Design of a new Ni@NCNT/graphene hybrid structured catalyst for high-performance electrochemical CO reduction: unravelling the roles of N-doping.

Doping strategies have been recognized as effective approaches for developing cost-effective and durable catalysts with enhanced reactivity and selectivity in the electrochemical synthesis of value-added compounds directly from CO. However, the reaction mechanism and the specific roles of heteroatom doping, such as N doping, in advancing the CO reduction reaction are still controversial due to the lack of precise control of catalyst surface microenvironments. In this study, we investigated the effects of N doping on the performances for electrochemically converting CO to CO over Ni@NCNT/graphene hybrid structured catalysts (Ni@NCNT/Gr).

A generative model for inorganic materials design.

The design of functional materials with desired properties is essential in driving technological advances in areas like energy storage, catalysis, and carbon capture. Generative models provide a new paradigm for materials design by directly generating novel materials given desired property constraints, but current methods have low success rate in proposing stable crystals or can only satisfy a limited set of property constraints . Here, we present MatterGen, a model that generates stable, diverse inorganic materials across the periodic table and can further be fine-tuned to steer the generation towards a broad range of property constraints.

A practical fluorometric and colorimetric dual-mode sensing platform based on two-dimensional porous organic nanosheets for rapid determination of trifluralin.

Trifluralin, a widely used dinitroaniline herbicide, poses significant toxic risks, necessitating the development of rapid detection methods for food safety. In this study, we prepared ultrathin two-dimensional triphenylamine porous organic nanosheets (TPA-PONs) through a facile liquid-phase exfoliation process. The TPA-PONs, characterized by their exceptional fluorescence properties and nanoscale thickness (1.

A multi-constraint representation learning model for identification of ovarian cancer with missing laboratory indicators.

Objectives: To evaluate the performance of a multi-constraint representation learning classification model for identifying ovarian cancer with missing laboratory indicators.

Methods: Tabular data with missing laboratory indicators were collected from 393 patients with ovarian cancer and 1951 control patients. The missing ovarian cancer laboratory indicator features were projected to the latent space to obtain a classification model using the representational learning classification model based on discriminative learning and mutual information coupled with feature projection significance score consistency and missing location estimation.

Longitudinal cardiac imaging for assessment of myocardial injury in non-hospitalised community-dwelling individuals after COVID-19 infection: the Rotterdam Study.

Background: The aim of this study was to assess the presence of myocardial injury after COVID-19 infection and to evaluate the relation between persistent cardiac symptoms after COVID-19 and myocardial function in participants with known cardiovascular health status before infection.

Methods: In the prospective population-based Rotterdam Study cohort, echocardiography and cardiovascular magnetic resonance (CMR) were performed among participants who recovered from COVID-19 at home within 2 years prior to inclusion in the current study. Persistent cardiac symptoms comprised only self-reported symptoms of chest pain, dyspnoea or palpitations lasting >4 weeks after COVID-19 infection.