Intrinsic negative Poisson's ratio of the monolayer semiconductor β-TeO2

Monolayer semiconductors with unique mechanical responses are promising candidates for novel electromechanical applications. Here, through first-principles calculations, we discover that the monolayer β-TeO2, a high-mobility p-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 2D β-TeO2. We further propose that the sign of the Raman frequency change under uniaxial tensile strain could assist in determining the lattice orientation of monolayer β-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 monolayer β-TeO2.

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.

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%.

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.

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.

Combined FTIR/Raman spectroscopic studies and ab initio electronic structure calculations of Dithiothreitol

A total of seven minimum energy geometries were obtained on exploring the conformational landscape of dithiothreitol (DTT) by varying the prominent dihedral angles in the molecule through a relaxed scan with a step size of 5° at B3LYP/cc-pVTZ with further geometry optimization at CCSD/cc-pVDZ level of theory. Single point energies were calculated for all the conformers at CCSD(T)/CBS limit with cc-pVNZ (N = T, Q) level of theory and revealed the similar energy pattern. The two conformers, namely G'TG'1 and G'TT, were found iso-energic even though they differed in their structure significantly and were of the lowest energy compared to others. Energies corresponding to the cyclic as well as other configurational counterpart of the global minimum were found much higher in energy compared to the global minimum structure. Intramolecular sulfur centered hydrogen bond was seen to stabilize the global minimum structure of DTT as revealed by AIM, NBO, FMO and ESP charge analysis. Computed NMR of DTT matched well with the experimental data gleaned from the literature. Vibrational spectra (Raman and IR) were measured and compared with computed normal modes of DTT, which were found in good agreement.

Rapid chiral purity identification of mandelic acid by Raman spectra in the O-H stretching region

Chiral molecules play a very important role in biological systems, and more and more chiral drugs are used in the treatment of diseases. Mandelic acid (MA) is an important chiral starting substance or the key intermediate of some chiral drugs, and the rapid detection of its chiral purity is very important in the synthesis, separation and detection of drugs. In this study, we developed a method for rapid determination of chiral purity of MA by Raman spectroscopy, and measured a series of Raman spectra of different chiral MA. Through the analysis, it is found that the OH stretching vibration peak can be used to identify the components of chiral molecules, and the enantiomeric excess (ee value) obtained is in good agreement with the real value, and the error is about 5%. The experimental detection speed is fast and the efficiency is high. Our work firstly provides a new idea for the purity detection of chiral molecules by the original Raman spectrum.

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.

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.

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.

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.

Variational Mode Decomposition for Raman Spectral Denoising

As a fast and non-destructive spectroscopic analysis technique, Raman spectroscopy has been widely applied in chemistry. However, noise is usually unavoidable in Raman spectra. Hence, denoising is an important step before Raman spectral analysis. A novel spectral denoising method based on variational mode decomposition (VMD) was introduced to solve the above problem. The spectrum is decomposed into a series of modes (uk) by VMD. Then, the high-frequency noise modes are removed and the remaining modes are reconstructed to obtain the denoised spectrum. The proposed method was verified by two artificial noised signals and two Raman spectra of inorganic materials, i.e., MnCo ISAs/CN and Fe-NCNT. For comparison, empirical mode decomposition (EMD), Savitzky-Golay (SG) smoothing, and discrete wavelet transformation (DWT) are also investigated. At the same time, signal-to-noise ratio (SNR) was introduced as evaluation indicators to verify the performance of the proposed method. The results show that compared with EMD, VMD can significantly improve mode mixing and the endpoint effect. Moreover, the Raman spectrum by VMD denoising is more excellent than that of EMD, SG smoothing and DWT in terms of visualization and SNR. For the small sharp peaks, some information is lost after denoising by EMD, SG smoothing, DWT and VMD while VMD loses fewest information. Therefore, VMD may be an alternative method for Raman spectral denoising.

Influence of the Matrix Material and Tribological Contact Type on the Antifriction Properties of Hybrid Reinforced Polyimide-Based Nano- and Microcomposites

This paper addresses peculiarities in the formation and adherence of a tribofilm on the wear track surface of antifriction PI- and PEI-based composites, as well as a transfer film (TF) on a steel counterface. It is shown that during hot pressing, PTFE nanoparticles melted and coalesced into micron-sized porous inclusions. In the PEI matrix, their dimensions were much larger (up to 30 µm) compared to those in the PI matrix (up to 6 µm). The phenomenon eliminated their role as effective uniformly distributed nanofillers, and the content of 5 wt.% was not always sufficient for the formation of a tribofilm or a significant decrease in the WR values. At the loaded content, the role of MoS2 and graphite (Gr) microparticles was similar, although filling with MoS2 microparticles more successfully solved the problem of adhering to a PTFE-containing tribofilm in the point tribological contact. This differed under the linear tribological contact. The higher roughness of the steel counterpart, as well as the larger area of its sliding surface with the same PTFE content in the three-component PI- and PEI-based composites, did not allow for a strong adherence of either the stable PTFE-containing tribofilm on the wear track surface or the TF on the steel counterpart. For the PEI-based composites, the inability to shield the steel counterpart from the more reactive polymer matrix, especially under the conditions of PTFE deficiency, was accompanied by multiple increases in the WR values, which were several times greater than that of neat PEI.

