Since its discovery in 1974, Surface-Enhanced Raman Spectroscopy (SERS) has evolved over five decades into a highly sensitive analytical technique with diverse application prospects. This review systematically summarizes key advances in enhancement mechanisms, active substrates, and technical methodologies, while outlining future developmental directions. The establishment of electromagnetic and chemical enhancement mechanisms laid the theoretical foundation for SERS, with the discovery of "hot spot" effects and development of quantitative enhancement factor evaluation systems further deepening the understanding of signal amplification principles. In terms of active substrates, research has expanded beyond noble metals like gold and silver to include transition metals, semiconductors, metal-organic frameworks, and various other material systems. The achievement of single-molecule SERS detection represents a significant breakthrough in technical sensitivity. Methodological innovations such as Shell-Isolated Nanoparticle-Enhanced Raman Spectroscopy (SHINERS) and Tip-Enhanced Raman Spectroscopy (TERS) have substantially improved the versatility and spatial resolution of SERS techniques. With the progressive standardization of performance evaluation systems and the emergence of new approaches like digital SERS, the technology is steadily advancing toward precise quantitative analysis. This review further elaborates on the broad application potential of SERS in catalysis, energy research, life sciences, clinical medicine, and food safety detection, highlighting important progress in both technical standardization and practical implementation. Looking forward, SERS research will focus on enhancing quantitative reliability, designing novel intelligent materials, integrating artificial intelligence for data interpretation, and expanding innovative applications in areas such as in vivo biological monitoring and industrial on-site detection. While challenges remain in establishing standardized databases, exploring spatiotemporal resolution limits, and developing multimodal integration technologies, SERS continues to offer vast development potential, maintaining its position at the forefront of analytical science.

The No.3 Bronze Mythical Tree unearthed from the Sanxingdui Site has a complex structure and lots of components, making it an important material object for studying the bronze casting technology and religious concepts of the Sanxingdui civilization. When unearthed, the tree was broken into several sections, suffering from various damages such as corrosion, fragmentation, and deformation, resulting in an overall poor state of preservation. To clarify its material composition, manufacturing process and corrosion characteristics, systematic testing and analysis were conducted on the fragments before restoration in this study. The results show that the main body of the No.3 Bronze Tree is a copper-tin-lead ternary alloy, with copper content exceeding 85% in most tested points, but the alloy ratio varies in different parts. Residual gold foil decorations were found on the surface, with a gold-to-silver ratio of approximately 85:15. Metallographic analysis indicates that the tree was cast and underwent a heating process. The surface corrosion products are mainly cuprite, malachite and cerussite, while the light-blue patina is a mixture of amorphous tin oxide and stable corrosion products, which has a loose structure and is easy to fall off. During the conservation and restoration, challenges such as the large number of broken parts and complex connections were addressed through integrated methods including 3D scanning, copper strip binding, and internal-external supports, achieving precise reassembly and structural reinforcement. Additionally, digital virtual restoration techniques were employed to reconstruct the original appearance of the bronze tree. The study shows that the combination of technological methods and traditional restoration not only effectively improves the scientificity and reliability of restoration of bronze, but also provides a feasible model for the preservation of large-scale composite bronze artifacts.

Painted cultural relics reflect the philosophy and aesthetics of the ancients. They have strong artistic value and research value, with rich historical information contained therein can be obtained through scientific and technological methods. Raman spectroscopy offers several advantages, including non-destructive analysis, accuracy, sensitivity, speed, and sensitive, fast and simple. It is very suitable for the analysis and detection of pigments in painted cultural relics. This work introduces the fundamental principles and evolution of Raman spectroscopy technology. Basing on research group’s comprehensive pigment analysis of painted cultural relics, a Raman spectral database of common pigments has been established to facilitate rapid preliminary identification. This study demonstrates various applications of this technique in heritage conservation, including investigation of discoloration mechanisms, determination of pigment properties and provenance, and reconstruction of painting techniques. Advanced applications are also be summarized such as quantitative analysis and spatial resolution imaging. Spatial resolution imaging enables both surface examination and subsurface analysis to support research on pigment degradation and complex multi-layered artworks. The findings provide valuable technical references for the scientific study of cultural heritage materials. Besides, for in-situ detection of immovable cultural relics and the analysis of organic dyes and other practical difficulties, breakthroughs can also be realized with the enhancement of Raman spectroscopy technology. In fact, Raman spectroscopy technology is also used in the analysis and detection of other types of cultural relics, and plays a very potential and irreplaceable role in the analysis, research and protection of cultural relics.

In recent years, Guatemalan jadeite has received more attention in China’s gemstone trading market, with green and gray-blue varieties being the most commonly encountered. This study focuses on blueish-violet Guatemala jadeite as the research subject. Building on conventional gemological testing, infrared spectroscopy, X-ray fluorescence spectroscopy (XRF)and Raman spectroscopy were employed to conduct an in-depth investigation into its gemological characteristics, spectroscopic properties, and color origin. The results indicate that the primary mineral composition of blueish-violet Guatemala jadeite, with characteristic peaks of jadeite identified in both infrared and Raman spectroscopy. DiamondViewTM samples have yellow-green fluorescence. Combined with the fluorescence characteristics and XRF test results, it shows that the Fe and Ti contents in the sample are relatively high, while the Mn content is relatively low. Ultraviolet-Visible absorption spectroscopy (UV-Vis) revealed absorption bands at 614 nm and 538 nm, attributed to Fe2+-Ti4+ and Fe2+-Fe3+ charge transfers, respectively, suggesting that the coloration is associated with Fe2+, Fe3+, and Ti4+.

