Analytical Methods最新文献

Immunoassay techniques are widely recognized for their sensitivity and selectivity in biomarker detection; however, their high cost, time-consuming protocols and limited stability often pose significant limitations. In this study, we address these challenges by developing an antibody-free fluorescent platform for the detection of glial fibrillary acidic protein (GFAP), a biomarker released from astrocytes, which plays a critical role in neurological diseases such as ischemic stroke and glioblastoma (GBM). Glutamic acid (GA), a neurotransmitter prevalent in the brain, was selected to quench a near-infrared (NIR) emitting carbon dot-based probe, exploiting the potential interaction between GA and GFAP. The probe demonstrated a turn-on response towards GFAP in the presence of various co-existing biomolecules and ions with a detection limit of 1.8 pg mL⁻¹. A real sample assay conducted in human serum further validated the performance of the probe, achieving a recovery rate of 85% to 97%, underscoring the potential of the probe as a reliable and cost-effective tool for GFAP detection in clinical settings.

Nimodipine (NMP) is a calcium channel blocker known for maintaining blood perfusion, particularly in the brain. Given its importance and widespread applicability, tracking NMP during industrial processes for both pharmaceutical dosage forms and raw material samples is crucial. This article discusses the use of a glassy carbon electrode (GCE) polished with commercial alumina for NMP determination. Atomic force microscopy (AFM) confirmed the presence of residual alumina on the surface of the GCE, and electrochemically active surface area (EASA) analysis demonstrated an enhancement in the active area. The alumina-polished electrode (GCE/AP) exhibited a superior response for NMP, with voltammograms obtained via differential pulse voltammetry (DPV). In this investigation, we employed density functional theory (DFT) calculations to examine the electrochemical characteristics of NMP from a theoretical standpoint. The geometry was optimized by employing the Generalized Gradient Approximation (GGA) functional, BP86, in conjunction with the Def2-TZVPPPD basis set and D3BJ dispersion corrections. To identify potential sites for oxidation and reduction, Fukui indices and dual descriptors were employed. The results indicate that the nitro group is the most probable site for reduction, whereas the dihydropyridine ring displays a proclivity for electrophilic attack. Regarding electroanalysis, two calibration curves were established through consecutive additions of NMP, with linear ranges of 0.20 to 49.0 μmol L⁻¹ and 2.92 to 13.5 μmol L⁻¹, respectively. The theoretical limits of detection (LOD) and quantification (LOQ) were, respectively, 0.06 μmol L⁻¹ and 0.20 μmol L⁻¹ for the first curve, and 4.0 μmol L⁻¹ and 12.0 μmol L⁻¹ for the second curve. Common constituents in pharmaceutical tablets were tested as potential interferents, and GCE/AP showed acceptable anti-interference ability. NMP was successfully quantified in tablet and raw material samples using the novel electrochemical method and High-Performance Liquid Chromatography (HPLC). Statistical analysis revealed no significant differences between the results. This confirms that the GCE/AP sensor was effectively applied to pharmaceutical samples, consistent with findings reported in the literature.

This study introduces a method for enhancing spectroelectrochemical sensor sensitivity by incorporating optical fiber technology. The sensor comprises a gold mesh electrode coated on the surface of an exposed optical fiber core. Total reflection attenuation spectroscopy was employed to measure the optical properties of the fiber core surface. To enhance sensitivity, we investigated surfactant addition to the sample, anticipating the formation of an electrostatic film on the optical fiber core surface. Spectroscopic measurements were conducted on 24 dyes, including cationic methylene blue and anionic indigosulfonic acid, as target substances. Consequently, adding surfactant at approximately one-tenth of the critical micelle concentration slightly improved the measurement sensitivity for cationic dyes, with a 2.3-fold increase observed for methylene blue. Previously challenging anionic dyes were successfully detected using this method. In addition, this technique was successfully applied to sulfide ion determination using the absorbance spectrophotometric method with methylene blue. The findings indicated that this approach markedly enhances the sensitivity and adaptability of spectroelectrochemical sensors using fiber optic, particularly in the detection of a wide variety of chemical substances.