Graphene-Based Physically Unclonable Functions with Dual Source of Randomness

There is growing interest in systems with randomized responses for generating physically unclonable functions (PUFs) in anticounterfeiting and authentication applications. Atomic-level control over its thickness and unique Raman spectrum make graphene an attractive material for PUF applications. Herein, we report graphene PUFs that emerge from two independent stochastic processes. Randomized variations in the shape and number of graphene adlayers were achieved by exploiting and improving the mechanistic understanding of the chemical vapor deposition of graphene. The randomized positioning of the graphene domains was then facilitated by dewetting the polymer film, followed by oxygen plasma etching. This approach yielded surfaces with randomly positioned and shaped graphene islands with varied numbers of layers and, therefore, Raman spectra. Raman mapping of surfaces resulted in multicolor images with a high encoding capacity. Advanced feature-matching algorithms were employed for the authentication of multicolor images. The use of two independent stochastic processes on a two-dimensional nanomaterial platform enables the creation of unique and complex surfaces that excessively challenge clonability.

Plasma Exosome Analysis for Protein Mutation Identification Using a Combination of Raman Spectroscopy and Deep Learning

Protein mutation detection using liquid biopsy can be simply performed periodically, making it easy to detect the occurrence of newly emerging mutations rapidly. However, it has low diagnostic accuracy since there are more normal proteins than mutated proteins in body fluids. To increase the diagnostic accuracy, we analyzed plasma exosomes using nanoplasmonic spectra and deep learning. Exosomes, a promising biomarker, are abundant in plasma and stably carry intact proteins originating from mother cells. However, the mutated exosomal proteins cannot be detected sensitively because of the subtle changes in their structure. Therefore, we obtained Raman spectra that provide molecular information about structural changes in mutated proteins. To extract the unique features of the protein from complex Raman spectra, we developed a deep-learning classification algorithm with two deep-learning models. Consequently, controls with wild-type proteins and patients with mutated proteins were classified with high accuracy. As a proof of concept, we discriminated the lung cancer patients with mutations in the epidermal growth factor receptor (EGFR), L858R, E19del, L858R + T790M, and E19del + T790M, from controls with an accuracy of 0.93. Moreover, the protein mutation status of the patients with primary (E19del, L858R) and secondary (+T790M) mutations was clearly monitored. Overall, our technique is expected to be applied as a novel method for companion diagnostic and treatment monitoring.

Serum Raman spectroscopy can be used to screen patients with early rheumatoid arthritis

In this study, Raman spectroscopy was used to analyze the serum of patients with early rheumatoid arthritis (RA), and to explore the screening value of Raman spectroscopy in patients with early RA. A total of 216 patients were included in the study. Fasting venous blood was collected for routine biochemical detection, and the remaining samples were tested by serum Raman spectroscopy. Support vector machine was used for model building and training. The area under the curve (AUC) values of the model were as follows: (1) healthy group versus early RA group: 0.860, (2) healthy group versus non-early RA group: 0.903, and (3) early RA group versus non-early RA group: 0.918. This study shows that serum Raman spectroscopy has a good ability to screen RA and can be staged according to the course of the disease, which can provide new ideas and technical support for the diagnosis or screening of early RA.

A double sliding-window method for baseline correction and noise estimation for Raman spectra of microplastics

When measuring microplastics of environmental samples, additives and attachment of biological materials may result in strong fluorescence in Raman spectra, which increases difficulty for imaging, identification, and quantification. Although there are several baseline correction methods available, user intervention is usually needed, which is not feasible for automated processes. In current study, a double sliding-window (DSW) method was proposed to estimate the baseline and standard deviation of noise. Simulated spectra and experimental spectra were used to evaluate the performance in comparison with two popular and widely used methods. Validation with simulated spectra and spectra of environmental samples showed that DSW method can accurately estimate the standard deviation of spectral noise. DSW method also showed better performance than compared methods when handling spectra of low signal-to-noise ratio (SNR) and elevated baselines. Therefore, DSW method is a useful approach for preprocessing Raman spectra of environmental samples and automated processes.

High Refractive Index GRIN Lens for IR Optics

Infrared gradient refractive index (GRIN) material lenses have attracted much attention due to their continuously varying refractive index as a function of spatial coordinates in the medium. Herein, a glass accumulation thermal diffusion method was used to fabricate a high refractive index GRIN lens. Six Ge_17.2As_17.2Se_xTe_(65-x) (x = 10.5-16) glasses with good thermal stability and high refractive index (n_{@10 μm} > 3.1) were selected for thermal diffusion. The refractive index span (∆n) of 0.12 was achieved in this GRIN lens. After thermal diffusion, the lens still had good transmittance (45%) in the range of 8-12 μm. Thermal imaging confirmed that this lens can be molded into the designed shape. The refractive index profile was indirectly characterized by the structure and composition changes. The structure and composition variation became linear with the increase in temperature from 260 °C to 270 °C for 12 h, indicating that the refractive index changed linearly along the axis. The GRIN lens with a high refractive index could find applications in infrared optical systems and infrared lenses for thermal imaging.

Modern Microscopic Approaches to Astrocytes

Microscopy started as the histological analysis based on intrinsic optical properties of tissues such as the refractive index and light absorption, and is expanding to include the visualization of organelles by chemical staining, localization of molecules by immunostaining, physiological measurements such as Ca²⁺ imaging, functional manipulation by optogenetics, and comprehensive analysis of chemical composition by Raman spectra. The microscope is one of the most important tools in neuroscience, which aims to reveal the complex intercellular communications underlying brain function and pathology. Many aspects of astrocytes, including the structures of their fine processes and physiological activities in concert with neurons and blood vessels, were revealed in the course of innovations in modern microscopy. The evolution of modern microscopy is a consequence of breakthroughs in spatiotemporal resolutions and expansions in molecular and physiological targets due to the progress in optics and information technology, as well as the inventions of probes using organic chemistry and molecular biology. This review overviews the modern microscopic approach to astrocytes.

