Application Of Spectroscopy Research Articles

Aerosol Fluorescent Labeling via Probe Molecule Volatilization.

The physicochemical properties of aerosols, including hygroscopicity, phase state, pH, and viscosity, influence important processes ranging from virus transmission and pulmonary drug delivery to atmospheric light scattering and chemical reactivity. Despite their importance, measurements of these key properties in aerosols remain experimentally challenging due to small particle sizes and low mass densities in air. Fluorescence probe spectroscopy is one of the only analytical techniques that is capable of experimentally determining these properties in situ in a nondestructive and minimally perturbative manner. However, the application of fluorescence probe spectroscopy to important classes of aerosols including exhaled respiratory and ambient atmospheric aerosols has been limited due to a typical reliance on premixing the probe molecule with particle constituents prior to particle generation, which is not always possible. Here, a method for aerosol fluorescent labeling based on probe molecule volatilization is developed. The method is first applied to label model polyethylene glycol (PEG) aerosols with two different polarity-sensitive probes, Nile red and Prodan. The similarity of the relative humidity-dependent fluorescent emission of each probe between prelabeled and volatilized-probe PEG particles validated the methodology. A preliminary application of the technique to indicate the hygroscopicity of artificial saliva respiratory particles and model atmospheric secondary organic aerosol particles is demonstrated. The methodology developed here paves the way for future studies applying powerful fluorescent probe-based analytical techniques to study exhaled or natural aerosols for which fluorescent prelabeling is not possible.

Highly efficient tunable mid-infrared luminescence achieved by crystal field modulation of CsPb1-xHoxBr3 halide perovskite AlF3-based fluoride glass

Mid-infrared (MIR) light sources have gained significant importance across various applications in spectroscopy, sensing, astronomy, communications and medical surgery. The diverse spectral characteristics of rare earth ions, particularly lanthanide ions, stemming from their distinctive 4f intershell transitions, offer a multitude of potential transitions spanning the UV-visible-infrared spectrum. Despite recent advancements in MIR gain luminescence, the investigation of tunable MIR luminescence mechanisms remains a major technical challenge. Herein, an effective mechanism to modulate the local crystal field of rare earth ions by altering its crystal structure has been revealed, resulting in tunable broad-spectrum emission in the MIR luminescence range of 2800-3000 nm and multi-peak emission in the near-infrared band of Ho3+. Notably, the local crystal field of Ho3+ is adjusted by manipulating the lattice symmetry of CsPb1-xHoxBr3 perovskite through the incorporation of fluoride glass reticulation to control the crystal size of the perovskite and thereby modify the lattice symmetry of CsPb1-xHoxBr3 perovskite. The energy level transition of Ho3+ is influenced by adjusting the crystal field asymmetry, resulting in the splitting of the 5I6 energy level depending on the crystal field. This cleavage affects the transitions from the 5I5 level to 5I6 at 1480 nm and from 5I6 to 5I7 at 2880 nm. As 5I6 acts as the common upper level for the two emission peaks, the infrared peaks at 1480 nm and 2880 nm widen and develop into a dual-peak emission phenomenon. The infrared luminescence produced aligns closely with the distinctive infrared absorption peaks of carbon dioxide, leading to the development of a convenient, high-precision device for monitoring of CO2 concentration in hydrogen energy in real time. These findings are anticipated to pave the way for extensive utilization of novel tunable MIR luminescence.

Another pipeline in local Partial Least Squares Regression (LPLS) methods: Assessing the impact of wavelet transform integration

This study evaluates the performance of four Partial Least Squares Regression (PLS) methods, focusing on a new Local Partial Least Squares Regression (LPLS) variant integrating wavelet transformation, named WLPLS, for analyzing feed using Near-Infrared (NIR) spectroscopy. While traditional PLS methods are effective for many spectroscopic applications, their global modeling approach often reduces predictive accuracy in large, heterogeneous datasets. In contrast, LPLS adapts models to local data characteristics, which can enhance prediction but also increase computational demands (power and time). WLPLS seeks to mitigate these demands by incorporating wavelet transformation to reduce data dimensionality while effectively managing spectral variances. This research conducts a comparative analysis of WLPLS against traditional PLS, LPLS, and another LPLS pipeline reducing data dimensionality: the LPLS on global PLS scores (LPLS-S). The performances of these methods were evaluated using a large feed database containing 24,644 samples, analyzing five key constituents: ash, crude fibers, fat, moisture, and proteins. The results demonstrate that local approaches outperformed the global PLS method for this dataset and that the performances of the local methods were relatively similar to each other. The selection of the optimal method therefore depends on the specific requirements of the application, such as dataset characteristics and the required prediction speed. Future studies should broaden this comparative framework to additional datasets and contexts to ensure they are adapted for diverse applications.

