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

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.

Molecules exhibiting aggregation-induced emission (AIE) characteristics have gained prominence fluorescence in aggregated state, offering distinct advantages compared to traditional fluorogenic probes. This study proposes a hydrazine modified tetrastyrene derivative (Q) with AIE enhancement effect, which is used for high selectivity and ultrasensitive recognition of Fe³⁺ and Cu²⁺ by constructing a new metal coordination supramolecular complex (Q-Fe/Cu). Quantitative analysis revealed detection limits (LODs) of 6.35 × 10^-9 M (Fe³⁺) and 4.37 × 10^-9 M (Cu²⁺). Significantly, the coordinated complex Q-Cu (formed through Cu²⁺ incorporation) achieved high sensitive CN^- quantification with a LOD of 6.89 × 10^-8 M. Meanwhile, Q-based test strips can serve as convenient and efficient reagent kits for detecting Fe³⁺ and Cu²⁺, while Q-Cu based test strips can detect CN^- in water. This supramolecular design strategy establishes a novel paradigm for sample ultrasensitive recognition through programmable coordination-driven assembly.

The creation of fluorescent probes with exceptional brightness holds the key to unlocking sensitive, reproducible, and reliable performance of fluorometry. Other than synthetizing novel fluorescent probes, enhancing the functionality of well-established, mass-produced existing probes is also very meaningful although thousands of organic small molecule fluorescent probes have been synthesized in recent years. Here, we demonstrate an unexpected and significant enhancement of brightness of Alizarin Red S (ARS)/PyB(OH)₂ (A/P) complexes, which we reported recently by coordination induced assembly with Al³⁺. A novel fluorescence turn-on method for Al³⁺ detection was developed based on the significant emission change. The introduction of Al³⁺ to the formed A/P/Al³⁺ ensembles make them highly suitable for constructing sensitive fluorescence methods because Al³⁺ provides recognition sites. The observed performance was rationalized through theoretical calculations, including HOMO-LUMO gaps and configuration changes. Thus, a highly sensitive sensing system for PPi and ALP was proposed. Unlike other Cu²⁺ or Cd²⁺ based ALP on-off assay, the proposed Al³⁺ based ALP assay allows off-on readout. In addition, the practicality of the proposed sensing system has been validated by detecting ALP in complex fetal bovine serum. The strategy can also be extended to enhance the brightness of other existing fluorescent probes, and related work is currently underway in our laboratory.

Surface-Enhanced Raman Spectroscopy (SERS) has long been a recognized method for the rapid and sensitive detection of low concentrations of analytes. Nevertheless, challenges still exist in the fabrication of practical, effective SERS substrates and the interpretation of complex SERS spectra. In this study, a machine learning (ML)-assisted SERS platform that is both scalable and practical is proposed. The SERS substrate, composed of silver nanoparticle-decorated nanofibers (AgNFs), was obtained by integrating nanoparticle-free reactive silver (Ag) ink into a nanofiber matrix containing polyvinylpyrrolidone (PVP) and poly (ethylene oxide) (PEO). The nanofibers served both as a mechanical scaffold and a nano-scale template, guiding the formation of homogeneously distributed Ag nanostructures throughout the substrate. This synergistic fiber-assisted growth produced a highly SERS-active substrate, resulting in an analytical enhancement factor (AEF) of 2.9 × 10⁷. This platform enabled detection of phenylalanine, proline, valine, alanine, and cysteine amino acids at concentrations as low as 0.3 μM. The results demonstrated a direct correlation between the analyte concentration and the SERS signal intensity, as evidenced by determination coefficients (R²) that exceeded 0.9. Furthermore, the spectral data were subjected to analysis using ML algorithms, thereby allowing for the classification of amino acids. The linear support vector machine (SVM) algorithm exhibited superior classification performance, with an accuracy rate of approximately 98%. The findings demonstrate the efficacy of the developed ML-enhanced SERS platform for detecting various analytes, as well as its advantageous properties of simplicity, scalability, reproducibility, and ultra-sensitive structure.

