Electrolyte design for the manipulation of gas bubble detachment during hydrogen evolution reaction

During electrochemical gas evolution reactions, the continuous and vigorous formation of gas bubbles hugely impacts the efficiency of the underlying electrochemical processes. In particular, enhancing the detachment of gas bubbles from the electrode surface has emerged as an effective strategy to improve reaction efficiency. In this study, we demonstrate that the detachment of H2 gas bubbles can be controlled by the electrolyte composition, which can be optimized. We employ a well-defined Pt microelectrode and utilize electrochemical oscillation analysis to elucidate the features of H2 gas bubble detachment. Our investigation explores how the behaviour of H2 gas bubbles responds to variations in electrolyte composition and concentration. The coalescence efficiency of electrochemically generated microbubbles, a critical factor determining the mode of H2 gas bubble detachment (random detachment vs. single H2 gas bubble detachment), is profoundly influenced by the electrolyte composition. Specifically, coalescence efficiency follows the Hofmeister series concerning anions and coalescence is consistently inhibited in the presence of alkali metal cations. Furthermore, we establish a comprehensive model that accounts for both thermal and solutal Marangoni effects, allowing us to rationalize the trend of detachment size and period of single H2 gas bubbles under various conditions.</p

Linescan microscopy data to extract diffusion coefficient of a fluorescent species using a commercial confocal microscope

We are grateful to the Max Delbrück Center for Molecular Medicine in the Helmholtz Association for core support and funding. P.A. and M.J.L. would like to acknowledge funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project-ID 421152132-SFB1423 subproject C03.We report here on the measurement of the diffusion coefficient of fluorescent species using a commercial microscope possessing a resonant scanner. Sequential linescans with a rate of up to 12 kHz yield a temporal resolution of 83 μs, making the setup amenable to measure diffusion rates over a range covering at least three orders of magnitude, from 100 μm2/s down to 0.1 μm2/s. We share representative data sets covering (i) the diffusion of a dye molecule, observed in media of different viscosities and (ii) the diffusion of a prototypical membrane receptor.  The data can be valuable for researchers interested in the rapid diffusion properties of nuclear, cytosolic or membrane bound proteins fused to fluorescent tags.Publisher PDFPeer reviewe

Algorithm-driven Artifacts in median polish summarization of Microarray data

<p>Abstract</p> <p>Background</p> <p>High-throughput measurement of transcript intensities using Affymetrix type oligonucleotide microarrays has produced a massive quantity of data during the last decade. Different preprocessing techniques exist to convert the raw signal intensities measured by these chips into gene expression estimates. Although these techniques have been widely benchmarked in the context of differential gene expression analysis, there are only few examples where their performance has been assessed in respect to coexpression-based studies such as sample classification.</p> <p>Results</p> <p>In the present paper we benchmark the three most used normalization procedures (MAS5, RMA and GCRMA) in the context of inter-array correlation analysis, confirming and extending the finding that RMA and GCRMA consistently overestimate sample similarity upon normalization. We determine that median polish summarization is responsible for generating a large proportion of these over-similarity artifacts. Furthermore, we show that most affected probesets show also internal signal disagreement, and tend to be composed by individual probes hitting different gene transcripts. We finally provide a correction to the RMA/GCRMA summarization procedure that massively reduces inter-array correlation artifacts, without affecting the detection of differentially expressed genes.</p> <p>Conclusions</p> <p>We propose tRMA as a modification of RMA to normalize microarray experiments for correlation-based analysis.</p

2nd Workshop on Evaluating Child Robot Interaction

Many researchers have started to explore natural interaction scenarios for children. No matter if these children are normally developing or have special needs, evaluating Child-Robot Interaction (CRI) is a challenge. To find methods that work well and provide reliable data is difficult, for example because commonly used methods such as questionnaires donot work well particularly with younger children. Previous research has shown that children need support in expressing how they feel about technology. Given this, researchers often choose time-consuming behavioral measures from observations to evaluate CRI. However, these are not necessarily comparable between studies and robots. This workshop aims to bring together researchers from differentdisciplines to share their experiences on these aspects. The main topics are methods to evaluate child-robot interaction design, methods to evaluate socially assistive child-robot interaction and multi-modal evaluation of child-robot interaction. Connected questions that we would like to tackle are for example: i) What are reliable metrics in CRI' ii) How can we overcome the pitfalls of survey methods in CRI' iii) How can we integrate qualitative approaches in CRI' iv) What are the best practices for in the wild studies with children? Looking across disciplinary boundaries, we want to discuss advantages and short-comings of using different evaluation methods in order to compile guidelines for future CRI research. This workshop is the second in a series that started at the International Conference on Social Robotics in 2015

SLocX: Predicting Subcellular Localization of Arabidopsis Proteins Leveraging Gene Expression Data

