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

Hollow defects seriously affect the quality and economic benefits of pecans, necessitating the removal of defective pecans from normal batches. In this study, near-infrared spectroscopy (NIRS) combined with multimodal data fusion and deep learning was proposed to detect hollow defects and their severity in pecans. Initially, different spectral preprocessing methods were applied and the preprocessed spectral data were fused to improve detection accuracy. Additionally, the physical parameters of pecans were incorporated to construct a "spectral features-physical parameters" multimodal fusion dataset. And traditional machine learning and deep learning methods were employed to develop binary and ternary classification models for detecting hollow defects in pecans. The results indicate that multimodal fusion significantly improves model performance. The optimized SVM model achieves overall accuracies of 94.19% and 88.37% for binary and ternary classifications, Deep learning models further enhances detection accuracy, with the CNN-MLP dual-stream model based on multimodal data fusion yielding the best results. The optimal binary classification model obtained an overall accuracy of 97.67%, while the ternary classification model achieved an accuracy of 90.7%. These results demonstrate that the CNN-MLP dual-stream model combined with multimodal data fusion can effectively improve the detection accuracy of hollow defects in pecans, providing a reliable and non-destructive detection method for pecan quality evaluation.

Cancer represents a significant challenge to people's health and safety. Tumor biomarker detection plays a vital role in the precise diagnosis of cancer and finds widespread applications in cancer screening and pathological diagnosis. Existing methods for tumor biomarker detection have drawbacks such as susceptibility to false positives, complexity of operation, and high costs. As a molecular-level fingerprint spectrum, Raman spectroscopy holds promise as a rapid and accurate method for tumor biomarker protein detection. This paper presents a high-confidence Raman spectra collection method for tumor biomarker proteins based on experimental and theoretical cross-validation. On the one hand, through ultrafiltration purification of protein samples, high-confidence spectra for four tumor biomarker proteins of breast cancer were experimentally acquired. On the other hand, using first-principles Density Functional Theory (DFT), the Raman spectra of the proteins were calculated theoretically. Experimental and theoretical spectra were mutually validated, confirming differences in spectral peak characteristics and their assignments for the four biomarker proteins. We also demonstrate improvement in AI-based protein classification through theoretical-experimental cross-validation, with 7.62% accuracy gain. The method proposed in this paper is well-suited for integration with high-throughput spectral analysis algorithms based on artificial intelligence. It holds the potential for developing deep learning models constrained by biological knowledge in the field of cancer screening and tissue biopsy pathological diagnosis in the future.

The widespread use of pentachloronitrobenzene (PCNB) poses a serious threat to environment and human health, making its precise detection in agricultural products and food samples crucial. In this study, a triphenylamine-based fluorescent probe TP was proposed and designed for the selective and accurate quantitative detection of PCNB. Probe TP combined a triphenylamine fluorescent group with an anthracene-based unit, which enabled a significant extension of the conjugation system and an enhanced π-π interaction, achieved specific recognition of PCNB based on intermolecular forces while maintaining the optical activity of the fluorophore. During the detection process, probe TP exhibited a low limit of detection (LOD) of 19 nM and showed remarkable selectivity for PCNB over other common pesticides widely used in agricultural activities, and demonstrated high accuracy in real herbal samples, nutraceuticals and vegetables, achieved spiked recoveries of 95.00% ∼ 103.75%. In addition, probe TP showed low cytotoxicity and cell membrane permeability, enabling imaging of PCNB-induced cells, was expected to serve as an interdisciplinary-integrated molecular tool for PCNB detection.