In recent years, there has been a steady increase in new drug substitutes and a greater variety of drug disguises, which has posed a challenge to the investigation and handling work of anti-drug departments. In the face of so many types of drugs and the increasing trend of new drugs, the establishment of rapid and effective detection methods is of great significance for effectively combating drug crimes and carrying out anti-drug rehabilitation work. Surface-enhanced Raman Spectroscopy (SERS) is a very promising technique for trace analysis, and many research results have been achieved in the field of drug detection. In this paper, the concept and characteristics of surface-enhanced Raman spectroscopy and its application in drug detection are summarized. This paper focuses on the analysis of SERS methods for drug detection in simple systems such as drug powders and solutions, and SERS detection methods developed in samples containing complex matrices such as saliva, urine, blood, hair and sewage. Finally, an outlook on the development trend of SERS in drug detection is made, with a view to providing a reference for the in-depth application of this technology in the field of anti-drugs.

Precise analysis of cultural relic materials is crucial for the scientific conservation and research of cultural heritage. Cultural relics are precious and irreplaceable. To obtain as much information as possible about them, scientists and technicians specializing in cultural relics typically employ multiple detection and analysis methods. Surface-enhanced Raman spectroscopy (SERS) technology has demonstrated significant potential in the field of analyzing cultural relic materials. SERS technology is particularly well-suited to this application due to its ultra-high sensitivity, molecular fingerprint identification capability, fluorescence quenching effect and minimally invasive/non-destructive testing advantages. This paper reviews the latest research progress and applications of SERS technology in analyzing cultural relic materials, focusing on four types of material: organic dyes, inorganic pigments, metal corrosion products and proteins. It provides detailed explanations of SERS substrate preparation, sample pretreatment and spectroscopic analysis methods. Research indicates that SERS technology will continue to provide strong technical support for the scientific understanding, verification of authenticity, and conservation and restoration of cultural heritage.

As augmented reality (AR) head-mounted displays rapidly advance, freeform optical systems have become one of the main solutions in optical design. This paper first analyzes the key issues and challenges faced by optical see-through head-mounted display systems, focusing on the balance between parameters such as field of view, exit pupil diameter, angular resolution, and system size, as well as the challenges posed by vergence-accommodation conflict. Solutions are proposed to optimize these parameters and enhance the user experience. The article then summarizes several structural types of freeform optical see-through head-mounted displays and recent research developments, providing an in-depth analysis of the advantages and disadvantages of various approaches. Finally, it explores trends in the field, particularly in achieving virtual reality interaction and addressing the vergence-accommodation conflict. The paper highlights the potential of technologies such as multi-focal displays, gesture recognition, and adaptive optics, noting that these innovations will further drive the application of freeform optical see-through head-mounted displays in both augmented and virtual reality. This work lays the theoretical foundation and research direction for designing more efficient, lightweight, and user-friendly head-mounted display optical systems.

Compton backscattering imaging is an important technique in the ray detection method. By detecting the Compton scattering photons emitted by the irradiated object, the irradiated object is imaged to obtain the defect information. This paper will review the Compton backscattering detection method, introduce the basic principle of Compton backscattering, and then discuss the research status of Compton backscattering detection technology in wall defects at home and abroad. Finally, the future development prospects of Compton backscattering imaging detection method are summarized and prospected.

Due to their high density, long life and low self-discharge rate, lithium-ion batteries are becoming more and more widely used and have become an indispensable energy choice in the aviation field. However, the risk of thermal runaway poses a significant challenge to aviation safety. Raman spectroscopy provides a method for the safety assessment of lithium ions due to its non-destructive and non-contact characteristics. In this paper, a Raman probe system that can be used in gas detection system is optimized, and Zemax is used to design the optical path, and the results show that the radius of the system point diagram is less than 19 μm, and the numerical aperture of the scattered light collection optical path reaches 0.36, which can effectively collect Raman scattered light. The simulation results show that the stray light PST of the improved optical system reaches less than 10-6, which effectively suppresses the stray light. A Raman library was established with standard gas as a sample, and the designed probe was tested, and the experimental data showed that the performance of the probe optical system was good. The real-time monitoring of the thermal runaway gas of lithium-ion batteries can accurately identify the Raman spectral characteristics before and after thermal runaway, which verifies the effectiveness of the Raman probe design and provides an effective means for the detection of thermal runaway gas in lithium-ion batteries.

Clear underwater imaging can effectively enhance the ability of visual underwater operations. Affected by backscattering, underwater imaging is prone to image blurring. The main reason is that small particles in the water scatter light beyond the reflection of the real target into the reflected light path, resulting in a decrease in image quality. Due to the effective utilization of polarization technology in underwater image backscattering and target reflection polarization characteristics, combined with underwater imaging models to achieve image clarity, polarization technology has been developed as a branch of underwater image deblurring in recent years. This article mainly introduces the basic principle, development path, and research status at home and abroad of underwater image clarity algorithms based on polarization technology, and analyzes and prospects its development trend.

In this study, glass samples with varying B2O3 contents (0~12 mol%) and basicity ratios CaO/SiO2=1.0 were prepared using aerodynamic levitation technology. The microstructural evolution of the CaO-SiO2-B2O3 ternary system in both molten and glassy states was investigated through in situ high-temperature Raman spectroscopy, quantum chemistry ab initio calculations, and molecular dynamics (MD) simulations. The deconvolution of Raman spectra revealed that B2O3 incorporation promotes the degree of network polymerization by increasing Q3 and Q4 species while reducing Q2,Q1, and Q0 species, this trend remains consistent across both molten and glassy states, with the molten state exhibiting a higher proportion of Q1 and Q0 species, while the glassy state displays a greater prevalence of Q3 and Q4. MD results are consistent with these results, showing that B2O3 promotes the conversion of non-bridging oxygen to bridging oxygen, thereby enhancing network connectivity. The combination of experiment and simulation verifies the accuracy and reliability of the conclusions. It provides critical insights into the structure modifications induced by B2O3, offering a theoretical foundation for considering and developing fluoride-free mold fluxes for continuous casting processes.

