Exploring microstructures of metal-doped oxides via simulated Raman spectrum.

Solid solution of metal-doped oxide has been widely used in material industry and catalysis process. Its performance is highly correlated with the distribution of doped ions. Due to the complex distribution of doped ions in solid solution and its variation with temperatures, to obtain the microstructures of metal-doped ions in solid solution remains a substantial challenge. Taken Ce1-xZrxO2 as a model, the global structure searching, structures proportion with temperature determined by Boltzmann distribution, and the weighted simulation Raman spectra were integrated to explore the microstructures of metal-doped solid solution oxides. It was further verified by application into rutile and anatase TiO2 mixture, indicating that the present method is feasible to deduce the microstructure of metal composite oxides. We anticipate that it provides a powerful solution to explore microstructures of solid solution and complex metal oxides.

The isosbestic point in the Raman spectra of the hydration shell.

Isosbestic point is often observed in a series of spectra, but their interpretation is still controversial, such as whether the continuum model can produce an isosbestic point. In order to answer this question, the Raman spectra of hydration shell with continuous distribution structure in different ionic aqueous solutions were separated by Raman ratio spectra, and an isosbestic point was successfully observed. Our experimental results show that the continuum model can indeed produce the isosbestic point. In order to deepen the understanding of the isosbestic point, we calculate the first moment of the Raman spectra and conduct molecular dynamics (MD) simulations. Both experimental and theoretical findings indicate that elevated temperatures lead to increased disorder among water molecules within the hydration shell.

Microplastic contamination in Ashtamudi Lake, India: Insights from a Ramsar wetland.

Estuaries function as temporary storage sites for plastic debris, influencing the distribution of microplastics (MPs) across ecosystems. This research delves into the presence of MPs in the water, sediment, fish, and shellfish of Ashtamudi Lake, a Ramsar wetland with brackish water located on the southwest coast of India. Given the lake's significance in supporting the livelihoods of numerous fishers and acting as a vital source of fishery resources for both local consumption and export, examining the contamination of the system by MPs becomes particularly pertinent. The highest percentage composition of MPs was found in macrofauna at 60.6% (with fish at 19.6% and shellfish at 40.9%), followed by sediment (22.8%) and water (16.7%). The primary types of MPs identified in all samples were fibers (35.6%), fragments (33.3%), and films (28%), with beads being the least represented at 3.03%. ATR-FTIR and Raman spectra analysis identified five polymers from shellfish (polypropylene, polyethylene, polystyrene, nylon, and polyvinyl chloride), five from fish guts (nylon, polypropylene, polyethylene, polyurethane, and polysiloxane), four in sediment (polypropylene, polyethylene, nylon, rayon), and four in water samples (polypropylene, polyethylene, nylon, and polystyrene). SEM-EDAX analysis of MPs obtained from the samples revealed degradation and the presence of inorganic elements such as Na, Mg, Al, Si, S, K, Cl, P, and Ca, as well as heavy metals like Pb, Mo, Rh, Pd, Ti, and Fe. The existence of these plastic polymers and heavy metals in microplastic samples poses a threat to vulnerable biota; people consume contaminated fish and shellfish, underscoring the importance of monitoring MPs in lake water. This investigation of MPs in Ashtamudi Lake highlights the system's susceptibility to plastic pollution and the bioavailability of smaller MPs to aquatic organisms. Identified sources of MPs in the lake include fishing and aquaculture activities, sewage pollution, improper solid waste management in lake watersheds, and unsustainable tourism. Upstream and downstream management interventions are recommended to address MP pollution in Ashtamudi Lake.

Prevalence and implications of microplastic contaminants in general human seminal fluid: A Raman spectroscopic study.

Microplastics are ubiquitous environmental contaminants that have been detected in human semen from polluted areas, yet their prevalence and effects in the general population remain largely unexplored. To examine microplastic presence, abundance, polymer types, and associations with semen quality parameters in individuals without occupational exposures, this study was conducted by collecting semen samples from 40 participants undergoing premarital health assessments in Jinan, China. Raman microspectroscopy was employed to identify, quantify, and categorize microplastic polymers, sperm motility was assessed via computer-assisted analysis, and morphology was evaluated through Diff-Quik staining. Correlations between demographics, semen parameters, and microplastic content were examined by statistical analysis. We found that microplastics were detected in all semen samples, with 2 particles per sample (ranging from 0.72 to 7.02 µm). Eight distinct polymers were identified, with polystyrene (31 %) being most prevalent. Semen exposed to polystyrene demonstrated higher sperm progressive motility as compared to polyvinyl chloride exposure group (43.52 ± 14.21 % vs 19.04 ± 13.46 %). Sperm morphological abnormalities were observed but not significantly associated with specific plastic types. In conclusion, this study reveals microplastic contamination in semen from individuals without occupational exposure, with PS, PE, and PVC being the most prevalent and exhibiting differential correlations with sperm progressive motility, and highlight the need for further research into the potential reproductive impacts of microplastic exposure.

Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers.

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.

Intrinsic negative Poisson's ratio of the monolayer semiconductorß-TeO2.

Monolayer semiconductors with unique mechanical responses are promising candidates for novel electromechanical applications. Here, through first-principles calculations, we discover that the monolayerß-TeO2, a high-mobilityp-type and environmentally stable 2D semiconductor, exhibits an unusual out-of-plane negative Poisson's ratio (NPR) when a uniaxial strain is applied along the zigzag direction. The NPR originates from the unique six-sublayer puckered structure and hinge-like Te-O bonds in the 2Dß-TeO2. We further propose that the sign of the Raman frequency change under uniaxial tensile strain could assist in determining the lattice orientation of monolayerß-TeO2, which facilitates the experimental study of the NPR. Our results is expected to motivate further experimental and theoretical studies of the rich physical and mechanical properties of monolayerß-TeO2.

A Molecular Typing Method for Invasive Breast Cancer by Serum Raman Spectroscopy.

BACKGROUND: The incidence of breast cancer ranks highest among cancers and is exceedingly heterogeneous. Immunohistochemical staining is commonly used clinically to identify the molecular subtype for subsequent treatment and prognosis. PURPOSE: Raman spectroscopy and support vector machine (SVM) learning algorithm were utilized to identify blood samples from breast cancer patients in order to investigate a novel molecular typing approach. METHOD: Tumor tissue coarse needle aspiration biopsy samples, and peripheral venous blood samples were gathered from 459 invasive breast cancer patients admitted to the breast department of Sichuan Cancer Hospital between June 2021 and September 2022. Immunohistochemical staining and in situ hybridization were performed on the coarse needle aspiration biopsy tissues to obtain their molecular typing pathological labels, including: 70 cases of Luminal A, 167 cases of Luminal B (HER2-positive), 57 cases of Luminal B (HER2-negative), 84 cases of HER2-positive, and 81 cases of triple-negative. Blood samples were processed to obtained Raman spectra taken for SVM classification models establishment with machine algorithms (using 80% of the sample data as the training set), and then the performance of the SVM classification models was evaluated by the independent validation set (20% of the sample data). RESULTS: The AUC values of SVM classification models remained above 0.85, demonstrating outstanding model performance and excellent subtype discrimination of breast cancer molecular subtypes. CONCLUSION: Raman spectroscopy of serum samples can promptly and precisely detect the molecular subtype of invasive breast cancer, which has the potential for clinical value.

Hydrated Electron Dynamics and Stimulated Raman Scattering in Water Induced by Ultrashort Laser Pulses.

For this study, we employed intense 400 nm, 100 fs pulses linearly propagated through a 50 cm water medium, initially self-stretching the excitation pulses to 2.50 ps. Subsequently, the self-stretched 2.50 ps pulses were focused into deionized water, and we conducted transient absorption experiments to measure and investigate the dynamics of hydrated electrons in water. The excess electrons generated were injected into the hydrogen bond network of the water cluster, leading to the observation of saturated hydrated electrons. Additionally, we observed the emergence of the forward stimulated Raman scattering (SRS) of water molecules. We report the experimental observation of a weak forward SRS emission at 463 nm (corresponding to 3400 cm-1), indicative of the ordinary OH stretching vibration in the liquid phase. Moreover, we observed an intense forward SRS emission at 460 nm in water, corresponding to two anomalous Raman shifts at 3260 cm-1 and 3355 cm-1. These anomalous Raman shifts resulted from changes in the hydrogen bond network structure. We determine that the formation of not fully hydrated and saturated hydrated electrons plays a crucial role in producing this phenomenon.

Comparative first principles investigation on the structural, optoelectronic and vibrational properties of strain-engineered graphene-like AlC3, BC3and C3N monolayers.

