Organic Phosphoric Acid Doped Polyaniline-Coupled g-C3 N4 for Enhancing Fire Safety of Intumescent Flame-Retardant Epoxy Resin.

Two kinds of polyaniline coupled graphitized carbon nitride nanosheets doped with different organic phosphoric acids (CP@PA, with phytic acid; CP@NP, with amino trimethyl phosphonic acid) are developed by in-situ polymerization. According to the analysis of the section morphology and element distribution of epoxy resin (EP) composites, although CP@PA and CP@NP show completely different morphology, they can significantly enhance the dispersion of graphitized carbon nitride nanosheets in EP. Moreover, the different oxidation states of phosphorus contained in the CP@PA and CP@NP lead to varying effects on the fire safety of EP. The flame retardancy Index (FRI) is a dimensionless index to evaluate the performance of flame retardants. When used as a flame retardant, CP@NP (FRI = 3.22) is better than CP@PA (FRI = 1.29) in flame retardant, especially in suppressing thermal hazards. As a synergist of intumescent flame retardants (IFR), CP@PA (FRI = 26.12) is most effective in improving the comprehensive fire safety property of EP and achieves an "Excellent" rating. Therefore, two different flame-retardant mechanisms of CP@PA and CP@NP are summarized by analyzing the combustion behavior and changes of condensed phase. In summary, this research may be helpful to the design of nano synergies for IFR systems.

Backscatter-Assisted Collision-Resilient LoRa Transmission.

Low-power wide-area networks (LPWANs), such as LoRaWAN, play an essential role and are expanding quickly in miscellaneous intelligent applications. However, the collision problem is also expanding significantly with the mass promotion of LPWAN nodes and providing collision-resilient techniques that are urgently needed for these applications. This paper proposes BackLoRa, a lightweight method that enables collision-resilient LoRa transmission with extra propagation information provided by backscatter tags. BackLoRa uses several backscatter tags to create multipath propagation features related to the LoRa nodes' positions and offers a lightweight algorithm to extract the feature and correctly distinguish each LoRa node. Further, BackLoRa proposes a quick-phase acquisition algorithm with low time complexity that can carry out the iterative recovery of symbols for robust signal reconstructions in low-SNR conditions. Finally, comprehensive experiments were conducted in this study to evaluate the performance of BackLoRa systems. The experimental results show th compared with the existing scheme, our scheme can reduce the symbol error rate from 65.3% to 5.5% on average and improve throughput by 15× when SNR is -20 dB.