Analytical Methods最新文献

A novel compact potentiometric electrode specifically designed for pH monitoring, featuring a good construction on a glass substrate coated with a cerium-doped tin oxide (Ce-doped SnO₂) layer. The Ce-doped SnO₂ thin film was created by spray-pyrolysis it on a glass substrate. Field emission scanning electron microscopy (FESEM) and X-ray diffraction (XRD) were used to characterize the deposited metal oxide coating. As a miniature potentiometric electrode, the synthesized Ce-doped SnO₂/glass substrate was utilized to measure a broad pH range (pH 2-12) in aqueous solutions. The electrode had a perfect near-Nernstian response (slope of -58.6 ± 0.7 mV per decade), high potential stability, mechanical durability, and great selectivity towards some common interfering cations and anions. These characteristics made it ideal for quality control and assurance purposes. The electrode's performance parameters and validation measurements were assessed using established procedures. The Ce-doped SnO₂-based electrode satisfactorily monitored the pH of several genuine water, drink, and fruit juice samples, and the data compared well with those obtained using a traditional pH glass electrode. The integration of Ce-doped SnO₂ as the active material marks a significant advancement, providing enhanced electrochemical stability, improved sensitivity, and a wider pH detection range compared to conventional electrodes. The compact design not only reduces the sensor's footprint but also facilitates its application in miniaturized and portable pH monitoring devices, making it highly suitable for advanced analytical and environmental sensing applications.

This paper presents functionalized magnetic nanoparticles (Fe₃O₄@Au MNPs) combined with the surface-enhanced Raman spectroscopy (SERS) technique for sensitive detection of colorectal cancer (CRC) protein biomarker carbohydrate antigen 19-9 (CA19-9). Fe₃O₄@Au MNPs were constructed by the PEI-mediated seed growth method. Then, the signal molecule 5-5'-dithiobis (succinimidyl-2-nitrobenzoic acid) was used as a bridging agent to link CA19-9 antibody, and the SERS sensor was prepared. Using this sensor, detection of CA19-9 can be realized with only a one step sampling reaction. Fe₃O₄@Au MNPs combine the advantages of magnetic materials and noble metal nanoparticles, effectively amplifying the signal by creating numerous "hot spots" within the Au particle gaps and enhancing magnetic enrichment. Consequently, this approach lowers the detection limit (LOD) and enhances detection sensitivity. The ratio of characteristic peak intensities I₁₃₉₂/I₁₀₆₉ was selected for calculation, and a linear equation was constructed with a LOD as low as 0.27 U mL^-1 by quantitative detections of the standard antigen. Finally, the sensor was used to analyze the clinical serum samples from CRC patients and healthy individuals, and the detection results were consistent with the actual results. This method exhibits notable advantages, including simplicity, high sensitivity, specificity, stability and reproducibility. It is expected to provide a new promising analytical method for clinical CA19-9 immunoassay.

Light sources exhibit significant absorption and scattering effects during the transmission through biological tissues, posing challenges in identifying heterogeneities in multi-spectral images. This paper introduces a fusion of techniques encompassing the spatial pyramid matching model (SPM), modulation and demodulation (M_D), and frame accumulation (FA). These techniques not only elevate image quality but also augment the precision of heterogeneous classification in multi-spectral transmission images (MTI) within deep learning network models (DLNM). Initially, experiments are designed to capture MTI of phantoms. Subsequently, the images are preprocessed separately through a combination of different techniques such as SPM, M_D and FA. Ultimately, multi-spectral fusion pseudo-color images derived from U-Net semantic segmentation are fed into VGG16/19 and ResNet50/101 networks for heterogeneous classification. Among them, different combinations of SPM, M_D and FA significantly enhance the quality of images, facilitating the extraction of heterogeneous feature information from multi-spectral images. In comparison to the classification accuracy achieved in the original image VGG and ResNet network models, all images after preprocessing effectively improved the classification accuracy of heterogeneities. Following scatter correction, images processed with 3.5 Hz modulation-demodulation combined with frame accumulation (M_D-FA) attain the highest classification accuracy for heterogeneities in the VGG19 and ResNet101 models, achieving accuracies of 95.47% and 98.47%, respectively. In conclusion, this paper utilizes different combinations of SPM, M_D and FA techniques to not only enhance the quality of images but also further improve the accuracy of DLNM in heterogeneous classification, which will promote the clinical application of MTI technique in breast tumor screening.

