Accumulation of Liquid Byproducts in an Electrolyte as a Critical Factor That Compromises Long-Term Functionality of CO2-to-C2H4 Electrolysis.

Electrochemical conversion of CO2 using Cu-based gas diffusion electrodes opens the way to green chemical production as an alternative to thermocatalytic processes and a storage solution for intermittent renewable electricity. However, diverse challenges, including short lifetimes, currently inhibit their industrial usage. Among well-studied determinants such as catalyst characteristics and electrode architecture, possible effects of byproduct accumulation in the electrolyte as an operational factor have not been elucidated. This work quantifies the influence of ethanol, n-propanol, and formate accumulation on selectivity, stability, and cell potential in a CO2-to-C2H4 electrolyzer. Alcohols accelerated flooding by degrading the hydrophobic electrode characteristics, undermining selective and stable ethylene formation. Furthermore, high alcohol concentrations triggered the catalyst layer's abrasion and structural disfigurements in the Nafion 117 membrane, leading to high cell potentials. Therefore, continuous removal of alcohols from the electrolyte medium or substantial modifications in the cell components must be considered to ensure long-term performing CO2-to-C2H4 electrolyzers.

Gas evolution in electrochemical flow cell reactors induces resistance gradients with consequences for the positioning of the reference electrode.

With the transfer of the electrochemical CO2-reduction from academic labs towards industrial application, one major factor is the increase in current density. This can be achieved via the usage of a gas diffusion electrode. It allows for electrochemical reactions at the three-phase boundary between gaseous CO2, liquid electrolyte and electrocatalyst. Thus, current densities in commercially relevant magnitudes of 200 mA cm-2 and beyond can be reached. However, when increasing the current density one faces a new set of challenges, unknown from low current experiments. Here, we address the issue of gas evolution causing a local increase in resistance and the impact on the operation of flow cells with gas diffusion electrodes. We set up a simple simulation model and compared the results with experiments on a real setup. As a result, the gas evolution's strong impact on current-, potential- and resistance-distributions along the flow axis can be described. Main consequence is that the positioning of the reference electrode has a significant effect on the locally measured IR-drop and thus on the measured or applied potential. Therefore, data from different setups must be compared with great care, especially with respect to the potentials, on which the cell is operated.

Automatic Tissue Differentiation Based on Confocal Endomicroscopic Images for Intraoperative Guidance in Neurosurgery.

Diagnosis of tumor and definition of tumor borders intraoperatively using fast histopathology is often not sufficiently informative primarily due to tissue architecture alteration during sample preparation step. Confocal laser microscopy (CLE) provides microscopic information of tissue in real-time on cellular and subcellular levels, where tissue characterization is possible. One major challenge is to categorize these images reliably during the surgery as quickly as possible. To address this, we propose an automated tissue differentiation algorithm based on the machine learning concept. During a training phase, a large number of image frames with known tissue types are analyzed and the most discriminant image-based signatures for various tissue types are identified. During the procedure, the algorithm uses the learnt image features to assign a proper tissue type to the acquired image frame. We have verified this method on the example of two types of brain tumors: glioblastoma and meningioma. The algorithm was trained using 117 image sequences containing over 27 thousand images captured from more than 20 patients. We achieved an average cross validation accuracy of better than 83%. We believe this algorithm could be a useful component to an intraoperative pathology system for guiding the resection procedure based on cellular level information.

Fractional exhaled nitric oxide measurement with a handheld device.

A sensing system for fractional exhaled nitric oxide (FeNO) measurement is presented, which is characterized by a compact setup and a cost potential to be made available for the patient at home. The sensing is based on the work function measurement of a phthalocyanine-type sensing material, which is shown to be sufficiently sensitive for NO(2) in the ppb range. The transducer used to measure the work function is a field effect transistor with a suspended gate electrode. Selectivity is given with respect to other breath components including typically metabolic by-products. The measurement system includes breath treatments in a simple setup, which essentially are dehumidification and a quantitative conversion of NO to NO(2) with a conversion rate of approx. 95%, using a disposable oxidation catalyst. The accomplishment of the correct exhalation maneuver and feeding of the suited portion of exhaled air to the sensor is provided by breath sampling means. The sensor is not gas consuming. This allows us to fill the measurement chamber once, instead of establishing a gas flow for the measurement. This feature simplifies the device architecture. In this paper, we report on sensor characteristics, system architecture and measurement with artificial breath-gas as well as with human breath with the device.