Detection Of Pollutants And Bioactive Substances In The Complex Compost Systerm Based On Highly Sensitive Functional Biosensor

In this paper, the highly sensitive biosensors were developed to determine the pollutants and special targets in the complex compost system. And the feasibility of biosensor and the electrochemical response mechanism were investigated. With the work we have accomplished by far, two immunosensors based on the immunoreaction between antigen and antibody, a DNA sensor and two mercuric sensors based on specific DNA biosensor, and a laccase sensor combining artificial neural network technique were respectively used to detect the targets in the complex compost system.Composting technology is widely applying in disposal of municipal solid waste. With the increase of the environmental standards and health consciousness, more attentions are focused on the security control in composting process and the safety standards of the composting products, which the relative detection requirements and methods should be accordingly matched. In municipal solid waste, heavy metals and organic pollutants are common. Due to the effect of accumulation and enrichment, heavy metals and organic pollutants, especially some refractory persistent organic pollutants（POPs） even if in low levels, have the potential to cause serious harm to the environment and human. However, the monitoring can not be executed easily because of the complexity of the complex compost system. Due to extensive source of municipal solid waste, the components of compost system are complicated and uncertain, which have various species and a great quantity and come from various sources. Additionally, the reactions in the compost system are successive and complicated, which relate to physical, chemical and biological reactions. The traditional detection techniques are complex and labor-intensive in operation, and are susceptible to interference due to the complexity, diversification and uncertain of the complex compost system, which are difficult to meet the development needs of real-time, in-situ, on-line modern detection technology.Nowadays, biosensor and online detection techniques have become a spot in monitoring field, which attributed to high selectivity and sensitivity, fast response, simple operation, portability, low consumption and other advantages. The proposed biosensors, combining nerual network technique, modern material technology and molecular biology detection technology, were developed to monitor the targets, e.g. heavy metals, organic pollutants, biomacromolecule in compost system. Microarray technology and high-throughput analysis of biological methodologies were also involved in relative researches. It is tried to use biosensor technology to obtain high sensitivity and selectivity, dynamic analysis and nondestructive testing methods, and multidetermination methods to realize an in situ, in vivo, in time detection of targets in environment, which as an important method would enhance the controlling in composting process and guarantee the products safety. When the biosensors are running in compost, the efficiency of the control in compost system will be improved much. This work would provide a new science tools to solve practical problems in environmental science and engineering field.The compost components is complex, theoretically, the involved detection items and range are wide. Therefore, this paper focuses on using the highly sensitive biosensor to detect some typical contaminants and related biological components in compost system. The details include the following four aspects:The first aspect involved the detection of the organic pesticides in compost based on the immunosensor. With certain molecular structure and functional group, some organics e.g. picloram can be as a hapten used in immune experiment. Herein, picloram, a widely used chlorinated herbicide was chosen as a representative of the organic pesticides in the relative study. In antibody incubation process, picloram was coupled with BSA through 1-Ethyl-3-（3-dimethyllaminopropyl）carbodiimide hydrochloride（EDC）, obtaining an antigen with immunogenicity and reactogenicity. The antigen of artificial synthesis was identified by combined with ultraviolet absorption spectrometry and infrared spectrometry, through the conversion of the absorbance of antigen gotten by ultraviolet absorption spectrometry, the crosslinking ratio was 19.88, which reflected good immunity. The obtained antigen was injected into rabbit to get immunity, and got the antibody of picloram after 8 weeks. The double immunodiffusion test and ELISA assay were used to identify the titer and performance of the antibody. After purification of antiserum, The Ig G was further purified, and detected by ultraviolet spectrophotometer at 280 nm. By calculated, the concentration of Ig G was 9.3 mg/m L. In the future, we are ready to develop an immunosensor to determine the picloram in the compost and Xiang Jiang River. The antibody is immobilized on the slide glass, and then the Fluorscence（FITC） is embed into the liposome modified with the picloram-antibody. The liposome embed with FITC can be used as a signal magnifier to realize the fluorescent detecting technique.First, a competitive immunoassay strategy based on the anti-picloram-Ig G was developed. The magnetic core-shell（Fe3O4-Si O2） nanoparticles modified with anti-picloram-Ig G were attached to the surface of carbon paste electrode with the aid of paramagnetism. The obtained electrode was immersed in block solution（p H=7.4） containing different concentrations of picloram and 1 mg/m L picloram-laccase to competitively react for 40 min at 37°C. Followed by placing in the detector cell with the PBS（p H=4.6） containing 50 μM hydroquinone, chronoamperometry was executed at the applied potential-0.139 V（vs. SCE）. The average decrease percentage DP（%） was linearly related to the natural logarithm of picloram concentration. The linear range for picloram detection was 1×10^-4-10 μg m L-1 with the correlation coefficient of 0.9936, and the detection limit is 1×10^-4 μg m L-1. The proposed immunosesnor was used to detect the compost and river water samples spiked with picloram, and also exhibits a good detection performance.Next, a fluorescence detection strategy based on the antibody labeled-liposome c fluorescein isothiocyanate（FITC） was developed. The liposomes were labeled by anti-picloram-Ig G, and encapsulated the FITC solution. A quartz glass plate functionalized with aldehyde groups was successively