Brominated flame retardants （BFRs） are widely used as organic flame retardants in plastic products， with most exhibiting strong biological toxicity as well as physical and chemical stability. BFRs inevitably remain in foods consumed on a daily basis through indirect or direct contact， thereby threatening human health. Therefore， establishing a fast and effective method for detecting and analyzing BFRs is imperative. Magnetic solid-phase extraction （MSPE） has been widely used in trace-analysis applications owing to advantages that include operational simplicity and rapid magnetic separability. The key to MSPE lies in the design and preparation of efficient magnetic adsorbents. In this study， a magnetic carbon aerogel （MCA） was prepared using a sol-gel method in combination with calcination. MCA was used as a magnetic solid-phase extractant to establish a new method for the analysis of four BFRs in mineral water and instant-noodle-bowl samples in combination with high performance liquid chromatography. Fourier-transform infrared （FT-IR） spectroscopy revealed peaks at 3 454， 1 590， 757， 1 349， 1 654， and 1 076 cm^-1 that are ascribable to -NH₂， -CH， triazine-ring， C-N， C=N， and C-O-C vibrations， respectively. Brunauer-Emmett-Teller （BET） analysis revealed values of 192.16 m²/g， 0.34 cm³/g， and 7.12 nm for the surface area， pore volume and pore size of the MCA， respectively. X-ray diffractometry （XRD） revealed a characteristic peak at 2θ=34.90° that corresponds to the （110） crystal plane of Fe₂O₃， and peaks at 2θ values of 44.72°， 65.01° and 82.42° that are ascribable to the （110）， （200）， and （211） crystal planes of CoFe/Co₃Fe₇. Vibrating sample magnetometry showed that the MCA is highly magnetic （35 emu/g）， which contributes to fast magnetic solid-liquid separation. The MCA was characterized by transmission electron microscopy （TEM）， which revealed a transparent gauze-like structure with nanometer-sized squares and circular particles evenly distributed between them. High-resolution TEM （HRTEM） showed that the square particles exhibit a 0.191 nm stripe spacing that belongs to the （311） crystal plane of Fe₂O₃， while the 0.245 nm stripe spacing observed for the circular particles corresponds to the （110） crystal plane of the CoFe alloy， in good agreement with the XRD results. X-ray photoelectron spectroscopy （XPS） revealed the presence of Co 2p， Fe 2p， O 1s， N 1s， and C 1s peaks. Taken together， these results show that the MCA， which contains various functional groups， had been successfully prepared. Factors that affect MSPE， such as solution pH， amount of material， adsorption time， the concentration and volume of the elution solvent， and sample volume， were investigated using the static adsorption method. BFR adsorption by the MCA was observed to increase with time， with equilibrium eventually reached. Tetrabromobisphenol A （TBBPA） reached adsorption equilibrium at 1 h with an adsorption rate close to 100%， whereas 3-bromobiphenyl （PBB-2）， 4，4′-dibromobiphenyl （PBB-15）， and 2，2′，4，4′-tetrabromodiphenyl ether （BDE-47） reached adsorption equilibria at 2 h. BDE-47 exhibited an adsorption rate close to 80% when 20 mg/L MCA was used， whereas the remaining three BFRs exhibited values close to 100%. Absorption by the MCA initially exhibited a constant trend with increasing sample volume but began to decline as the sample volume exceeded 100 mL. BFR adsorption by the MCA was found to be almost pH-independent， which indicates that the MCA is stable over a wide pH range. In addition， the analytes were effectively eluted in 30 min using 5 mL of acetonitrile. Based on the results presented above， the optimal adsorption and desorption conditions are： 20 mg/L of adsorbent， a sample volume of 100 mL， an adsorption time of 2 h without pH adjustment， 5 mL of acetonitrile as the eluent， and desorption time of 30 min. TBBPA， PBB-2， and PBB-15 exhibited limits of detection （LODs， S/N≥3） of 0.005 mg/L each under the optimal conditions， while BDE-47 exhibited a value of 0.010 mg/L， with corresponding RSDs of 7.35%， 5.12%， 3.66%， and 5.58% （n=5， C=0.02 mg/L）， respectively， and actual enrichment times of 50， 40， 51， and 61 min， respectively. The developed method was used to determine four BFRs in mineral water and instant-noodle-bowl samples， with satisfactory recoveries obtained， thereby providing a new fast and sensitive method for the analysis of brominated flame retardants.

