Catalytic N−H bond activation and breaking by well‐defined molecular complexes or their heterogeneous analogues is considered to be a challenge in chemical science. Metal(0) nanoparticles catalytically decompose NH3; they are, however, ill defined and contain a range of contiguous metal sites with varying coordination numbers and catalytic properties. So far, no well‐defined/molecular Mn+‐containing materials have been demonstrated to break strong N−H bonds catalytically, especially in NH3, the molecule with the strongest N−H bonds. Recently, noncatalytic activation of NH3 with the liberation of molecular H2 on an organometallic molybdenum complex was demonstrated. Herein, we show the catalytic activation and breaking of N−H bonds on a singly dispersed, well‐defined, and highly thermally resistant (even under reducing environments) CoII1O4 site of a heterogeneous catalyst for organic (ethylamine) and inorganic (NH3, with the formation of N2 and H2) molecules. The single‐site material serves as a viable precursor to ultrasmall (2.7 nm and less) silica‐supported cobalt nanoparticles; thus, we directly compare the activity of isolated cationic cobalt sites with small cobalt nanoparticles. Density functional theory (DFT) calculations suggest a unique mechanism involving breaking of the N−H bonds in NH3 and N−N coupling steps taking place on a Co1O4 site with the formation of N2H4, which then decomposes to H2 and N2H2; N2H2 subsequently decomposes to H2 and N2. In contrast, Co1N4 sites are not catalytically active, which implies that the ligand environment around a single atom of a heterogeneous catalyst largely controls reactivity. This may open a new chapter for the design of well‐defined heterogeneous materials for N−H bond‐activation reactions.

Healthcare is one of the most promising domains for the application of Internet of Things- (IoT-) based technologies, where patients can use wearable or implanted medical sensors to measure medical parameters anywhere and anytime. The information collected by IoT devices can then be sent to the health care professionals, and physicians allow having a real-time access to patients’ data. However, besides limited batteries lifetime and computational power, there is spatio-temporal correlation, where unnecessary transmission of these redundant data has a significant impact on reducing energy consumption and reducing battery lifetime. Thus, this paper aims to propose a routing protocol to enhance energy-efficiency, which in turn prolongs the sensor lifetime. The proposed work is based on Energy Efficient Routing Protocol using Dual Prediction Model (EERP-DPM) for Healthcare using IoT, where Dual-Prediction Mechanism is used to reduce data transmission between sensor nodes and medical server if predictions match the readings or if the data are considered critical if it goes beyond the upper/lower limits of defined thresholds. The proposed system was developed and tested using MATLAB software and a hardware platform called “MySignals HW V2.” Both simulation and experimental results confirm that the proposed EERP-DPM protocol has been observed to be extremely successful compared to other existing routing protocols not only in terms of energy consumption and network lifetime but also in terms of guaranteeing reliability, throughput, and end-to-end delay.

No abstract

No abstract

No abstract