Objective To develop a gas chromatography-mass spectrometry solid-phase extractionfor detectingphthalic acid esters (PAEs) in the serum of patients with thyroid cancer. Four PAEs, namely, dimethyl phthalate (DMP), diethyl phthalate (DEP), dibutyl phthalate (DBP), and di (2-ethyl) hexyl phthalate (DEHP), were used in experiments. Methods Samples of PAEs were extracted and enhanced using a solid-phase extraction glass column. The column temperature parameters were as follows: the initial temperature was${60}^{\circ}\mathrm{C}$. The temperature was maintained at${60}^{\circ}\mathrm{C}$for 1 min and then increased by${20}^{\circ}\mathrm{C}/\mathrm{{min}}$to${220}^{\circ}\mathrm{C}$. The temperature was then increased by${5}^{\circ}\mathrm{C}/\mathrm{{min}}$to${290}^{\circ}\mathrm{C}$and maintained at${290}^{\circ}\mathrm{C}$for 2 min. The mass spectrometry conditions were as follows: the ionization mode was electron impact, the source energy was${70}\mathrm{{eV}}$, the ion source temperature was${240}^{\circ}\mathrm{C}$, the quadrupole temperature was${150}^{\circ}\mathrm{C}$, and the scanning mode was selective ion monitoring (SIM). These parameters were qualitatively based on the target compound’s retention time and the mass-to-charge ratio (m/z). Quantitative analysis was conductedusing the standard internal method. Results The standard curve regression equations of DMP, DEP, DBP, and DEHP all had excellent linear relationships with correlation coefficients greater than 0.997. The precision of DMP, DEP, DBP, and DEHP was 2.60%, 4.73%, 5.50% and 5.70%, respectively. The average recoveries were in the average range of${94.5}\%$. The detection rate of DBP, DMP, DEP and DEHP were 100%,31.6%,73.46%, and${100}\%$, respectively. Conclusion The method is suitable for simultaneous detection of PAEs in human serum, with low organic dose, short extraction time, good recovery and reproducibility, and low detection limit.

Objective To study error compensation techniques in high-precision pressure detection systems and improve measurement accuracy. Methods A comprehensive error compensation method based on neural networks was proposed, and a pressure sensor error model was established. An adaptive neural network compensator was designed. Results This method achieves effective compensation for nonlinear errors, and experimental results show that the systematic nonlinear error decreases from 0.25% FS to 0.015% FS and the maximum relative error from 0.38% to 0.022%. After temperature compensation, the temperature coefficient decreased to${0.002}\%\mathrm{{FS}}/{}^{\circ}\mathrm{C}$, which increased by${80}\%$compared to$\pm {0.01}\%$FS/${}^{\circ}\mathrm{C}$before compensation. The dynamic response time is in the${1.7}\sim {2.0}\mathrm{\;{ms}}$range, with a$-3\mathrm{\;{dB}}$bandwidth of${850}\sim {870}\mathrm{\;{Hz}}$. Conclusion The error compensation method proposed in this paper can effectively improve the measurement accuracy of pressure detection systems and provide new ideas for the development of high-precision pressure detection technology.

With the rapid development of information technology, commodity barcode, as an effective information carrier, plays an increasingly important role in laboratory quality control. This paper discusses how barcode standardization can improve the efficiency and accuracy of product quality inspection through an in-depth analysis of the basic composition and function of commodity barcodes and their application in the laboratory. This paper focuses on the role of commodity barcodes in improving data traceability, optimizing quality monitoring processes, and enhancing product quality compliance, and demonstrates the practical application effect of commodity barcode standardization in laboratory quality control through specific case analysis. The results show that the standardization of commodity barcodes is one of the key technologies to improve the efficiency and quality management level of laboratories.

Objective To establish a method for the determination of total boron in soil by alkali fusion-inductively coupled plasma emission spectrometry to meet the quality control requirements of the third national soil census. Methods In this experiment, the alkali fusion method was used for sample pretreatment. By optimizing experimental conditions and screening the optimal curve preparation method, three standard substances with different contents were determined by inductively coupled plasma atomic emission spectrometry. Results The linear equation and correlation coefficient of the calibration curve prepared with experimental water are$y ={1171.470x}+ {12.981}\left({{r}^{2}= {1.000}}\right)$. The accuracy test was carried out with national standard substances, and the relative error is 13%~33%. The linear equation and correlation coefficient of the curve prepared with matrix matching solution are$y ={818.529x}+ {10.124}\left({{r}^{2}= {0.999}}\right)$. The accuracy test was carried out with national standard substances, and the relative error is${1.9}\%\sim {7.7}\%$. Conclusion This shows that preparing the calibration curve with matrix matching solution can eliminate the matrix interference caused by alkali fusion and obtain satisfactory results.

