Spectroscopy Technology Research Articles

Rice Origin Tracing Technology Based on Fluorescence Spectroscopy and Stoichiometry.

The origin of agricultural products is crucial to their quality and safety. This study explored the differences in chemical composition and structure of rice from different origins using fluorescence detection technology. These differences are mainly affected by climate, environment, geology and other factors. By identifying the fluorescence characteristic absorption peaks of the same rice seed varieties from different origins, and comparing them with known or standard samples, this study aims to authenticate rice, protect brands, and achieve traceability. The study selected the same variety of rice seed planted in different regions of Jilin Province in the same year as samples. Fluorescence spectroscopy was used to collect spectral data, which was preprocessed by normalization, smoothing, and wavelet transformation to remove noise, scattering, and burrs. The processed spectral data was used as input for the long short-term memory (LSTM) model. The study focused on the processing and analysis of rice spectra based on NZ-WT-processed data. To simplify the model, uninformative variable elimination (UVE) and successive projections algorithm (SPA) were used to screen the best wavelengths. These wavelengths were used as input for the support vector machine (SVM) prediction model to achieve efficient and accurate predictions. Within the fluorescence spectral range of 475-525 nm and 665-690 nm, absorption peaks of nicotinamide adenine dinucleotide (NADPH), riboflavin (B2), starch, and protein were observed. The origin tracing prediction model established using SVM exhibited stable performance with a classification accuracy of up to 99.5%.The experiment demonstrated that fluorescence spectroscopy technology has high discrimination accuracy in tracing the origin of rice, providing a new method for rapid identification of rice origin.

Use of spectroscopic process analytical technology for rapid quality evaluation during preparation of CHO cell culture media.

Media preparation parameters contribute significantly to media quality, cell culture performance, productivity, and product quality. Establishing proper media preparation procedures is critical for ensuring a robust CHO cell culture process. Process analytical technology (PAT) enables unique ways to quantify assessments and improve media quality. Here, cell culture media were prepared under a wide range of temperatures (40-80°C) and pH (7.6-10.0). Media quality profiles were compared using three real-time PATs: Fourier-transform infrared (FTIR) spectroscopy, Raman spectroscopy, and excitation-emission matrix (EEM) spectroscopy. FTIR and Raman spectroscopies identified shifts in media quality under high preparation temperature (80°C) and at differing preparation pH which negatively impacted monoclonal antibody (mAb) production. In fed-batch processes for production of three different mAbs, viable cell density (VCD) and cell viability were mostly unaffected under all media preparation temperatures, while titer and cell specific productivity of mAb decreased when cultured in basal and feed media prepared at 80°C. High feed preparation pH alone was tolerated but cell growth and productivity profiles deviated from the control condition. Further, charge variants (main, acidic, basic species) and glycosylation (G0F, afucosylation, and high mannose) were examined. Statistically significant differences were observed for one or more of these quality attributes with any shifts in media preparation. In this study, we demonstrated strong associations between media preparation conditions and cell growth, productivity, and product quality. The rapid evaluation of media by PAT implementation enabled more comprehensive understanding of different parameters on media quality and consequential effects on CHO cell culture.

Deep learning based event reconstruction for cyclotron radiation emission spectroscopy

The objective of the cyclotron radiation emission spectroscopy (CRES) technology is to build precise particle energy spectra. This is achieved by identifying the start frequencies of charged particle trajectories which, when exposed to an external magnetic field, leave semi-linear profiles (called tracks) in the time–frequency plane. Due to the need for excellent instrumental energy resolution in application, highly efficient and accurate track reconstruction methods are desired. Deep learning convolutional neural networks (CNNs) - particularly suited to deal with information-sparse data and which offer precise foreground localization—may be utilized to extract track properties from measured CRES signals (called events) with relative computational ease. In this work, we develop a novel machine learning based model which operates a CNN and a support vector machine in tandem to perform this reconstruction. A primary application of our method is shown on simulated CRES signals which mimic those of the Project 8 experiment—a novel effort to extract the unknown absolute neutrino mass value from a precise measurement of tritium β −-decay energy spectrum. When compared to a point-clustering based technique used as a baseline, we show a relative gain of 24.1% in event reconstruction efficiency and comparable performance in accuracy of track parameter reconstruction.

