Layer-by-layer immobilization of carbon dots fluorescent nanomaterials on single optical fiber

We report within this paper the development of a fiber-optic based sensor for Hg(II) ions. Fluorescent carbon nanoparticles were synthesized by laser ablation and functionalized with PEG200 and N-acetyl-l-cysteine so they can be anionic in nature. This characteristic facilitated their deposition by the layer-by-layer assembly method into thin alternating films along with a cationic polyelectrolyte, poly(ethyleneimine). Such films could be immobilized onto the tip of a glass optical fiber, allowing the construction of an optical fluorescence sensor. When immobilized on the fiber-optic tip, the resultant sensor was capable of selectively detecting sub-micromolar concentrations of Hg(II) with an increased sensitivity compared to carbon dot solutions. The fluorescence of the carbon dots was quenched by up to 44% by Hg(II) ions and interference from other metal ions was minimal.

Optical fiber sensor for Hg(II) based on carbon dots

An optical fiber sensor for Hg(II) in aqueous solution based on sol–gel immobilized carbon dots nanoparticles functionalized with PEG200 and N-acetyl-l-cysteine is described. This sol–gel method generated a thin (about 750 nm), homogenous and smooth (roughness of 2.7±0.7 a˚ ) filmthat immobilizes the carbon dots and allows reversible sensing of Hg(II) in aqueous solution. A fast (less than 10 s), reversible and stable (the fluorescence intensity measurements oscillate less than 1% after several calibration cycles) sensor system was obtained. The sensor allow the detection of submicron molar concentrations of Hg(II) in aqueous solution. The fluorescence intensity of the immobilized carbon dots is quenched by the presence of Hg(II) with a Stern-Volmer constant (pH = 6.8) of 5.3×105M−1.

Quantum-based Particle Swarm Optimization Application to Studies of Aggregated Consumption Shifting and Generation Scheduling in Smart Grids

Demand response programs and models have been developed and implemented for an improved performance of electricity markets, taking full advantage of smart grids. Studying and addressing the consumers’ flexibility and network operation scenarios makes possible to design improved demand response models and programs. The methodology proposed in the present paper aims to address the definition of demand response programs that consider the demand shifting between periods, regarding the occurrence of multi-period demand response events. The optimization model focuses on minimizing the network and resources operation costs for a Virtual Power Player. Quantum Particle Swarm Optimization has been used in order to obtain the solutions for the optimization model that is applied to a large set of operation scenarios. The implemented case study illustrates the use of the proposed methodology to support the decisions of the Virtual Power Player in what concerns the duration of each demand response event.