This work presents a straightforward, highly sensitive, and cost-effective method for the simultaneous determination of V, Ti, Ni and Ga by high resolution-continuum source electrothermal atomic absorption spectrometer (HR-CS ETAAS) in aqueous environmental samples (tap and seawater samples). The system is based on retention of the analyte onto a novel magnetic nanomaterial (M@GO magnetic graphene oxide) functionalised with methylthiosalicilate (MTS). The formed complexes between the M@GO-MTS and the target analytes were broken, adding 1 mL of nitric acid (6%) and sonication for 5 min. The optimized method achieved detection limits of 0.71 μg L^-1 for Ti, 0.20 μg L^-1 for V, 0.04 μg L^-1 for Ga, 0.66 μg L^-1 for Ni. The accuracy of the proposed method was demonstrated by analysing two certified reference materials and by determining the analyte content in spiked environmental water samples. The results obtained using this method were in good agreement with the certified values of the standard reference materials, and the recoveries for the spiked tap water and seawater samples ranged from 94% to 120%.

PPG signals are a new means of non-invasive detection of blood glucose, but there are still shortcomings of poor time adaptability and low prediction accuracy of blood glucose quantitative models. Few studies discuss prediction accuracy in the case of a large time interval span between modeling and prediction. This paper proposes an automatic optimal threshold baseline removal algorithm based on variational mode decomposition (AOT-VMD), which can adaptively eliminate high-frequency noise and baseline interference for each decomposed IMF modal component and reduce the baseline difference of PPG signals from different days. Furthermore, a fuzzy integral multi-model decision fusion algorithm based on error weight is proposed. The fuzzy integral operator is introduced to make the features with large contributions in each sub-model maintain a high-weight value in the overall prediction mechanism, which improves the prediction accuracy of blood glucose. In this paper, a self-developed portable PPG glucose meter is used to collect PPG signals, and the true blood glucose values for 8 consecutive days are collected by CGM. The proposed algorithm is used to build a model with the first day's data and predict the blood glucose values for the remaining 7 days. The experimental results show that the AOT-VMD preprocessing algorithm and the quantitative regression algorithm of the fuzzy integral multiple model decision fusion algorithm proposed in this paper perform well in measurement accuracy and time adaptability compared with the traditional methods. In addition, the proposed method requires less invasive calibration samples in the modeling stage, achieving high-precision prediction for a long period. 100% of the samples are located in areas A and B of the Clarke area in this experiment, and the algorithm has strong time generalization ability. This innovative method can promote the development of a home blood glucose noninvasive detector.

A new technique has been developed to enhance the stability of laser-induced breakdown spectroscopy (LIBS) in the analysis of dry droplets by mitigating the coffee ring effect (CRE) on substrates with superhydrophobic microstructured grooves. The substrate was prepared from a laser-etched pure copper base, resembling the surface of a lotus leaf, creating a biomimetic superhydrophobic substrate. The superhydrophobic microstructured grooved substrate contained an array of dome-shaped cones with heights of approximately 140 μm and 100 μm, arranged in a periodic pattern of high-low-high. The superhydrophobic properties of the substrate not only evaporation-induced thermal capillary action but also initiated the Marangoni flow, which moves from the periphery to the center of the droplet as it evaporates. This flow mechanism effectively mitigated the CRE by transporting the analyte from the bottom edge of the droplet across its surface to the central peak. To assess how these superhydrophobic microstructured grooved substrates impede the formation of coffee rings, LIBS was deployed to analyze samples from both structured and unstructured grooved substrates. The results indicated that the relative standard deviation (RSD) of the spectral intensity for Sr I at 407.67 nm in substrates with a superhydrophobic microstructured groove edge length of 0.8 mm was 3.6%. In contrast, for the unstructured grooved substrate and a side length of 0.9 mm, the RSD was significantly higher at 25.4%. This research demonstrates that substrates with superhydrophobic microstructured grooves are capable of effectively mitigating the CRE. Additionally, the study examined how the dimensions of these grooves impact the plasma characteristics across two distinct configurations. Based on these observations, calibration curves for Sr were developed using substrates with groove side lengths of 0.6 mm and 0.8 mm. The performance of the superhydrophobic microstructured grooved substrate was satisfactory, exhibiting determination coefficients (R²) of 0.994 and 0.995 for the Sr element. The detection limits (LOD) were notably low at 0.16 μg mL^-1 and 0.11 μg mL^-1. The average relative standard deviations (ARSD) were 7.2% and 4.9%, respectively. These results demonstrate that the superhydrophobic microstructured grooved substrate effectively mitigates the CRE, thereby enhancing the detection sensitivity and prediction accuracy for heavy metals. This provides a robust reference for selecting platforms using LIBS technology in the pre-treatment process.