High-performance full-etched fiber-to-chip grating couplers at 3.7-micron wavelength on silicon

Mid-infrared (MIR) silicon photonics have attracted great interest for potential applications in on-chip spectroscopy, free-space communication, and remote imaging. Currently, the high-performance and fabrication-friendly fiber-to-chip grating couplers operating at 3–4 μm wavelength band are still desired urgently. Here, we propose an efficient method for designing a 2D subwavelength grating coupler. The few analytical fitting parameters defining the subwavelength grating periods and fill factors in two dimensions enable a high design degree of freedom and a low search space dimension, resulting in high figure-of-merit and fast convergence simultaneously. The developed 3.7 μm grating coupler showcases record-high coupling efficiency both theoretically (−2.2 dB) and experimentally (−4.4 dB) among the 3–4 μm MIR counterparts. This study provides an effective method for designing fiber-to-chip grating couplers, by which the MIR grating couplers with high performance and fabrication ease are developed, thus paving the way for developing high-performance silicon photonic integrated circuits operating in the MIR wavelength band.

A short review of application of near-infrared spectroscopy (NIRS) for the assessment of microvascular post-occlusive reactive hyperaemia (PORH) in skeletal muscle

Near-infrared spectroscopy (NIRS) is an optical technique that can be used to non-invasively interrogate haemodynamic changes within skeletal muscle. It can be combined with a short (3–5 min) arterial cuff-occlusion to quantify post-occlusive reactive hyperaemia (PORH). This technique has utility in tracking changes in vascular health in relation to exercise, disease progression or treatment efficacy. However, methods for assessing PORH vary widely and there is little consensus on methodological approaches such as sampling frequency, correction for adipose tissue or the analysis endpoints. The purpose of this review was to: (1) summarise recent advances; (2) compare different methodological approaches and (3) identify current knowledge gaps and future objectives for use of NIRS for vascular assessment. We propose key areas for future work, including optimising occlusion duration and comparing methods of correction for the ischemic stimulus, standardising methods for adjustment of adipose tissue thickness, cross-device comparisons and establishing a standard for minimum sampling rate. Comparisons with alternative methods of capturing PORH or upstream vasodilatory responses would be valuable. Addressing these methodological considerations will aid our understanding of this useful, non-invasive tool for characterising PORH within skeletal muscle and facilitate interpretation of results across studies.

Flashforward: The Current and Future Applications of Vibrational Spectroscopy for Forensic Purposes

ABSTRACTVibrational spectroscopy combined with machine learning has a great potential for forensic applications. For example, handheld Raman spectroscopic instruments are already used by law enforcement agencies for precise, confirmatory identification of drugs. Beyond drug identification, several emerging technologies based on vibrational spectroscopy are currently under development for forensic investigative purposes, including the analysis of questioned documents, gunshot residue, fabrics, soil, hair, nails, and nail polish. This article provides a comprehensive overview of the application of vibrational spectroscopy in various areas of forensic analysis, particularly focusing on forensic serology and the analysis of trace evidences. In the case of forensic serology, the methodology allows for determining complex aspects of serological casework, including the time since deposition of a stain, as well as the phenotypic profile of the stain donor—namely, sex, race, and age. Furthermore, gunshot residues can be accurately identified by grain, caliber, and manufacturer when Raman spectroscopy is paired with machine learning. This integration of advanced spectroscopic techniques with machine learning holds great promise for furthering both the accuracy and efficiency of investigations, helping to reduce the total backlog of evidence investigation currently plaguing modern forensic laboratories.

