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

Raman spectroscopy can play a crucial role in coal rank identification by providing direct insights into the structural evolution of carbon during coalification. Unlike any traditional method such as vitrinite reflectance, which rely on specific macerals and often face limitations in low-vitrinite or compositionally altered samples, Raman offers a non-destructive and comprehensive assessment of all organic matter types. It detects changes in chemical bonding, aromaticity, and the degree of structural order-key indicators of coal maturity-by analyzing vibrational energy levels. Its ability to differentiate between sp²- and sp³-hybridized carbon and monitor the transition from amorphous to graphitic structures makes it especially valuable for evaluating thermal evolution. This research investigates the evolution of coal's chemical composition and microstructure during maturation using Raman spectroscopy, supported by Fourier Transform Infrared (FT-IR) and X-ray photoelectron spectroscopy (XPS). The transformation of peat into coal, driven by microbial activity, thermochemical degradation and burial, increases carbon concentration and releases volatiles. Traditional methods such as vitrinite reflectance suffer from limitations, particularly in samples with low or absent vitrinite content, or when vitrinite is suppressed by bitumen or enhanced by recycled particles. Measurement errors may arise from polishing issues, anisotropy or instrument calibration, but Raman spectroscopy offers more profound insights into carbon structure changes during coalification and graphitization. Initially, coal and lignite samples of varying ranks (from lignite to semi-anthracite) were analyzed using traditional methods such as proximate analysis and vitrinite reflectance to determine coal rank. Subsequently, spectroscopic techniques were employed to evaluate carbon structure, functional groups, and sp²/sp³ carbon ratios. FT-IR identified functional groups, while XPS examined surface elements. Raman spectra revealed a clear relationship between coal rank and carbon structure, showing higher-rank coals with greater sp² carbon ordering, resembling graphite. Gaussian fitting confirmed that sp² content increases while sp³ content decreases with rank, consistent with XPS and IR findings. The G-band shift and broadening indicated increased nanocrystalline graphite from sub-bituminous to anthracite, while lignite displayed more sp³ content and amorphous carbon. XPS confirmed that higher-rank coals have more CC bonds, while lignite contains CO and COOH groups. The study provides a novel framework for assessing coal rank and maturation, enhancing understanding of coal's metamorphic history, and offering insights for optimizing coal utilization and expanding geological knowledge of organic sediment metamorphism.

In this work, small angle neutron scattering (SANS) data of branched polyethyleneimine (bPEI)/TEMPO-oxidized and ultrasonicated cellulose nanofiber (TOUS-CNF) xerogels, namely cellulose nano-sponges (CNSs), at different hydration level (h) and cross-linker amount, were analyzed through a combined approach involving generalized (2DCOS) and perturbation-correlation moving window (PCMW2D) two-dimensional correlation spectroscopy. The aim was to get novel insights into the sequence of structural changes experienced by the xerogel moieties upon hydration, based on the assessment of the cross-correlations existing at different length scales retrieved by the synchronous (SCMs) and asynchronous (ACMs) 2DCOS and PCMW2D correlation maps calculated upon variation in the chosen perturbation variable. It is worth noting that the application of 2DCOS and PCMW2D on SANS data enabled the identification of structural transitions that are not readily apparent from conventional SANS analysis, highlighting the sensitivity of this method in detecting structural dynamics as well as any minor changes in the polymer arrangement at both low and high spatial scales.

Uric acid (UA), the terminal metabolite of purine catabolism, is a critical biomarker requiring precise monitoring. Conventional detection methods (e.g., enzymatic assays, HPLC) suffer from limited sensitivity and operational complexity. To address this, we engineered four colloidal SERS substrates (Ag NPs, AuAg NPs, Au NPs, Au NRs) for signal amplification and fabricated hydrophobic surfaces via hydrochloric acid etching and evaporative deposition of heptadecafluorodecyl trimethoxysilane to minimize analyte dispersion. Integration of optimized Ag NPs with the hydrophobic substrate enabled quantitative UA detection by SERS. This approach achieves rapid (∼1 min), demonstrating significant potential for point-of-care UA monitoring.

Acrylamide is a cancer-causing substance that forms in food by Maillard reaction when free asparagine (an amino acid) and sugars, both naturally present, undergo high-temperature processing (>120 °C) and low humidity conditions. The European Food Safety Authority (EFSA) has identified acrylamide as a significant contaminant that needs to be monitored and minimized in certain food products. This work reports the first step to detect the presence of acrylamide in aqueous solution by Surface-Enhanced Infrared Absorption (SEIRA) spectroscopy. This work applies Density Functional Theory (DFT) to calculate the vibrational frequencies and infrared (IR) absorption cross-sections of molecular acrylamide in close proximity to gold nanoantenna arrays in water-based solutions. These IR nanoantennas are designed through electromagnetic calculations of their electromagnetic response by tunning their plasmon resonance to match the characteristic vibrational frequencies of acrylamide, enhancing the molecular spectroscopic signal. Comprehensive characterization of the SEIRA signal for various gold nanorod antennas (AuNRA) coupled with a covering shell containing molecular acrylamide enables direct identification of its vibrational modes. This work shows how concentrations of up to 500 ng/ml of acrylamide in water can be detected by SEIRA. It also provides guidelines to apply SEIRA for the detection of acrylamide in challenging samples, such as those typically found in the food industry.