Here we propose a Convolutional Neural Network with a Spatial-Channel Attention Mechanism combined with Gradient-Weighted Class Activation Mapping (Grad-CBAM-CNN) for the classification of serum SERS spectra from patients with systemic lupus erythematosus (SLE) and neuropsychiatric systemic lupus erythematosus (NPSLE). The classification model can capture major SERS spectral features effectively and demonstrate an accuracy of 96.7% with a small dataset of 300 samples for NPSLE diagnosis by optimizing network architecture and incorporating an attention mechanism. The classification performances from various CNN models are analyzed, by which the model configuration is optimized to significantly reduce the requirement of dataset volume and computing power of CNNs. Four SERS characteristic peaks for NPSLE are identified and visualized by the collaboration of the developed four high-performance models. This work not only advances the application of deep learning in the classification of limited biomedical datasets, but also provides a robust framework for the identification of potential specific biomarkers in serum for early diagnosis of systemic autoimmune disease for clinical applications.

This study investigates the molecular structural variations of konjac glucomannan (KGM) under different processing conditions, including heating (thermal treatment), soaking (aqueous treatment), and drying (dehydration treatment). Fourier Transform Infrared Spectroscopy (FTIR) and second-derivative spectroscopy were used to analyze key structural changes, focusing on β-glycosidic bonds (896 cm-1), C—O—C bonds (1156 cm-1, 1078 cm-1), and C—H bonds (2944 cm-1, 2882 cm-1). The results show that thermal treatment (95 ℃, 3 h) significantly destabilized C—O—C bonds, as indicated by a 15.4% decrease in the 1156 cm-1 peak area and a 41.2% decrease in the 1078 cm-1 peak area (P<0.05). In contrast, the β-glycosidic bond peak area increased by 300.0% (P<0.01), suggesting molecular rearrangement or crosslinking at high temperatures. Aqueous treatment (20 ℃, 24 h) had a stabilizing effect on the C—O—C backbone, with peak areas increasing by 15.4% (1156 cm-1) and 25.5% (1078 cm-1). Hydration also improved β-glycosidic bond integrity, increasing its peak area by 50.0%, which may enhance KGM stability in food applications. Dehydration treatment (50 ℃, 48 h) had minimal effects on the primary KGM backbone, but reduced the β-glycosidic bond peak area by 50.0% (P<0.05). This decline may negatively impact KGM’s gelling and functional properties. Second-derivative spectroscopy improved peak resolution for 896 cm-1, 1156 cm-1, and 1078 cm-1, further validating the accuracy of FTIR analysis. These findings provide insights into the stability of KGM under different processing conditions, offering a scientific basis for optimizing its functional applications in food processing and storage. Additionally, this study highlights FTIR combined with second-derivative spectroscopy as a robust tool for exploring the structure-function relationships of polysaccharides.

As a vital organic solvent,Ethanol is widely used in various production processes including pharmaceuticals, fuel, and chemicals. Methods for the rapid detection of its concentration holds practical significance. Raman spectral data of ethanol solutions with different concentrations (10% to 100% at 10% intervals) were measured to establish a dataset. The data were divided into a training set and a testing set. Principal Component Analysis (PCA) was employed to reduce dimensionality by integrating it with Support Vector Machine (SVM) classification. The experimental results indicate that the classification accuracy of the test set data reached 100% by adopting the PCA-SVM algorithm. The study demonstrates the effectiveness of the proposed method.

The criteria for classifying jade in ancient times differed significantly from those employed in the contemporary era. The methods that ancient peoples utilized to identify jade represent a critical area of study for modern archaeologists. When irradiated with a 785 nm laser via a laser confocal micro-Raman spectrometer, ancient jade specimens displayed a hump-shaped fluorescence background in their Raman spectra. This type of fluorescence arises from the formation of conjugated carbonyl (C=O) within the organic matter present in the jade. Rocky jade, organic jade, freshly mined nephrite, jadeite, Green jade, Dushan jade, Lantian jade, Chinese white jade, mica, talc, southern red agate, turquoise, lapis lazuli, and other common ancient jades all exhibit hump-shaped fluorescence backgrounds in their Raman spectra when heated, indicating the presence of organic matter within the jade. Analysis of crystal-enclosed organic matter extracted from freshly mined jade using a pyrolysis-gas chromatography-mass spectrometry (Py-GC/MS) analyzer revealed that it primarily consisted of various carboxylic salts. There are two potential sources of organic matter within jade: one originates from the sedimentary organic matter in mudstone or carbonate rocks, and the other comes from metal-organic complexes in hydrothermal fluids. By analyzing the common characteristics of various jade varieties, this research concludes that most types of jade recognized in antiquity typically contain organic components, which give them an organic luster (The luster of jade). Drawing from references in texts such as the ShuowenJiezi and other scholarly works, jade can be systematically categorized into six classifications: “Jade, Beauty of Stone,” “Stones Marginally Inferior to Jade,” “Stones Resembling Jade,” “Aesthetic Stones,” “Shells and Clams,” and “Western Region Stones.”