Polycyclic aromatic hydrocarbons （PAHs） are organic pollutants that are ubiquitous in nature and in environments affected by anthropogenic activities. PAHs enter the human body through inhalation， ingestion， or skin contact， and consequently threaten human health. Urban areas are mostly affected by PAHs pollution， which is ascribable to dense population， heavy traffic， and limited air-pollutant diffusion. In this study， we assessed the current status of PAHs exposure among non-occupationally exposed urban residents using isotope dilution combined with liquid-liquid extraction-gas chromatography-high resolution dual-focus magnetic mass spectrometry （GC-HRMS）. The burden of PAHs of 92 permanent residents aged 2 to 80 years in Beijing was investigated. Ten monohydroxypolycyclic aromatic hydrocarbons （OH-PAHs） were measured in urine samples， namely hydroxynaphthalene （OHNap， including 1-OHNap and 2-OHNap）， hydroxyfluorene （OHFlu， including 2-OHFlu， 3-OHFlu， and 9-OHFlu）， hydroxyphenanthrene （OHPhe， including 1-OHPhe， 2-OHPhe， 3-OHPhe， and 4-OHPhe）， and 1-hydroxypyrene （1-OHPyr）. The OH-PAH levels were corrected with urinary creatinine， with results below LODs replaced with half the value of LODs. Correlations between OH-PAHs were assessed using Spearman’s rank correlation analysis （two-tailed）. The nonparametric Mann-Whitney U test and the Kruskal-Wallis H test were used to compare the distribution of OH-PAH levels in different populations. The results showed that six OH-PAHs （1-OHNap， 2-OHNap， 2-OHFlu， 9-OHFlu， 1-OHPhe， and 2-OHPhe） were detected in all the urine samples. The total contents （ΣOH-PAHs） of the 10 OH-PAHs ranged from 661 to 33 782 ng/g， with an overall geometric mean （GM） of 2 775 ng/g and significant inter-individual differences. The following content-distribution trend was observed： OHNap>OHFlu>OHPhe>1-OHPyr， with a significant negative correlation with molecular size recorded. OHNap was mainly observed， accounting for 62.2% of the total. Complex correlations were found to existed between the OH-PAHs， with 9-OHFlu exhibiting unique exposure patterns. Urinary OH-PAH levels were found to correlate with gender and age， and smoking was also observed to be a significant influencing factor. ΣOH-PAHs peaked in the youth group （0‒15 years）， with a GM of 3 940 ng/g. Levels of ΣOH-PAHs were similar in the working-age group （16‒59 years， GM： 2 598 ng/g） and in the elderly group （≥60 years， GM： 2 639 ng/g）. These suggest that age is a key PAH-exposure factor. Habitual smoking was found to consistently and significantly affect OH-PAH levels， with smokers generally having higher levels of OH-PAH than non-smokers. While males exhibited higher overall exposure levels than females， females exhibited significantly higher levels of 1-OHPyr than males （p=0.03） when smoking was excluded， which suggests that metabolic and behavioral differences between genders impact PAHs exposure. This study revealed the exposure and distribution characteristics of OH-PAHs in Beijing residents. It provides a scientific basis for studying PAHs pollution and its health effect， as well as epidemiological investigations， disease-burden assessments， and policy formulation.

Polychlorinated biphenyls （PCBs） are hazardous， persistent organic pollutants that are widely used industrially. Although the use of PCBs is banned in many countries， they are still present at trace levels in food and the environment. PCBs are highly chemically stable and lipophilic； hence， they are easily enriched and accumulate in the human body through milk and dairy products. PCBs residues pose serious threats to human health； therefore establishing a reliable enrichment method is an important objective. Sample pretreatment is required to efficiently extract target PCBs owing to sample-matrix complexity and their low contents. Efficient adsorbents form the cores of novel sample-pretreatment technologies， and designing new stable adsorbents is crucial for the further development of pretreatment techniques. Zeolitic imidazolate frameworks （ZIFs） are a family of metal-organic frameworks composed of imidazole linkers and metal ions. Their large surface areas， good stabilities， high porosities， and ease of modification are distinct advantages； consequently， ZIFs are widely used to adsorb organic pollutants. However， powdered ZIFs are difficult to separate and collect， which provides reuse challenges； hence， preparing ZIF composites with other functional materials is a highly effective way of addressing this challenge. Chitosan （CS） is an inexpensive and biodegradable natural polysaccharide that gelates easily. The structure of CS contains many free amino and hydroxyl groups that facilitate chemical modification and hybridization； consequently， CS is a matrix commonly used in composite materials. In this study， we prepared CS@ZIF-8 composite beads by the in-situ synthesis of ZIF-8 on chitosan through acid-solubilization/base-fixation. An analytical method for determining 18 PCBs in milk was developed using CS@ZIF-8 composite microspheres as the adsorbent for dispersive solid-phase extraction （DSPE） coupled with gas chromatography-mass spectrometry （GC-MS）.The CS@ZIF-8 composite microspheres were characterized by scanning electron microscopy （SEM）， Fourier-transform infrared （FT-IR） spectroscopy， X-ray diffractometry （XRD）， and nitrogen-adsorption-desorption experiments， which confirmed that the material had been successfully prepared. How adsorbent dosage， extraction and desorption times， and type