RESUMO
The synthesis and reactivity of an air and water stable Bicyclic (alkyl)(amino)carbene (BICAAC) stabilized phosphenium cation (1) is reported. Air and water stable phosphenium cation are rare in the literature. Compound 1 is obtained by reaction of BICAAC with Ph2PCl in THF followed by anion exchange with LiOTf. The reduction and oxidation of 1 yielded corresponding α-radical phosphine species (2) and BICAAC stabilized phosphenium oxide (3) respectively. All compounds are well characterized by single crystal X-ray diffraction studies. The Lewis acidity of compounds 1 and 3 are determined by conducting fluoride ion affinity experiments using UV-Vis spectrophotometry and multinuclei NMR spectroscopy. Compounds 1 and 3 exhibited selective binding to fluoride anion but did not interact with other halides (Cl- and Br-). Quantum chemical calculations were performed to understand the structure and nature of bonding interactions in these compounds, as well as to comprehend the specific bonding affinity to fluoride over other halide ions.
RESUMO
Preterm babies in the Neonatal Intensive Care Unit (NICU) have to undergo continuous monitoring of their cardiac health. Conventional monitoring approaches are contact-based, making the neonates prone to various nosocomial infections. Video-based monitoring approaches have opened up potential avenues for contactless measurement. This work presents a pipeline for remote estimation of cardiopulmonary signals from videos in NICU setup. We have proposed an end-to-end deep learning (DL) model that integrates a non-learning-based approach to generate surrogate ground truth (SGT) labels for supervision, thus refraining from direct dependency on true ground truth labels. We have performed an extended qualitative and quantitative analysis to examine the efficacy of our proposed DL-based pipeline and achieved an overall average mean absolute error of 4.6 beats per minute (bpm) and root mean square error of 6.2 bpm in the estimated heart rate.