Schinus molle extract mediated green synthesis of iron niobate photocatalyst for the degradation of methyl orange dye under visible light

FeNbO4 monoclinic nanocomposite semiconductors were synthesised using hydrothermal and sol gel methods; photocatalysts were then calcined at 800 °C. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), UV–Vis spectroscopy (UV/Vis) and X-ray diffraction (XRD) technologies were used to investigate crystallinity, morphology and optical properties of the photocatalysts. Fourier transform infrared spectroscopy (FTIR) was used to determine the functional groups of both treated and untreated FeNbO4. It was found that the S. molle extract-treated FeNbO4 prepared using the hydrothermal method (FeNbO4-HT + S. molle) has the smallest nanoparticles (22.8 nm) with the smallest band gap energy (2.78 eV). X-ray photoelectron spectroscopy verified the presence of the elements of FeNbO4 as well as their oxidation states. The photodegradation reactions of 10-ppm methyl orange dye solutions using FeNbO4-sol gel, FeNbO4-HT and FeNbO4-HT + S. molle were carried out under visible light (>420 nm) for 50 min. The reactions resulted in degradation percent of 74 %, 78 % and 96 % by using FeNbO4-sol gel, FeNbO4-HT and FeNbO4-HT + S. molle respectively. The photocatalytic activity of FeNbO4 treated with S. molle extract demonstrated superior light absorption and photostability, which is attributed to the consistency in the photocatalysts’ morphology, optical band gap, particle size distribution, and porosity. The photocatalysts remained stable and effective for five degradation cycles.

Warning deterrence or knowledge guidance? Research on triggering mechanism of phishing sensitivity

Using S-O-R model, this study explores the triggering mechanism of phishing sensitivity caused by security warning deterrence, security knowledge guidance, and security warning deterrence + security knowledge guidance. Study 1 explores the triggering effect of these three types of stimuli on phishing sensitivity through online experiments, and considers the moderating effect based on phishing situation. The results show that knowledge guidance and warning deterrence + knowledge guidance have significant triggering effects on phishing sensitivity, whereas warning deterrence has no such effects. The phishing situation plays a moderating role. In study 2, a cognitive neural experiment is conducted. The functional near-infrared spectroscopy (fNIRS) technology is used to explore S-O-R triggering mechanism of phishing sensitivity mediated by cognitive processing and trust. The results show that both cognitive processing and trust play a mediating role between security intervention and phishing sensitivity. The research results have important theoretical value and practical significance for improving phishing sensitivity.

Multi-View Synthesis of Sparse Projection of Absorption Spectra Based on Joint GRU and U-Net

Tunable diode laser absorption spectroscopy (TDLAS) technology, combined with chromatographic imaging algorithms, is commonly used for two-dimensional temperature and concentration measurements in combustion fields. However, obtaining critical temperature information from limited detection data is a challenging task in practical engineering applications due to the difficulty of deploying sufficient detection equipment and the lack of sufficient data to invert temperature and other distributions in the combustion field. Therefore, we propose a sparse projection multi-view synthesis model based on U-Net that incorporates the sequence learning properties of gated recurrent unit (GRU) and the generalization ability of residual networks, called GMResUNet. The datasets used for training all contain projection data with different degrees of sparsity. This study shows that the synthesized full projection data had an average relative error of 0.35%, a PSNR of 40.726, and a SSIM of 0.997 at a projection angle of 4. At projection angles of 2, 8, and 16, the average relative errors of the synthesized full projection data were 0.96%, 0.19%, and 0.18%, respectively. The temperature field reconstruction was performed separately for sparse and synthetic projections, showing that the application of the model can significantly improve the reconstruction accuracy of the temperature field of high-energy combustion.