Despite the growing volume of experimentally validated knowledge about the subcellular localization of plant proteins, a well performing in silico prediction tool is still a necessity. Existing tools, which employ information derived from protein sequence alone, offer limited accuracy and/or rely on full sequence availability. We explored whether gene expression profiling data can be harnessed to enhance prediction performance. To achieve this, we trained several support vector machines to predict the subcellular localization of Arabidopsis thaliana proteins using sequence derived information, expression behavior, or a combination of these data and compared their predictive performance through a cross-validation test. We show that gene expression carries information about the subcellular localization not available in sequence information, yielding dramatic benefits for plastid localization prediction, and some notable improvements for other compartments such as the mitochondrion, the Golgi, and the plasma membrane. Based on these results, we constructed a novel subcellular localization prediction engine, SLocX, combining gene expression profiling data with protein sequence-based information. We then validated the results of this engine using an independent test set of annotated proteins and a transient expression of GFP fusion proteins. Here, we present the prediction framework and a website of predicted localizations for Arabidopsis. The relatively good accuracy of our prediction engine, even in cases where only partial protein sequence is available (e.g., in sequences lacking the N-terminal region), offers a promising opportunity for similar application to non-sequenced or poorly annotated plant species. Although the prediction scope of our method is currently limited by the availability of expression information on the ATH1 array, we believe that the advances in measuring gene expression technology will make our method applicable for all Arabidopsis proteins

Performance Enhancement of Electrocatalytic Hydrogen Evolution through Coalescence-Induced Bubble Dynamics

The evolution of electrogenerated gas bubbles during water electrolysis can significantly hamper the overall process efficiency. Promoting the departure of electrochemically generated bubbles during (water) electrolysis is therefore beneficial. For a single bubble, a departure from the electrode surface occurs when buoyancy wins over the downward-acting forces (e.g., contact, Marangoni, and electric forces). In this work, the dynamics of a pair of H2 bubbles produced during the hydrogen evolution reaction in 0.5 M H2SO4 using a dual platinum microelectrode system is systematically studied by varying the electrode distance and the cathodic potential. By combining high-speed imaging and electrochemical analysis, we demonstrate the importance of bubble-bubble interactions in the departure process. We show that bubble coalescence may lead to substantially earlier bubble departure as compared to buoyancy effects alone, resulting in considerably higher reaction rates at a constant potential. However, due to continued mass input and conservation of momentum, repeated coalescence events with bubbles close to the electrode may drive departed bubbles back to the surface beyond a critical current, which increases with the electrode spacing. The latter leads to the resumption of bubble growth near the electrode surface, followed by buoyancy-driven departure. While less favorable at small electrode spacing, this configuration proves to be very beneficial at larger separations, increasing the mean current up to 2.4 times compared to a single electrode under the conditions explored in this study.</p

Gene expression profiling in susceptible interaction of grapevine with its fungal pathogen Eutypa lata: Extending MapMan ontology for grapevine

<p>Abstract</p> <p>Background</p> <p>Whole genome transcriptomics analysis is a very powerful approach because it gives an overview of the activity of genes in certain cells or tissue types. However, biological interpretation of such results can be rather tedious. MapMan is a software tool that displays large datasets (e.g. gene expression data) onto diagrams of metabolic pathways or other processes and thus enables easier interpretation of results. The grapevine (<it>Vitis vinifera</it>) genome sequence has recently become available bringing a new dimension into associated research. Two microarray platforms were designed based on the TIGR Gene Index database and used in several physiological studies.</p> <p>Results</p> <p>To enable easy and effective visualization of those and further experiments, annotation of <it>Vitis vinifera </it>Gene Index (VvGI version 5) to MapMan ontology was set up. Due to specificities of grape physiology, we have created new pictorial representations focusing on three selected pathways: carotenoid pathway, terpenoid pathway and phenylpropanoid pathway, the products of these pathways being important for wine aroma, flavour and colour, as well as plant defence against pathogens. This new tool was validated on Affymetrix microarrays data obtained during berry ripening and it allowed the discovery of new aspects in process regulation. We here also present results on transcriptional profiling of grape plantlets after exposal to the fungal pathogen <it>Eutypa lata </it>using Operon microarrays including visualization of results with MapMan. The data show that the genes induced in infected plants, encode pathogenesis related proteins and enzymes of the flavonoid metabolism, which are well known as being responsive to fungal infection.</p> <p>Conclusion</p> <p>The extension of MapMan ontology to grapevine together with the newly constructed pictorial representations for carotenoid, terpenoid and phenylpropanoid metabolism provide an alternative approach to the analysis of grapevine gene expression experiments performed with Affymetrix or Operon microarrays. MapMan was first validated on an already published dataset and later used to obtain an overview of transcriptional changes in a susceptible grapevine – <it>Eutypa lata </it>interaction at the time of symptoms development, where we showed that the responsive genes belong to families known to be involved in the plant defence towards fungal infection (PR-proteins, enzymes of the phenylpropanoid pathway).</p

Influence of Solder Pads to PERC Solar Cells for Module Integration

AbstractThe majority of screen printed solar cells has silver pads at the rear side to enable soldering for the module manufacturing. The pads increase the recombination at the silicon/metal interface due to the absence of a back surface field (BSF) at the solder pads. This reduces the efficiency of full-area Al-BSF solar cells. For passivated emitter and rear cells (PERC), a large area fraction of the rear side is covered with the passivation layer. When using specially designed Ag pastes for the rear side of PERC cells, the passivation of this layer is maintained, and the rear recombination is reduced.A comparison of solar cells with and without solder pads confirms that there is no loss in solar cell performance, both cell types achieve an efficiency of 19.6%. We investigate the influence of solder pads to PERC solar cells by calculating the effective rear surface recombination. The calculations confirm that there is a loss in open circuit voltage of less than 2mV due to the solder pads.A 54-cell PERC PV module is manufactured. The cell-to-module loss reveals that the module process is still to be optimized. Comparable modules made from 9 solar cells lost less than 1% relative in all J-V parameters after a 1000h damp-heat test