Three cardinal two-dimensional semiconductorsviz., AlC3, BC3and C3N, closely resembling the graphene structure, are intriguing contenders for emerging optoelectronic and thermomechanical applications. Starting from a critical stability analysis, this density functional theory study delves into a quantitative assessment of structural, mechanical, electronic, optical, vibrational and thermodynamical properties of these monolayers as a function of biaxial strain(ε)in a sublinear regime(-2%⩽ε⩽4%)of elastic deformation. The structures with cohesive energies slightly smaller than graphene, manifest exceptional mechanical stiffness, flexibility and breaking stress. The mechanical parameters have been deployed to further cultivate acoustic attributes and thermal conductivity. The hexagonal structures with mixed ionic-covalent molecular bonds have indirect electronic band-gap and work-function acutely sensitive toε. Dispersions of optical dielectric function, energy loss, refractive index, extinction coefficient, reflectivity, absorption coefficient and conductivity are deciphered in the UV-Vis-NIR regime against strain, where particular frequency bands featuring high polarization, dissipation, absorbance or reflectance are identified. Phonon band-structure and density of states testify dynamic stability in the ground state for all systems except the compressed ones. A comprehensive group theoretical analysis is performed to cultivate rotational; infrared and Raman-active modes, and the nature of molecular vibrations is delineated. The red-shifting of phonon bands andE2g/A1gRaman peaks with increasingε, associates estimation of Grüneisen parameter. Finally, strain-induced alterations of thermodynamic quantities such as entropy, enthalpy, free energy, heat capacity and Debye temperature are studied, followed by a molecular dynamics-based stability assessment under canonical ensemble.

Image-free recognition of moderate ROP from mild with machine learning algorithm on plasma Raman spectrum.

The retinopathy of prematurity (ROP) can cause serious clinical consequences and, fortunately, it is remediable while the time window for treatment is relatively narrow. Therefore, it is urgent to screen all premature infants and diagnose ROP degree timely, which has become a large workload for pediatric ophthalmologists. We developed a retinal image-free procedure using small amount of blood samples based on the plasma Raman spectrum with the machine learning model to automatically classify ROP cases before medical intervention was performed. Statistical differences in infrared Raman spectra of plasma samples were found among the control, mild (ZIIIS1), moderate (ZIIIS2 & ZIIS1), and advanced (ZIIS2) ROP groups. With the different wave points of Raman spectra as the inputs, the outputs of our support vector machine showed that the area under the curves in the receiver operating characteristic (AUC) were 0.763 for the pair comparisons of the control with the mild groups, 0.821 between moderate and advanced groups (ZIIS2), while more than 90% in comparisons of the other four pairs: control vs. moderate (0.981), control vs. advanced (0.963), mild vs. moderate (0.936), and mild vs. advanced (0.953), respectively. Our study could advance principally the ROP diagnosis in two dimensions: the moderate ROPs have been classified remarkably from the mild ones, which leaves more time for the medical treatments, and the procedure of Raman spectrum with a machine learning model based on blood samples can be conveniently promoted to those hospitals lacking of the pediatric ophthalmologists with experience in reading retinal images.

Raman signal optimization based on residual network adaptive focusing.

Due to its high sensitivity and specificity, Micro-Raman spectroscopy has emerged as a vital technique for molecular recognition and identification. As a weakly scattered signal, ensuring the accurate focus of the sample is essential for acquiring high quality Raman spectral signal and its analysis, especially in some complex microenvironments such as intracellular settings. Traditional autofocus methods are often time consuming or necessitate additional hardware, limiting real-time sample observation and device compatibility. Here, we propose an adaptive focusing method based on residual network to realize rapid and accurate focusing on Micro-Raman measurements. Using only a bright field image of the sample acquired on any image plane, we can predict the defocus distance with a residual network trained by Resnet50, in which the focus position is determined by combining the gradient and discrete cosine transform. Further, detailed regional division of the bright field map used for characterizing the height variation of actual sample surface is performed. As a result, a focus prediction map with 1µm accuracy is obtained from a bright field image in 120 ms. Based on this method, we successfully realize Raman signal optimization and the necessary correction of spectral information. This adaptive focusing method based on residual network is beneficial to further enhance the sensitivity and accuracy of Micro-Raman spectroscopy technology, which is of great significance in promoting the wide application of Raman spectroscopy.

Surface-Enhanced Raman Spectroscopy Substrate Time Stability Improvement Using an External Oxygen Barrier Method.