Non-invasive screening and subtyping for breast cancer by serum SERS combined with LGB-DNN algorithms

Early detection of breast cancer and its molecular subtyping is crucial for guiding clinical treatment and improving survival rate. Current diagnostic methods for breast cancer are invasive, time consuming and complicated. In this work, an optical detection method integrating surface-enhanced Raman spectroscopy (SERS) technology with feature selection and deep learning algorithm was developed for identifying serum components and building diagnostic model, with the aim of efficient and accurate noninvasive screening of breast cancer. First, the high quality of serum SERS spectra from breast cancer (BC), breast benign disease (BBD) patients and healthy controls (HC) were obtained. Chi-square tests were conducted to exclude confounding factors, enhancing the reliability of the study. Then, LightGBM (LGB) algorithm was used as the base model to retain useful features to significantly improve classification performance. The DNN algorithm was trained through backpropagation, adjusting the weights and biases between neurons to improve the network's predictive ability. In comparison to traditional machine learning algorithms, this method provided more accurate information for breast cancer classification, with classification accuracies of 91.38 % for BC and BBD, and 96.40 % for BC, BBD, and HC. Furthermore, the accuracies of 90.11 % for HR+/HR- and 88.89 % for HER2+/HER2- can be reached when evaluating BC patients' molecular subtypes. These results demonstrate that serum SERS combined with powerful LGB-DNN algorithm would provide a supplementary method for clinical breast cancer screening.

Plasma-assisted manipulation of vanadia nanoclusters for efficient selective catalytic reduction of NOx

Supported nanoclusters (SNCs) with distinct geometric and electronic structures have garnered significant attention in the field of heterogeneous catalysis. However, their directed synthesis remains a challenge due to limited efficient approaches. This study presents a plasma-assisted treatment strategy to achieve supported metal oxide nanoclusters from a rapid transformation of monomeric dispersed metal oxides. As a case study, oligomeric vanadia-dominated surface sites were derived from the classic supported V2O5-WO3/TiO2 (VWT) catalyst and showed nearly an order of magnitude increase in turnover frequency (TOF) value via an H2-plasma treatment for selective catalytic reduction of NO with NH3. Such oligomeric surface VOx sites were not only successfully observed and firstly distinguished from WOx and TiO2 by advanced electron microscopy, but also facilitated the generation of surface amide and nitrates intermediates that enable barrier-less steps in the SCR reaction as observed by modulation excitation spectroscopy technologies and predicted DFT calculations.

Detection of protein content in alfalfa using visible/ near-infrared spectroscopy technology

In this study, a quantitative model was developed using near-infrared spectroscopy to analyze protein content in dried purple alfalfa, employing preprocessing methods (SG, SNV, MSC, FD) and variable selection algorithms (CARS, IRIV) to optimize spectra. Models using ELM, PLSR, SVM, and LSTM were tested; the MSC-CARS-PLSR-SVM model achieved the highest accuracy, with a calibration determination coefficient (R²) of 0.9982 and root mean square error (RMSE) of 0.1088, and a prediction R² of 0.9645 with RMSE of 0.5230, offering a precise and reliable method for protein content prediction.

Low temperature catalytic activity of molybdenum promoted two-dimensional boron nitride in propane oxidative dehydrogenation reaction

The catalytic oxidation dehydrogenation of propane to propylene, achieved at low temperatures and with high selectivity on catalysts supported by non-noble metals, exhibits significant potential for a wide range of applications. The performance of boron-based catalysts in ODHP is remarkable; however, there is a dearth of research on their low-temperature range performance. The present study employed a straightforward impregnation method to synthesize a range of Mo/h-BN-γ-Al2O3 catalysts with varying molybdenum loadings, focusing on the comparison between commercial h-BN and h-BN-γ-Al2O3 mixed support. The Mo/h-BN-γ-Al2O3 catalyst demonstrates remarkable low-temperature ODHP activity, with an initial conversion rate of 2.36 % and a propylene selectivity of 96 % at a reaction temperature of 260 ℃, exhibiting good stability over 50 h, the catalytic performance of Mo/h-BN-γ-Al2O3 in the low-temperature range surpasses that of commercial h-BN by a significant margin. Comparative experiments, along with physical and chemical characterization techniques, demonstrate that the incorporation of an appropriate quantity of Mo enhances the catalyst's acidity and redox capability. Furthermore, the synergistic effect between MoOx and BOx promotes low-temperature ODHP. The mechanism underlying the catalyst's low-temperature reaction was investigated using in-situ infrared spectroscopy technology.