ZnTe Crystal Multimode Cryogenic Thermometry Using Raman and Luminescence Spectroscopy

In this study, ZnTe crystal was applied to provide precise thermal sensing for cryogenic temperatures. Multiple techniques, namely Raman and photoluminescence spectroscopies, were used to broaden the operating temperature range and improve the reliability of the proposed thermometers. Raman-based temperature sensing could be applied in the range of 20-100 K, while luminescence-based thermometry could be utilized in a narrower range of 20-70 K. However, the latter strategy provides better relative thermal sensitivity and temperature resolution. The best thermal performances based on a single temperature-dependent parameter attain S_r = 3.82% K^-1 and ΔT = 0.12 K at T = 50 K. The synergy between multiple linear regression and multiparametric thermal sensing demonstrated for Raman-based thermometry results in a ten-fold improvement of _Sr and a two-fold enhancement of ΔT. All studies performed testify that the ZnTe crystal is a promising multimode contactless optical sensor for cryogenic thermometry.

The landscape of micron-scale particles including microplastics in human enclosed body fluids

Exogenous microparticles including microplastics are novel pollutants that could persist in the environment with potential health effects, while crucial data on their exposure in humans are still lacking. To understand the panorama of microparticles including microplastics exposure and distribution characteristics in different kinds of body fluids. A non-targeted microparticle internal exposure landscape analysis was done in thirteen kinds of human enclosed body fluids covering eight body systems. Totally 104 patients aged 24-96 years with an average age of 56 years were included in this study. After sample digestion, non-soluble microparticles were detected and identified with one Raman Microspectroscope under a strict quality control-particle detection system. Totally 702 microparticles with size ranging from 2.15 to 103.27 µm were detected in samples. Microparticles were identified into 84 substances or 66 molecules, most of which were firstly reported inside human body. Nine kinds of microplastics were originally reported in human body fluids with their size ranging from 19.66 to 103.27 µm. Microparticles exposure was unexpectedly high inside the human body despite the protection of biological barriers and membranes, raising awareness of the impact of particle pollution on sustainable development.

Bayesian Fusion Model Enhanced Codfish Classification Using Near Infrared and Raman Spectrum

In this study, a Bayesian-based decision fusion technique was developed for the first time to quickly and non-destructively identify codfish using near infrared (NIRS) and Raman spectroscopy (RS). NIRS and RS spectra from 320 codfish samples were collected, and separate partial least squares discriminant analysis (PLS-DA) models were developed to establish the relationship between the raw data and cod identity for each spectral technique. Three decision fusion methods: decision fusion, data layer or feature layer, were tested and compared. The decision fusion model based on the Bayesian algorithm (NIRS-RS-B) was developed on the optimal discrimination features of NIRS and RS data (NIRS-RS) extracted by the PLS-DA method whereas the other fusion models followed conventional, non-Bayesian approaches. The Bayesian model showed enhanced classification metrics (92% sensitivity, 98% specificity, 98% accuracy) that were significantly superior to those demonstrated by any of other two spectroscopic methods (NIRS, RS) and the two data fusion methods (data layer fused, NIRS-RS-D, or feature layer fused, NIRS-RS-F). This novel proposed approach can provide an alternative classification for codfish and potentially other food speciation cases.

Distributed Raman Spectrum Data Augmentation System Using Federated Learning with Deep Generative Models

Chemical agents are one of the major threats to soldiers in modern warfare, so it is so important to detect chemical agents rapidly and accurately on battlefields. Raman spectroscopy-based detectors are widely used but have many limitations. The Raman spectrum changes unpredictably due to various environmental factors, and it is hard for detectors to make appropriate judgments about new chemical substances without prior information. Thus, the existing detectors with inflexible techniques based on determined rules cannot deal with such problems flexibly and reactively. Artificial intelligence (AI)-based detection techniques can be good alternatives to the existing techniques for chemical agent detection. To build AI-based detection systems, sufficient amounts of data for training are required, but it is not easy to produce and handle fatal chemical agents, which causes difficulty in securing data in advance. To overcome the limitations, in this paper, we propose the distributed Raman spectrum data augmentation system that leverages federated learning (FL) with deep generative models, such as generative adversarial network (GAN) and autoencoder. Furthermore, the proposed system utilizes various additional techniques in combination to generate a large number of Raman spectrum data with reality along with diversity. We implemented the proposed system and conducted diverse experiments to evaluate the system. The evaluation results validated that the proposed system can train the models more quickly through cooperation among decentralized troops without exchanging raw data and generate realistic Raman spectrum data well. Moreover, we confirmed that the classification model on the proposed system performed learning much faster and outperformed the existing systems.

Serum Raman spectroscopy combined with multiple classification models for rapid diagnosis of breast cancer

Breast cancer is a malignant tumor with the highest incidence rate in women. Current diagnostic methods are time-consuming, costly, and dependent on physician experience. In this study, we used serum Raman spectroscopy combined with multiple classification algorithms to implement an auxiliary diagnosis method for breast cancer, which will help in the early diagnosis of breast cancer patients. We analyzed the serum Raman spectra of 171 invasive ductal carcinoma (IDC) and 100 healthy volunteers; The analysis showed differences in nucleic acids, carotenoids, amino acids, and lipid concentrations in their blood. These differences provide a theoretical basis for this experiment. First, we used adaptive iteratively reweighted penalized least squares (airPLS) and Savitzky-Golay (SG) for baseline correction and smoothing denoising to remove the effect of noise on the experiment. Then, the Principal component analysis (PCA) algorithm was used to extract features. Finally, we built four classification models: support vector machine (SVM), decision tree (DT), linear discriminant analysis (LDA), and Neural Network Language Model (NNLM). The LDA, SVM, and NNLM achieve 100% accuracy. As supplementary, we added the classification experiment of the raw data. By comparing the experimental results of the two groups, We concluded that the NNLM was the best model. The results show the reliability of the combination of serum Raman spectroscopy and classification models under large sample conditions.