Real-time monitoring of viral particles can have a crucial impact on vaccine manufacturing and can alleviate public health challenges by supporting continuous supply. Spectroscopic methods such as Raman spectroscopy can provide rapid and non-invasive measurements. Here, we have developed a Raman spectroscopy-based tool to monitor the quality and quantity of viral particles in a continuous flow setup. We characterized the attenuated human cytomegalovirus (CMV) across a wide range of concentrations (1.45 × 10¹⁰ to 2.90 × 10¹¹ particles/mL) and flow rates (100 μm/s to 1000 μm/s) within a square quartz capillary. This process analytical technology (PAT) tool enables the detection of viral particles even at high flow rates such as 1000 μm/s. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and dynamic light scattering (DLS) demonstrated that the samples maintain their integrity even after laser exposure, reiterating the non-invasive nature of Raman spectroscopy. To the best of our knowledge, this is the first report on characterizing CMV particles using Raman spectroscopy, especially under flow conditions. We have also demonstrated the limit of detection (LOD_min) (2.01 × 10¹⁰ particles/mL) for CMV particles in continuous flow (1000 μm/s) (via the Raman spectroscopy method), addressing the effects of flow rate, concentration, and sample integrity. This technology could enable process control in the bio-manufacturing of vaccines.

The determination of the α-glucosidase (α-Glu) activity is crucial for the early screening of diabetes. However, traditional α-Glu detection methods mainly rely on a single-signal readout system, which is inevitably subjected to interference from a complicated detection environment. To address this practical issue, a fluorescence-colorimetric dual-mode sensor based on green-emitting nitrogen-doped carbon dots (GN-CDs) was designed for the sensitive and portable detection of α-Glu. The detection strategy of this sensor was based on the enzymatic reaction of α-Glu with the specific substrate p-nitrophenyl-α-D-glucopyranoside (PNPG) to generate p-nitrophenol (PNP), which not only displayed stronger absorbance at 400 nm but also could effectively quench the fluorescence of GN-CDs through the inner filter effect (IFE), thereby realizing the dual-mode detection of α-Glu. Through the mutual calibration of the double signals, the accuracy and anti-interference ability of α-Glu detection in complex environments were significantly improved, with the detection limits as low as 0.023 U/L in fluorescence mode and 0.045 U/L in colorimetric mode, respectively. Furthermore, portable α-Glu detection was successfully achieved by integrating GN-CDs loaded hydrogel microspheres and smartphone-based image analysis technology. Therefore, the developed dual-signal sensor provided an accurate and portable strategy for α-Glu detection, demonstrating great application potential in the sensing of diabetes-related biomarkers.

Fluorescence excitation-emission matrix (EEM) spectroscopy is a crucial analytical tool for characterizing dissolved organic matter in aquatic systems. The factorization of mixed spectral components within EEMs has long been the main subject of data interpretation, prompting widespread adoption of trilinear decomposition such as parallel factor analysis (PARAFAC). However, the requirements of multi-sample dataset and manual judgment pose limitations to PARAFAC analysis, particularly hindering the real-time and in-situ applications. This study introduces a rapid decomposition approach capable of automatically decomposing single EEM input into fluorescent components. The proposed approach, termed empirical initialization non-negative matrix factorization (EI-NMF), comprises three core steps: (1) chemical rank estimation via singular value decomposition (SVD), (2) empirical initialization based on statistical analysis, and (3) non-negative matrix factorization with multiplicative updates. Simulated data and natural water samples were used to verify the feasibility of proposed approach. Validation on simulated data yielded satisfactory results: EI-NMF achieved accurate chemical rank determination and component spectral recovery (Tucker congruence coefficients >0.9) relative to the true component spectra. Decomposition results of unseen natural samples further confirmed that EI-NMF can effectively processes single EEM inputs, yielding decomposition outcomes with excellent accuracy and chemical interpretability. This computationally efficient framework enables real-time decomposition of individual EEMs (processing time <0.1 s), offering significant potential for in situ monitoring of aquatic fluorescent components.

