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Herein, a novel visual method for detecting triazophos based on competitive bio-barcode immunoassay was described. The competitive immunoassay was established by gold nanoparticles (AuNPs), magnetic microparticle (MMPs) and triazophos, combined with biochip hybridization system to detect the residual of triazophos in water and apple. Because AuNPs carried many bio-barcodes, which hybridized with labeled DNA on the biochip, catalyzed signal amplification using silver staining was detected by grayscale values as well as the naked eye. Notably, the grayscale values decreases with increasing the concentrations of triazophos, and the color change weakened gradually. The detection range was in between 0.05 and 10 ng/mL and the minimum detection limit was set at 0.04 ng/mL. Percent recovery calculated from spiked water and apple samples ranged between 55.4 and 107.8% with relative standard deviations (RSDs) of 12.4-24.9%. It has therefore been shown that this protocol provides a new insight for rapid detection of small molecule pesticides in various matrices.

The existing coal mine equipment digital twin modeling method mainly focuses on single equipment modeling. It lacks three machine coupling collaborative relationship analysis. In order to solve the above problems, the paper puts forward three machine digital twin and collaborative modeling method for fully mechanized working face. By adopting an intelligent modeling method, the method constructs agent-based models of a coal mining machine, a hydraulic support and a scraper conveyor which comprise a sensing unit, a control unit and an execution unit. The method constructs corresponding visual models according to a three-dimensional modeling process. The method drives the three-dimensional models to move by the intelligent models. The combination of the two forms a digital twin model of three machines. A discrete event modeling method is used to construct a collaborative process model covering the interaction process of the three machine digital twin model. The three machine mining process is sorted out according to the time sequence to form a three machine collaborative process time sequence table. The digital twin model is used to describe the state and behavior of the three machines in fully mechanized mining and to simulate the calculation at the individual level. The collaborative process model is used to represent the sequential action transformation between digital twin models and realize the deduction of the whole three machine collaborative process. The simulation of rocker lifting and lowering for the digital twin model of the shearer is carried out. The simulation results show that compared with the measured data of real equipment, the model error is small, an average error of rocker arm dip angle is 2.3°. The simulation of continuous column lifting action for the digital twin model of hydraulic support is carried out. The simulation results show good consistency between the model and real equipment. Compared with the measured data of the real equipment, the average angle error is 0.14° and the average stroke error is 6.3 mm. Combined with the actual production log of the coal mine, the virtual and real simulation experiment of the three machine collaborative model is carried out. The results show that the three machine digital twin model of the fully mechanized working face and real equipment realize mutual mapping. The simulation results are close to the real records. The three machine collaborative model can accurately reflect the collaborative mining process. The method of three machine digital twin and collaborative modeling for fully mechanized working face provides a new idea for the digital twin modeling of fully mechanized coal mining equipment and its collaborative relationship.

Although the toxicity of triazophos is high and it has been pulled from the market in many countries; it is still widely used and frequently detected in agricultural products. While conventional analyses have been routinely used for the quantification and monitoring of triazophos residues, those for detecting low residual levels are deemed necessary. Therefore, we developed a novel and sensitive fluorometric signal amplification immunoassay employing bio-barcodes for the quantitative analysis of triazophos residues in foodstuffs and surface water. Herein, monoclonal antibodies (mAbs) attached to gold nanoparticles (AuNPs) were coated with DNA oligonucleotides (used as a signal generator), and a complementary fluorogenic RNA was used for signal amplification. The system generated detection signals through DNA-RNA hybridization and subsequent dissociation of fluorophores by Ribonuclease H (RNase H). It has to be noted that RNase H can only disintegrate the RNA in DNA-RNA duplex, but not cleave single or double-stranded DNA. Hence, with iterative cycles of DNA-RNA hybridization, sufficient strong signal was obtained for reliable detection of residues. Furthermore, this method enables quantitative detection of triazophos residues through fluorescence intensity measurements. The competitive immunoassay shows a wide linear range of 0.01-100 ng/mL with a limit of detection (LOD) of 0.0032 ng/mL. The assay substantially meets the demand for the low residue detection of triazophos residues in agricultural products and water samples. Accuracy (expressed as spiked recovery %) and coefficient of variation (CV) were ranged from 73.4% to 116% and 7.04% to 17.4%, respectively. The proposed bio-barcodes immunoassay has the advantages of being stable, reproducible, and reliable for residue detection. In sum, the present study provides a novel approach for detection of small molecules in various sample matrices.