Bacterial adsorption on mineral surface is one of the key steps in bioleaching process. The bacteria adsorb on the mineral surface via the extracellular polymeric substances (EPS) layer. In this paper, the behavior of glucuronic acid, one of the key substances in EPS layer, adsorbed on the pyrite surface is studied using DFT and electrochemical methods. Adsorption capacity of glucuronic acid is stronger than that of water. Glucuronic acid adsorbs on pyrite surfaces and it follows a mixed type of interactions (physisorption and chemisorption). Adsorption of glucuronic acid on pyrite surface followed Langmuir’s adsorption isotherm with adsorption standard free energy of –27.67kJ mol–1. The structural and electronic parameters were calculated and discussed.

The individual identification of communication emitters is a process of identifying different emitters based on the radio frequency fingerprint features extracted from the received signals. Due to the inherent non-linearity of the emitter power amplifier, the fingerprints provide distinguishing features for emitter identification. In this study, approximate entropy is introduced into variational mode decomposition, whose features performed in each mode which is decomposed from the reconstructed signal are extracted while the local minimum removal method is used to filter out the noise mode to improve SNR. We proposed a semi-supervised dimensionality reduction method named exponential semi-supervised discriminant analysis in order to reduce the high-dimensional feature vectors of the signals, and LightGBM is applied to build a classifier for communication emitter identification. The experimental results show that the method performs better than the state-of-the-art individual communication emitter identification technology for the steady signal data set of radio stations with the same plant, batch and model.