The poor time stability of surface-enhanced Raman scattering (SERS) substrates greatly limits their application potential. Although core-shell structures are commonly used to enhance stability, their complex preparation processes, high costs, and susceptibility under acidic or alkaline conditions result in serious disadvantages for practical applications. Here, we propose a new method of external oxygen barrier to improve spectral stability, in which SERS substrates are stored in an oxygen-free environment. Controlled experiments are carried out under air and vacuum. Raman spectrum intensity is measured 11 times within six months for each group. Using the attenuation formula, the Raman spectrum intensity decay results of each SERS substrate over time are obtained. The effectiveness of the external oxygen barrier method is demonstrated through curve fitting using the corresponding function. The substrate spectral attenuation rates of the vacuum group and the argon group within six months are <20%, proving the effectiveness of the external oxygen barrier method.

Efficient, automatic, and optimized portable Raman-spectrum-based pesticide detection system.

Raman spectroscopy can be used for accurately detecting pesticides and determining the chemical composition of a pesticide. To facilitate field detection, the present study used a portable Raman spectrometer for analysis. However, this spectrometer was found to be susceptible to noise interference and signal offsets, which increased the difficulty of pesticide identification. The most commonly used algorithm for Raman spectrum identification is principal component analysis (PCA). However, accurate classification often cannot be achieved with PCA because of the offset and noise in the Raman spectrum data. Therefore, in this study, after the collected Raman spectrum data were processed using the small-step, center-weighted moving-average method, these data were employed to train a convolutional neural network (CNN) model for prediction. To optimize the CNN model, the hyperparameters of the CNN were adjusted using various optimization algorithms, and the optimal solution was obtained after multiple iterations. Data preprocessing and architecture training models were then constructed in a self-optimized manner to improve the ability of the algorithm model to handle diverse types of data. Finally, a CNN model optimized using the cat swarm optimization algorithm was developed. This model was trained on 3000 samples containing three pesticides, and its accuracy for pesticide composition identification was discovered to be 89.33%.

Comparison of biofilm models for producing artificial active white spot lesions

Abstract Creating artificial caries-like lesions that mimic the complex changes observed in natural caries is essential for properly evaluating new strategies, dental materials, and devices designed to arrest their progression and avoid more costly and invasive treatments. Objective This study compared three protocols for developing artificial white spot lesions (WSL) using biofilm models. Methodology In total, 45 human enamel specimens were sterilized and allocated into three groups based on the biofilm model: Streptococcus sobrinus and Lactobacillus casei (Ss+Lc), Streptococcus sobrinus (Ss), or Streptococcus mutans (Sm). Specimens were incubated in filter-sterilized human saliva to form the acquired pellicle and then subjected to the biofilm challenge consisting of three days of incubation with bacteria (for demineralization) and one day of remineralization, which was performed once for Ss+Lc (four days total), four times for Ss (16 days total), and three times for Sm (12 days total). After WSL creation, the lesion fluorescence, depth, and chemical composition were assessed using Quantitative Light-induced Fluorescence (QLF), Polarized Light Microscopy (PLM), and Raman Spectroscopy, respectively. Statistical analysis consisted of two-way ANOVA followed by Tukey's post hoc test (α=0.05). WSL created using the Ss+Lc protocol presented statistically significant higher fluorescence loss (ΔF) and integrated fluorescence (ΔQ) in comparison to the other two protocols (p<0.001). Results In addition, Ss+Lc resulted in significantly deeper WSL (137.5 µm), followed by Ss (84.1 µm) and Sm (54.9 µm) (p<0.001). While high mineral content was observed in sound enamel surrounding the WSL, lesions created with the Ss+Lc protocol showed the highest demineralization level and changes in the mineral content among the three protocols. Conclusion The biofilm model using S. sobrinus and L. casei for four days was the most appropriate and simplified protocol for developing artificial active WSL with lower fluorescence, higher demineralization, and greater depth.

Symmetry-Engineering-Induced In-Plane Polarization Enhancement in Ta2NiS5/CrOCl van der Waals Heterostructure.

Van der Waals (vdW) interfaces can be formed via layer stacking regardless of the lattice constant or symmetry of the individual building blocks. Herein, we constructed a vdW interface of layered Ta2NiS5 and CrOCl, which exhibited remarkably enhanced in-plane anisotropy via polarized Raman spectroscopy and electrical transport measurements. Compared with pristine Ta2NiS5, the anisotropy ratio of the Raman intensities for the B2g, 2Ag, and 3Ag modes increased in the heterostructure. More importantly, the anisotropy ratios of conductivity and mobility in the heterostructure increased by one order of magnitude. Specifically speaking, the conductivity ratio changed from ~2.1 (Ta2NiS5) to ~15 (Ta2NiS5/CrOCl), while the mobility ratio changed from ~2.7 (Ta2NiS5) to ~32 (Ta2NiS5/CrOCl). Such prominent enhancement may be attributed to the symmetry reduction caused by lattice mismatch at the heterostructure interface and the introduction of strain into the Ta2NiS5. Our research provides a new perspective for enhancing artificial anisotropy physics and offers feasible guidance for future functionalized electronic devices.