Objective To improve the determination of free silica content in dust by pyrophosphoric acid heated by silicon carbide in order to ensure the accuracy of detection. Methods Analyze the influencing factors and controls during the experimental process, including the determination of dust sample composition, sample processing, preparation of pyrophosphate, control of heating temperature, hot filtration, ashing and weighing. Results By controlling and improving the influencing factors during the experimental process, measurement errors can be effectively avoided. Conclusion The importance of determining the characteristics of dust samples during the measurement process lies not only in the preparation of pyrophosphate and strict temperature control, but also in the accuracy of data analysis for quality control sample analysis.

In this study, a detection method based on infrared thermal imaging technology is developed for the defect detection problem of agricultural distribution network lines. This method combines advanced laser technology and infrared thermal imaging analysis to effectively improve the accuracy and efficiency of detection. The laser device is used to thermal motivate the rural distribution network line from multiple angles, and then the thermal image data is collected through an infrared thermal imager. The in-depth analysis of the thermal images accurately identifies the tiny cracks and other potential defects in the line. Experimental data show that this method can significantly improve the SNR of defect identification and reduce the missed detection rate, and it is an efficient detection technology adapted to meet the needs of modern agricultural power grid. This technology not only optimizes the detection process, but also greatly shortens the detection time, and improves the operation safety and reliability of the rural distribution network.

With the development of modern medicine, fluorescence immunochromatography plays an important role in the detection of pathogenic microorganisms because of its rapid, simple and high sensitivity. In this paper, the principle and application process of the technique are systematically described, including sample pretreatment, preparation of fluorescently labeled antibodies, assembly of immunochromatographic apparatus, detection reaction and result interpretation. The application examples of the technology in the detection of bacteria, viruses and fungi were introduced, and the advantages and challenges of the technology were analyzed. The results show that fluorescence immunochromatography provides an effective tool for rapid identification and monitoring of pathogenic microorganisms, and has a wide application prospect in clinical diagnosis and public health.

Due to the strong randomness of fault distribution and low reliability of detection results in zinc oxide lightning arresters, a fault detection method for zinc oxide lightning arresters based on infrared thermal imaging technology is proposed. The FLIR T600 infrared thermal imager with a resolution of${640}\times {480}$pixels is used to capture thermal sensing images of lightning arresters. The fault area is identified through temperature characteristics of the fault location and binary image processing, and the detection output of temperature abnormal areas is obtained. The test results show that the detection results of different areas of the zinc oxide lightning arrester device are completely consistent with the manual detection results, and there are no missed or false detections.

In recent years, laboratory accidents in colleges and universities have occurred from time to time, and controlling laboratory safety has been the focus of university managers in recent years. In the practice of whole-process laboratory management, further exploration is needed. Combined with the current situation of the laboratory where the management of temporary hazards is difficult, the equipment is incompatible with the laboratory environment, and the improvement of students' safety literacy is limited, it is proposed to integrate the concept of dynamic management into the whole process management process, and realize the whole process dynamic safety management of the four elements of personnel, reagents, equipment and environment with the help of the school laboratory safety intelligent monitoring and control system, which can effectively improve the whole process management process of the laboratory.

University laboratories play an increasingly important role in enhancing the ability of scientific research and innovation, ensuring the quality of teaching, promoting interdisciplinary integration and promoting the sustainable development of the university. However, at present, the management of university laboratory is faced with some problems, such as imperfect management system, outdated facilities, weak talent team, lack of open sharing and lagging information level. This paper proposes to implement 6S management model to clarify the responsibility system, increase investment to accelerate the upgrading of facilities, strengthen the construction of talent team to optimize the management structure, expand open and sharing channels to improve resource utilization efficiency, deepen information construction to improve the intelligent level of management and other coping strategies. By analyzing successful cases, we summarize experiences and lessons. In order to provide reference and reference for university laboratory management.