Petrochemical plastic pollution has become a global environmental pollution issue, and replacing petrochemical plastics with biodegradable plastics is a significant trend for sustainable human development and technological civilization in the future. However, the degradation conditions of different biodegradable plastics vary. Classifying and identifying them is necessary to improve the degradation rate and shorten the degradation cycle of biodegradable plastics. This paper proposes a support vector machine algorithm combined with micro Raman spectroscopy technology to identify different types of biodegradable plastics rapidly. Obtain standard vibration spectra of different types of biodegradable plastics through a micro Raman spectrometer, analyze the Raman spectral characteristic peaks and corresponding molecular vibration bonds of various biodegradable plastics, interpret the spectral characteristics of different biodegradable plastics, and establish a corresponding database of biodegradable plastic standard samples. Based on Raman spectroscopy database, the influence of different kernel functions on support vector machine classification models was compared, and the classification performance of the models was evaluated through confusion matrix and macro-average method. The accuracy of SVC for the optimal classification result of the test set was 0.996, and the recall rate was 0.996. The research results indicate that the combination of support vector machine algorithm and micro Raman spectroscopy technology can achieve the classification and recognition of different types of biodegradable plastics. This technology provides methodological references for addressing the related waste disposal issues after the large-scale promotion of biodegradable plastics in future human society.

Doping testing is a core component in ensuring fairness in sports competitions. Testosterone Propionate (TP), as a representative of synthetic metabolic steroids, faces severe challenges in trace residue detection. The aim of this study is to establish a support vector machine (SVM) model based on surface-enhanced Raman spectroscopy (SERS) technology, combined with a new intelligent optimization algorithm-Hippopotamus Optimization Algorithm (HOA), to achieve highly sensitive, specific and rapid detection of trace residual testosterone propionate in athlete blood samples. Firstly, the simulated blood samples of athletes containing TP were preprocessed, and SERS signal enhancement was performed using silver nanosol to obtain the characteristic Raman spectral fingerprint information of TP. In response to the problems of high dimensionality, complex background, and difficult feature extraction in SERS spectral data, this study innovatively introduces the Hippopotamus Optimization Algorithm (HOA). By utilizing the powerful global search and optimization capabilities of HOA, the key parameters of the SVM model are automatically optimized to construct the optimal HO-SVM classification model. Through HOA optimization, the generalization ability and classification accuracy of SVM models are effectively improved. The results indicate that SERS exhibits excellent detection sensitivity for TP in blood. In addition, the SVM model optimized by HOA (HO-SVM) performs well in TP quantification, with a test set determination coefficient of 0.9610 and a root mean square error of 0.0597. The HO-SVM model can effectively process the complex information of SERS spectra and achieve efficient recognition and classification of TP characteristic peaks. This study successfully developed a novel detection method based on the combination of SERS technology and Hippopotamus Optimization Support Vector Machine (HO-SVM) algorithm. This method fully utilizes the high sensitivity and fingerprint recognition characteristics of SERS, as well as the advantages of HOA in optimizing SVM hyperparameters, significantly improving the detection performance of testosterone propionate in blood. This method provides a powerful technical means for precise and efficient screening of prohibited substances in athletes’ biological samples, and has important application prospects in the field of anti doping.

Infrared spectroscopy technology was applied to analyze the spectra of cefuroxime axetil during the heating process from 20 ℃ to 180 ℃, and fails to find significant changes in the characteristic infrared absorption peaks of cefuroxime axetil within 180 ℃. After conducting two-dimensional correlation spectroscopy analysis on the series of infrared spectra collected during the heating process, it was found that the absorption peaks of double bond C=C (1666 cm-1) and C=O (1716 cm-1) were very sensitive to temperature changes and interaction between them were confirmed. Combined with the moving two-dimensional window correlation spectrum, it was further confirmed that the C=C and C=O of cefuroxime axetil started to change from 85 ℃. In this article, the process that cefuroxime axetil would isomerize into δ-isomer of Cefuroxime axetil when heated to 85 ℃ could be characterized by variable temperature Infrared combining two-dimensional correlation spectroscopy analysis. Rapid and non-destructive testing techniques and analysis methods for the study of thermal stability of drug structures were provided.

Based on the principle of intermodal interference in optical fibers, a fully fiber optic sensor with a single-mode-coreless-single-mode structure was designed and simulated. An in-situ measurement device was developed using this sensor to measure the concentration of saturated sodium chloride (NaCl) solution at different temperatures. Finite element simulation was employed to optimize the geometrical dimensions of the sensor, and a quantitative relationship between the interference intensity and the NaCl solution concentration was established. Using the in-situ measurement system based on the fiber-optic sensor, the concentrations of saturated NaCl solution at 25 ℃, 30 ℃, 35 ℃, 40 ℃, and 50 ℃ were measured, yielding values of 25.62%, 26.11%, 26.49%, 26.92%, and 27.38%, respectively. These result-s show good agreement with those obtained via the evaporation method, with a maximum absolute error of 0.86%. The fiber-optic sensor enabled simultaneous in-situ measurement of concentration and temperature while maintaining high measurement accuracy, indicating its potential applications in fields such as chemical engineering, pharmaceuticals, battery health monitoring, and extreme condition detection.

With the increasing status of Afghan emeralds in the emeralds market, Effectively Iidentifying the Characteristics of Afghan emeralds Has become the Main Research Focus at Present. This Article Uses Large-scale Instruments Such as The Stereomicroscope, Fourier Ttransform Infrared Spectrometer, Ultraviolet Spectrometer, and Micro Area Laser Raman Spectrometer to Test and Analyze the Gemological and Spectral Characteristics of Afghan Emerald Samples. The UV Spectrum Shows that the Green of Afghan Emerald due to The Combined Action of Cr3+and V3+, The Absorption of Fe2+ Result in a Blue Tone in the Grandmother green band. The Infrared Spectrum Reveals that Afghan Emerald Belongs to the Type II Water Dominated Emerald. The Laser Raman Spectrum Shows that the Overall Vibration Intensity of the Groups in Afghan Emerald is Greater in the Vertical c-axis Direction than in the Parallel c-axis Direction of the Crystal.