Microcystin-LR (MC-LR) could be largely released in water environment during the cyanobacterial blooms, endangering the health of plants, animals, and even humans. Numerous evidences had demonstrated a strong correlation between the toxicities of MC-LR and the oxidative stress induced by MC-LR. Hydrogen peroxide (H₂O₂) is one of the primary constituents of reactive oxygen species (ROS) and tends to be overproduced under oxidative stress. Therefore, detecting the changes in H₂O₂ levels in organisms exposed to MC-LR can serve as an indicator of MC-LR-induced oxidative damage. However, the studies of directly detecting H₂O₂ levels in organisms exposed to MC-LR are lacking. In this work, we developed a novel near-infrared probe, DSP-B, to detect H₂O₂ under MC-LR-induced oxidative stress in organisms. DSP-B exhibited high sensitivity and specificity to H₂O₂, and the detection ability of DSP-B to endogenous and exogenous H₂O₂ has also been validated. Then DSP-B was applied to detect the H₂O₂ level in cells and zebrafishes treated with MC-LR to elucidate the effect of oxidative stress caused by MC-LR. Moreover, DSP-B was utilized for tissue visualization imaging in MC-LR-poisoned loaches model, enabling the upregulation of H₂O₂ to be successfully observed. This study offers a novel strategy for analyzing the MC-LR-induced oxidative stress and demonstrates the potential of using probe for MC-LR toxicity research. This probe is expected to provide assistances in evaluating the risks and hazards of MC-LR exposure to organisms in the environment.

Paraquat (PQ) is the herbicide that has been proven to be harmful to humans and poses significant risks. Rapid and accurate determination of PQ in clinical practice would aid in the correct diagnosis and timely treatment of patients with PQ poisoning. This study aims to utilize surface-enhanced Raman spectroscopy (SERS) for the rapid and accurate detection of PQ in human plasma. Ag nanoparticles modified metal-organic framework (Ag-MOF) was prepared by sodium citrate reduction method as SERS substrate. The octahedral NH₂-MIL-88B(Fe) was selected as the MOF matrix due to its porous structure and excellent adsorption capacity, which enabled the adsorbed PQ to be brought closer to the "hot spots" generated by the local surface plasmon resonance (LSPR) of Ag. Consequently, the Ag-MOF exhibited good PQ detection performance, with limits of detection (LOD) as low as 3.36 × 10^-9 M (0.86 μg/L) and 4.98 × 10^-9 M (1.28 μg/L) for aqueous solutions and human plasma samples, respectively. Furthermore, the prepared Ag-MOF SERS substrate demonstrated good selectivity, reproducibility, and stability, providing a simple, rapid, and sensitive method for detecting PQ in human plasma. It is expected to be applied in clinical PQ detection.

Charge recombination, which causes photocurrent loss, is a critical factor limiting the performance of dye-sensitized solar cells (DSSCs). Introducing alkyl or alkoxy chains into molecular structures has been established as an effective strategy to mitigate this process. Therefore, this study systematically investigated the influence of alkoxy groups in triphenylamine (TPA) and alkyl/alkoxy phenyl groups in phenothiazine (PTZ) on DSSC performance using density functional theory (DFT). The photophysical and electrochemical properties of four molecules, TP-C₆, TP-PhOC₆, C₆O-TP-C₆, and C₆O-TP-PhOC₆, were computationally analyzed. Results show that the alkoxy phenyl groups in PTZ and the absence of alkoxy groups in TPA within TP-PhOC₆ enhance molecular planarity, induce redshifted absorption spectra, and improve charge transfer performance. Adsorption simulations further reveal that dye@TiO₂ system exhibits a reduced bandgap and redshifted absorption, thereby enhancing spectral response and facilitating electron injection. Moreover, the application of an external electric field is shown to significantly improve electron transfer, optical properties, and interfacial electron injection at the dye/TiO₂ interface. Photoelectric performance predictions confirm that TP-PhOC₆ achieves the highest power conversion efficiency (PCE) of 6.509%, attributed to its high short-circuit current density (J_sc = 11.96 mA cm^-2) and open-circuit voltage (V_oc = 0.650 V). In the molecular design, TPA was substituted with carbazole in the donor unit, and the benzene ring in the π bridge was replaced by thiophene. The thiophene substitution was found to broaden the light absorption range and enhance light-harvesting efficiency, resulting in a higher J_sc. Consequently, the designed molecule exhibits superior PCE compared to the experimental molecules. Theoretical simulations further validate the feasibility of developing high-performance DSSCs.

