Herein, we investigated the chemical, electrochemical, and spectroscopic characteristics of humic acids (HAs) extracted from sediments of different origin [Ling Qiao river, Xi Xi wetland, Qi Zhen lake (QZ), and Hu Zhou pond in Zhejiang province, China], paying particular attention to their role in the enhancement of nitrate and FeOOH reduction. Notably, the highest C/N ratio (16.16), O/C ratio (1.89), and Fe content (11.57 g kg-1 sample) were observed for HAs extracted from QZ sediment. Cyclic voltammetry analyses confirmed that all HAs contained redox-active groups and exhibited redox potentials between -0.36 and -0.28 V vs. the standard hydrogen electrode. All HAs showed similar Fourier transform infrared spectra with variable absorption intensity, the spectra verified the presence of aromatic C=C, C–H, and C=O of quinone ketones group in HAs. Electron spin resonance suggested that quinone moieties within HAs are the redox-active centers. All HAs promoted the microbial reduction of nitrate and amorphous FeOOH by Shewanella oneidensis strain MR-1, achieving high nitrate reduction extents of 79–98.4%, compared to the biotic and abiotic control values of 29.6 and 0.006%, respectively. The corresponding extents of Fe(II) production equaled 43.25–60.5%, exceeding those of biotic and abiotic controls (28.5 and 0.005%, respectively). In addition to the highest C/N, O/C ratio, and Fe content, HA extracted from QZ sediment also exhibited the highest nitrate and FeOOH reduction performances. Although the proportion of organic redox-active carbon is small, the potential electron-mediating ability is not ignorable. HAs are redox active for enhancing microbial reduction of nitrate and amorphous FeOOH regardless of the location or texture of parent sediments, implying their great potential for acting as redox mediator in enhancing multiple microbial reduction, thereby affecting various biogeochemical processes (i.e., iron cycle, nitrogen cycle, etc.) as well as in situ remediation in anaerobic environment.

ABSTRACT:The effects of molecular weight on electroluminescent properties of the light emitting polymeric nanocomposite, poly(9,9-dioctylfluorene-alt-thiophene) with chemically bonded gold nanoparticles (PDOFT-Au), have been studied under a condition of constant polymer-to-gold weight ratio. The polymer, PDOFT, was first synthesized via the Suzuki cross-coupling reaction and then bonded to in situ-formed gold nanoparticles (AuNPs) via terminal thiol functional groups which had been generated during the quenching of polymerization. A series of PDOFT-Au's of various molecular weights have thus been synthesized. At a constant polymer-to-gold weight ratio, the average size of gold nanoparticles (AuNPs) increased with an increase in the molecular weight of PDOFT. Although an increased molecular weight led to a red shift in UV-absorption and PL spectra as well as an increased PL quantum efficiencies (U PL ) for all samples (both PDOFT and PDOFT-Au), the effect of AuNPs bonding became more noticeable when the molecular weight was higher. As for light emitting diode (LED) device fabrication, an increase in the molecular weight of PDOFT also led to a red shift in the EL spectra of the fabricated LED devices. Nevertheless, PDOFT-Au, compared with PDOFT, had a lower threshold voltage, an increased brightness and current density, and an improved photometric efficiency. Moreover, the photometric efficiency increased with an increase in the molecular weight of the polymer, from 0.298 cd/A for PDOFT-Au3 up to 0.645 cd/A for PDOFT-Au1.

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The cross-correlation performance between epilepsy electroencephalogram (EEG) signals reflects the status of epilepsy patients which has importance for analyzing long-range correlation of nonstationary signals. For the first time, detrended cross-correlation analysis (DCCA) was applied to analyze the α wave of different physiological and pathological states of epilepsy EEG signals. It were compared the difference of DCCA values between epilepsy patients' EEG signals and normal subjects' EEG signals. It was found that the DCCA values of epilepsy patients' EEG signals increased compared the normal subjects' EEG signals which can be helpful for medical diagnosis and treatment.

Abstract. In order to realize the prediction of air quality of pollution sources, this paper uses the method of wavelet neural network. In this paper, the pollutant concentration values of pollution sources were predicted, so as to realize the monitoring and early warning of pollution source emission. In this paper, the main chemical plants in Taizhou city are selected as the research object. The wavelet neural network is used to forecast the concentration of sulfur dioxide, nitrogen oxide of the chemical plant emissions. Finally, the experiments show that the wavelet neural network is useful to forecast the concentration of pollution sources.