A sustainable CVD approach for ZrN as a potential catalyst for nitrogen reduction reaction.

In pursuit of developing alternatives for the highly polluting Haber-Bosch process for ammonia synthesis, the electrocatalytic nitrogen reduction reaction (NRR) using transition metal nitrides such as zirconium mononitride (ZrN) has been identified as a potential pathway for ammonia synthesis. In particular, specific facets of ZrN have been theoretically described as potentially active and selective for NRR. Major obstacles that need to be addressed include the synthesis of tailored catalyst materials that can activate the inert dinitrogen bond while suppressing hydrogen evolution reaction (HER) and not degrading during electrocatalysis. To tackle these challenges, a comprehensive understanding of the influence of the catalyst's structure, composition, and morphology on the NRR activity is required. This motivates the use of metal-organic chemical vapor deposition (MOCVD) as the material synthesis route as it enables catalyst nanoengineering by tailoring the process parameters. Herein, we report the fabrication of oriented and facetted crystalline ZrN thin films employing a single source precursor (SSP) MOCVD approach on silicon and glassy carbon (GC) substrates. First principles density functional theory (DFT) simulations elucidated the preferred decomposition pathway of SSP, whereas ab initio molecular dynamics simulations show that ZrN at room temperature undergoes surface oxidation with ambient O2, yielding a Zr-O-N film, which is consistent with compositional analysis using Rutherford backscattering spectrometry (RBS) in combination with nuclear reaction analysis (NRA) and X-ray photoelectron spectroscopy (XPS) depth profiling. Proof-of-principle electrochemical experiments demonstrated the applicability of the developed ZrN films on GC for NRR and qualitatively hint towards a possible activity for the electrochemical NRR in the sulfuric acid electrolyte.

The STOIC2021 COVID-19 AI challenge: Applying reusable training methodologies to private data.

Challenges drive the state-of-the-art of automated medical image analysis. The quantity of public training data that they provide can limit the performance of their solutions. Public access to the training methodology for these solutions remains absent. This study implements the Type Three (T3) challenge format, which allows for training solutions on private data and guarantees reusable training methodologies. With T3, challenge organizers train a codebase provided by the participants on sequestered training data. T3 was implemented in the STOIC2021 challenge, with the goal of predicting from a computed tomography (CT) scan whether subjects had a severe COVID-19 infection, defined as intubation or death within one month. STOIC2021 consisted of a Qualification phase, where participants developed challenge solutions using 2000 publicly available CT scans, and a Final phase, where participants submitted their training methodologies with which solutions were trained on CT scans of 9724 subjects. The organizers successfully trained six of the eight Final phase submissions. The submitted codebases for training and running inference were released publicly. The winning solution obtained an area under the receiver operating characteristic curve for discerning between severe and non-severe COVID-19 of 0.815. The Final phase solutions of all finalists improved upon their Qualification phase solutions.

Application of Ion Chromatography for the Reliable Quantification of Ammonium in Electrochemical Ammonia Synthesis Experiments: A Practical Guide.

Assessing novel electrocatalysts for the electrochemical ammonia synthesis (EAS) requires reliable quantitative trace analysis of electrochemically produced ammonia to infer activity and selectivity. This study concerns the development of an ion chromatography (IC) method for quantitative trace analysis of ammonium in 0.1 M sulfuric acid electrolyte, which is applied to EAS gas-diffusion electrode (GDE) experiments with commercial chromium nitride as electrocatalyst. The developed IC method is highly sensitive, versatile, and reliable, achieving a limit of quantification (LOQ) of 6 µg l-1 (6 ppbmol ) ammonium. The impacts of the sample matrix, dilution, and neutralization, as well as contamination, on the quantitative analysis by IC are analyzed. Experimental constraints result in an effective LOQ including dilution of 60 µg l-1 for the determination of ammonium in 0.1 M sulfuric acid electrolyte, owing to necessary sample dilution. The practical guide presented herein is intended to be very relevant for the field of EAS as a guideline and applicable to a broad range of catalyst systems and ion chromatography devices.