Owing to the great potential in photochemical and photophysical applications, extensive efforts have been devoted to developing quantum dot-molecule (QD-M) hybrid systems. Particularly in the senario of photon upconversion, triplet energy transfer (TET) from the QD to molecule is a crutial process directly determining the performance. However, surface defects generally emerging in bare QDs will trap carriers and compete with TET process, which is harmful for efficient photon upconversion. Surface passivation by capping shell on QD can reduce carrier trapping. Nevertheless, it simultaneously suppresses TET. Herein, the ZnSe/ZnS-PTA (9-phenanthrene carboxylic acid) QD-M systems with different ZnS shell thickness are synthesized for synergistically passivating surface defects and optimizing TET towards high photon upconversion quantum yield (UCQY). Through combined spectral analysis, the results show unambiguous carrier dynamics of TET and carrier trapping, providing solid evidence for how TET competes with carrier trapping and achieving the highest UCQY. This systematic study could provide further insight for exploring the carrier dynamics in QD-M hybrid systems and benefit new applications in photon-converting nanomaterials.

Fluorescence properties of nonconventional fluorophores, particularly those emitting under ultraviolet light without any external treatment, have garnered significant attention in recent years. In this study, we report the intrinsic visible emission of ammonium carboxylate salts (-NH₃⁺-OOC^-) in both concentrated solutions and solid powders, which is attributed to a clustering-triggered emission (CTE) mechanism. The CTE mechanism is further supported by single-crystal structural analysis, revealing space electronic communication facilitated by intermolecular interactions. Additionally, the fluorescence of ammonium carboxylate salts exhibits high sensitivity to pH changes and selective fluorescence quenching in the presence of Fe³⁺ ions.

This work presents the first systematic in-situ investigation of surface adsorption and photocatalytic reaction pathways on metal-free nanomaterials, namely graphitic carbon nitride and boron carbon nitride (g-C₃N₄ and BCN). Although g-C₃N₄- and BCN-based photocatalysts are widely studied, mechanistic understanding of surface-bound intermediates during photocatalysis is still often inferred rather than directly tracked in real time. Here, in situ attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy is used to directly monitor the adsorption and subsequent light-driven transformation of three representative organics: methanol, phenol, and methylene blue (MB) at the catalyst/water interface. Methanol adsorption and its time-dependent spectral evolution are consistent with formation of surface methoxy species followed by stepwise oxidation toward carbonyl/formate-type intermediates and eventually CO₂. The spectral evolution of phenol is consistent with its interaction via hydrogen bonding and π-π interactions, followed by hydroxylation, quinone formation, and ring-opening pathways. MB adsorbs strongly through electrostatic and π-π interactions and undergoes N-demethylation, chromophore disruption, and sequential oxidation. Across all probes, BCN shows stronger adsorbate-induced spectral perturbation and more pronounced intermediate evolution than g-C₃N₄, consistent with boron-induced modification of surface polarity/acid-base character and charge-transfer behavior that promotes interfacial transformation. The results demonstrate the value of operando ATR-FTIR for resolving surface-controlled photocatalytic reaction sequences and diagnosing intermediate accumulation and interfacial OH/water dynamics, which are directly relevant to activity and stability considerations under more complex treatment conditions.

Based on the surface plasmon resonance (SPR) technique, the proposed biosensor is investigated as an SPR-based sensing platform for detecting breast cancer cells, specifically MCF-7 and MDA-MB-231 cells. Developed biosensor features an octagonal cylinder-shaped resonator design composed of two novel materials: an octagon-shaped structure made of gold (Au) and a cylinder-shaped structure made of silver (Ag). Graphene is also integrated to achieve high sensitivity in the detection for the two breast cancer cell lines based on the refractive index values, within the wavelength range of 1650-1700 nm, yielding sensitivity rates of 714.28 nm/RIU (MCF-7) and 785.71 nm/RIU (MDA-MB-231). The proposed Graphene Octagonal Cylinder-Shaped Surface Plasmon Resonance (GOCSPR) biosensor consists of two layers, with a ceramic substrate made of aluminum nitride (AlN), and exhibits good quality factors of 560 for MCF-7 and 557 for MDA-MB-231. The analysis of layer height and cylinder radius, along with optimization using a Linear Regression machine learning algorithm and the corresponding R² values, is also presented in the manuscript. The designed graphene-based structure can be used for detecting breast cancer cell with high efficiency for medical applications.