reacted with picloram and 1:10 diluted liposomes-labeled anti-picloram-Ig G 60 min and 40 min. Following the lysis of the liposomes using 130 μL of 0.2%（v/v） Triton-X 100 in 20%（v/v） methanol-water solution for 8 min to release the encapsulated FITC, the fluorescence of FITC was measured by a fluorimeter. The fluorescence intensity was linearly related to the logarithm of picloram concentration, ranging from 1.0×10-13 to 1.0×10-7 g m L-1 with the correlation coefficient of 0.996, and the detection limit is 1.0×10-14 g m L-1. Picloram concentration in compost extract and wastewater samples were determined by the proposed method and HPLC, the results of the two methods were approximately the same. The proposed method showed high sensitivity and good selectivity, and could be an efficient tool for picloram quantitative analysis.The second aspect involved the detection of specific targets based on highly sensitive biosensors using the environmental functional substance, new nano-materials and biomolecular materials.First, an electrochemcal DNA sensor using the complementary gene sequence to detect the conserved DNA sequence of lignin peroxidase（Li P） in compost was developed. Li P, from microorganisms secreting, is believed to be a key enzyme to trigger lignin biodegradation in composting process. To identify the specific gene fragment of Li P would reflect the dynamic changes of Li P during microbial degradation of lignin, and would monitor the municipal solid waste composting process.Multiwalled carbon nanotubes（MWCNTs）, gold nanoparticles（GNPs）, and chitosan were used successively to coat on electrode surface. The thiolated capture probe was assembled and competitively hybridized with the target nucleic acid and biotinylated response probe. The sensor performance was analyzed by introducing the notion of detection efficiency. The experimental results showed that although the electron transfer capability of CPE is less strong than that of metal electrode used in DNA sensor, the materials modified on the CPE could significantly improve the performance. A detection limit of 1 n M of target DNA and the sensitivity of 2.707×103 m A M-1 cm-2 were obtained.Next, two sensors based on the mercury-specific oligonucleotide technique and new nano-materials to detect the Hg²⁺ in compost and other environmental samples were developed. The thymine-Hg²⁺-thymine(T-Hg²⁺-T) coordination chemistry and the resulting Hg²⁺-stabilized hybridization of oligonucleotides with T-T mismatches has specific recognition ability of Hg²⁺ with convincing detection accuracy.One is a three-dimensional configuration of mercuric sensor using mercury-specific oligonucleotide, gold nanocluster, and anionic intercalator. Due to the stereo reaction field on the electrode surface microenvironment formed by gold nanoclusters, the sensor could spatially capture mercuric and electric-active indicator to realize trace mercury measurement with high sensitivity, the sensor exhibited strong environmental adaptability, high selectivity, and etc. advantages. Under optimal conditions, mercuric ion could be detected in the range from 0.05 to 350 n M, and the detection limit is 0.01 n M. In addition, sensor performance was also analyzed by introducing the notion of detectivity. This accurate method has a potential to be deployed in field measurement for mercury in environmental media.Another mercuric sensor was based on nanomaterials and the relative functionalized strategies. Graphene and nano Au were successively electrodeposited on a glass carbon electrode surface to improve the electrode conductivity and functionalize with the 10-mer thymine-rich DNA probe（P1）. Nano Au carriers functionalised with 29-mer guanine-rich DNA probe（P3） labeled methyl blue（MBnano Au-P3s） were used to further strengthen signal response. In the presence of Hg²⁺, a T-T mismatched ds DNA would occur between P1 and a 22-mer thymine-rich DNA probe（P2） on the electrode surface due to T-Hg²⁺-T coordination chemistry. Followed by adding the MB-nano Au-P3 s for hybridization with P2, square wave voltammetry was executed. Under optimal conditions, Hg²⁺ could be detected in the range from 1.0 a M to 100 n M with a detection limit of 0.001 a M.The third aspect involved the effect of functionalized electrodes and nano Au carrier signal amplification on electrochemical DNA sensing strategy to effectively develop a suitable sensing strategy for composting system detection.Four kinds of electrodes including an electrodeposited nano Au functionalized glassy carbon electrode, an electrodeposited nano Au clusters functionalized glassy carbon electrode, a carbon nanotubes-nano Au functionalized glassy carbon electrode, and an electrodeposited graphene-nano Au functionalized glassy carbon electrode, and the methylene blue labeled DNA signal probe with or without the nano Au carrier amplification strategy were carried out in this study. In comparison with the effect of the signal amplification strategy, the functionalized electrodes are dominant to change the current response in this sensing system. It can be concluded that the improvement in the capacity of electronic conduction of the sensing unit would have a decisive impact on the increasing current response, and under the similar capacity of electronic conduction condition, the specific surface of the sensing unit would play an important role.The fourth aspect involved an on-line system of detection and prediction by combining a laccase sensor and artificial neural network（ANN） to realize nonlinear detection of hydroquinone in a compost system. The direct detection range for hydroquinone in compost system using biosensor reached 1.5×10-8³.6×10^-4 M. Meanwhile, the performance of the ANN model was compared with a nonlinear regression model with respect to the simulation accuracy and adaptability to uncertainty, etc. The results illustrated that the combined application of biosensor measurement and artificial neural network analysis was a rapid, sensitive and robust method in a quantitative study of a composting system.In conclusion, the application of biosensor to determine the pollutants and biologically active substance in complex compost system, can overcome some shortcomings and limits of the traditional determining methods to make an exact detection, or to realize the pollutant determination which can not be completed by traditional determining methods. This is an advanced, efficient, economical, convenient and operable method for monitoring of targets in environment and compost system.