Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy最新文献

Submerged oil remains suspended in water bodies for extended periods, exhibiting high concealment and significant hazards. Accurate quantification of its concentration is crucial for formulating emergency response strategies. Accordingly, a comparative evaluation was carried out to examine the performance of four multi-way calibration strategies for the quantitative determination of submerged oil concentrations using time-resolved fluorescence spectroscopy (TRFS) data. The investigated approaches included parallel factor analysis/alternating penalty trilinear decomposition combined with partial least squares regression (PARAFAC/APTLD-PLSR), N-way partial least squares (N-PLS), as well as N-PLS combined with residual bilinearization (N-PLS/RBL). The results indicate that the N-PLS/RBL approach effectively leverages the ability of N-PLS to capture multidimensional spectral information while simultaneously benefiting from the second-order advantage introduced by the RBL algorithm. Consequently, this method exhibits superior prediction accuracy and greater robustness compared with the other calibration strategies. Among them, crude oil samples exhibited the most outstanding performance, with a residual predictive deviation (RPD) of 14.90 and a coefficient of determination of 0.99 for the validation set. In addition, the limits of detection (LOD) for the four oil types ranged from 0.15 to 1.03 mg·L^-1, indicating high sensitivity that meets the requirements for rapid on-site detection. Overall, this study provides a reliable methodological foundation and technical pathway for the development of low-cost, highly interference-resistant, and rapid on-site detection technologies for submerged oil pollution.

Accurate discrimination of dark tea vintages is crucial for quality assurance and market value assessment. In this study, a multiple carbon quantum dots-enhanced excitation-emission matrix (MCQDs-EEM) fluorescence strategy was developed for the rapid identification of Anhua dark tea from different production years. Three functional carbon quantum dots with complementary responses to pH, tea polyphenols, and amino acids were combined into an integrated fluorescent chemical sensor, generating enhanced and information-rich fluorescence fingerprints upon interaction with tea infusions. The MCQDs-EEM dataset was decomposed using the Alternating Trilinear Decomposition (ATLD) algorithm, which revealed four chemically significant components that showed differences in Anhua dark tea from thirteen different years. Based on the MCQDs-EEM data, partial least squares discriminant analysis (PLS-DA) and k-nearest neighbors (k-NN) models were constructed. The PLS-DA model achieved 100% classification accuracy for both the training and test sets, while the k-NN model attained accuracies of 96.59% and 100%, respectively. These results demonstrate clear superiority over the traditional fluorescence strategy based on carbon quantum dots (CQDs-FL), which relies on the splicing of three distinct emission spectra. The MCQDs-EEM strategy not only simplifies the implementation process but also demonstrates higher accuracy, which can be regarded as an effective tool for tea vintage authentication and quality control.

The development of fluorescent sensing systems that are suitable for the detection of protamine and trypsin is of significant practical value, given their vital role in physiological and medical processes. The utilization of commercially available fluorescent probes is a favored option due to the inherent advantages of convenience and exemption from complex synthesis that these probes possess. Consequently, the development of sensing systems based on commercially available fluorescent probes merits further consideration. In this research, a highly efficient sensing system for protamine has been achieved by employing a commercially available dye, eosin Y (EY), as the probe. Subsequent to the electrostatic assembly of EY with protamine, a substantial fluorescence quenching is observed. Meanwhile, a drastic change in the absorption spectra is observed, accompanied by a color change from yellowish-green to pink in the solution. EY was confirmed to be an effective probe for the fluorescence detection of protamine with a limit of detection of 4.03 ng/mL. A smartphone-based fluorescence colorimetric analysis platform has been developed for the purpose of achieving rapid and sensitive analysis of protamine in buffer solution. Furthermore, when trypsin is present, the hydrolysis of protamine induces the progressive disaggregation of EY-protamine complex into EY monomer, resulting in a direct, highly sensitive signal for monitoring trypsin activity in both buffer solution and artificial urine. The EY-based sensing system facilitates continuous real-time monitoring of trypsin activity and visual detection of protamine, and shows great potential in the field of clinical diagnostics and drug discovery. This work also serves as a catalyst for further research, particularly in the exploration of additional detection systems based on the available fluorescent dyes.