Organic photosensitizers (PSs) with organelle targeting and photodynamic therapy (PDT) performance have received extensive attention from scientific researchers. In this study, diphenylamine-xanthene compounds with aggregation-induced emission (AIE) properties were designed and synthesized. The different organelle-targeting performance of the photosensitizer was achieved by subtly regulating the functional group connected to the aldehyde group. These aggregation-induced emission luminogens (AIEgens) can not only specifically target organelles, but also effectively produce reactive oxygen species (ROS) under visible light irradiation. Mitochondria-targeting AIE compounds with cationic groups (OCH₃-AP) have better ROS generation capacity and cell phototoxicity than compound (OCH₃-AM) targeting lipid droplets (LDs). Through in vivo experiments, it was found that OCH₃-AP has a long retention ability in live tumor tissues and have potential application value in photodynamic cancer therapy.

To achieve rapid, non-destructive detection of food quality indicators, this study introduces a novel method that combines near-infrared (NIR) spectroscopy with the Extreme learning machine (ELM) model. Eight spectral preprocessing methods and three wavelength selection algorithms were evaluated for predicting total ginsenoside content (TGC) and protein content (PC), along with a comparative analysis of the ELM model's performance against support vector regression and random forest. Results showed that Savitzky-Golay smoothing with standard normal variate was the best preprocessing method, K-means clustering provided the optimal wavelength selection algorithm, and the ELM model demonstrated the best performance. Specifically, the ELM based on K-means method achieved optimal results: R² of 0.9431, RMSE of 0.2933 mg/g, rRMSE of 0.0824, RPD of 4.2386, and P-time of 4 × 10^-7 s for TGC; and R² of 0.9764, RMSE of 4.1361 mg/g, rRMSE of 0.0337, RPD of 6.1295, and P-time of 2 × 10^-7 s for PC. In summary, combining NIR spectroscopy with the ELM model and clustering-based wavelength selection algorithm offers a reliable and practical solution for rapid, non-destructive, and accurate detection of food quality indicators.

Rationale: Procaine HCl (PrHCl), a protic ionic liquid (PIL), exhibits intricate relaxation due to ionic and neutral species interactions. However, its glass-forming ability is limited. To overcome these limitations, this study explores the synthesis of a novel PIL, PrHIb, by replacing Cl^- with the bulky, asymmetric ibuprofen anion. This modification is expected to introduce steric hindrance, restrict molecular mobility, and decouple relaxation processes, thereby enhancing thermal stability, glass-forming ability, and pharmaceutical functionality.

Aim: To synthesize and characterize a novel protic ionic liquid, PrHIb, with a focus on relaxation dynamics, glass-forming ability, and dual therapeutic potential.

Method: PrHIb synthesis was validated by density functional theory (DFT), Fourier Transform Infrared Spectrscopy (FTIR), and Fourier Transform Raman Spectroscopy (FT-RS). The antibacterial and anti-inflammatory properties of PrHIb were evaluated. Thermal and molecular dynamics were studied by differential scanning calorimetry (DSC) and broadband dielectric spectroscopy (BDS). Molecular mobility was assessed to decipher the decoupling of ionic and neutral molecules. The polymer matrices were explored to enhance performance and applicability by restricting molecular mobility.

Results and discussion: DFT confirmed PrHIb formation as endothermic and non-spontaneous under standard conditions with positive enthalpy (∆H = -3.88× 10⁵ kcal/mol) and Gibbs free energy (∆G = -3.88× 10⁵ kcal/mol), while negative entropy (∆S = -56.43 cal/molK) reflects reduced system disorder. FTIR and FT-RS validated the incorporation of functional groups from procaine and ibuprofen. PrHIb exhibited enhanced antimicrobial efficacy against E. coli (62 %) and Pseudomonas (80 %) compared to PrHCl and retained strong anti-inflammatory activity (95 %). DSC and BDS studies confirmed glass-forming behavior, with a T_g of 266 K. Replacement of Cl^- with Ibu^- led to decoupled relaxation and the lack of secondary relaxation. Two distinct relaxations in M″(ω) were attributed to ionic conductivity and structural relaxation. All data were fit the Havriliak-Negami equation. Vogel-Fulcher-Tammann (VFT) showed high fragility (m = 102), indicating sharp viscosity changes near T_g. polyvinylpyrrolidone (PVP) confinement reduced fragility (m = 36), suppressed ionic hopping, and improved stability with a T_g of ∼269 K.

Conclusion: Replacing Cl^- with bulky, asymmetric Ib^- in PrHIb restricts molecular mobility, leading to decoupled relaxation processes and enhanced dynamic heterogeneity. Confinement in a PVP matrix further stabilizes PrHIb by reducing fragility and improving its glass-forming ability. These features, along with dual antimicrobial and anti-inflammatory activity, h