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Segmentation is required to quantify cellular structures in microscopic images. This typically requires their fluorescent labeling. Convolutional neural networks (CNNs) can detect these structures also in only transmitted light images. This eliminates the need for transgenic or dye fluorescent labeling, frees up imaging channels, reduces phototoxicity and speeds up imaging. However, this approach currently requires optimized experimental conditions and computational specialists. Here, we introduce "aiSEGcell" a user-friendly CNN-based software to segment nuclei and cells in bright field images. We extensively evaluated it for nucleus segmentation in different primary cell types in 2D cultures from different imaging modalities in hand-curated published and novel imaging data sets. We provide this curated ground-truth data with 1.1 million nuclei in 20,000 images. aiSEGcell accurately segments nuclei from even challenging bright field images, very similar to manual segmentation. It retains biologically relevant information, e.g. for demanding quantification of noisy biosensors reporting signaling pathway activity dynamics. aiSEGcell is readily adaptable to new use cases with only 32 images required for retraining. aiSEGcell is accessible through both a command line, and a napari graphical user interface. It is agnostic to computational environments and does not require user expert coding experience. Author summary: Fluorescence microscopy is the most widely used method to monitor cellular structures in space and time. Fluorescently labeling cellular structures is typically required to localize ("segment") them in electronic images for subsequent quantification. Deep learning approaches can detect these structures also in only bright field images. This eliminates the need for a fluorescent label, frees up imaging channels, speeds up imaging, and reduces the harmful effects of exposing cells to high intensity light. However, label free segmentation currently requires optimized experimental conditions and computational specialists. Therefore, we developed "aiSEGcell" a user-friendly deep learning-based software to segment nuclei and cells in only bright field images. We extensively evaluated aiSEGcell on different common experimental conditions and showed that biologically even sensitive relevant information is retained. Furthermore, we demonstrated that aiSEGcell is adaptable by retraining to new applications with very little required data. We make it accessible for users with no required expert coding experience in a wide range of computational environments. Finally, we openly share our very large imaging data sets to further the development of other segmentation approaches. [ABSTRACT FROM AUTHOR]

The direct conversion of syngas into hydrocarbons, catalyzed by a combination of metal oxide and acidic zeolite, has garnered increasing attention in the scientific community. The remarkable hydrocarbon selectivity achieved in the OX‐ZEO process is a key factor in this growing interest. This process involves two distinct steps: CO activation over the oxide and subsequent C−C coupling within the zeolite pores, connected by an unidentified oxygenate intermediate. In this study, we explored three proposed oxygenates as potential intermediates and compared their conversion within the OX‐ZEO process. Using a bifunctional ZnCr2O4/H‐MOR catalyst in a syngas‐fed batch reactor, we found that methanol (MeOH) and dimethyl ether (DME) produced a hydrocarbon distribution similar to syngas conversion. However, the conversion of acetic acid (AcOH) resulted in a notably distinct range of hydrocarbons, including short olefins not detected with other reagents. Our findings suggest that methanol/DME might serve as active intermediates in the OX‐ZEO process over the ZnCr2O4/H‐MOR catalyst. This study provides a deeper insight into the transformation of proposed intermediates, contributing to the identification of the bridging intermediate that links the two catalytic functions. [ABSTRACT FROM AUTHOR]

For a carbon-neutral society, the production of hydrogen as a clean fuel through water electrolysis is currently of great interest. Since water electrolysis is a laborious energetic reaction, it requires high energy to maintain efficient and sustainable production of hydrogen. Catalytic electrodes can reduce the required energy and minimize production costs. In this context, herein, a bifunctional electrocatalyst made from iron nickel sulfide (FeNi 2 S 4 [FNS]) for the overall electrochemical water splitting is introduced. Compared to Fe 2 NiO 4 (FNO), FNS shows a significantly improved performance toward both OER and HER in alkaline electrolytes. At the same time, the FNS electrode exhibits high activity toward the overall electrochemical water splitting, achieving a current density of 10 mA cm -2 at 1.63 V, which is favourable compared to previously published nonprecious electrocatalysts for overall water splitting. The long-term chronopotentiometry test reveals an activation followed by a subsequent stable overall cell potential at around 2.12 V for 20 h at 100 mA cm -2 ., (© 2024 The Authors. Small published by Wiley‐VCH GmbH.)

Electrocatalytic hydrogenations (ECH) enable the reduction of organic substrates upon usage of electric current and present a sustainable alternative to conventional processes if green electricity is used. Opposed to most current protocols for electrode preparation, this work presents a one-step binder- and additive-free production of silver- and copper-electroplated electrodes. Controlled adjustment of the preparation parameters allows for the tuning of catalyst morphology and its electrochemical properties. Upon optimization of the deposition protocol and carbon support, high faradaic efficiencies of 93 % for the ECH of the Vitamin A- and E-synthon 2-methyl-3-butyn-2-ol (MBY) are achieved that can be maintained at current densities of 240 mA cm -2 and minimal catalyst loadings of 0.2 mg cm -2 , corresponding to an unmatched production rate of 1.47 kg MBE  g cat -1  h -1 . For a continuous hydrogenation process, the protocol can be directly transferred into a single-pass operation mode giving a production rate of 1.38 kg MBE  g cat -1  h -1 . Subsequently, the substrate spectrum was extended to a total of 17 different C-C-, C-O- and N-O-unsaturated compounds revealing the general applicability of the reported process. Our results lay an important groundwork for the development of electrochemical reactors and electrodes able to directly compete with the palladium-based thermocatalytic state of the art., (© 2023 The Authors. Chemistry - A European Journal published by Wiley-VCH GmbH.)