High-power pulsed Raman fiber laser with wavelength over 2.4 μm

In this paper, the compact high-power pulsed Raman fiber lasers based on single-pass stimulated Raman scattering have been experimentally presented. Home-built noise-like pulse laser centered at ∼1994.7 nm with an envelope width of ∼20.8 ns was utilized as the pump laser, which can provide a maximal output power of ∼19.1 W without a notable spectral broadening. The 1st-order Raman laser operated at ∼2190.8 nm with a maximal output power of ∼4.11 W and a spectral purity of ∼93 % was achieved. Furthermore, by finely optimizing the length of Raman-gain fiber, the 2nd-order Raman laser centered at ∼2408.5 nm with a maximal power of ∼2.36 W was achieved, which represents the maximal value of Raman laser at 2.4 μm in silica-core fiber, to the best of our knowledge. The high-power pulsed Raman fiber lasers may have the potential applications in polymer material processing and spectroscopy.

Online Monitoring of Catalytic Processes by Fiber-Enhanced Raman Spectroscopy

An innovative solution for real-time monitoring of reactions within confined spaces, optimized for Raman spectroscopy applications, is presented. This approach involves the utilization of a hollow-core waveguide configured as a compact flow cell, serving both as a conduit for Raman excitation and scattering and seamlessly integrating into the effluent stream of a cracking catalytic reactor. The analytical technique, encompassing device and optical design, ensures robustness, compactness, and cost-effectiveness for implementation into process facilities. Notably, the modularity of the approach empowers customization for diverse gas monitoring needs, as it readily adapts to the specific requirements of various sensing scenarios. As a proof of concept, the efficacy of a spectroscopic approach is shown by monitoring two catalytic processes: CO2 methanation (CO2 + 4H2 → CH4 + 2H2O) and ammonia cracking (2NH3 → N2 + 3H2). Leveraging chemometric data processing techniques, spectral signatures of the individual components involved in these reactions are effectively disentangled and the results are compared to mass spectrometry data. This robust methodology underscores the versatility and reliability of this monitoring system in complex chemical environments.

Application of proton magnetic resonance spectroscopy in metabolic alterations of prefrontal white and gray matter in depression adolescents

BACKGROUND Cases of depression among adolescents are gradually increasing. The study of the physiological basis of cognitive function from a biochemical perspective has therefore been garnering increasing attention. Depression has been hypothesized to be associated with the brain biochemical metabolism of the anterior cingulate gyrus, frontal lobe white matter, and the thalamus. AIM To explore the application of proton magnetic resonance spectroscopy (1H-MRS) in the metabolic alterations in the prefrontal white matter (PWM) and gray matter (GM) in adolescents with depression. METHODS 1H-MRS was performed for semi-quantitative analysis of the biochemical metabolites N-acetylaspartate (NAA), choline (Cho) complexes, creatine (Cr), and myo-inositol (mI) in bilateral PWM, anterior cingulate GM, and thalami of 31 adolescent patients with depression (research group) and 35 healthy adolescents (control group), and the NAA/Cr, Cho/Cr, and mI/Cr ratios were calculated. Meanwhile, Hamilton Depression Scale (HAMD) and Wechsler Memory Scale were used to assess the degree of depression and memory function in all adolescents. The correlation of brain metabolite levels with scale scores was also analyzed. RESULTS The research group had markedly higher HAMD-24 scores and lower memory quotient (MQ) compared with the control group (P < 0.05). Adolescents with depression were found to have lower bilateral PWM NAA/Cr and Cho/Cr ratios compared with healthy adolescents (P < 0.05). The mI/Cr ratios were found to be similar in both groups (P > 0.05). The bilateral anterior cingulate GM NAA/Cr, Cho/Cr, and mI/Cr also did not demonstrate marked differences (P > 0.05). No statistical inter-group difference was determined in NAA/Cr of the bilateral thalami (P > 0.05), while bilateral thalamic Cho/Cr and mI/Cr were reduced in teenagers with depression compared with healthy adolescents (P < 0.05). A significant negative correlation was observed between the HAMD-24 scores in adolescents with depression with bilateral PWM NAA/Cr and Cho/Cr and were inversely linked to bilateral thalamic Cho/Cr and mI/Cr (P < 0.05). In adolescents with depressions, MQ positively correlated with right PWH NAA/Cr, left PWH Cho/Cr, and bilateral thalamic Cho/Cr and mI/Cr. CONCLUSION PWM and thalamic metabolic abnormalities might influence teen depression, and the reduction in bilateral PWM NAA/Cr and Cho/Cr could be related to the neuropathology of adolescents with depression suffering from memory impairment. There exists a possibility of dysfunction of nerve cell membrane phospholipids in the thalami of adolescent patients with depression.

