical layers, putative inhibitory interneurons on average proffered higher neurometric sensitivity than putative excitatory neurons. In infragranular layers, neurons increasing firing rate in response to stimulation featured higher sensitivities than neurons decreasing firing rate. Offline machine-learning-based analysis of videos of behavioral sessions showed that mice performed better when not moving, which at the neuronal level, was reflected by increased stimulus-evoked firing rates.
Nowadays, robots are found in a growing number of areas where they collaborate closely with humans. Enabled by lightweight materials and safety sensors, these cobots are gaining increasing popularity in domestic care, where they support people with physical impairments in their everyday lives. However, when cobots perform actions autonomously, it remains challenging for human collaborators to understand and predict their behavior, which is crucial for achieving trust and user acceptance. One significant aspect of predicting cobot behavior is understanding their perception and comprehending how they “see” the world. To tackle this challenge, we compared three different visualization techniques for Spatial Augmented Reality. All of these communicate cobot perception by visually indicating which objects in the cobot’s surrounding have been identified by their sensors. We compared the well-established visualizations Wedge and Halo against our proposed visualization Line in a remote user experiment with participants suffering from physical impairments. In a second remote experiment, we validated these findings with a broader non-specific user base. Our findings show that Line, a lower complexity visualization, results in significantly faster reaction times compared to Halo, and lower task load compared to both Wedge and Halo. Overall, users prefer Line as a more straightforward visualization. In Spatial Augmented Reality, with its known disadvantage of limited projection area size, established off-screen visualizations are not effective in communicating cobot perception and Line presents an easy-to-understand alternative.
Different charge treatment approaches are examined for cyclotide-induced plasma membrane disruption by lipid extraction studied with dissipative particle dynamics. A pure Coulomb approach with truncated forces tuned to avoid individual strong ion pairing still reveals hidden statistical pairing effects that may lead to artificial membrane stabilization or distortion of cyclotide activity depending on the cyclotide’s charge state. While qualitative behavior is not affected in an apparent manner, more sensitive quantitative evaluations can be systematically biased. The findings suggest a charge smearing of point charges by an adequate charge distribution. For large mesoscopic simulation boxes, approximations for the Ewald sum to account for mirror charges due to periodic boundary conditions are of negligible influence.
The use of molecular string representations for deep learning in chemistry has been steadily increasing in recent years. The complexity of existing string representations, and the difficulty in creating meaningful tokens from them, lead to the development of new string representations for chemical structures. In this study, the translation of chemical structure depictions in the form of bitmap images to corresponding molecular string representations was examined. An analysis of the recently developed DeepSMILES and SELFIES representations in comparison with the most commonly used SMILES representation is presented where the ability to translate image features into string representations with transformer models was specifically tested. The SMILES representation exhibits the best overall performance whereas SELFIES guarantee valid chemical structures. DeepSMILES perform in between SMILES and SELFIES, InChIs are not appropriate for the learning task. All investigations were performed using publicly available datasets and the code used to train and evaluate the models has been made available to the public.
The development of deep learning-based optical chemical structure recognition (OCSR) systems has led to a need for datasets of chemical structure depictions. The diversity of the features in the training data is an important factor for the generation of deep learning systems that generalise well and are not overfit to a specific type of input. In the case of chemical structure depictions, these features are defined by the depiction parameters such as bond length, line thickness, label font style and many others. Here we present RanDepict, a toolkit for the creation of diverse sets of chemical structure depictions. The diversity of the image features is generated by making use of all available depiction parameters in the depiction functionalities of the CDK, RDKit, and Indigo. Furthermore, there is the option to enhance and augment the image with features such as curved arrows, chemical labels around the structure, or other kinds of distortions. Using depiction feature fingerprints, RanDepict ensures diversely picked image features. Here, the depiction and augmentation features are summarised in binary vectors and the MaxMin algorithm is used to pick diverse samples out of all valid options. By making all resources described herein publicly available, we hope to contribute to the development of deep learning-based OCSR systems.