The expression levels of small extracellular vesicles (sEVs) are closely associated with several significant biological processes, which can be used as a crucial biomarker for cancer diagnosis, such as colorectal cancer. More efforts are still necessary to amplify sEV detection sensitivity, as their expression is minimal during the early stages of colorectal cancer. Through the integration of a catalytic assembly-triggered DNAzyme motor and gold nanoparticle (AuNP) aggregation, we have developed a triple signal amplified biosensor for the detection of sEVs. In this method, the catalytic assembly triggered DNAzyme motor continuously cleaved on the hairpin probe which is fixed on the surface of AuNPs, leaving a single-stranded sequence on the surface of AuNPs to induce the aggregation. This approach employs a triple signal amplification process to enhance the efficiency of the reaction and circumvent the issue of expensive and readily degradable proteases. The signal output system is based on dynamic light scattering technology, which enables ultra-sensitive detection of sEVs with a detection limit of 3.08 particles per μL. The present strategy exhibits significant potential for the analysis of a variety of additional analytes in clinical research disciplines due to its appealing analytical capabilities.

The forest frog (Rana ridibunda Pollas) is a traditional medicinal source rich in active protein compounds. In order to extract these compounds, six extraction methods were employed, including freeze-thaw and stirring techniques. Three different solvents were utilized in this process: 0.15 M sodium chloride (NaCl), 0.05 M phosphate buffer (PB), and 0.05 M phosphate-buffered saline (PBS). The objective was to identify the most effective extraction method. The extraction efficiencies, protein content, structure, and physicochemical properties of the extracts were compared. Additionally, antioxidant activity and free amino acid composition were analyzed. The highest-scoring extract, denoted as M1, obtained through freeze-thaw extraction using 0.15 M NaCl, exhibited an extraction rate of 7.79 ± 0.71% and a protein content of 60.36 ± 2.12%. M1 also showed antioxidant activity against DPPH˙, ABTS⁺˙, and ˙OH free radicals, with IC₅₀ values of 0.41, 0.41, and 0.39 mg mL^-1, respectively. The freeze-thaw extraction method utilizing 0.15 M NaCl has been identified as effective for extracting proteins from dried forest frogs, confirming their potential as a source of antioxidant proteins for scientific research and application.

Lead (Pb²⁺) and hexavalent chromium (Cr⁶⁺) are highly toxic pollutants with no safe exposure levels, posing significant health risks globally, especially in developing countries. Current detection methods for these metals are often complex and inaccessible, highlighting the urgent need for innovative approaches. In this study, we present a rapid, cost-effective colorimetric assay utilizing ascorbic acid-capped gold nanoparticles (AuNPs) for the selective detection of Pb²⁺ and Cr^3+/6+ ions at levels recommended by regulatory bodies such as the WHO and EPA. The synthesis of our AuNPs was achieved by reducing gold(III) chloride with ascorbic acid, resulting in stable, negatively charged nanoparticles, as characterized by dynamic light scattering, UV-vis spectroscopy and high-resolution transmission electron microscopy. Our method demonstrated high sensitivity, with limits of detection (LOD) of 5.4 ± 0.25 ppb for Pb²⁺, and 6.3 ± 0.23 ppb for Cr⁶⁺, confirming specificity towards these ions in various water samples. The assay's efficacy was validated in real-world applications, including testing drinking water from multiple sources and assessing the performance of filtration systems. This straightforward assay offers a promising tool for monitoring water quality, enhancing public health initiatives and accessibility to critical environmental testing.

Warfarin (WA), the most prescribed oral anticoagulant in patients with atrial fibrillation, is widely utilized for the treatment of various diseases, such as vascular disorders, venous thrombosis, and atrial fibrillation. However, its abnormal concentration is linked to a variety of disorders and diseases, namely bleeding while brushing teeth, skin tissue issues, hair loss, and chest pain. Therefore, WA monitoring in blood serum is vital due to its narrow therapeutic window. Accordingly, WA determination has been conducted using various methods, such as high-performance liquid chromatography, fluorescent, surface-enhanced Raman scattering, and electrochemical methods. Electrochemical methods have received considerable attention due to their outstanding selectivity, remarkable sensitivity, great time efficiency, and cost-effectiveness. Herein, a comprehensive literature survey on electrochemical methods for determining WA is presented. This review discusses the development of various chemically modified electrodes (CMEs). These CMEs, such as multi-wall carbon nanotubes, molecularly imprinted polymers, metal oxide nanoparticles, and polymer nanocomposites, owing to their morphology and structure, high selectivity, high conductivity, and high volume/area ratio, are designed to overcome the limitations of bare electrodes, which include reduced electrocatalytic activity, slower electron transfer rates, and poor sensitivity. Also, this review presents the advantages and disadvantages of various modified electrodes applied in WA detection.