Melanoma remains one of the most aggressive and lethal cutaneous malignancies, underscoring the need for objective and label-free diagnostic methods that complement traditional histopathology. In this study, Laser-Induced Breakdown Spectroscopy (LIBS) and Raman spectroscopy were jointly applied to characterize formalin-fixed paraffin-embedded (FFPE) melanoma and normal human tissues, and the resulting spectra were modeled using an Extreme Learning Machine (ELM) under five activation functions. Model interpretability was achieved through SHapley Additive exPlanations (SHAP) analysis. Among the tested activation functions, the average test accuracies were achieved by sine for LIBS (92.43%) and sigmoid for Raman (74.41%) under stratified spectrum-wise cross-validation, while feature-level fusion achieved 77.88% average test accuracy with sigmoid. SHAP analysis revealed that K (∼766/769 nm) and Ca (∼422 nm) emission lines in LIBS and ∼ 3164 and ∼ 1163 cm^-1 bands in Raman were the dominant discriminative features, corresponding to ionic imbalance, calcium signaling dysregulation, as well as protein conformational changes (amide A/N-H stretching) and protein-lipid-associated C-C/C-N vibrational alterations characteristic of melanoma progression. These results establish a coherent and interpretable framework linking spectral signatures to biochemical mechanisms and demonstrate the potential of compact, multimodal, and mechanism-driven optical diagnostics for precise, transparent cancer assessment.

Heparin is a highly sulfated linear glycosaminoglycan with anticoagulant properties, and its excessive use may lead to many diseases. Therefore, developing sensitive detection techniques for precise detection of heparin is of utmost importance. Herein, we reported a novel peptide-based fluorescent probe (TPE-GRGRG) based on tetraphenylethylene (TPE)-labeled pentapeptide (Gly-Arg-Gly-Arg-Gly-NH₂) for the highly selective and outstanding sensitive detection of heparin. TPE-GRGRG exhibited a large Stokes shift (132 nm) and typical aggregation-induced emission (AIE) characteristics in DMSO/H₂O binary mixtures. TPE-GRGRG demonstrated a significant enhancement of fluorescence intensity in the presence of heparin with a response time of under 30 s and the limit of detection (LOD) as low as 0.30 nM. A series of characterizations revealed that TPE-GRGRG and heparin formed nanoaggregates via electrostatic interactions, including fluorescence spectroscopy, UV-Vis, FTIR, CD, zeta potential, DLS measurements and fluorescence lifetime analyses, which in turn restricted the intramolecular rotation and triggered the fluorescence enhancement. TPE-GRGRG enabled heparin detection over a wide pH range and exhibited good biocompatibility, and was successfully applied to image heparin in living cells and zebrafish larvae. Furthermore, this study established detection platforms based on swab tests for visual qualitative analysis and smartphone RGB analysis, thereby achieving portable and visual heparin monitoring with the LOD of 0.93 μM. Finally, heparin detection was achieved in 0.1% fetal bovine serum with the LOD of 1.46 nM. TPE-GRGRG offers a reliable strategy for point-of-care testing of heparin, holding potential application value in clinical diagnosis and biosensing.