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Here, we report a facile approach for synthesizing M–N–C catalysts (M = Co, Fe, Ni) at a commercial scale without employing organic solvents. Our characterization efforts indicate that single atomic catalysts with high surface areas were successfully obtained. Electrochemical measurements demonstrate that, among the three synthesized catalysts, Ni–N–C exhibits the highest performance in the electrochemical CO2 reduction reaction (CO2RR) to carbon monoxide (CO), affording 80% Faradaic efficiency (FE) of CO production at –0.49 VRHE with a turnover frequency (TOF) of 57,379 h–1. Large-scale synthesis coupled with high performance allows moving forward with the practical implementation of M–N–C catalysts for industrially relevant CO2RR.

This manuscript reports the electrosynthesis of ammonia from nitrate catalysed Cu derived from Cu2O materials. Cu2O (111) and (100) preferential grain orientations were prepared through electrodeposition. Cu derived from Cu2O (111) is more active and selective for ammonia formation than Cu2O (100) derived Cu. The highest faradaic efficiency (FE) was achieved for both catalysts at −0.3 V vs RHE, with Cu derived from Cu2O (111) reaching up to 80 %. Additional measurements with quasi-in situ X-ray photoelectron spectroscopy and in situ Raman spectroscopy revealed that Cu0 is the active phase during the reaction. The stability of the catalysts was examined by ex situ methods such as SEM, XRD and ICP elemental analysis. The catalysts underwent severe morphological changes as a function of the applied potential and the reaction time, most likely due to the dissolution and redeposition of Cu. After 3 hours of reaction, the entire surface of the catalysts was reconstructed into nanoneedles. The FE after 3 hours remained higher for the Cu derived from Cu2O (111), suggesting that the activity is dependent on the initial structure and the different rates of dissolution and re-deposition.

The forthcoming assembly of the adultDrosophila melanogastercentral brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here, we create a leaky integrate-and-fire computational model of the entireDrosophilabrain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of theDrosophilabrain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.

This is a case for the Speciality Certificate Examination of a rare benign skin tumour and the associated histopathological features along with the histopathological features of differential diagnoses.