and volume of the desorption solvent affect the extraction efficiency were investigated， with the following optimal extraction conditions determined： 20 mg of CS@ZIF-8 as the adsorbent， 30 min of extraction by shaking， and 8 min of ultrasonic desorption with 1 mL of n-hexane. The 18 PCBs exhibited good linearities in the 1–200 μg/L under these optimal conditions， with coefficients of determination （r²） exceeding 0.999. Detection limits （S/N=3） ranged between 0.06 and 0.24 μg/L， with quantification limits （S/N=10） of 0.19–0.79 μg/L. Repeatability experiments were performed by the addition of 100 μg/L of the 18 PCBs， which exhibited intra-day and inter-day precisions （n=6） of 2.5%–5.3% and 4.3%–5.9%， respectively， while inter-batch material precisions （n=3） ranged between 4.9% and 9.7%. The applicability of the developed method was investigated by selecting whole milk and skim milk as samples based on PCBs lipophilicity. Spiked recovery experiments were conducted at three concentrations （5， 20， and 100 μg/L）， with the 18 PCBs exhibiting spiked recoveries of 84.8%–114.3%. CS@ZIF-8 not only has a larger specific surface area than CS， but it also adsorbs PCBs through π-π interactions and hydrophobicity， leading to superior extraction efficiency. CS@ZIF-8 exhibited spiked recoveries exceeding 70% for all samples after four adsorption-desorption cycles during reproducibility testing. The developed method provides a simplified extraction process by eliminating the need for centrifugation or filtration steps that are usually associated with conventional DSPE. In addition， the developed method is highly sensitive， precise， and accurate， with adsorbent reusability a noteworthy feature， thereby supporting the simple and efficient detection of PCBs in milk samples.

Crude oils are complex mixtures of thousands of organic compounds that differ significantly in relative molecular mass， volatility， content， and polarity. Traditional methods for analyzing crude oil often involve complicated steps， consume large amounts of organic solvents， and require long sample-preparation times. These limitations lead to inefficient and time-consuming analysis processes. Crude oil is commonly analyzed by gas chromatography-mass spectrometry （GC-MS）. However， this technique is incapable of effectively separating complex crude-oil components owing to its low resolution and peak capacity， resulting in overlapping peaks that can lead to inaccurate compound identification and quantification. These challenges highlight the need for advanced analytical techniques. Comprehensive two-dimensional gas chromatography （GC×GC） is a novel separation technique that has been widely used to analyze complex samples， such as food， environmental samples， natural products， and crude oil. GC×GC has several advantages over traditional GC. Firstly， it offers higher resolution and peak capacity， thereby improving separation efficiency. Secondly， its high separation power reduces the need for complex sample pretreatment. Thirdly， the ordered separation and “tile effect” in a GC×GC chromatogram facilitate easier compound identification and quantification in complex mixtures.

In this study， we developed a gas purge microsyringe extraction （GPMSE） method for the rapid pretreatment of crude-oil samples. This method reduces sample processing time to only 10 min while minimizing organic solvent consumption. The chemical compositions of 45 crude oil samples were analyzed using GC×GC-time-of-flight mass spectrometry （GC×GC-TOFMS）， which helped to establish detailed chemical fingerprints for each sample. The GC×GC-TOFMS data were processed using multivariate statistical methods， including redundancy analysis （RDA） and Monte Carlo permutation testing， which identified 36 biomarkers that are strongly associated with the origin of the crude oil （p<0.05）. A classification model was constructed using a training set of 28 samples. Four single-source and 13 mixed-source samples were used to validate the model. The GPMSE-GC×GC-TOFMS method was demonstrated to be highly efficient and accurate. A discrimination accuracy of 97.8% was achieved during the identification of crude-oil sources. The developed method not only provides a powerful tool for tracing crude oil but also has broad applications potential， including for the detection of adulterated crude oil， tracking oil-spill sources， and monitoring oilfield development. This study offers several significant benefits. For example， it helps to address crude-oil trade fraud and supports national energy security. Additionally， it provides scientific support in relation to crude-oil quality control and risk assessment. The developed method is fast， reliable， and environmentally friendly； hence， it is expected to be a valuable tool for use in the oil industry. The GPMSE-GC×GC-TOFMS method is cost-effective and requires minimal solvent； consequently， it is an attractive option for reducing environmental impacts in laboratory and industrial settings. Furthermore， the high throughput and accuracy of the developed method make it suitable for large-scale analyses. In conclusion， this study demonstrated the effectiveness of combining GPMSE with GC×GC-TOFMS for analyzing crude oil； the ability of the method to identify biomarkers and classify crude-oil sources in a highly accurate manner represents a significant advancement in the field. Future studies are expected to further explore its applications in related areas， such as oil refining and environmental monitoring.