One-Step Exfoliation Method for Plasmonic Activation of Large-Area 2D Crystals

Advanced exfoliation techniques are crucial for exploring the intrinsic properties and applications of 2D materials. Though the recently discovered Au-enhanced exfoliation technique provides an effective strategy for the preparation of large-scale 2D crystals, the high cost of gold hinders this method from being widely adopted in industrial applications. In addition, direct Au contact could significantly quench photoluminescence (PL) emission in 2D semiconductors. It is therefore crucial to find alternative metals that can replace gold to achieve efficient exfoliation of 2D materials. Here, the authors present a one-step Ag-assisted method that can efficiently exfoliate many large-area 2D monolayers, where the yield ratio is comparable to Au-enhanced exfoliation method. Differing from Au film, however, the surface roughness of as-prepared Ag films on SiO₂ /Si substrate is much higher, which facilitates the generation of surface plasmons resulting from the nanostructures formed on the rough Ag surface. More interestingly, the strong coupling between 2D semiconductor crystals (e.g., MoS₂ , MoSe₂ ) and Ag film leads to a unique PL enhancement that has not been observed in other mechanical exfoliation techniques, which can be mainly attributed to enhanced light-matter interaction as a result of extended propagation of surface plasmonic polariton (SPP). This work provides a lower-cost and universal Ag-assisted exfoliation method, while at the same time offering enhanced SPP-matter interactions.

Cheminformatics Study on Structural and Bactericidal Activity of Latest Generation β-Lactams on Widespread Pathogens

Raman spectra of oxacillin (OXN), carbenicillin (CBC), and azlocillin (AZL) are reported for the first time together with their full assignment of the normal modes, as calculated using Density Functional Theory (DFT) methods with the B3LYP exchange-correlation functional coupled to the 6-31G(d) and 6-311+G(2d,p) basis sets. Molecular docking studies were performed on five penicillins, including OXN, CBC, and AZL. Subsequently, their chemical reactivity and correlated efficiency towards specific pathogenic strains were revealed by combining frontier molecular orbital (FMO) data with molecular electrostatic potential (MEP) surfaces. Their bactericidal activity was tested and confirmed on a couple of species, both Gram-positive and Gram-negative, by using the disk diffusion method. Additionally, a surface-enhanced Raman spectroscopy (SERS)-principal component analysis (PCA)-based resistogram of A. hydrophila is proposed as a clinically relevant insight resulting from the synergistic cheminformatics and vibrational study on CBC and AZL.

Oil Palm Fruits Ripeness Classification Based on the Characteristics of Protein, Lipid, Carotene, and Guanine/Cytosine from the Raman Spectra

The capacity of palm oil production is directly affected by the ripeness of the fresh fruit bunches (FFB) upon harvesting. Conventional harvesting standards rely on rigid harvesting scheduling as well as the number of fruitlets that have loosened from the bunch. Harvesting is usually done every 10 to 14 days, and an FFB is deemed ready to be harvested if there are around 5 to 10 empty sockets on the fruit bunch. Technology aided by imaging techniques relies heavily on the color of the fruit bunch, which is highly dependent on the surrounding light intensities. In this study, Raman spectroscopy is used for ripeness classification of oil palm fruits, based on the molecular assignments extracted from the Raman bands between 1240 cm^-1 and 1360 cm^-1. The Raman spectra of 52 oil palm fruit samples which contain the fingerprints of different organic compounds were collected. Signal processing was applied to perform baseline correction and to reduce background noises. Characteristic data of the organic compounds were extracted through deconvolution and curve fitting processes. Subsequently, a correlation study between organic compounds was developed and eight hidden Raman peaks including protein, beta carotene, carotene, lipid, guanine/cytosine, chlorophyll-a, and tryptophan were successfully located. Through ANOVA statistical analysis, a total of six peak intensities from proteins through Amide III (β-sheet), beta-carotene, carotene, lipid, guanine/cytosine, and carotene and one peak location from lipid were found to be significant. An automated oil palm fruit ripeness classification system deployed with artificial neural network (ANN) using the seven signification features showed an overall performance of 97.9% accuracy. An efficient and accurate ripeness classification model which uses seven significant Raman peak features from the correlation analysis between organic compounds was successfully developed.

Calculation of absolute Raman scattering cross-sections using vibrational self-consistent field/vibrational configuration interaction wave functions

In the present study, the differential scattering cross-sections, depolarization ratios and Raman shifts of small molecular systems are obtained from configuration iteration wave functions of vibrational self-consistent field (VSCF) states. The transition polarizabilities were modeled using the Placzek approximation, neglecting those contributions not arising from the electric dipole mechanism. This theoretical approach is considered a good approximation for samples that absorb in the UV range if the excitation radiation falls in the visible region, as is the case of the molecules selected for the present study, namely: water, methane, and acetylene. Potential energy and electronic polarizability surfaces are calculated by the CCSD(T) and CC3 methods with aug-cc-p(C)V(T,Q,5)Z basis sets. The vibrational Hamiltonian includes the vibrational angular momentum contribution of the Watson kinetic energy operator. As expected, due to the variational nature of the VSCF and vibrational configuration interaction (VCI) methods, the Raman transition wavenumbers are substantially improved over the harmonic predictions. Surprisingly, the scattering cross-sections obtained using the harmonic approximation or the VSCF method better agrees with the experimental values than those cross-sections predicted using VCI wave functions. The more significant deviations of the VCI results from the experimental reference may be related to the significant uncertainties of the measured cross-sections. Still, it may also indicate that the VCI Raman transition moments may require a more accurate description of the electronic polarizability surface. Finally, the depolarization ratios calculated for H₂ O and C₂ D₂ using harmonic and VCI wave functions have similar accuracy, whereas, for C₂ H₂ and C₂ HD, the VCI results are more accurate.