Semi-continuous mesophilic anaerobic digestion of date palm (Phoenix dactelifyra L.) leaflets of the H'mira cultivar from the Adrar region of Southern Algeria using an alkaline pre-treatment

This research paper aim to investigate the enhancement potential of mesophilic Anaerobic Digestion (AD) of Algerian date palm (Phoenix dactylifera L.) leaflets from H'mira cultivars using an alkaline pretreatment approach. The experiment was conducted for the first time in semi-continuous digesters. NaOH was applied at various percentages (6 %, 12 % and 18 %) to chemically pretreat the substrate for 5 days at 37 °C. The chemical pretreatment results were interpreted for the first time with in-depth FTIR spectroscopic analyses. The Hydraulic Retention Time (HRT) was fixed at 15 days, and the Organic Loading Rate (OLR) at 2 g MO/L/day. The results showed that the methane (CH4) production values were stabilized with a clear distinction at averages of 211.85, 177.26, 143.36 and 123.07 ml of CH4/day, i.e. a CH4 yield of 105.92, 88.63, 71.68 and 61.53 ml of CH4/gVS/day for the reactors containing substrates pre-treated with 12 %, 6 %, 18 % NaOH and the substrate without pre-treatment respectively. Compared to the substrate without pre-treatment, the CH4 production rates were improved 1.72, 1.44 and 1.16 times for the reactors containing 12 %, 6 % and 18 % NaOH respectively. These findings were perfectly in accordance with FTIR spectroscopy results. These findings demonstrate that the semi-continuous AD process can be an attractive energy recovery solution from such waste compared to batch digesters, (on one day, it produce 77–105 % of the CH4 produced during 21–55 days in batch digesters), as well as the importance of adequate pre-treatment prior to the AD. FTIR Spectroscopic analysis technology can also be widely used to analyze pre-treatment data for lignocellulosic biomass, as it gives very encouraging results.

Research on classification methods for rubber based on terahertz time-domain spectroscopy with data fusion strategy

Rubber, an essential industrial product, plays a crucial role in industry and human life. However, some manufacturers of rubber use inferior, recycled or low-cost rubber materials to replace high-quality rubber products. Therefore, in order to ensure industrial production, protect natural environment and ensure personal safety, it is of great economic and social significance to enable rapid and non-destructive qualitative identification of rubber products. In this paper, eight different types of black rubber, including Nitrile Butadiene Rubber (NBR), Choloroprene Rubber (CR) and Ethylen Propylen Diene Monomer (EPDM), were selected as research objects. Terahertz Time-Domain Spectroscopy (THz-TDS) technology was utilized to get the absorption spectra, refractive index spectra and time-domain spectra of these samples for exploring the differences between different types of rubber in terahertz spectra. To reduce the data volume, three band selection algorithms were employed to extract features from the absorption spectra and refractive index spectra, and temporal features of time-domain spectra were extracted for subsequent data processing. In the selection of classification model algorithms, K-Nearest Neighbor (KNN), Random Forest (RF) and Supervised Kohonen Neural Networks (S-Kohonen) were employed to recognize these spectral data. To fully exploit the latent information within these data and considering the possibility of the loss of partial information in single-type signal, a feature-level data fusion method was applied to different optical parameter signals to obtain more information about terahertz spectra during the process of data modeling. The results of the research indicated that the model with the highest prediction accuracy was the KNN model, with an accuracy of 97.37%, when refractive index spectra and time-domain spectra were fused as inputs, the F1-score reached 99.64. The performance of data fusion was noticeably better than that of a single-type signal. This study validates the feasibility of using terahertz time-domain spectroscopy for qualitative identification of different types of rubber.

