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In recent years, noble metal-based solar absorbers have been extensively studied as their pronounced plasmonic resonances and high solar-to-thermal conversion efficiency. However, the high cost of noble metals is the unavoidable roadblock restricting the way towards scalability. In this work, we report a nickel-based photothermal absorbers, which is capable of realizing an average solar absorption of ∼97% in the range of 400–2500 nm originating from relatively weaker collective plasmonic resonances but more pronounced single electron excitation. Importantly, it is easily fabricated via the straightforward physical deposition and cost-effective with a raw material price of ∼0.3% gold and ∼20% of silver. We used it for interfacial solar vapor generation and realized an evaporation rate of ∼0.9 kg m−2 h−1 under one sun, almost comparable to the counterparts made from noble metals. The excellent performance combined with the cost effective and scalable fabrication process makes it be a promising candidate for mass off-grid solar desalination.

This paper employs dispersed nanoparticles (NPs) to build an adaptive neuro-fuzzy system (ANFIS) for predicting their thermal conductivity (TC) and viscosity according to the most important input data including concentration, size, the thickness of the interfacial layer, and intensive properties of NPs. In this regard, we gather an extensive and comprehensive data set from different sources. Here, the ANFIS model factors are optimized by using the particle swarm optimization (PSO) technique. Afterward, the obtained results are compared with previously published models which did a better job in predicting target values. In the following, to investigate the validity of our proposed model, statistical and graphical techniques are employed and it was proved that this model is efficient to evaluate the output values. Amounts of results obtained from the PSO-ANFIS model evaluation are 0.988 and 0.985 for the R2 and 0.0156 and 0.0876 for root mean squared error (RMSE) of TC ratio and viscosity ratio values, respectively, letting out a valid forecast of targets. Finally, by performing various statistical analyzes, it can be said that this model shows a high ability to predict target values and can be considered a good alternative to previous models.

We herein report a facile one-pot synthesis of MnO/N-doped carbon (NâC) composites via a sustainable cotton-template glycineânitrate combustion synthesis to yield superior anode materials for Li ion batteries. MnO nanoparticles with several nanometers were well-embedded in a porous N-doped carbon matrix. It displays the unique characteristics, including the shortened Li+-ion transport path, increased contact areas with the electrolyte solution, inhibited volume changes and agglomeration of nanoparticles, as well as good conductivity and structural stability during the cycling process, thereby benefiting the superior cycling performance and rate capability. This favorable electrochemical performance of obtained MnO/NâC composites via a one-pot biomass-templated glycine/nitrate combustion synthesis renders the suitability as anode materials for Li-ion batteries. Keywords: Biomass, Cotton, Manganese oxide, Lithium ion battery, Porous carbon