Rapid Detection and Analysis of Raman Spectra of Bacteria in Multiple Fields of View Based on Image Stitching Technique.

BACKGROUND: Due to antibiotic abuse, the problem of bacterial resistance is becoming increasingly serious, and rapid detection of bacterial resistance has become an urgent issue. Because under the action of antibiotics, different active bacteria have different metabolism of heavy water, antibiotic resistance of bacteria can be identified according to the existence of a C-D peak in the 2030-2400 cm-1 range in the Raman spectrum. METHODS: To ensure data veracity, a large number of bacteria need to be detected, however, due to the limitation of the field of view of the high magnification objective, the number of single cells in a single field of view is very small. By combining an image stitching algorithm, image recognition algorithm, and processing of Raman spectrum and peak-seeking algorithm, can identify and locate single cells in multiple fields of view at one time and can discriminate whether they are Antimicrobial-resistant bacteria. RESULTS: In experiments 1 and 2, 2706 bacteria in 9 × 11 fields of view and 2048 bacteria in 11 × 11 fields of view were detected. Results showed that in experiment 1, there are 1137 antibiotic-resistant bacteria, accounting for 42%, and 1569 sensitive bacteria, accounting for 58%. In experiment 2, there are 1087 antibiotic-resistant bacteria, accounting for 53%, and 961 sensitive bacteria, accounting for 47%. It showed excellent performance in terms of speed and recognition accuracy as compared to traditional manual detection approaches. And solves the problems of low accuracy of data, a large number of manual experiments, and low efficiency due to the small number of single cells in the high magnification field of view and different peak-seeking parameters of different Raman spectra. CONCLUSIONS: The detection and analysis method of bacterial Raman spectra based on image stitching can be used for unattended, automatic, rapid and accurate detection of single cells at high magnification with multiple fields of view. With the characteristics of automatic, high-throughput, rapid, and accurate identification, it can be used as an unattended, universal and non-invasive means to measure antibiotic-resistant bacteria to screen for effective antibiotics, which is of great importance for studying the persistence and spread of antibiotics in bacterial pathogens.

A highlightedly improved method for isolating and characterizing calcium oxalate crystals from tubercles of Mammillaria schumannii.

BACKGROUND: Calcium oxalate (CaOx) is the most prevalent and widespread biomineral in plants and is involved in protective and/or defensive functions against abiotic stress factors. It is, however, expected that this function has an extremely significant contribution to growth processes in plants bearing large amounts of CaOx, such as cacti growing in desert environment. RESULTS: In our research, small-sized CaOx crystals (≤ 20 µm) with tetrahedral or spherical shapes were observed to dominate in each epidermal and cortical cell from the tubercles of Mammillaria schumannii, a species from the Cereoideae subfamily, having tubercles (main photosynthetic organs) united with adjacent ones almost into ridges on its stem. Because they have potential significant functions, differential centrifugations after mechanical blending were used to obtain these small-sized CaOx crystals, which extremely tend to adhere to tissue or suspend in solution. And then the combined Scanning Electron Microscope Energy Dispersive System (SEM-EDS) and Raman spectroscopy were further performed to demonstrate that the extracted crystals were mainly CaC2O4·2H2O. Interestingly, spherical druses had 2 obvious abnormal Raman spectroscopy peaks of -CH and -OH at 2947 and 3290 cm-1, respectively, which may be attributed to the occluded organic matrix. The organic matrix was further extracted from spherical crystals, which could be polysaccharide, flavone, or lipid compounds on the basis of Raman spectroscopy bands at 2650, 2720, 2770, and 2958 cm-1. CONCLUSIONS: Here we used a highlightedly improved method to effectively isolate small-sized CaOx crystals dominating in the epidermal and cortical cells from tubercles of Mammillaria schumannii, which extremely tended to adhere plant tissues or suspend in isolation solution. And then we further clarified the organic matrix getting involved in the formation of CaOx crystals. This improved method for isolating and characterizing biomineral crystals can be helpful to understand how CaOx crystals in cacti function against harsh environments such as strong light, high and cold temperature, and aridity.

Automatic classification of microplastics and natural organic matter mixtures using a deep learning model.

