Lignosulfonate in situ-modified reduced graphene oxide biosensors for the electrochemical detection of dopamine.

Lignosulfonate (LS), a biomass by-product from sulfite pulping and the paper-making industry, which has many excellent characteristics, such as renewable, environmentally friendly, amphiphilic nature, and especially the abundant content of hydrophilic functional groups in its architecture, making it highly reactive and can be used as a sensitive material in sensors to show changes in electrical signals. Herein, we report a one-step method to fabricate lignosulfonate-modified reduced graphene oxide (LS-rGO) green biosensors, which can be used for the sensitive electrochemical detection of dopamine without interference from uric acid and ascorbic acid. The modified LS molecular layers act as chemical-sensing layers, while the rGO planar sheets function as electric-transmitting layers in the as-assembled dopamine biosensors.

TRSWA-BP Neural Network for Dynamic Wind Power Forecasting Based on Entropy Evaluation.

The performance evaluation of wind power forecasting under commercially operating circumstances is critical to a wide range of decision-making situations, yet difficult because of its stochastic nature. This paper firstly introduces a novel TRSWA-BP neural network, of which learning process is based on an efficiency tabu, real-coded, small-world optimization algorithm (TRSWA). In order to deal with the strong volatility and stochastic behavior of the wind power sequence, three forecasting models of the TRSWA-BP are presented, which are combined with EMD (empirical mode decomposition), PSR (phase space reconstruction), and EMD-based PSR.