Confocal controlled laser-induced breakdown spectroscopy for quantitative detection of cadmium in soil

Soil, one of the most precious natural resources on Earth, gradually accumulates heavy metals, inevitably causing significant damage to the ecological environment. Here, we introduce confocal controlled laser induced breakdown spectroscopy (CCLIBS) technology for the quantitative analysis of the heavy metal cadmium in soil for the first time. CCLIBS offers better spatial consistency and stable plasma temperature during sample ablation compared to traditional LIBS, thereby reducing matrix effects to improve the accuracy of the quantitative results. The fluctuation of the spectrum and limit of detection are reduced by 0.6 times and 0.39 times, respectively. An effective prediction model was established using the partial least squares method, with a determination coefficient increased to 0.96. The root mean square error of prediction and average relative error are reduced to 67.67 and 0.20, respectively. These results indicate that CCLIBS provides consistent ablation conditions for elemental quantification and yields reliable test results, which is significant for monitoring heavy metals in the ecological environment and effectively intervene and mitigate environmental contamination.

Short-Wavelength Infrared Characteristics and Indications of Exploration of the Jiawula Silver–Lead–Zinc Deposit in Inner Mongolia

For the Jiawula lead–zinc deposit, as easily accessible resources become depleted, mines are becoming deeper to replenish ore reserves. Identifying large, continuous, and high-grade ore bodies in deep areas has become a daunting problem. Moreover, separating lead–zinc-bearing complex ore bodies from waste material and extracting them from associated minerals are also difficult. Thus, pioneering exploratory strategies and technological methodologies are required to make breakthroughs in mineral discovery. Based on extensive-scale structural lithofacies mapping, this paper uses short-wave infrared (SWIR) spectroscopy technology to investigate hydrothermal alteration minerals in the mining area. It has identified a total of 16 hydroxyl-bearing alteration minerals, including chlorite, muscovite, illite, calcite, ankerite, kaolinite, and smectite. These minerals establish zoning characteristics around the ore bodies and on their flanks. They comprise a segmented assemblage that follows the pattern of comb-textured quartz–illite–chlorite–carbonate → muscovite–illite–chlorite–ankerite → illite–smectite–chlorite → chlorite–kaolinite–calcite. Deep-zone illitization with a lower Al–OH absorbance peak wavelength (<2206 nm) and higher crystallinity indices (>1.1) and chloritization with higher Fe–OH absorbance peak wavelengths (>2254) and higher crystallinity indices (>3.0) are indicators of potential hydrothermal centers in the deeper regions. By finding hydrothermal centers and connecting their spatial distribution with existing ore bodies, a pertinent relationship between diabase + andesite, Fe-chlorite + illite, and high-grade mineralization has been established. They correspond well with the lithology-alteration mineralization. This research provides a basis for predicting the positioning of concealed ore bodies deep inside a mine or at the periphery.

Photogenerated carrier dynamics of Mn2+ doped CsPbBr3 assembled with TiO2 systems: Effect of Mn doping content.

In recent years, all-inorganic perovskite materials have become an ideal choice for new thin film solar cells due to their excellent photophysical properties and have become a research hotspot. Studying the ultrafast dynamics of photo-generated carriers is of great significance for further improving the performance of such devices. In this work, we focus on the transient dynamic process of CsPbBr3/TiO2 composite systems with different Mn2+ doping contents using femtosecond transient absorption spectroscopy technology. We used singular value decomposition and global fitting to analyze the transient absorption spectra and obtained three components, which are classified as hot carrier cooling, charge transfer, and charge recombination processes, respectively. We found that the doping concentration of Mn2+ has an impact on all three processes. We think that the following two factors are responsible: one is the density of defect states and the other is the bandgap width of perovskite. As the concentration of doped Mn2+ increases, the charge transfer time constant shows a trend of initially increasing, followed by a subsequent decrease, reaching a turning point. This indicates that an appropriate amount of Mn2+ doping can effectively improve the photoelectric performance of solar cell systems. We proposed a possible charge transfer mechanism model and further elucidated the microscopic mechanism of the effect of Mn2+ doping on the interface charge transfer process of the CsPbBr3/TiO2 solar cell system.