Temperature Distribution of Vessel Tissue by High Frequency Electric Welding with Combination Optical Measure and Simulation

In clinical surgery, high frequency electric welding is routinely utilized to seal and fuse soft tissues. This procedure denatures collagen by electrothermal coupling, resulting in the formation of new molecular crosslinks. It is critical to understand the temperature distribution and collagen structure changes during welding in order to prevent thermal damage caused by heat generated during welding. In this study, a method combining optical measurement and simulation was presented to evaluate the temperature distribution of vascular tissue during welding, with a fitting degree larger than 97% between simulation findings and measured data. Integrating temperature distribution data, strength test data, and Raman spectrum data, it is discovered that optimal parameters exist in the welding process that may effectively prevent thermal damage while assuring welding strength.

Raman spectrum classification based on transfer learning by a convolutional neural network: Application to pesticide detection

Pesticide detection is of tremendous importance in agriculture, and Raman spectroscopy/Surface-Enhanced Raman Scattering (SERS) has proven extremely effective as a stand-alone method to detect pesticide residues. Machine learning may be able to automate such detection, but conventional algorithms require a complete database of Raman spectra, which is not feasible. To bypass this problem, the present study describes a transfer learning method that improves the algorithm's accuracy and speed to extract features and classify Raman spectra. The transfer learning model described here was developed through the following steps: (1) the classification model was pre-trained using an open-source Raman spectroscopy database; (2) the feature extraction layer was saved after training; and (3) the training model for the Raman spectroscopy database was re-established while using self-tested pesticides and keeping the feature extraction layer unchanged. Three models were evaluated with or without transfer learning: CNN-1D, Resnet-1D, and Inception-1D, and they have improved the accuracy of spectrum classification by 6%, 2%, and 3%, with reduced training time and increased curve smoothness. These results suggest that transfer learning can improve the feature extraction capability and therefore accuracy of Raman spectroscopy models, expanding the range of Raman-based applications where transfer learning model can be used to identify the spectra of different substances.

Modifying Jahn-Teller distortion by epitaxial stress in LaMnO3films for tunning electron localization

The effect of epitaxial stress on Jahn-Teller (JT) distortion in epitaxial LaMnO₃(LMO) films has been investigated. Both2θ-ωscans and reciprocal space maps (RSMs) indicate that LMO samples are subjected to compressive stress. Obvious Laue oscillations can be detected in2θ-ωscans, indicating the high quality of samples. RSMs of symmetry peak (001) and asymmetry peak (-103) imply different epitaxial stress for LMO films deposited on different substrates. Raman spectra measurements reveal that the degree of JT distortion can be well tuned via the epitaxial stress which may further influence on the electron localization in the films. This study might benefit to understanding the correlation between crystalline structure and electrical transport properties of LMO films and related LMO-based superlattices.

The effect of cross-linking degree on physicochemical properties of surimi gel as affected by MTGase

BACKGROUND

The transglutaminase-induced cross-linking reaction can enhance the textural properties of surimi gels. However, when the cross-linking degree exceeds a certain range, surimi gels become brittle, giving the gel a special mouthfeel. Little information is to be found regarding the brittleness of surimi gel and its formation mechanism. The present study aimed to investigate the effects of cross-links on physicochemical properties of surimi gel and to analyze the reason for the textural variation of surimi gels regulated by cross-links.

RESULTS

When the cross-linking degree was lower than about 30%, the surimi gel could not be fractured and the surimi gel was mushy. When the cross-linking degree was around 40-55%, surimi gels without microbial transglutaminase (MTGase) presented an elastic texture, while surimi gels with MTGase displayed a tough texture. When the cross-linking degrees were 64.1% and 76.5%, surimi gels became brittle. Water holding capacity decreased with the increase in cross-linking degree and the cross-links released some combined water to free water. With the increase in cross-links, the pore equivalent diameter and the fractal dimension first decreased and then increased. When the cross-linking degree exceeded approximately 50%, hydrophobic interaction increased, consistent with the exposure of hydrophobic residues and the decrease in the water gap between proteins.

CONCLUSION

The changes in gel texture were related to micro-network structure, water status and microenvironment of proteins. It was demonstrated that the texture of slowly heated surimi-based products could be regulated by controlling the TGase-induced cross-links. © 2021 Society of Chemical Industry.

Raman Spectroscopy as a Novel Method for the Characterization of Polydioxanone Medical Stents Biodegradation

Polydioxanone (PPDX), as an FDA approved polymer in tissue engineering, is an important component of some promising medical devices, e.g., biodegradable stents. The hydrolytic degradation of polydioxanone stents plays a key role in the safety and efficacy of treatment. A new fast and convenient method to quantitatively evaluate the hydrolytic degradation of PPDX stent material was developed. PPDX esophageal stents were degraded in phosphate-buffered saline for 24 weeks. For the first time, the changes in Raman spectra during PPDX biodegradation have been investigated here. The level of PPDX hydrolytic degradation was determined from the Raman spectra by calculating the area under the 1732 cm^-1 peak shoulder. Raman spectroscopy, unlike Fourier transform infrared (FT-IR) spectroscopy, is also sensitive enough to monitor the decrease in the dye content in the stents during the degradation. Observation by a scanning electron microscope showed gradually growing cracks, eventually leading to the stent disintegration. The material crystallinity was increasing during the first 16 weeks, suggesting preferential degradation of the amorphous phase. Our results show a new easy and reliable way to evaluate the progression of PPDX hydrolytic degradation. The proposed approach can be useful for further studies on the behavior of PPDX materials, and for clinical practice.