Mid-infrared characterization of NbTiN superconducting nanowire single-photon detectors on silicon-on-insulator

Superconducting nanowire single-photon detectors are widely used for detecting individual photons across various wavelengths from ultraviolet to near-infrared range. Recently, there has been increasing interest in enhancing their sensitivity to single photons in the mid-infrared spectrum, driven by applications in quantum communication, spectroscopy, and astrophysics. Here, we present our efforts to expand the spectral detection capabilities of U-shaped NbTiN-based superconducting nanowire single-photon detectors, fabricated in a 2-wire configuration on a silicon-on-insulator substrate, into the mid-infrared range. We demonstrate saturated internal detection efficiency extending up to a wavelength of 3.5 μm for a 5 nm thick and 50 nm wide NbTiN nanowire with a dark count rate less than 10 counts per second at 0.9 K and a rapid recovery time of 4.3 ns. The detectors are engineered for integration on waveguides in a silicon-on-insulator platform for compact, multi-channel device applications.

6.7-8.1 µm tunable single-frequency difference frequency generation in ZGP for high-resolution spectroscopy.

Difference frequency generation (DFG) based tunable single-frequency mid-infrared (MIR) light sources are desirable for high-resolution spectroscopy, sensing, and imaging. In this work, we demonstrate a continuous-wave (CW) single-frequency DFG in a ZnGeP2 (ZGP) crystal driven by all-fiber near-infrared (NIR) fiber lasers, for the first time to our knowledge. The all-fiber NIR laser sources consist of a 1.5 µm erbium-doped fiber amplifier seeded by a CW tunable fast scanning single-frequency laser and a 1.9 µm CW tunable single-frequency thulium-doped fiber laser. Taking advantage of the high nonlinear coefficient and large birefringence of the ZGP crystal, single-frequency DFG in ZGP achieves a broad spectral tuning range from 6.7 to 8.1 µm, with an output power at the 10 µW level. Precise detection of C2H2 gas by continuously scanning the DFG source across a spectral range of 1.1 THz (∼34 cm-1) is also presented, highlighting the potential of the tunable DFG source for high-resolution optical spectroscopy applications. We anticipate this work will provide what we believe to be a new platform for spectroscopy in the molecular fingerprint spectral region.

Application of excitation-emission matrix fluorescence spectroscopy and chemometrics for quantitative analysis of emulsified oil concentration

Emulsified oil concentration is an important index for quantitative analysis of sea surface oil spill pollution, and the development of a fast and effective quantitative analysis method for emulsified oil concentration plays a crucial role in the estimation of oil spill volume and post-spill assessment. A quantitative analysis method for emulsified oil concentration based on excitation-emission matrix (EEM) fluorescence spectroscopy and chemometrics was proposed. Firstly, the EEM fluorescence spectra of two emulsified oils were measured using a FLS1000 fluorescence spectrometer. Then, the measured EEM fluorescence spectra were decomposed by parallel factor analysis (PARAFAC), and several key excitation wavelengths were filtered from the loading matrix obtained from the decomposition. Subsequently, the three-band fluorescence index (TBFI) at these excitation wavelengths was calculated and combined with the optimal band selection algorithm, from which the optimal emission band combinations were selected. Finally, the selected optimal emission bands were combined with partial least squares regression (PLSR) to establish a prediction model for emulsified oil concentration. By comparing the prediction results with those based on PARAFAC-PLSR and multivariate curve resolved-alternating least squares (MCR-ALS)-PLSR models, the TBFI-PLSR model showed the best results in the quantitative analysis of emulsified oil concentration. The coefficient of determination, mean square relative error, and ratio of performance to interquartile distance for the gasoline and diesel fuel emulsion validation sets were 0.93, 3.67%, 4.72, and 0.93, 3.72%, 4.60, respectively.