Haloacetic acids (HAAs) are among the most common drinking water disinfection byproducts (DBPs) and are of widespread concern due to their potential carcinogenicity and reproductive health risks. However, data on human exposure to HAAs are scarce. HAAs in urine have been identified as useful biomarkers for assessing exposure to DBPs via drinking chlorinated water. In this study, a suitable method for determining eight HAAs (including iodoacetic acid (IAA)) in human urine was developed and validated based on USEPA 552.3. HAAs were extracted from 5 mL of urine with methyl tert-butyl ether (MTBE), derivatized with acidified methanol and analyzed via gas chromatography with an electron capture detector (GC-ECD). Several optimization experiments were performed for the sample pretreatment step to establish a more sensitive and efficient analysis scheme. The method was tested in terms of linearity, accuracy, precision, and detection limits. The linear range of the method was 0.1–100 μg L⁻¹, with recovery rates from blank matrix-spiked samples ranging from 84.9% to 122.6%, relative standard deviations between 1.7% and 14.2%, detection limits from 0.013 to 0.3 μg L⁻¹, and quantitation limits from 0.043 to 1.0 μg L⁻¹. Finally, the method was applied to detect HAAs in actual human urine samples. Six HAAs were detected in 460 urine samples to varying degrees, with geometric mean concentrations ranging from 0.07 to 0.89 μg L⁻¹. The results demonstrate that the method is practical, and suitable for routine biomonitoring of HAAs in populations.

Diabetes mellitus is a disease that affects a large number of people around the world. There are no effective methods to completely cure diabetes, and patients have to constantly monitor their blood sugar levels, so there is still a need for improved sensors. In addition to diabetes, quantitative values of glucose levels affect the development of some endocrine diseases and problems with nervous tissue. In this review, we will describe existing developments, the principles of glucose measurement, sensor designs, the materials they are made of, and how nanotechnology is improving the sensors under development by increasing sensitivity and surface area and improving catalytic properties.

Microcystin-lr (MC-LR) is one of the most toxic and ubiquitous microcystins (MCs) released by cyanobacteria. Exposure to MC-LR can cause multiple organ damage and even death of the organism. Therefore, creating highly sensitive and dependable methods for detecting trace MC-LR is crucial. Herein, we developed a novel fluorescence aptasensor aided by exonuclease III (Exo III) for the highly sensitive detection of MC-LR. In the presence of MC-LR, the affinity interaction between MC-LR and aptamer A was triggered, leading to the release of blocker B. This unbound blocker can initiate Exo III-mediated signal amplification to digest the probe H, thereby recovering the fluorescence signal for readout. The proposed Exo III-assisted sensing platform demonstrated remarkable sensitivity and selectivity, achieving a limit of detection (LOD) of 0.37 ng L⁻¹. Furthermore, it is robust and has been effectively utilized on water samples, achieving acceptable recovery rates (95.04–107.01%). With excellent sensitivity, high selectivity, efficient signal amplification, and fluorescence readout, the proposed biosensor offered a new and reliable alternative for the detection of trace MC-LR in the environment and the early warning of algal toxins.

Frankincense is a resin drug used widely in the medical field. Comprehensive qualitative and quantitative analyses of the differences in the chemical composition of frankincense, stir-fried frankincense and vinegar frankincense were done by combining GC-MS and LC-MS. GC-MS revealed six chemical compositions with high content in frankincense: β-ocimene, 1-octanol, acetic acid, octyl ester, nerolidol, lauric acid and incensole acetate. LC-MS revealed eight more widely studied chemical compositions: 11-keto-β-boswellic acid, β-elemolic acid, acetyl-11-keto-β-boswellic acid, β-elemonic acid, α-boswellic acid, β-boswellic acid, 3-O-acetyl-α-boswellic acid and 3-O-acetyl-β-boswellic acid. In addition, the chemical composition of frankincense was identified by the fragmentation information afforded by LC-MS.