Raman Spectrum-Based Diagnosis Strategy for Bladder Tumor

Raman spectroscopy is an optical technique that can potentially serve as a molecular diagnosis method. This approach is excellent in many aspects for diagnosing bladder tumors, and over the last 20 years, there has been a rapid increase in the number of related studies. However, no review article has covered the wide use of Raman spectroscopy in bladder tumors. A total of 26 original studies have suggested that Raman spectroscopy shows good performance in diagnosing bladder tumors from 4 aspects, including tissue sections, endoscopic methods, cell screening, and biomarkers. However, Raman spectroscopy needs to be modified by combining it with other techniques, and studies based on a large population are still urgently needed to expand its clinical value.

Effect of thickness and stacking order on Raman spectrum of layered CrCl3

Layer- and stacking-dependent physical properties discovered in van der Waals materials have attracted a lot of attention recently. In this work, we have investigated the Raman spectrum of layered magnetic CrCl₃with different atomic thickness and stacking order from first principles. Our study indicates Raman shift ofR3̄andC2/mphase is near degenerate except that the first and fourth Raman resonant modes exhibit a marked peak difference about 3 cm^-1, which can be understood by stacking-dependent vibrational eigenvectors. Moreover, we also find that both frequency and intensity of Raman modes of few-layer CrCl₃increase with the number of layers simultaneously. Furthermore, the Raman tensor and polarization-dependent Raman intensity are also evaluated in details, which are found to be in good agreement with available experiments. Our results proposed several distinctive signals to identify layer number and stacking order of layered CrCl₃, which will provide an important reference to further experimental study.

Raman Scattering Study on the Influence of E-Beam Bombardment on Si Electron Lens

Microcolumns have a stacked structure composed of an electron emitter, electron lens (source lens), einzel lens, and a deflector manufactured using a micro electro-mechanical system process. The electrons emitted from the tungsten field emitter mostly pass through the aperture holes. However, other electrons fail to pass through because of collisions around the aperture hole. We used Raman scattering measurements and X-ray photoelectron spectroscopy analyses to investigate the influence of electron beam bombardment on a Si electron lens irradiated by acceleration voltages of 0, 20, and 30 keV. We confirmed that the crystallinity was degraded, and carbon-related contamination was detected at the surface and edge of the aperture hole of the Si electron lens after electron bombardment for 24 h. Carbon-related contamination on the surface of the Si electron lens was verified by analyzing the Raman spectra of the carbon-deposited Si substrate using DC sputtering and a carbon rod sample. We report the crystallinity and the origin of the carbon-related contamination of electron Si lenses after electron beam bombardment by non-destructive Raman scattering and XPS analysis methods.

Magnetic phase transition and lattice dynamic features in ErB2C borocarbide

A sample of erbium borocarbide ErB₂C was synthesized from a stoichiometric mixture of erbium, boron, and pyrographite hydride. Temperature dependent magnetic susceptibility, heat capacity and lattice parameters of borocarbide at 2-300 K were experimentally investigated, the Raman spectrum was determined and analyzed. Sharp anomalies in the heat capacity and magnetic properties of ErB₂C nearT= 16.3 K, caused by the transition to the antiferromagnetic state, as well as diffuse anomalies at a higher temperature, caused by the effect of the crystal field (Schottky anomalies) were revealed. It was found that the magnetic phase transition does not cause measurable anomalies in the lattice parameters of borocarbide due to the weak coupling between the Er³⁺ion subsystem and the B-C layers. Almost zero thermal expansion of erbium borocarbide in the basal plane (alongaandbaxes) (anisotropic invar effect) as well as an unusual character of thec-axis thermal expansion were observed. The first of these features is due to the high bonding energy in the B-C layers and the weak bond between the layers in the borocarbide crystal lattice. It was found that the anomalous expansion along thecaxis is a consequence of the action of the crystal field. The splitting scheme for the ground level of Er³⁺ions by crystal field was determined from the analysis of the Schottky anomaly of the heat capacity.

Recognition of chronic renal failure based on Raman spectroscopy and convolutional neural network

PURPOSE

Chronic renal failure (CRF) is a disease with a high morbidity rate that can develop into uraemia, resulting in a series of complications, such as dyspnoea, mental disorders, hypertension, and heart failure. CRF may be controlled clinically by drug intervention. Therefore, early diagnosis and control of the disease are of great significance for the treatment and prevention of chronic renal failure. Based on the complexity of CRF diagnosis, this study aims to explore a new rapid and noninvasive diagnostic method.

METHODS

In this experiment, the serum Raman spectra of samples from 47 patients with CRF and 53 normal subjects were obtained. In this study, Serum Raman spectra of healthy and CRF patients were identified by a Convolutional Neural Network (CNN) and compared with the results of identified by an Improved AlexNet. In addition, different amplitude of noise were added to the spectral data of the samples to explore the influence of a small random noise on the experimental results.

RESULTS

A CNN and an Improved AlexNet was used to classify the spectra, and the accuracy was 79.44 % and 95.22 % respectively. And the addition of noise did not significantly interfere with the classification accuracy.

CONCLUSION

The accuracy of CNN of this study can be as high as 95.22 %, which greatly improves its accuracy and reliability, compared to 89.7 % in the previous study. The results of this study show that the combination of serum Raman spectrum and CNN can be used in the diagnosis of CRF, and small random noise will not cause serious interference to the data analysis results.