The quality of gasoline directly affects the combustion efficiency of gasoline, the lifespan of engines and the emissions of exhaust gases, etc. Therefore, it is very important to test the quality of gasoline. This paper adopts Raman spectroscopy technology combined with machine learning algorithms to construct a rapid identification model for gasoline quality. Firstly, mix No. 92, No. 95, and No. 98 gasoline in pairs with a 10% gradient to create fuel samples of different proportions. Based on the collection of Raman spectral data of all types of gasoline samples, a Raman spectral dataset of gasoline was established. Then, the Raman spectral data are preprocessed by using the asymmetric least square method and mean normalization to extract spectral features. Finally, four machine learning algorithms including Gradient Boosting Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Random Forest were respectively used to train and predict the preprocessed data and evaluate the model. The experimental results show that Gradient Boosting Decision Tree has the best recognition effect, with classification accuracy, precision, recall rate and the harmonic mean of precision and recall for all types of gasoline samples reaching 98.18%, 98.32%, 98.18%, and 98.18% respectively. This paper provides a rapid method for identifying the quality of gasoline, and offers a rapid detection solution for the supervision of the gasoline market in the circulation link and the protection of consumers' legitimate rights and interests.

In order to realize focusing light at multiple points and flexibly control the spot intensity of each point after passing through the disordered medium, we delve into algorithm associated with wavefront shaping technique in this paper. Firstly, we introduced the fundamental principle of the traditional wavefront shaping algorithm for achieving focusing of the scattering light field based on the transmission process of coherent light waves within disordered media. Then, we illustrate the challenges of uneven spot brightness distribution and uncontrollable intensity inherent in the traditional approach by analyzing the focusing of scattered light fields at two-point positions as a case study. Subsequently, we proposed a multi-point focusing strategy for the scattering light field based on weight control. This strategy optimizes the phase modulation scheme of the scattering sub-waves by introducing a normalization process and a weights coefficient methodology, thereby enabling flexible control of the spot intensity at the focal point. Finally, the performance of the traditional algorithm and the weight control algorithm in multipoint focusing is compared by simulation experiments. The experimental results demonstrate that, in comparison with the conventional wavefront shaping algorithm, the weights control algorithm is capable of effectively focusing the scattering light field at multiple points, and the spot intensity can be flexibly controlled by modifying the value of the weights coefficients. It is proved that the method has evident advantages in the application of multi-target complex scattering light field regulation.

Bone loss in space is a worldwide problem that has not yet been completely solved. Early rapid diagnosis and treatment are equally important. Due to the space station’s particular application scenario, the realization of noninvasive evaluation of clinical diagnosis is the key to engineering application. Calcium ion is a typical marker of bone loss, and it is mainly excreted through urine. Determining the urinary calcium value in urine is helpful in indirectly evaluating spatial bone loss. Therefore, this paper proposes a non-invasive detection method of bone loss markers based on laser-induced breakdown spectroscopy combined with a machine learning algorithm. Firstly, a biochemical analysis of urinary calcium value was performed on the urine samples collected clinically. Then, the LIBS system was used to measure the urine samples adsorbed by the test strip, and the collected LIBS spectra were preprocessed. Secondly, the plasma emission spectra of urine samples were interpreted to analyze the correlation between the characteristic peak intensity of calcium and urinary calcium value. Finally, the partial least squares regression algorithm was used to establish the quantitative urinary calcium value prediction model. The R2 of the training and validation sets were 0.9362 and 0.9328, respectively, and the RMSE were 0.5142 and 0.5091, respectively. In conclusion, this paper proposes and verifies a new method for the noninvasive detection of bone loss markers, which has a specific reference value for the early diagnosis of spatial bone loss.

The strong coupling of light and matter is a key research direction in the field of modern optics, which is of great significance to the development of cutting-edge technologies such as quantum information processing, high sensitivity sensing and optoelectronic devices. Focusing on monolayer tungsten disulfide (WS2) in 2D transition metal sulfur compounds, this study demonstrates strong coupling with the composite structure formed by Au nanorods on the hydrogel substrate. The Rabi splitting energy of the Au nanorods-WS2-hydrogel composite can reach up to 103 meV, meeting the strong coupling criterion. By utilizing the reversible deformation properties of the double crosslinked network hydrogel, the peak position and intensity of WS2 were adjusted through mechanical stretching, ultimately achieving the strong coupling effect of the composite structure under strain regulation. This research has proven that a robust coupling system with significant Rabi splitting energy and dynamic control can be achieved at room temperature. This offers new possibilities for accurately managing strong coupling systems and has the potential for broad application in flexible electronics.

Optical tweezer–confocal microscopy is an effective technique for observing the spatially resolved Raman spectra of individual suspended droplets. In this work, a compact, integrated optical tweezer–confocal micro-Raman system (Optwee 100) was custom-designed and constructed to investigate the intensity distribution of spontaneous and stimulated Raman signals within microdroplets. The stimulated Raman signals in microdroplets correspond to whispering gallery modes (WGMs) formed by the resonance of Raman signals at specific wavelengths inside the droplet cavity. These modes exhibit strong spatial resolution and narrow linewidths, making them high-quality targets for single-droplet spatially resolved measurements. Under constant humidity conditions, the spatially resolved Raman signals of ammonium sulfate microdroplets were measured and identified via WGM modes calculations, yielding precise values for the microdroplet radius and refractive index. A series of WGMs ( P n l ) assigned and with different polarizations ( P =TE and P =TM), mode numbers ( n ), and mode orders ( l ) were analyzed in terms of their frequencies, linewidths, and intensity distributions. The influence of laser power on spatially resolved droplet observations was discussed. By fitting the stimulated Raman signals, a spatial resolution of approximately 0.80 μm and a spectral resolution of 1.40 cm-1 were achieved. These results demonstrate that the self-developed Optwee 100 system exhibits excellent performance in spatially resolved Raman spectroscopy and serves as a powerful experimental platform for high-resolution Raman measurements of levitated single microdroplets.

