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This work compares various existing rough-wall models on a large collection of rough surfaces with different characteristics and studies the potential of these models in accommodating new datasets. We consider three empirical roughness correlations, two physics-based models, and one data-driven machine-learning model on 68 rough surfaces inside and outside the Roughness Database1. Results show that correlation-type models and machine-learning models do not extrapolate outside the dataset against which they are calibrated or trained. In contrast, the physics-based sheltering model performs well in extrapolation. Recalibrating a roughness correlation against a large dataset proves unfruitful. However, retraining a machine learning model yields good results. We do not pursue further retraining and recalibrating of a physics-based model, as it requires new physical insights. Overall, our findings suggest that a universal rough-wall model is yet to be found. The capability of extrapolation will likely come from incorporating physics. Data, on the other hand, benefits machine learning models.

 

The Earth’s atmosphere is composed of an enormous variety of chemical species associated with trace gases and aerosol particles whose composition and chemistry have critical impacts on the Earth’s climate, air quality, and human health. Mass spectrometry analysis as a powerful and popular analytical technique has been widely developed and applied in atmospheric chemistry for decades. Mass spectrometry allows for effective detection, identification, and quantification of a broad range of organic and inorganic chemical species with high sensitivity and resolution. In this review, we summarize recently developed mass spectrometry techniques, methods, and applications in atmospheric chemistry research in the past several years. Specifically, new developments of ion-molecule reactors, various soft ionization methods, and unique coupling with separation techniques are highlighted. The new mass spectrometry applications in laboratory studies and field measurements focus on improving the detection limits for traditional and emerging volatile organic compounds, characterizing multiphase highly oxygenated molecules, and monitoring particle bulk and surface compositions. 

Electrochemical upcycling of nitrate into ammonia at ambient conditions offers a sustainable synthesis pathway that can complement the current industrial NH₃production from the Haber–Bosch process. One of the key rate‐limiting steps is the effective desorption of gaseous or interfacial bubble products, mainly NH₃with some minor side products of nitrogen and hydrogen, from the electrode surfaces to sustain available sites for the NO₃⁻reduction reaction. To facilitate the gaseous product desorption from the catalytic sites, hydrophobic polytetrafluoroethylene (PTFE) nanoparticles are blended within a CuO catalyst layer, which is shown to eliminate the undesirable accumulation and blockage of electrode surfaces and largely decouples the electron‐ and phase‐transfer processes. The NH₃partial current density normalized by the electrochemically active surface area (ECSA) increases by nearly a factor of 17.8 from 11.4 ± 0.1 to 203.3 ± 1.8 mA cm⁻²_ECSA. The DFT and ab‐initio molecular dynamics simulations suggest that the hydrophobic PTFE nanoparticles may serve as segregated islands to enhance the spillover and transport the gaseous products from electrocatalysts to the PTFE. Thus, a higher ammonia transfer is achieved for the mixed PTFE/CuO electrocatalyst. This new and simple strategy is expected to act as inspiration for future electrochemical gas‐evolving electrode design.

 

Recent advances and achievements of artificial intelligence (AI) as well as deep and graph learning models have established their usefulness in biomedical applications, especially in drug–drug interactions (DDIs). DDIs refer to a change in the effect of one drug to the presence of another drug in the human body, which plays an essential role in drug discovery and clinical research. DDIs prediction through traditional clinical trials and experiments is an expensive and time-consuming process. To correctly apply the advanced AI and deep learning, the developer and user meet various challenges such as the availability and encoding of data resources, and the design of computational methods. This review summarizes chemical structure based, network based, natural language processing based and hybrid methods, providing an updated and accessible guide to the broad researchers and development community with different domain knowledge. We introduce widely used molecular representation and describe the theoretical frameworks of graph neural network models for representing molecular structures. We present the advantages and disadvantages of deep and graph learning methods by performing comparative experiments. We discuss the potential technical challenges and highlight future directions of deep and graph learning models for accelerating DDIs prediction.