Background: Microfluidic chip detection technology is considered a potent tool for many bioanalytic applications. Rapid detection of foodborne pathogens in the early stages is imperative to prevent the outbreak of foodborne diseases, known as a severe threat to human health. Conventional bacterial culture methods for detecting foodborne pathogens are time-consuming, laborious, and lacking in pathogen diagnosis. To overcome this problem, we have created an embedded paper-based microchip based on isothermal loop amplification (LAMP), which can rapidly and sensitively detect foodborne pathogens. Results: We embed paper impregnated with LAMP reagent and specific primers in multiple reaction chambers of the microchip. The solution containing the target pathogen was injected into the center chamber and uniformly distributed into the reaction chamber by centrifugal force. The purified DNA of Escherichia coli O157:H7, Salmonella spp., Staphylococcus aureus, and Vibrio parahaemolyticus has been successfully amplified and directly detected on the microchip. The E. coli O157:H7 DNA was identified as low as 0.0134 ng μL^− 1. Besides, the potential of this microchip in point-of-care testing was further tested by combining the on-chip sample purification module and using milk spiked with Salmonella spp.. The pyrolyzed milk sample was filtered through a polydopamine-coated paper embedded in the inside of the sample chamber. It was transported to the reaction chamber by centrifugal force for LAMP amplification. Then direct chip detection was performed in the reaction chamber embedded with calcein-soaked paper. The detection limit of Salmonella spp. in the sample measured by the microchip was approximately 12 CFU mL^− 1. Conclusion: The paper embedded LAMP microchip offers inexpensive, user-friendly, and highly selective pathogen detection capabilities. It is expected to have great potential as a quick, efficient, and cost-effective solution for future foodborne pathogen detection.

An environment-friendly device based on ozone-induced chemiluminescence (CL) technology was developed and optimized for the rapid measurement of chemical oxygen demand (COD) in waters. The purpose of this paper was to introduce a system that could be used in the field and has the characteristics of simple operation and avoiding the use of chemical reagents. Moreover, no more than 3 mL was introduced into the CL cell by using a syringe and mixed with ozone gas flow that could be produced online using an electrolytic ozone generator, which could lead to generating CL immediately and then the weak emission was focused into a photomultiplier tube through a specially designed optical system. Although the measurement procedure is simple, the method was sensitive as sharp peaks appeared within seconds for milligrams of COD, and the lower limit of detection was 0.1 mg L⁻¹. The light intensity difference (ΔI) of the CL detection system between deionized water and real sample water was proportional to the COD value of the water sample. A complete analysis can be performed in 2 minutes including sample addition and signal processing. Compared with conventional methods, this method required a smaller volume of water sample (3 mL vs. 30 mL) and was less time-consuming (2 min vs. 2 h), and was free of chemical reagents. The correlation between the presented method and the conventional permanganate chemical oxygen demand (COD_Mn) for several real water samples showed a good determination coefficient R² = 0.9947. Owing to its characteristics of rapid, simple, sufficient sensitivity and low cost, this method provides a promising tool for large-scale environmental assessment and industrial process control.

A paper-based microfluidic disc is used for the rapid determination of various nutrients in water.

Computer vision and artificial intelligence (AI) have become increasingly important in behavioral analysis across biological research. In contrast to well-established methods for individual behavior analysis, computational frameworks for quantitatively assessing zebrafish shoaling behavior remain limited. To address this gap, we propose a cascaded detection–tracking framework that integrates multi-scale object detection with adaptive motion tracking for zebrafish shoaling behavior analysis. A multidimensional feature set was developed to extract both kinematic and spatial distribution metrics from tracked trajectories. Behavioral analysis revealed a biphasic effect of ethanol: low concentrations increased global motion intensity (hyperactivity), whereas higher concentrations reduced locomotor activity and disrupted shoal cohesion.

Small short-arc xenon flash lamps, yielding high energy and ultraviolet radiation without cooling structures, provide ideal analytic-instrument light. Improving instrument designs requires accurately determining spatial optical-flux distribution. Thus, this paper presents rapid scanning of a xenon lamp’s central light intensity using a high-sensitivity photodiode to capture short light pulses. Results show two-dimensional optical illumination patterns. In the horizontal mode, the anode and cathode lie in the target plane. In the vertical mode, the two electrodes are centered in a circle. Thus, because the xenon lamp’s spatial light flux varies by orientation and main driving voltage, we recommend sampling light horizontally in front of the lamp across small angles.

The global issue of oil spreading in water poses a significant environmental challenge, emphasizing the critical need for the accurate determination and monitoring of oil content in aquatic environments to ensure sustainable development of the environment. However, the complexity arises from challenges such as oil dispersion, clustering, and non-uniform distribution, making it difficult to obtain real-time oil concentration data. This paper introduces a sophisticated system for acquiring induced fluorescence spectra specifically designed for the quantitative analysis of oil pollutants. The paper involved measuring the fluorescence spectra across 20 concentration gradients (ranging from 0 to 1000 mg/L) for four distinct oil samples: 92# Gasoline, Mobil Motor Oil 20w-40, Shell 10w-40 engine oil, and Soybean Oil. The research focused on establishing a relationship model between relative fluorescence intensity and concentration, determined at the optimal excitation wavelength, utilizing the segmented Random Sample Consensus (RANSAC) algorithm. Evaluation metrics, including standard addition recovery, average recovery, relative error, and average relative error, were employed to assess the accuracy of the proposed model. The experimental findings suggest that the average recovery rates for the four samples ranged between 99.61% and 101.15%, with the average relative errors falling within the range of 2.04% to 3.14%. These results underscore the accuracy and efficacy of the detection methodology presented in this paper. Importantly, this accuracy extends to scenarios involving heavier oil pollution. This paper exhibits exceptional sensitivity, enabling precise detection of diverse oil spills within the concentration range of 0~1000 mg/L in water bodies, offering valuable insights for water quality monitoring and sustainable development of the environment.