With increasing attention to health and environmental protection, the issue of heavy metal contamination in sports equipment has garnered significant concern. Heavy metals pose a threat to human health and may also result in long-term impacts on the environment. To effectively detect heavy metal pollution in sports equipment, this paper proposes a detection method based on the integration of the LightGBM algorithm and LIBS (Laser-Induced Breakdown Spectroscopy) technology. This method aims to enhance the accuracy and efficiency of heavy metal detection. The quantitative precision of LIBS is influenced by chemical matrix effects; to address this limitation, wavelet algorithms, and covariance are introduced in this paper for data processing and feature extraction. After preprocessing LIBS spectral data and extracting key features, a model correlating heavy metal content with spectral features is established using LightGBM. This model effectively reduces the impact of noise on detection results while improving the accuracy of heavy metal content prediction. In the experimental section, various sports equipment samples were selected, and their spectral data was obtained through LIBS technology before being analyzed using the LightGBM algorithm. Experimental results demonstrated that the LIBS-based detection method integrated with LightGBM exhibited outstanding performance in predicting heavy metal content, outperforming traditional methods in accuracy and robustness. In summary, the proposed method combining the LightGBM algorithm with LIBS technology provides an innovative solution for detecting heavy metal contamination in sports equipment, offering significant application value and promising prospects. Future research could explore its potential applications in other fields to promote environmental safety and public health.

Research on jade artifacts from the Neolithic period has become a major focus in archaeological and material culture studies, owing to their crucial value in exploring processes of social complexity, interregional resource exchange, and the evolution of early crafting technologies. The jade, stone, and shell ornaments unearthed from Daxi Culture sites constitute an important component of its material assemblage, and their material composition, resource procurement, and technological choices are essential for understanding the technological system and cultural interaction networks of prehistoric societies. In this study, Raman spectroscopy was employed to perform non-destructive material identification on 29 jade artifacts unearthed from the Daxi and Dashuitian sites. The results reveal a diverse combination of materials, including marble, serpentine, quartzite jade, turquoise, malachite, black talc, jet, and shell. These findings indicate that the ornaments are not confined to the narrow definition of “jade artifacts” but instead exhibit a multi-material structure dominated by locally available carbonate and silicate rocks, supplemented by non-local minerals and organic materials. By integrating spectral characteristics, microscopic features, and archaeological typology, this study further investigates the variability in resource selection, manufacturing techniques, and aesthetic preferences within the Daxi Culture, and interprets material combinations in relation to regional exchange systems and technological traditions. The results demonstrate that Raman spectroscopy provides irreplaceable advantages in distinguishing compositionally similar minerals and detecting low-concentration impurities, thereby offering a refined analytical approach for the systematic study of prehistoric jade and related ornaments.

In this paper, the fibers, tissue structure and surface colored pigment components of the "Sakyamuni Buddha" Tangka of the XiXia were detected and analyzed by using micro-morphological observation, scanning electron microscope, near-infrared imaging technology, X-ray fluorescence spectrometry and laser Raman spectrometry. These detection means determined that the silk thread material of the Tangka painting core is natural mulberry silk and the silk thread weaving method is plain weave, which provides important basis for understanding material and production technology of the Tangka. In addition, the pigment components of the Tangka color painting were clarified, such as red is painted with a mixed pigment of lead and cinnabar; white is painted with lead white pigment; the main green is painted with a green pigment containing Cu element, which helps to deeply understand thement usage of the Tangka. It is of great significance for the research on the painting process, age identification and cultural connotation of the Tangka.

The gold and silver-painted grand wine bottle and bearing plate, as the inscribed bronze relics unearthed from the tombs of the marquises of the Eastern Han Dynasty, It was of great significance to study the establishment of the official and the casting of bronze ware in western Sichuan at that time, and the casting of bronze vessels. These were analyzed and tested before protected, the Raman spectroscopy and polarized light microscopic analysis were adopted for the scientific detection and analysis of them, and it was found that the rusts included cuprite, malachite, azurite, lanarkite and atacamite, among which, atacamite was a harmful rust, which must be cleared.

In order to rapidly classify and identify fluorite and its processed products, the Raman spectra of fluorite, calcite, and quartz, which have very similar appearances, were first detected. Then, the Raman spectra of fluorite from different origins, as well as the raw and processed products of fluorite, were analyzed. A classification and identification model combining Raman spectroscopy with Principal Component Analysis (PCA)-Support Vector Machine (SVM) algorithm was established, and the Raman spectral data were classified and identified. The results show that for the similar appearance of fluorite, calcite, and quartz, there are obvious differences in Raman spectra, so these different kinds of mineral medicines can be accurately classified and identified by the naked eye observing or the PCA-SVM algorithm identifying the obvious differences in the Raman spectra. Although the Raman spectra of the fluorite from different origins, the Raman spectra of fluorite and the processed fluorite are so similar that it is almost impossible to differentiate them with the naked eye, the PCA-SVM algorithm is also able to accurately classify and identify based on small differences in the Raman spectral data. Meanwhile, the accuracy rate can reach 100%. This method has the advantages of being fast, accurate, non-destructive, convenient, portable, and low-cost, which is of great value for the classification and identification of mineral medicines and their quality control.