Synergistic effects and competitive relationships between DOC and DOX as acting on DNA molecules: Studied with confocal Raman spectroscopy and molecular docking technology

Docetaxel (DOC) is one of the second-generation antineoplastic drugs of the taxanes family with excellent antitumor activity. However, the mechanism of DOC inducing tumor cell apoptosis and treating cancer diseases, especially its interaction with DNA in the nucleus, and its adjuvant or combined Doxorubicin (DOX) acting on DNA molecules are unclear. In this study, the interaction mechanism between DOC and DNA, as well as the synergistic effects and competitive relationships among DOC and DOX when they simultaneously interact with DNA molecules were studied by laser confocal Raman spectroscopy combined with UV–visible absorption spectroscopy and molecular docking technology. The spectroscopic results showed that the binding constant of DOC to DNA is 5.25 × 103 M−1, the binding modes of DOC and DNA are non-classical intercalation and electrostatic binding, and the DNA-DOC complex has good stability. When DOC or DOX interacts with DNA alone, both of them can bind with bases and phosphate backbone of DNA, and also lead to DNA conformation changes; when DOC and DOX interact with DNA at the same time, the orders of interaction not only affect their binding sites with DNA, but also cause changes in the surrounding environment of the binding sites. In addition, the molecular docking results further verified that DOC and DOX have synergy and competition when they interact with DNA molecules simultaneously. The docking energies of DNA-DOC and DNA-DOX indicate the important role of van der Waals forces and hydrogen bonds. This study has practical significance for the design and development of antitumor drugs with less toxic based on the taxanes family and the combination with other drugs for the treatment of cancer.

A breakthrough in phytochemical profiling: ultra-sensitive surface-enhanced Raman spectroscopy platform for detecting bioactive components in medicinal and edible plants.

A perennial challenge in harnessing the rich biological activity of medicinal and edible plants is the accurate identification and sensitive detection of their active compounds. In this study, an innovative, ultra-sensitive detection platform for plant chemical profiling is created using surface-enhanced Raman spectroscopy (SERS) technology. The platform uses silver nanoparticles as the enhancing substrate, excess sodium borohydride prevents substrate oxidation, and methanol enables the tested molecules to be better adsorbed onto the silver nanoparticles. Subsequently, nanoparticle aggregation to form stable "hot spots" is induced by Ca2+, and the Raman signal of the target molecule is strongly enhanced. At the same time, deuterated methanol was used as the internal standard for quantitative determination. The method has excellent reproducibility, RSD ≤ 1.79%, and the enhancement factor of this method for the detection of active ingredients in the medicinal plant Coptis chinensis was 1.24 × 109, with detection limits as low as 3 fM. The platform successfully compared the alkaloid distribution in different parts of Coptis chinensis: root > leaf > stem, and the difference in content between different batches of Coptis chinensis decoction was successfully evaluated. The analytical technology adopted by the platform can speed up the determination of Coptis chinensis and reduce the cost of analysis, not only making better use of these valuable resources but also promoting development and innovation in the food and pharmaceutical industries. This study provides a new method for the development, evaluation, and comprehensive utilization of both medicinal and edible plants. It is expected that this method will be extended to the modern rapid detection of other medicinal and edible plants and will provide technical support for the vigorous development of the medicinal and edible plants industry.