Enhanced Quantum Efficiency via Co-Substitution in Red-Emitting Phosphor Sr2[MgAl5N7] : Eu2+ for Advanced Spectroscopic Applications Including Laser Displays with Ultra-High Luminescence Saturation Threshold.

In order to obtain novel and more efficient red light-emitting materials, a series of Sr2[Mg1-xLixAl5-xSixN7] : 0.01Eu2+ (SMAN-xLS, 0≤x≤0.5) red phosphors were devised and successfully synthesized via the high temperature solid state reaction and the effects of the co-substitution of [Mg-Al]5+ by [Li-Si]5+ on structural and luminescence properties is investigated in detail. A series of powder XRD data and Rietveld refinement indicate that [Li-Si]5+ co-substitution can successfully enter the Sr2[MgAl5N7] : 0.01Eu2+ (SMAN) lattice. With the entry of [Li-Si]5+ into the lattice, the substitution of Al3+ by Si4+ leads to the weakening of the nephelauxetic effect of the 5d level of Eu2+, which shifts the emission peak from 657 nm to 647 nm and reduces the excitation in the green region, i.e., lowers the absorption of green light. When the amount of [Li-Si]5+ co-substitution is x=0.1, the luminescence intensity and thermal stability of the sample are enhanced, in which the external quantum efficiency (EQE), reflecting the luminescence intensity, is elevated by 49.6 %. The increase in lattice rigidity gives rise to higher luminescence intensity, and the introduced trap levels for the compensation of luminescence enhances the thermal stability. Under blue laser excitation, the SMAN-0.1LS can achieve an ultra-high luminescence saturation threshold of 52.22 W/mm2, which is remarkably superior to other existing red phosphors, a breakthrough performance that has enormous potential for application in high-power laser display light sources. Cathodoluminescence (CL) characterization and measurements attest to the favorable CL properties of SMAN-0.1LS. By measuring the pressure-dependent luminescence of SMAN-0.1LS, the emission peak can be shifted from 650 nm to 702 nm with the increase of pressure, and the sensitivity dλ/dP is 5.07 nm/GPa, which is indicative of the potential application of this system as an optical pressure sensor.

Enhanced Quantum Efficiency via Co‐Substitution in Red‐Emitting Phosphor Sr2[MgAl5N7] : Eu2+ for Advanced Spectroscopic Applications Including Laser Displays with Ultra‐High Luminescence Saturation Threshold

AbstractIn order to obtain novel and more efficient red light‐emitting materials, a series of Sr2[Mg1−xLixAl5−xSixN7] : 0.01Eu2+ (SMAN‐xLS, 0≤x≤0.5) red phosphors were devised and successfully synthesized via the high temperature solid state reaction and the effects of the co‐substitution of [Mg−Al]5+ by [Li−Si]5+ on structural and luminescence properties is investigated in detail. A series of powder XRD data and Rietveld refinement indicate that [Li−Si]5+ co‐substitution can successfully enter the Sr2[MgAl5N7] : 0.01Eu2+ (SMAN) lattice. With the entry of [Li−Si]5+ into the lattice, the substitution of Al3+ by Si4+ leads to the weakening of the nephelauxetic effect of the 5d level of Eu2+, which shifts the emission peak from 657 nm to 647 nm and reduces the excitation in the green region, i.e., lowers the absorption of green light. When the amount of [Li−Si]5+ co‐substitution is x=0.1, the luminescence intensity and thermal stability of the sample are enhanced, in which the external quantum efficiency (EQE), reflecting the luminescence intensity, is elevated by 49.6 %. The increase in lattice rigidity gives rise to higher luminescence intensity, and the introduced trap levels for the compensation of luminescence enhances the thermal stability. Under blue laser excitation, the SMAN‐0.1LS can achieve an ultra‐high luminescence saturation threshold of 52.22 W/mm2, which is remarkably superior to other existing red phosphors, a breakthrough performance that has enormous potential for application in high‐power laser display light sources. Cathodoluminescence (CL) characterization and measurements attest to the favorable CL properties of SMAN‐0.1LS. By measuring the pressure‐dependent luminescence of SMAN‐0.1LS, the emission peak can be shifted from 650 nm to 702 nm with the increase of pressure, and the sensitivity dλ/dP is 5.07 nm/GPa, which is indicative of the potential application of this system as an optical pressure sensor.