Gel electrophoresis is used to separate and analyze macromolecules （such as DNA， RNA， and proteins） and their fragments， and highly reproducible and efficient automatic band-detection methods have been developed to analyze gel images. Uneven background， low contrast， lane distortion， blurred band edges， and geometric deformation pose detection-accuracy challenges during automatic band detection. In order to address these issues， various correction algorithms have been proposed； however， these algorithms rely on researcher experience to adjust and optimize parameters based on image characteristics， which introduces human error while qualitatively and quantitatively processing bands. Isoelectric focusing （IEF） gel electrophoresis separates proteins with high-resolution based on isoelectric point （pI） differences. Microarray IEF （mIEF） is used for the auxiliary diagnosis of diabetes and adult β-thalassemia owing to operational ease， low sample consumption， and high throughout. This diagnostic method relies on accurately positioning and precisely determining protein bands. To avoid errors associated with correction algorithms during band analysis， this paper introduces a method for rapidly recognizing bands in gel electrophoresis patterns that relies on a deep learning object detection algorithm， and uses it to quantify and classify the IEF electrophoresis pattern of hemoglobin （Hb）. We used mIEF experiments to collect 1 665 pI-marker-free Hb IEF images as a model dataset to train the YOLOv8 model. The trained model accepts a Hb IEF image as input and infers band bounding boxes and classification results. Using inference data， the gray intensities of the pixels in each band area are summed to determine the content of each protein. The background and foreground of the image need to be separated prior to summing the abovementioned gray intensities， and the threshold method is used to achieve this. The threshold is defined as the average intensity of the background area， which is obtained by summing and averaging the background intensities of gel areas between the detection bounding boxes of each protein band. The baseline band areas are unified after removing the background. This method only requires the input image， directly outputs the corresponding electrophoretic band information， and does not rely on the experience of professionals nor is it affected by factors such as lane distortion or band deformation. In addition， the developed method does not depend on pI markers for qualitatively determining bands， thereby reducing experimental costs and improving detection efficiency. YOLOv8n delivered a detection accuracy of 92.9% and an inference time of 0.6 ms while using limited computing resources. Using Hb A2 as an example， we compared its content measured using the developed method with clinical data. The quantitative results were subjected to regression analysis， which delivered a linearity of 0.981 2 and a correlation coefficient of 0.980 0. We also used the Bland-Altman analysis method to verify that these two values are highly consistent. Compared with the traditional automatic band detection methods， the method developed in this study is fast， accurate， more repeatable， and stable， and can be used to determine the Hb A2 content in clinical practice， thereby potentially assisting in the auxiliary diagnosis of adult β-thalassemia.

Organic synthesis experiments are often deficient and involve relatively independent experimental instrumental-analysis methods that lack integration. Accordingly， an open-instrument-analysis experiment was designed to address these shortcomings； this experiment combines organic synthesis with instrumental analysis to deliver an innovative undergraduate experiment that includes material preparation and characterization， investigating the adsorption performance of hydroxylated polychlorinated biphenyl， and exploring detection methods. First， Fe₃O₄ was treated with tetraethyl orthosilicate， after which 3-aminopropyltrimethoxysilane was introduced to prepare amino-modified Fe₃O₄. Trimesoyl chloride and p-phenylenediamine are then added as monomers to synthesize a magnetic covalent organic framework （MCOF）. The surface groups and thermal stability of the MCOF were then characterized using Fourier-transform infrared spectroscopy and thermogravimetric analysis， after which the MCOF was used to determine hydroxylated polychlorinated biphenyl in Liaohe River water samples using liquid chromatography. During this experiment， students master separation and detection methods for hydroxylated polychlorinated biphenyl， while also learning about its levels in the Liaohe River. Students will recognize the important role that instrumental analysis plays in environmental monitoring by analyzing and discussing the experimental results； they will also improve their abilities to comprehensively apply basic inorganic chemistry， organic chemistry， and instrumental-analysis knowledge， while also improving their abilities to operate， analyze， and solve problems. Implementing this open experiment will help to improve the use of laboratory equipment and fully utilize existing laboratory resources.