A combination of analytical techniques—including three-dimensional video microscopy, infrared spectroscopy, color difference analysis, surface-enhanced Raman spectroscopy (SERS), density functional theory (DFT) calculations, and ultra-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS)-was applied to comprehensively characterize the craftsmanship and dyes of the mounting textiles of the Qing-dynasty “Imperial Approved Stone Classics” from the National Museum of China. The results show that the material is silk, with two types of weaving structures identified: the case and cover exhibit a twill weave (categorized as ling), while the label features a plain weave (categorized as chou). The yellow dye was confirmed to be a combination of Sophora japonica buds and Phellodendron amurense bark. This study provides the first systematic clarification of the material composition and technical characteristics of the stone classics’ mounting, thereby filling a gap in its material research and offering key evidence for the scientific conservation of the artifact and for research on Qing court textile history.

In recent years, hyperspectral analysis technology combined with chemometrics has been widely used in the field of forensic science, which has achieved accurate identification and analysis of physical evidence. This study briefly introduces the concept of hyperspectral imaging technology and commonly used chemometrics methods, compares their applicability, and focuses on the research and application of hyperspectral imaging combined with chemometrics in the inspection of document materials. The problems existing in the research are prospected in order to promote the further research and application of hyperspectral imaging technology in document inspection.

Hemerocallis citrina is widely consumed for its nutritional and medicinal benefits. However, whether it contains colchicine or colchicine-like toxic structures remains a subject of scientific debate. In this study, Fourier transform infrared spectroscopy (FTIR) was employed to investigate the presence and relative expression levels of colchicine-related structures in different parts of H. citrina (root, stem, and flower). A spectral reference system was established using a colchicine standard and Colchicum autumnale corm (a known colchicine-rich plant) to identify functional group-specific absorption peaks, including amide C=O, methoxy C—O, and ether C—O—C. Six characteristic peaks were selected to perform normalized absorbance and peak area ratio analyses. Furthermore, a composite metric—Colchicine-related Expression Index (CEI)—was developed to semi-quantitatively estimate the structural expression intensity across tissues. The results showed that the flower exhibited the strongest absorption features and the highest CEI values, suggesting it as a potential accumulation site of colchicine-like structures. This work introduces a spectral strategy integrating functional group recognition, multi-peak ratio modeling, and index-based evaluation, offering a rapid and non-destructive tool for trace-level toxic structure screening in complex plant matrices.

Accurately identifying their ingredients with the widespread application of sports nutrient supplements in fitness and exercise has become particularly important. Traditional detection methods are often time-consuming and costly and require highly professional skills from operators. This study aims to explore an efficient and accurate method for identifying the components of sports nutrient supplements by combining a recursive feature elimination-gradient boosting decision tree (RFE-GBDT) algorithm with Raman spectroscopy technology. Firstly, the collected Raman spectroscopy data is preprocessed, including steps such as denoising, normalization, and feature extraction, to improve its quality and usability. Secondly, after feature selection, the RFE-GBDT algorithm is used to construct quantitative models, and their accuracy and robustness in component discrimination are evaluated through coefficient of determination and root mean square error. Finally, after experimental verification, the predicted parameters R2 and RMSE of the RFE-GBDT algorithm were 0.9632 and 3.6713, respectively. The model combining the RFE-GBDT algorithm with Raman spectroscopy performed well in identifying the components of sports nutrient supplements. The experimental results show that this method improves the accuracy of component identification and significantly shortens the time required for analysis. Compared with traditional detection methods, the proposed method has significant advantages in efficiency and economy. This study demonstrates that combining the RFE-GBDT algorithm and Raman spectroscopy provides a novel and effective solution for identifying the components of sports nutrient supplements with broad application prospects. Future research can further explore combining other machine learning algorithms and spectroscopic techniques to promote the development of sports nutrient analysis technology and provide consumers with safer and more effective product protection.

The Dunhuang art of the Cao family’s Guiyi Circuit period, inheriting the artistic legacies of the mid-to-late Tang Dynasty and setting stylistic precedents for later polities (including the Xi Xia Dynasty and Uighur regimes), was a pivotal transitional stage in Dunhuang art history—bridging earlier and later traditions. Concurrently, its murals reflect the technical norms and contemporary features of the painting academy system. This study focuses on Caves 33, 34, and 35 of the Yulin Grottoes. By comprehensively utilizing techniques such as portable X-ray fluorescence (pXRF), X-ray diffraction (XRD), and Raman spectroscopy, we systematically analyzed the phase composition of the pigments. Among these, Raman spectroscopy provided crucial fingerprint evidence for the accurate identification of various pigments. The analysis reveals that the red pigment is hematite; the green pigment belongs to the atacamite group of minerals; the blue pigment is lapis lazuli; the yellow pigments identified are orpiment and massicot; and the white base layers consist of calcite and anhydrite, respectively. Through the scientific analysis of mural pigments from the Guiyi Circuit period under the Cao family, this research provides empirical support for understanding the transitional phase of Dunhuang art, which " which inherited from the Tang Dynasty and initiated the Xia Dynasty," and offers a scientific basis for comprehending the origins and development of late Dunhuang art.