Nanozymes have garnered growing interest due to their potential to act as substitutes for natural enzymes across a range of biomedical applications, including biosensing, catalysis, and immunoassays. Conventional approaches in biosensing application face several challenges due to their complicated methodology and high cost, thus restricting their application in on-site detection. To overcome these challenges, herein, we developed gold coated copper sulfide (CuS@Au) nanozymes possessing synergistic peroxidase mimetic activity. Reports support this observation and can be assumed due to noncovalent Cu⁺(d¹⁰)-Au⁺(d¹⁰) metallophilic interactions. Peroxidase mimetic activity was evaluated and validated using chromogenic substrate TMB in the presence of H₂O₂, found to obey Michaelis-Menten enzymatic equation. In comparison to many reported nanozymes, advancement of developed system is to possess catalytic potential at physiological pH, which proves to more important in biological system. Developed nanozyme was further applied as colorimetric biosensor for rapid and sensitive detection of xanthine and ascorbic acid in biological matrices. Colorimetric sensing primarily based on xanthine (presence of xanthine oxidase) and ascorbic acid, ability to promote and inhibit the oxidation of TMB, respectively. Results showed that for xanthine and ascorbic acid, linear detection range were 0-750 μM and 10-200 μM with detection limit of 1.42 μM and 2.4 μM, respectively. Further, colorimetric method was also efficiently used in detection of xanthine and ascorbic in real samples with high recovery. Overall, the CuS@Au nanozyme exhibits significant peroxidase-like activity, characterized by its affordability, high efficiency, and cost effectiveness, thereby offering an effective and sensitive platform for monitoring xanthine and ascorbic acid as a substitute for conventional methods.

The controlled synthesis of the nanomaterials is imperative to develop efficient sensing probes. Herein, we report a simple, selective, and sensitive colorimetric sensor for Cr(III) detection based on citrate-capped gold nanoparticles (AuNPs) synthesized under varying temperature conditions. Using a one-step synthesis approach, five types of citrate capped AuNPs (AuNPs-I to AuNPs-V) were prepared at 4, 25, 37, 50, and 100 °C synthesis temperature, respectively. Upon addition of Cr(III), the developed AuNPs displayed color change (from red to blue) and a red shift in the localized surface plasmon resonance (LSPR) peak due to nanoparticle aggregation. The AuNPs aggregation is driven by strong Cr(III)-citrate interactions, where each Cr(III) ion coordinates with two citrate molecules to disrupt their colloidal stability. Among the developed AuNPs variants, AuNPs-I demonstrated the highest sensitivity, with a detection limit of 38.04 ppb. Notably, all the developed probes exhibited excellent selectivity for Cr(III) over other metal ions. Moreover, the practical applicability of the probes was accessed by evaluating their performance on tap water and grape juice.

Simple and cost-effective glyphosate assays were developed based on competitive demetallation using the dyes pyrocatechol violet (PV) and zincon (ZCN). The dyes served as colorimetric probes in the form of metal-catechol dye complexes: [Cu₂^IIPV] at pH 6.5 and [Zn^IIZCN] at pH 7.4 or pH 9.0. UV-Vis spectroscopy measurements were used to monitor complex formation and demetallation (taken to determine optimal reaction media), and to assess and optimize assay stability (with cooling shown to retard probe decomposition by up to a factor of 17). Subsequently, assay selectivity was evaluated based on complex stability constants to assess interference from complexing agents that may be present in real samples. This approach can be used to judge the applicability of the assays to real samples. Finally, calibration curves from large-scale UV-Vis measurements yielded low detection limits of 0.80-1.91 μM (0.14-0.32 mg/L). Most importantly, a novel smartphone-based colorimetric detection system was developed. It is the first of its kind to combine all features necessary for a convenient and user-friendly smartphone detection platform. Key novelties include the field-ready controlled lighting enclosure (3D-printed cuvette holder within a photo box), robust image calibration, automated color extraction based on markers, simultaneous evaluation of a large number of images with an automated output of annotated images, and a user-friendly color analysis software. The Blue_proportion = B/(R + G + B) parameter was identified as optimal, with detection limits of 0.87-1.68 μM (0.15-0.28 mg/L) using [Zn^IIZCN] at pH 7.4, being comparable to results wit a commercial UV-Vis spectrometer.