The concept of molecular scaffolds as defining core structures of organic molecules is utilised in many areas of chemistry and cheminformatics, e.g. drug design, chemical classification, or the analysis of high-throughput screening data. Here, we present Scaffold Generator, a comprehensive open library for the generation, handling, and display of molecular scaffolds, scaffold trees and networks. The new library is based on the Chemistry Development Kit (CDK) and highly customisable through multiple settings, e.g. five different structural framework definitions are available. For display of scaffold hierarchies, the open GraphStream Java library is utilised. Performance snapshots with natural products (NP) from the COCONUT (COlleCtion of Open Natural prodUcTs) database and drug molecules from DrugBank are reported. The generation of a scaffold network from more than 450,000 NP can be achieved within a single day.
We propose a quantum-mechanical model to calculate the nonlinear differential conductance of a single molecular junction immersed in a solvent, either in pure form or as a binary mixture with varying volume fraction. The solvent mixture is captured by a dielectric continuum model for which the resulting spectral density is determined within the Gladstone-Dale approach. The conductance of the molecular junction is calculated by a real-time diagrammatic technique. We find a strong variation of the conductance maximum for varying volume fraction of the solvent mixture. Importantly, the calculated molecular nonlinear conductance shows a very good agreement with experimentally measured data for common molecular junctions in various polar solvent mixtures.
The printing variable least addressed in previous research aiming to reveal the effect of the FFF process parameters on the printed PLA part’s quality and properties is the filament color. Moreover, the color of the PLA, as well as its manufacturer, are rarely mentioned when the experimental conditions for the printing of the samples are described, although current existing data reveal that their influence on the final characteristics of the print should not be neglected. In order to point out the importance of this influential parameter, a natural and a black-colored PLA filament, produced by the same manufacturer, were selected. The dimensional accuracy, tensile strength, and friction properties of the samples were analyzed and compared for printing temperatures ranging from 200 C up to 240 C. The experimental results clearly showed different characteristics depending on the polymer color of samples printed under the same conditions. Therefore, the optimization of the FFF process parameters for the 3D-printing of PLA should always start with the proper selection of the type of the PLA material, regarding both its color and the fabricant.
For proton exchange membrane water electrolysis (PEMWE) to become competitive, the cost of stack components, such as bipolar plates (BPP), needs to be reduced. This can be achieved by using coated low-cost materials, such as copper as alternative to titanium. Herein we report on highly corrosion-resistant copper BPP coated with niobium. All investigated samples showed excellent corrosion resistance properties, with corrosion currents lower than 0.1 µA cm−2 in a simulated PEM electrolyzer environment at two different pH values. The physico-chemical properties of the Nb coatings are thoroughly characterized by scanning electron microscopy (SEM), electrochemical impedance spectroscopy (EIS), X-ray photoelectron spectroscopy (XPS), and atomic force microscopy (AFM). A 30 µm thick Nb coating fully protects the Cu against corrosion due to the formation of a passive oxide layer on its surface, predominantly composed of Nb2O5. The thickness of the passive oxide layer determined by both EIS and XPS is in the range of 10 nm. The results reported here demonstrate the effectiveness of Nb for protecting Cu against corrosion, opening the possibility to use it for the manufacturing of BPP for PEMWE. The latter was confirmed by its successful implementation in a single cell PEMWE based on hydraulic compression technology.
Third-party tracking is a common and broadly used technique on the Web. Different defense mechanisms have emerged to counter these practices (e.g. browser vendors that ban all third-party cookies). However, these countermeasures only target third-party trackers and ignore the first party because the narrative is that such monitoring is mostly used to improve the utilized service (e.g. analytical services). In this paper, we present a large-scale measurement study that analyzes tracking performed by the first party but utilized by a third party to circumvent standard tracking preventing techniques. We visit the top 15,000 websites to analyze first-party cookies used to track users and a technique called “DNS CNAME cloaking”, which can be used by a third party to place first-party cookies. Using this data, we show that 76% of sites effectively utilize such tracking techniques. In a long-running analysis, we show that the usage of such cookies increased by more than 50% over 2021.