Study of photo induced charge transfer mechanism of PEDOT with nitro groups of RDX, HMX and TNT explosives using anti-stokes and stokes Raman lines ratios

The paper reports the charge transfer mechanism between poly (3,4-ethylenedioxythiophene) (PEDOT) and high energy materials such as RDX, HMX and TNT, respectively in terms of ratios of anti-stokes (AS) and stokes(S) Raman lines of NO₂ bands. Generally it works as an effective sensing medium for the detection of explosives when mixed in an equal proportion and are subjected to 532 nm wavelength without any chemical treatment [1]. The pristine PEDOT is less sensitive to 532 nm wavelength (2.33 eV) but influences the Raman S and AS lines of explosives in the mixture. The study also reveals that a small quantity (one milligram) of PEDOT is sufficient to initiate the positive charge transfer mechanism between its oxidized state to the lone pairs of electrons on the oxygen atoms of the nitro group of the explosive molecules. Consequently, the intensity of the Raman spectra of RDX, HMX and TNT is dropped by an order of 22.5, 11.45 and 17.2 times, respectively along with the shift of the NO₂ vibrational modes. It is also attributed to Photon-electron-phonon interaction. Finally, we have estimated the reduced mass of the functional group to ascertain the force constant and the intensity ratios of AS /S lines to confirm the charge transfer mechanism. The effect of charge transfer mechanism is also reflected in drastic change in transmission /absorption characteristics of FTIR spectra of same PEDOT and explosive mixtures.

Dual Tunable MZIs Stationary-Wave Integrated Fourier Transform Spectrum Detection

In order to resolve spectral alias due to under sampling in traditional stationary-wave integrated Fourier transform (SWIFT) spectrometers, an all-on-chip waveguide based on dual tunable Mach-Zehnder interferometer (MZI) stationary-wave integrated Fourier transform technology (DTM-SWIFT) is proposed. Several gold nanowires are asymmetrically positioned at two sides of zero optical path difference and scatter the interference fringes information, which can avoid aliasing of spectral signals and help to gain high spectral resolution. A systematic theoretical analysis is carried on in detail, including the optical distribution characteristics based on multi-beam interference, stationary-wave theorem and signal reconstruction method based on the FT technology. The results show that the method can complete a resolution of 6 nm for Gauss spectrum reconstruction using only 6 gold nanowires, and a resolution of 5 cm^-1 for Raman spectrum reconstruction using 25 gold nanowires.

Synthesis and Spectral Characteristics Investigation of the 2D-2D vdWs Heterostructure Materials

Due to the attractive optical and electrical properties, van der Waals (vdWs) heterostructures constructed from the different two-dimensional materials have received widespread attention. Here, MoS₂/h-BN, MoS₂/graphene, WS₂/h-BN, and WS₂/graphene vdWs heterostructures are successfully prepared by the CVD and wet transfer methods. The distribution, Raman and photoluminescence (PL) spectra of the above prepared heterostructure samples can be respectively observed and tested by optical microscopy and Raman spectrometry, which can be used to study their growth mechanisms and optical properties. Meanwhile, the uniformity and composition distribution of heterostructure films can also be analyzed by the Raman and PL spectra. The internal mechanism of Raman and PL spectral changes can be explained by comparing and analyzing the PL and Raman spectra of the junction and non-junction regions between 2D-2D vdWs heterostructure materials, and the effect of laser power on the optical properties of heterostructure materials can also be analyzed. These heterostructure materials exhibit novel and unique optical characteristics at the stacking or junction, which can provide a reliable experimental basis for the preparation of suitable TMDs heterostructure materials with excellent performance.

Evaluation of nutrition components in Lanzhou lily bulb by confocal Raman microscopy

Lanzhou lily is a famous lily variety in China, which has many advantages different from other lily varieties. It is rich in nutrients and can be used as medicine or food. The present study is performed to evaluate the quality of Lanzhou lily by Raman spectroscopy. Here, Raman spectra of lily bulbs were collected by confocal Raman microscopy. Through study of a variety of samples, we found that Raman peaks of several important nutrients including starch, sucrose and amino acids were clearly observed from scales of lily bulb, while strong characteristic peaks of ferulic acid were observed at the epidermis of the same scale due to the stimulation of the external environment. We also compared lily bulbs with various sizes and shapes using an average Raman spectrum of selected area. Then, changes of nutrients were quantitively analyzed in different storage period. The results show that the nutrient components including starch, sucrose, amino acids and ferulic acid can be evaluated by Raman spectroscopy. Then the quality of Lanzhou lily can be evaluated by Raman spectroscopy. This is valuable for quality evaluation of lily using non-destructive methods.

Research on the Preparation and Spectral Characteristics of Graphene/TMDs Hetero-structures

The Van der Waals (vdWs) hetero-structures consist of two-dimensional materials have received extensive attention, which is due to its attractive electrical and optoelectronic properties. In this paper, the high-quality large-size graphene film was first prepared by the chemical vapor deposition (CVD) method; then, graphene film was transferred to SiO₂/Si substrate; next, the graphene/WS₂ and graphene/MoS₂ hetero-structures were prepared by the atmospheric pressure chemical vapor deposition method, which can be achieved by directly growing WS₂ and MoS₂ material on graphene/SiO₂/Si substrate. Finally, the test characterization of graphene/TMDs hetero-structures was performed by AFM, SEM, EDX, Raman and PL spectroscopy to obtain and grasp the morphology and luminescence laws. The test results show that graphene/TMDs vdWs hetero-structures have the very excellent film quality and spectral characteristics. There is the built-in electric field at the interface of graphene/TMDs heterojunction, which can lead to the effective separation of photo-generated electron-hole pairs. Monolayer WS₂ and MoS₂ material have the strong broadband absorption capabilities, the photo-generated electrons from WS₂ can transfer to the underlying p-type graphene when graphene/WS₂ hetero-structures material is exposed to the light, and the remaining holes can induced the light gate effect, which is contrast to the ordinary semiconductor photoconductors. The research on spectral characteristics of graphene/TMDs hetero-structures can pave the way for the application of novel optoelectronic devices.

Machine Learning Analysis of Raman Spectra of MoS2

Defects introduced during the growth process greatly affect the device performance of two-dimensional (2D) materials. Here we demonstrate the applicability of employing machine-learning-based analysis to distinguish the monolayer continuous film and defect areas of molybdenum disulfide (MoS₂) using position-dependent information extracted from its Raman spectra. The random forest method can analyze multiple Raman features to identify samples, making up for the problem of not being able to effectively identify by using just one certain variable with high recognition accuracy. Even some dispersed nucleation site defects can be predicted, which would commonly be ignored under an optical microscope because of the lower optical contrast. The successful application for classification and analysis highlights the potential for implementing machine learning to tap the depth of classical methods in 2D materials research.