This study presents a digital image colorimetric method for hydrogen sulfide (H₂S) detection using citrate-capped silver nanoprisms (AgNPrs) synthesized via a photo-mediated process. Upon exposure to H₂S, the AgNPrs sensor exhibited a distinct color change from orange to gray, consistent with sulfidation and Ag₂S formation. Key analytical parameters were optimized, including buffer pH (9.0), reaction time (10 min), and sensor volume (60 μL). The method showed two linear response ranges with limits of detection and quantification of 1.87 and 6.24 ppmv, respectively, which are relevant to occupational safety and onsite screening applications. Accuracy and precision were assessed using spike recovery experiments in natural water samples, intra-day and inter-day precision studies, and comparison with a commercial lead acetate paper. The proposed system offers a portable and cost-effective approach for onsite sulfide detection, emphasizing procedural simplicity through additive-free synthesis and smartphone-based analysis.

Uterine smooth muscle tumors include a broad range of neoplasms, from benign leiomyomas (LMs) to malignant leiomyosarcomas (LMS), as well as intermediate forms classified as Smooth Muscle Tumors of Uncertain Malignant Potential (STUMP). An accurate diagnosis of these tumor types is essential for their appropriate clinical management; however, it remains challenging due to possible overlapping of histological features. In this study, a multidisciplinary approach combining Fourier Transform Infrared Imaging (FTIRI) spectroscopy, a label-free and non-destructive analytical technique, with histology and statistical analyses have been exploited for investigating the morpho-chemical characteristics of these uterine smooth muscle tumors. The analysis aimed to identify new reliable and diagnostic spectral markers, complementary to traditional histology, and thus useful for improving accuracy in cases with uncertain morphological features. Tissue samples including different leiomyoma histological subtypes, such as usual, cellular, apoplectic, and bizarre, were analyzed and compared with LMS and healthy myometrium. The analysis of IR data, submitted to univariate and multivariate statistical approaches, such as Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), revealed distinctive spectral profiles associated with each tumor type and indicated changes in collagen content and organization as key features for a reliable discrimination not only between benign and malignant tissues but also among different LM histotypes.

Precise control of the graft distribution, represented by the degree of grafting (DoG), in anion exchange membranes (AEMs) prepared by radiation-induced grafting (RIG), is critical for alkaline fuel cells' performance, especially aiming to improve water management. However, current methods offer only bulk or qualitative assessments of DoG, limiting the ability to understand and optimize local membrane properties. In this work, we present a novel Raman-based chemometric method for the spatially resolved quantification of DoG using micro-Raman spectroscopy. By applying classical least squares (CLS) fitting to decompose Raman spectra into contributions from the polymer base and grafted side chains, we establish a direct correlation between CLS scores and the local DoG. This approach enables, for the first time to our knowledge, the use of a multivariate technique for quantitative mapping of grafting profiles across the membrane cross-section using a widely accessible and non-destructive technique. The method is validated on membranes with known grafting levels and applied to asymmetric DoG AEMs, revealing detailed insights into spatial variations in functionalization. Moreover, the approach is broadly applicable to any grafted copolymer system with side-chain functionalization, beyond the specific membranes studied here. By combining spatially resolved measurement with rigorous chemometric analysis, this technique offers a robust tool for the design and optimization of next-generation ion-conducting membranes in electrochemical energy systems.

Background: Quantitative determination of total phosphorus (TP), an indirectly absorbing aquatic indicator, using near-infrared (NIR) spectroscopy is challenged by high-dimensional, noisy, and nonlinear spectral data. Furthermore, traditional data-driven models tend to neglect underlying physical principles, resulting in overfitting and physically inconsistent predictions.

Method: We propose PICSEN, a Physics-Informed Convolutional-Sequential Dual-Branch Fusion Network. Its architecture synergistically fuses global representations, extracted by a CNN from PCA features, with localized sequential dependencies captured by a GRU from key spectral sequences. To enhance physical consistency, a specialized regularization term is introduced. Unlike traditional methods, it learns an effective absorption proxy to reconstruct the original spectra, thereby embedding implicit physical constraints tailored for TP's indirect optical response within an end-to-end training framework.

Significant findings: Through rigorous repeated validation and statistical testing, PICSEN achieved an average R² of 0.9380 ± 0.0191, demonstrating competitive and robust performance across all benchmarks (p<0.05). Ablation studies confirmed the critical contributions of both the dual-branch architecture and the physics constraint, with the latter serving as a primary driver for model stability. The model demonstrated high stability across random seeds and enhanced resilience to Gaussian noise. SHAP analysis and saliency maps further validated that PICSEN aligns with known physicochemical absorption regions, indicating strong physical consistency within the studied aquatic matrix. While the current findings are based on a specific river basin (N=235), the adaptable nature of the effective absorption proxy provides a robust framework for regional water quality monitoring, with promising potential for recalibration across diverse hydrological environments.