This study employed Attenuated Total Reflection Fourier Transform Infrared Spectroscopy (ATR-FTIR) and second derivative spectroscopy for the analysis of 10 apple varieties, complemented by Convolutional Neural Network (CNN) and its hybrid with Long Short-Term Memory Network (CNN-LSTM) for discrimination. In the FTIR spectra of the 10 apple varieties, strong absorption was observed in the ranges of 3500~2850 cm-1, 1650~1400 cm-1, and 1200~900 cm-1, indicating that apples are rich in carbohydrates, vitamins, amino acids, lipids, organic acids, phenols, and flavonoids. Notably, in the fingerprint region of 1200~900 cm-1, variations in the contents of carbohydrates, pectin, and other components were observed among different apple varieties. Second derivative spectral analysis was performed on the 1200~900 cm-1 fingerprint region. In the second derivative spectra, differences in chemical composition among different apple varieties could be identified. Based on the changes in peak positions and shapes, the Yellow Delicious varieties from Gansu and Xinjiang could be effectively distinguished, and the structural characteristics of different lines of Red Fuji and Qinguan could also be differentiated. This study provides a reliable fingerprint basis for quality control and traceability identification of apples. Further CNN analysis was performed on 329 spectra of 10 apple varieties. A stratified sampling method and K-fold cross-validation were used to divide the spectral data of each variety into a training set and a prediction set at a ratio of 7:3. After a certain number of iterations and training, the CNN model achieved an optimal state with 100% classification accuracy on the training set. Then, the spectra of 66 samples were predicted, and the accuracy in the CNN analysis was 86.4%. To improve the model accuracy, a hybrid CNN-LSTM model combining CNN and LSTM was further applied for discrimination, which increased the accuracy to 90.9%. Both models exhibited excellent classification accuracy. Therefore, the analytical methods of ATR-FTIR, second derivative spectroscopy, CNN, and CNN-LSTM neural networks complement each other in apple analysis and discrimination research, enabling accurate classification of apples. Moreover, this methodological framework can be extended to the classification and discrimination analysis of other substances.

SAPO-34; Methanol-to-Olefins (MTO);  Operando ; UV-Raman spectroscopy; Coke

 

To study the structure and composition of the corrosion products, and corrosion mechanisms, samples from 24 bronze weapons unearthed at the Gaoquanshan Tombs in Miluo, Hunan Province were analyzed by optical microscopy, Raman spectrometry and scanning electron microscopy. It also analyzed the pH value and anion concentration of the adhering soil with a pH meter and ion chromatograph. The results showed that these bronze weapons could be categorized into three types based on the corrosion degree of cross-sections: lightly corroded, moderately corroded and completely mineralized bronzes. Cuprite, cassiterite, and malachite were detected on all three types of bronze. Lead sulfate was primarily detected on moderately corroded and completely mineralized bronze, while cerussite was only detected on moderately corroded samples. The completely mineralized bronzes could be further subdivided into two subtypes: one exhibiting no distinct layering, featuring only cassiterite corrosion products; and another with clear layering and diverse corrosion products including cuprite, cassiterite, malachite, lead sulfate, azurite, brochantite, atacamite and clinoatacamite. Atacamite and clinoatacamite were detected on three fully mineralized bronzes, and their formation was associated with chloride ions present in the soil. The acidic soil, high temperatures and heavy rainfall have caused severe leaching of copper ions, resulting in extensive mineralization of these bronze weapons. It is indicated that while the alloy composition may influence the types of corrosion products, the burial environment plays a dominant role in governing the corrosion process and final corrosion product assemblage of bronze artifacts.

Jade has held a unique position in ancient Chinese culture and archaeological research. Nephrite jade and turquoise unearthed from archaeological contexts were central components of the ritual and decorative systems in the early stages of Chinese civilization. Infrared (FTIR) and Raman spectroscopy, as non-destructive analytical techniques, play a crucial role in the material identification of excavated jade artifacts. This study applies both methods to characterize the mineral composition of nephrite jade and turquoise artifacts unearthed from sites such as Jiahu, Daxi, Panlongcheng, Guojiamiao, Guozishan, Xuewei No. 1, and the Tomb of Prince Liangzhuang. The results indicate that nephrite jade exhibits diagnostic absorption peaks at 467 cm-1 and 994 cm-1, allowing clear differentiation from quartzite, talc, and marble; turquoise shows characteristic phosphate-group peaks at 840 cm-1 and 650 cm-1, distinct from malachite, mica, and glass imitations. Even for heavily weathered or corroded samples, these spectroscopic techniques successfully identified the principal mineral components, demonstrating their reliability and efficiency in cultural heritage analysis. Nevertheless, several methodological limitations remain—such as fluorescence interference, surface encrustation, sample heterogeneity, and point-selection bias—which may affect data accuracy. This study summarizes these challenges and discusses possible strategies, including multi-spot sampling, signal enhancement, and combined multi-spectral analysis, to improve analytical precision. The findings highlight not only the advantages but also the technical challenges of spectroscopic methods in jade identification, offering methodological insights for developing a high-accuracy, non-destructive testing system in archaeological material studies. This research highlights the methodological value of infrared and Raman spectroscopy in the study of excavated nephrite jade and turquoise artifacts. It also underscores the ongoing interdisciplinary contributions of Gemmological Institute’s Team at China University of Geosciences (Wuhan). The findings advance the scientific study of jade material identification—particularly for nephrite jade and turquoise—and provide valuable insights for cultural heritage conservation and the study of early inter-civilizational interactions.

Fourier-transform infrared (FTIR) diffuse reflectance spectroscopy was employed for the first time to characterize the spectral differences between the native surfaces and corresponding fracture planes of conch pearls and queen conch shells. These observations were further correlated with scanning electron microscopy (SEM) and X-ray diffraction (XRD) analyses to elucidate the underlying mechanisms. The results were shown that: the intensity ratio of ν3 to ν2 bands of biogenic aragonite on native surfaces significantly exceeded that of aragonite on fracture planes.The ν2 band of surface aragonite exhibited a notable frequency-shift compared to fractured aragonite, accompanied by reduced full-width at half-maximum (FWHM).A characteristic peak at 842 nm was identified in fracture-plane aragonite but absent on native surfaces.SEM and XRD analyses revealed that crystallographic anisotropy of aragonite crystals between surface and fracture orientations directly accounts for these spectroscopic and diffraction singularities. This work provides critical insights for understanding biomineralization processes and offers practical guidance for distinguishing conch pearls from polished queen conch shell.