Integrating progressive screening strategy-based continuous wavelet transform with EfficientNetV2 for enhanced near-infrared spectroscopy

Near-infrared (NIR) spectroscopy has gained wide acceptance across various fields as a result of advances in portable equipment that can record spectra on site or at production lines. Continuous wavelet transform (CWT) can transform traditional one-dimensional (1D) NIR spectra into more informative two-dimensional (2D) spectrograms, thus enhancing the analysis and interpretation of spectral information. This study introduces a high-efficiency 2D CWT-EfficientNetV2 regression model to optimize NIR spectroscopy applications. A novel progressive screening strategy is employed to select the optimal wavelet functions and scales for CWT, which are then used to transform the features into wavelet coefficient matrices. Direct digital mapping (DDM) with Gray colormap generates 2D spectrograms from matrices, significantly preserving the representation of wavelet coefficients. The 2D CWT-EfficientNetV2 model was used to predict the content of five polyphenols in tobacco leaf samples with superior performance compared to partial least squares regression (PLSR) and other high-efficiency models. Moreover, to further validate the robustness and reliability of the proposed method, two additional public NIR spectral datasets were included in this study. The model achieves lower root mean square error of prediction (RMSEP), as well as higher coefficient of determination of prediction (RP2) and the ratio of the standard error of prediction to the standard deviation of the reference values (RPD) on the test datasets. These results demonstrate that the 2D CWT-EfficientNetV2 model is a robust and efficient approach for the accurate quantification of various target compounds utilizing NIR spectroscopy.

Unravelling the electrochemical impedance spectroscopy of hydrogenated amorphous silicon cells for photovoltaics

Abstract This research reveals the application of electrochemical impedance spectroscopy (EIS) in analyzing and improving the performance of hydrogenated amorphous silicon (a-Si: H) based photovoltaic cells. As a non-destructive technique, EIS provides deep insight into the electrochemical characteristics of photovoltaic cells, including series resistance, layer capacitance, recombination mechanisms, and charge transport. The impedance data is obtained and analyzed using small AC potential signals at various frequencies via Nyquist diagrams and Bode plots. This analysis allows the identification of resistive and capacitive elements as well as the evaluation of the quality of the interface between the active layer and the electrode. The results show that EIS can identify internal barriers that reduce the efficiency of a-Si: H solar cells, such as dominant recombination mechanisms and inefficient charge transport. Using equivalent circuit models, electrochemical parameters are extracted to reveal cell behavior and performance. In addition, these results also confirm that EIS is an important tool in design optimization and performance improvement of a-Si: H photovoltaic cells, providing a solid scientific basis for the development of more efficient and sustainable solar cell technology. These findings contribute to efforts to increase solar energy efficiency, supporting broader and more effective use of photovoltaic technology in meeting global sustainable energy needs.

Dual trace detection of hazardous substances by aptamer-functionalized gold nanofilms based on surface-enhanced Raman spectroscopy

This work proposes the application of multi-residue Surface-enhanced Raman spectroscopy (SERS) detection based on an internal standard and aptamer-functionalized gold nanofilms. First, two types of complementary DNA (cDNA) are fixed on the self-assembled monolayer of a halide ion-regulated nano-membrane through gold-sulfur bonds. Then, two signal probe molecules are prepared and modified. The aptamer specifically binds to cDNA, generating SERS signals. Detection of the target analytes is achieved by measuring the Raman intensity and using the internal standard method. The results show that the concentrations of thiamethoxam and dichlorodiphenyltrichloroethane (DDT) at 10-12 M can be effectively measured under optimal experimental conditions. The Raman intensity exhibits a linear relationship at the shifts of 1085 cm-1 for 4-mercaptopyridine (4-MPY) and 1583 cm-1 and 2223 cm-1 for 4-mercaptobenzoic acid (4-MBN). When applied to the detection of thiacloprid in actual lake water samples, the results show no significant difference compared to the standard.