Several preprocessing procedures are required for the classification of microplastics (MPs) in aquatic systems using spectroscopic analysis. Procedures such as oxidation, which are employed to remove natural organic matter (NOM) from MPs, can be time- and cost-intensive. Furthermore, the identification process is prone to errors due to the subjective judgment of the operators. Therefore, in this study, deep learning (DL) was applied to improve the classification accuracies for mixtures of microplastic and natural organic matter (MP-NOM). A convolutional neural network (CNN)-based DL model with a spatial attention mechanism was adopted to classify substances from their Raman spectra. Subsequently, the classification results were compared with those obtained using conventional Raman spectral library software to evaluate the applicability of the model. Additionally, the crucial spectral band for training the DL model was investigated by applying gradient-weighted class activation mapping (Grad-CAM) as a post-processing technique. The model achieved an accuracy of 99.54%, which is much higher than the 31.44% achieved by the Raman spectral library. The Grad-CAM approach confirmed that the DL model can effectively identify MPs based on their visually prominent peaks in the Raman spectra. Furthermore, by tracking distinctive spectra without relying solely on visually prominent peaks, we can accurately classify MPs with less prominent peaks, which are characterized by a high standard deviation of intensity. These findings demonstrate the potential for automated and objective classification of MPs without the need for NOM preprocessing, indicating a promising direction for future research in microplastic classification.

Single-Grating Monolithic Spatial Heterodyne Raman Spectrometer: An Investigation on the Effects of Detector Selection.

Spatial heterodyne Raman spectrometers (SHRSs) are modified forms of Michelson interferometers, except the mirrors in a Michelson interferometer are replaced with stationary diffraction gratings. This design removes the need for an entrance slit, as is the case in a dispersive spectrometer, and removes the need to scan the spectrum by using a moving mirror in a modern Michelson interferometer. In previous studies, various SHRS variants, such as free-standing two-grating SHRS, single-grating SHRS (1g-SHRS), monolithic SHRS (mSHRS), and single-grating mSHRS (1g-mSHRS), have been evaluated. However, the present study exclusively focuses on the 1g-mSHRS configuration. The 1g-mSHRS and 1g-SHRS increase the spectral range at fixed grating line density while trading off spectral resolution and resolving power. The mSHRS benefits from increased rigidity, lack of moving parts, and reduced footprint. In this study, we investigate how the choice of detector impacts the performance of the 1g-mSHRS system, with a specific focus on evaluating the performance of three types of cameras: charged-coupled device (CCD), intensified CCD (ICCD), and complementary metal-oxide-semiconductor (CMOS) cameras. These systems were evaluated using geological, organic, and inorganic samples using a 532 nm continuous wave laser for the CMOS and CCD cameras, and a 532 nm neodymium-doped yttrium aluminum garnet pulsed laser for the ICCD camera. The footprint of the 1g-mSHRS was 3.5 × 3.5 × 2.5 cm3 with a mass of 272 g or 80 g, depending on whether the monolith housing is included or not. We found that increasing the number of pixels utilized along the x-axis of the camera increases fringe visibility (FV) and optimizes the resolution (by capturing the entirety of the grating and magnifying the fringes). The number of pixels utilized in the y-axis, chip size, and dimensions, affect the signal-to-noise ratio of the systems. Additionally, we discuss the effect of pixel pitch on the recovery of Fizeau fringes, including the relationship between the Nyquist frequency, aliasing, and FV.

Study on Asymmetric Vibrational Coherent Magnetic Transitions and Origin of Fluorescence in Symmetric Structures.

In this work, the physical mechanisms of three highly efficient circularly polarized luminescent materials are introduced. The UV-vis spectra are plotted; the transition properties of their electrons at the excited states are investigated using a combination of the transition density matrix (TDM) and the charge difference density (CDD); combining the distribution of electron clouds, the essence of charge transfer excitation in three structures is explained. The resonance Raman spectrum of the three structures at the S1 and S2 excited states are calculated. The M, M-4 and M, M-5 structures are found to produce novel chirality by electronic circular dichroism (ECD) spectrum, and the reasons for the chirality of the M, M-4 and M, M-5 structures are discussed by analyzing the density of transition electric/magnetic dipole moments (TEDM/TMDMs) in different orientations. Finally, the Raman optical activity (ROA) of M, M-4, and M, M-5 are calculated, and the spectra are plotted. This study will provide guidance for the application of carbon-based nanomaterials in organic electronic devices, solar cells, and optoelectronics.