The study of ultrasound and iontophoresis on oxaprozin transdermal penetration using surface-enhanced Raman spectroscopy

The potential for physicochemical driving forces facilitating topical transport of the lipid-soluble drug oxaprozin (OXA) was investigated using surface-enhanced Raman spectroscopy (SERS) in this study. Azone, iontophoresis (IP), and sonophoresis (SP) were combined and performed on mouse skin for the OXA transdermal penetration, and the synergistic effect was analyzed using Raman spectroscopy. The data of characteristic peak intensity were processed with overlapping peak resolving and standard normalization. The results showed that Azone promoted the transdermal penetration of OXA (5.9-fold greater than the OXA concentration of normal penetration); SP enhanced OXA transdermal penetration (5.5-fold); IP enhanced OXA transdermal penetration (4.2-fold); the combined application of Azone and SP (Azone+SP) and SP+IP can improve the enhancement coefficient of OXA transdermal penetration (8.4-fold and 6.1-fold, > 5.9, > 5.5, > 4.2), and their combined application has a synergistic effect; Azone+IP does not have a synergistic effect while the enhancement coefficient of Azone+IP (5.3-fold, < 5.9) and Azone+SP+IP (7.2-fold, < 8.4) was slightly reduced. As for the drug OXA, Azone+SP is an effective method of transdermal penetration.

Blind source separation of coexisting background in Raman spectra

Due to the Raman signal coexists with other scatter spectra which leads to the low ratio of the wanted signal and high background, the appropriate method should be applied to enhance this ratio. The nature of raw spectra is a multi-source system, so its determinacy must be ensured by multi-input. Besides, the faithfulness of output should be provided. Then, the huge fall within the frequencies of Raman and background almost satisfies separating demand for independent component analysis (ICA), and this analysis can give help to the achievement of the two type signals classing and estimate the optimal number of source and match ICA output signals to Raman or background. Thus, based on ICA and the mixing-entropy criteria, the background and Raman adapting calibration kit (BRACK) method is proposed, which is a kind of multiple raw spectral inputs and multiple output (MIMO) method. This method firstly divides the raw data into two parts of Raman and background by ICA, identifies Raman signal by entropy criterion, then restores the part of Raman signal. BRACK method obtains several advantages, for instance, well-adapted, no need for any additional option or extra-intervention, high fidelity, and no unwanted external information. In principle, the correction of background and Raman signals can be expected to be completed by BRACK method.

The application of label-free imaging technologies in transdermal research for deeper mechanism revealing

The penetration behavior of topical substances in the skin not only relates to the transdermal delivery efficiency but also involves the safety and therapeutic effect of topical products, such as sunscreen and hair growth products. Researchers have tried to illustrate the transdermal process with diversified theories and technologies. Directly observing the distribution of topical substances on skin by characteristic imaging is the most convincing approach. Unfortunately, fluorescence labeling imaging, which is commonly used in biochemical research, is limited for transdermal research for most topical substances with a molecular mass less than 500 Da. Label-free imaging technologies possess the advantages of not requiring any macromolecular dyes, no tissue destruction and an extensive substance detection capability, which has enabled rapid development of such technologies in recent years and their introduction to biological tissue analysis, such as skin samples. Through the specific identification of topical substances and endogenous tissue components, label-free imaging technologies can provide abundant tissue distribution information, enrich theoretical and practical guidance for transdermal drug delivery systems. In this review, we expound the mechanisms and applications of the most popular label-free imaging technologies in transdermal research at present, compare their advantages and disadvantages, and forecast development prospects.

Comprehensive Performance Quasi-Non-Volatile Memory Compatible with Large-Scale Preparation by Chemical Vapor Deposition

Two-dimensional materials with atomic thickness have become candidates for wearable electronic devices in the future. Graphene and transition metal sulfides have received extensive attention in logic computing and sensing applications due to their lower power dissipation, so that their processes have been relatively mature for large-scale preparation. However, there are a few applications of two-dimensional materials in storage, which is not in line with the development trend of integration of storage and computing. Here, a charge storage quasi-non-volatile memory with a lanthanum incorporation high-k dielectric for next-generation memory devices is proposed. Thanks to the excellent electron capture capability of LaAlO₃, the MoS₂ memory exhibits a very comprehensive information storage capability, including robust endurance and ultra-fast write speed of 1 ms approximately. It is worth mentioning that it exhibits a long-term stable charge storage capacity (refresh time is about 1000 s), which is 10⁵ times that of the dynamic random access memory (refresh time is on a milliseconds timescale) so that the unnecessary power dissipation greatly reduces caused by frequent refresh. In addition, its simple manufacturing process makes it compatible with various current two-dimensional electronic devices, which will greatly promote the integration of two-dimensional electronic computing.

[Application of Raman Spectroscopy in the Diagnosis of Oral Cancer]

Oral cancer is a common and deadly malignancy.While multidisciplinary treatment(mainly surgery)has been applied in the treatment of cancer treatment,early diagnosis and complete removal of the primary lesion are essential for a better prognosis.Raman spectroscopy is an optical technique that detects inelastic scattered light generated by the interaction of light and matter.It can detect the vibrational spectra of biochemical and biomolecular structures and tissue conformations,and can provide the "molecular fingerprint" for cells,tissues,and biological fluids.With the development of related technologies and optical instruments,Raman spectroscopy has been widely applied in medical fields.This article reviews the research advances and application of Raman spectroscopy in the diagnosis of oral cancer.