Speciation methods provide a more detailed picture regarding human exposure to toxic metals/metalloids and their effects on human health. The toxicity of methylmercury (MeHg) differs considerably from inorganic mercury (iHg), such that their separation and quantification in whole blood is helpful in identifying sources and possible pathways of exposure. Liquid chromatography (LC) has several advantages over gas chromatography (GC) for the separation of iHg from MeHg due to the former's compatibility with uptake rates of common nebulizer systems used with ICP-MS and the latter's requirement for a derivatization step to produce gaseous Hg species for an effective separation. Here we report an improved method that was developed to separate and quantify MeHg and iHg species in whole blood using isocratic LC elution with determination by vapor generation (VG) coupled with ICP-MS/MS. Chromatographic separation of MeHg and iHg is achieved in ∼4 minutes on a C8 reversed phase column. In those rare cases where there may be human exposure to ethylmercury (EtHg), or where a certified reference material (CRM) is known to contain EtHg (e.g., NIST SRM 955c), all three Hg species can be separated by extending the LC elution time to 8 minutes. Adding VG post column boosts the signal-to-noise ratio, and lowers the LOD. With optimized sample preparation, the LC-VG-ICP-MS/MS method LOD for both iHg and MeHg is 0.2 μg L⁻¹. Method validation was conducted using NIST SRM 955c Toxic Metals in Caprine Blood and NIST SRM 955d Toxic Elements and Metabolites in Frozen Human Blood. Additional validation data were generated using archived blood reference materials from multiple Proficiency Testing programs and External Quality Assessment schemes. Blood-based quality control materials, previously analyzed for Hg species using isotope dilution with GC coupled to ICP-MS, were provided by the US CDC.

A terephthalaldehyde-based AIE fluorescent probe has been widely used in bioimaging and sensor technology. But most of them have complex structures and are difficult to synthesize. In our experiments, an ethyl butyrate side chain was introduced on the benzene ring of terephthalaldehyde, diethyl 4,4′-((2,5-diformyl-1,4-phenylene)bis(oxy))dibutyrate (FPB), which enhances its Aggregation-Induced Emission (AIE) properties. This enhancement is attributed to the intermolecular hydrogen bond interaction. These findings are corroborated by results from X-ray single crystal diffraction. Meanwhile, it is well known that the aldehyde group and bisulfite (HSO₃⁻) exhibit a specific reaction through nucleophilic addition. We found that the product formed can disrupt intermolecular hydrogen bonding, thereby affecting the AIE properties. Based on the reaction of HSO₃⁻ and the aldehyde groups of FPB, the AIE fluorescent molecule (FPB) can be used to rapidly detect sulfur dioxide. Under the optimized experimental conditions, FPB shows high selectivity and anti-interference to HSO₃⁻ and fast response (less than 1 minute). The detection limit of bisulfite is 24.4 nM. Finally, FPB molecule was used to detect HSO₃⁻ in some vegetables purchased from local supermarkets and some dried fruits purchased online. The measurement results were compared with the test results of China's national standard method (GB 5009.34-2022). The data measured by FPB showed good consistency with the results measured by the national standard method.

The use of Raman spectroscopy for real-time gas monitoring has the advantages of response speed, high sensitivity and low cost. However, due to the overlap of each peak position, the Raman spectral data usually exhibit high dimensionality, complex nonlinear relationships and significant noise interference, which makes it difficult to directly determine the composition of the mixture using traditional data processing methods. This work focuses on the optimization of a machine learning model, XGBoost, for predicting gas composition based on Raman spectral data, enhancing predictive accuracy through three different feature extraction and feature selection methods. The superior performance of the XGBoost model is demonstrated by comparison with other machine learning models, including decision trees, random forests, support vector machines and neural networks, using the Raman spectrum of a gas mixture of hydrogen, ethylene, propylene and butene. The results show that XGBoost has better accuracy and generalization ability for quantitative analysis of Raman spectra, making it suitable for complex chemical process monitoring.