Investigating the effect of non-resonant background variation on the CARS data analysis of bacteria samples and classification using machine learning

Non-resonant background (NRB) plays a significant role in coherent anti-Stokes Raman scattering (CARS) spectroscopic applications. All the recent works primarily focused on removing the NRB using different deep learning methods, and only one study explored the effect of NRB. Hence, in this work, we systematically investigated the impact of NRB variation on Raman signal retrieval. The NRB is simulated as a linear function with different strengths relative to the resonant Raman signal, and the variance also changes for each NRB strength. The resonant part of nonlinear susceptibility is extracted from real experimental Raman data; hence, the simulated CARS data better approximate the experimental CARS spectra. Then, the corresponding Raman signal is retrieved by four different methods: maximum entropy method (MEM), Kramers-Kronig (KK), convolutional neural network (CNN), and long short-term memory (LSTM) network. Pearson correlation measurements and principal component analysis combined with linear discriminant analysis modeling revealed that MEM and KK methods have an edge over LSTM and CNN for higher NRB strengths. It is also demonstrated that normalizing the input data favors LSTM and CNN predictions. In contrast, background removal from the predictions significantly influenced Pearson correlation but not the classification accuracies for MEM and KK. Further, the LSTM performance is found to be limited and can only be applied for low NRB strengths. This comprehensive study has the potential to impact the CARS spectroscopy and microscopy applications in different areas.

Application of Quantitative Proton Nuclear Magnetic Resonance Spectroscopy for Determination of the Content of Geniposide in Gardeniae fructus

Background: Gardeniae fructus (Zhi-Zi) is the dry ripe fruit of the plant Gardenia jasminoides Ellis (Rubiaceae), which can be used as both food and medicine. Geniposide, a key constituent of Gardeniae fructus, serves as an indicator component for evaluating the quality of Gardeniae fructus. Traditionally, the quantification of geniposide in Gardeniae fructus is achieved through High-performance Liquid Chromatography (HPLC)-based methods. Objective: The present study aimed to introduce a rapid approach to quantifying geniposide content in Gardeniae fructus along with validating its effectiveness. Methods: The experiments involved finding a suitable deuterium solvent, Internal Standard (IS), specific peak, and Nuclear Magnetic Resonance (NMR) parameters for quantitation, and validating specificity, accuracy, precision, and stability. Results: The results have indicated that methanol-d4 as a solvent has facilitated excellent signal separation in the proton Nuclear Magnetic Resonance (1H NMR) spectroscopy, with trimethyl 1,3,5- benzenetricarboxylate emerging as the ideal IS. The specific signal at δ 7.45, corresponding to H-3 in geniposide, has been identified as the optimal peak for integration. The application of distinctive signals from the 1H NMR spectroscopy has allowed for the precise quantification of geniposide in Gardeniae fructus. Conclusion: This study has suggested using 1H NMR to quantify geniposide in Gardeniae fructus to be a viable alternative to HPLC-based methods, providing a suitable approach for quality control of Gardeniae fructus.

Exploring the role of graphene-metal hybrid nanomaterials as Raman signal enhancers in early stage cancer detection

Molecular diagnosis plays a significant role in detection of biomolecules linked to early stage cancer since it offers greater sensitivity and reliability for identification of biomarker level changes as the disease progresses. The application of vibrational spectroscopy in biomarker detection is defined by the fingerprint spectrum of a molecule originating from single-molecule vibrations. This characteristic makes surface enhanced Raman spectroscopy (SERS) a promising tool for identification of biomarkers. The performance of the SERS technique largely depends on the material being used as the SERS substrate. Graphene, with its large surface area and abundance of aromatic regions, is considered advantageous as SERS substrate. Combining graphene with metal nanomaterials considerably increases SERS signal intensity, thereby enhancing detection sensitivity. Therefore, this review emphasizes the significance of selecting graphene-metal nanohybrids as suitable SERS substrates for signal amplification. The detail understanding of the mechanism of graphene-metal hybrid in SERS based detection of early stage cancer is also presented. Furthermore, several examples demonstrated the application of graphene-metal hybrid nanomaterials in detecting biomarkers and cancer cell differentiation using SERS imaging.