Discovery of new antibiotics using AI-guided spectroscopy and 3D drug-protein computer simulation technologies to combat MDR bacteria-associated mortality

Multidrug-resistant (MDR), extensively drug-resistant (XDR), and totally drug-resistant bacteria can cause sepsis and death in patients due to their ability to inactivate most antibiotics, including ampicillin, tetracycline, streptomycin, chloramphenicol, erythromycin, and ciprofloxacin. This paper aims to review recent advancements in synthetic antibiotics, lantibiotics, and phytoantibiotics and to present our research on phytoantibiotics, specifically focusing on CU1 and NU2. While third- and fifth-generation synthetic antibiotics such as meropenem, moxifloxacin, amikacin, and tigecycline are currently relied upon for treating MDR infections, research is underway to develop peptide antibiotics known as lantibiotics (e.g., nisins, bacteriocins, and salivaricins). Lantibiotics such as nisin-A and salivaricin-B have demonstrated efficacy in curing numerous MDR infections, while phytochemicals such as artemisinin and quinine have shown effectiveness against chloroquine-resistant Plasmodium falciparum infections (malaria). In our study, we utilized techniques such as mass spectroscopy, nuclear magnetic resonance, and Fourier transform infrared spectroscopy in conjunction with artificial intelligence (AI) and computer simulation technologies to determine the structure of phytochemicals. Our results revealed that CU1, derived from Cassia fistula bark ethanol extract, exhibits potent antibiotic activity against XDR bacteria by targeting the RNA polymerases of Escherichia coli and Mycobacterium tuberculosis. Consequently, our MDR-Cure extract containing CU1 represents a promising antibacterial Ayurvedic medicine specifically tailored for skin and nail infections. Similarly, NU2 poly-fluorophosphate-glycosides from Suregada multiflora roots ethanol extract exhibited strong inhibitory effects on XDR bacteria by targeting DNA topoisomerase I. Recently, many cyclic peptide antibiotics have been synthesized in vitro using computer-guided AI technologies to predict 3D drug-enzyme interactions and are currently undergoing clinical trials. Our ultimate goal is to combat XDR bacteria-associated deaths, which are predicted to escalate as we approach 2050.

Simultaneous detection of multiple aging characteristic components in oil-paper insulation using sensitive Raman technology and microfluidics

The aging characteristic components of oil-paper insulation reflect the aging status of the power equipment. In this study, we designed a novel microfluidic chip capable of automatic and rapid extraction of aging components from insulating oil. Combined with Raman spectroscopy technology, it enables simultaneous detection of various aging components. By optimizing the microfluidic chip structural and adopting an optical window encapsulation, it eliminates interference from the Polydimethylsiloxane (PDMS). Measurements and analyses were carried out on multiple oil samples containing three aging products (furfural, acetone, and methanol). The results indicate that this novel microfluidic chip facilitates simultaneous detection of multiple components, significantly improving the detection sensitivity of complex oil. The detection limits for furfural, acetone, and methanol in insulating oil are 0.43 mg/L, 1.04 mg/L, and 2.31 mg/L, respectively. This provides a new approach for the online detection of oil-paper insulation equipment.

SERS-Based Microneedle Biosensor for In Situ and Sensitive Detection of Tyrosinase.

Tyrosinase (TYR) emerges as a key enzyme that exerts a regulatory influence on the synthesis of melanin, thereby assuming the role of a critical biomarker for the detection of melanoma. Detecting the authentic concentration of TYR in the skin remains a primary challenge. Distinguished from ex vivo detection methods, this study introduces a novel sensor platform that integrates a microneedle (MN) biosensor with surface-enhanced Raman spectroscopy (SERS) technology for the in situ detection of TYR in human skin. The platform utilized dopamine (DA)-functionalized gold nanoparticles (Au NPs) as the capturing substrate and 4-mercaptophenylboronic acid (4-MPBA)-modified silver nanoparticles (Ag NPs) acting as the SERS probe. Here, the Au NPs were functionalized with mercaptosuccinic acid (MSA) for DA capture. In the presence of TYR, DA immobilized on the MN is preferentially oxidized to dopamine quinone (DQ), a process that results in a decreased density of SERS probes on the platform. TYR concentration was detected through variations in the signal intensity emitted by the phenylboronic acid. The detection system was able to evaluate TYR concentrations within a linear range of 0.05 U/mL to 200 U/mL and showed robust anti-interference capabilities. The proposed platform, integrating MN-based in situ sensing, SERS technology, and TYR responsiveness, holds significant importance for diagnosing cutaneous melanoma.