Fe³⁺ and nitrofurazone (NFZ) can raise a risk for environment and human health due to accumulation in water. Hence, a post-synthetic modification strategy was employed to develop a visualization sensor for detecting Fe³⁺ and NFZ. In this study, we selected a Zinc-based metal organic framework in combination with fluorescein to develop a fluorescent dyes-encapsulated sensor (Flu@Zn-MOF). The sensing experimental indicated that the Flu@Zn-MOF demonstrated excellent recognition performance for Fe³⁺ and NFZ. The detection limits (LODs) of Fe³⁺ and NFZ are 1.27 and 0.89 μM, respectively. Furthermore, the color of Flu@Zn-MOF turn from yellow-green to blue in the presence of NFZ. The proposed sensor can accurately identify Fe³⁺ and three nitrofuran analogs (NFZ, NFT and FTD) by principal component analysis (PCA). In addition, satisfactory recovery rates and low standard deviation for detection of Fe³⁺ and NFZ in real water samples confirms the practicability of Flu@Zn-MOF. Then, a sensing system was created that leverages smartphone technology for the quantitative and real-time monitoring of NFZ. Therefore, this work presents a new strategy for designing a novel sensing method for on-site recognition of nitrofurans pollutants in water.

Long-wavelength near-infrared small-molecule dyes hold enormous application potential in the field of biophotonics. Traditional strategies achieve red-shifted wavelengths by expanding aromatic structures, but often sacrifice application performances such as photostability, cell permeability, and functionality. Given the limitations of aromatic structures, this study turns to ground-state antiaromaticity to explore a new approach: by substituting the nitrogen-containing groups at the 3,6-positions and the phenyl group at the 9-position of the fluorene skeleton, the intramolecular electron push-pull effect is enhanced, aiming to optimize the comprehensive performance of near-infrared dyes by inhibiting the twisted intramolecular charge transfer (TICT) effect. Comparison with traditional rhodamine dyes shows that the fluorene dye skeleton exhibits excellent structural stability; even after introducing electron-withdrawing substituents that inhibit TICT at its 3,6-positions for group modification, its photophysical properties can still remain stable, this result further confirms that the fluorescence quantum yield of fluorene-skeleton dyes is not regulated by the TICT effect. This design successfully improves the water solubility of fluorene skeleton dyes, and SAF-1 and SAF-2 still show excellent second near-infrared window imaging capabilities. We used the amphiphilic material DSPE-PEG2000 to encapsulate them, among which DSPE-PEG2000-FA@SAF-1 successfully achieved imaging of mouse tumor tissues. The above results indicate that ground-state antiaromaticity is an effective strategy for the development of long-wavelength dyes.

The excited-state proton transfer (ESPT) behavior of the PiQ molecule was systematically investigated in both liquid and solid phases. The asymmetric molecular structure and intramolecular hydrogen bonding interactions were analyzed through geometry optimization, potential energy surface calculations, and frontier molecular orbitals analysis. It was demonstrated that in the liquid phase, PiQ undergoes a concerted excited-state intramolecular double proton transfer (ESDPT), resulting in green fluorescence at 510 nm. In contrast, in the solid phase, only the initial excited-state intramolecular single proton transfer (ESISPT). Crystalline packing in the solid state restricts molecular rearrangement, destabilizes the TD* (ESDPT) form, and thus inhibits the second step of proton transfer, resulting in the formation of TB* (ESISPT) and blue fluorescence emission at 487 nm. RMSD analysis supports this inhibition by revealing minimal geometric deviation between the ground and excited states in the solid phase. These findings demonstrate that phase-dependent ESPT pathways govern the emission behavior of PiQ, enabling visually perceptible phase recognition and offering a viable strategy for the development of multi-responsive luminescent materials.