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In this work, we present a comprehensive experimental and theoretical study of the vibrational spectra of PAH molecules recently detected in the interstellar medium: 1-cyanonaphthalene and 2-cyanonaphthalene. The room temperature IR spectra of 1- and 2-cyanonaphthalene in the region 100-3100 cm-1 and their vibrational Raman spectra in the region 35-3100 cm-1 are reported here for the first time. A detailed spectral analysis is carried out using quantum chemical calculations employing the DFT methodology. Anharmonic corrections using the VPT2 method yield excellent agreement with the experimental spectra. A re-investigation of the vibrational spectrum of the parent molecule: naphthalene validates the experimental and theoretical methods used. A consistent set of assignments is reported for the fundamental bands of 1- and 2-cyanonapththalene. The experimental and theoretical data presented here would be useful inputs for modelling the role of cyanonaphthalene in astrophysical processes.

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Raman spectroscopy is an important technique for analyzing the chemical composition of samples in many fields. A severe challenge often encountered in Raman measurements is the presence of a concurrent fluorescence background, especially in biological samples. In order to obtain accurate Raman spectra, the fluorescence background must be subtracted from the original Raman spectra. We proposed a shifted ratio spectrum method to subtract the strong fluorescence background from the original Raman spectrum. First, the original Raman spectrum is divided into multiple regions according to the spectral shape of the shifted ratio spectra, and then, Gaussian fitting is performed in each region. The fitting results are stitched together in order to obtain the complete fluorescence background. Finally, this fluorescence background is subtracted from the original spectrum to obtain a pure Raman spectrum. This method can accurately subtract the fluorescence background of Rhodamine 6G (R6G)/ethanol solution and serum. This highlights the great potential of this method for applications in both biological and non-biological samples.

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BACKGROUND: Upcoming inexpensive, compact Internet of Things (IoT) microcontrollers i.e., tiny-machine learning (TinyML) takes the ML driven Raman spectroscopy one step ahead for realization of more affordable and highly compact field deployable instruments. Further, lack of large spectral datasets and need for numerous specialized SERS substrates impede the development of various ML-based surface enhanced Raman spectroscopy (SERS) applications. The aim is to introduce TinyML analysis on wide range of spectra classes using customized dataset obtained with low-cost SERS. In this regard, it is vital to establish an optimum ML model and efficient data handling methodology for low memory TinyML units. RESULTS: We introduce a novel TinyML methodology for accurate classification of large spectra classes with smartphone assistance for data communication and results visualization. To generate large customized spectral dataset, we present a facile, micro-drop SERS using Au colloids and reusable grooved Al substrates. The results demonstrated that memory efficient 8-bit data quantization based convolutional neural network is effective for accurate identification of 22 different spectra classes of trace dye-pesticide mixtures and pharmaceuticals. In this novel quantized data analysis on significantly varied intensity and complex variation spectra classes i.e., many individual, binary-mixtures and some with varied compositions, data normalization is shown to be powerful for improving ML classification accuracy from about 55 % to >99.5 %. Its robustness is demonstrated using inter-instrument driven data variations such as spectral shifts, high noise, and abscissa-flip, with five-fold cross validation of model performance. In addition, on-site quantification of analyte through spectral intensity is also demonstrated. SIGNIFICANCE: This study opens up a new approach of ML analysis towards realization of next generation field deployable analytical instruments maintaining data privacy. It presents a detailed procedure of quantized spectral data analysis and its implementation in TinyML, attractive for various users and instrument manufacturers. The presented innovative computer-free ML analysis can be employed in all types of spectrometers, meeting the common goal of Raman spectroscopy i.e., accurate identification of complex spectra classes.

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Feature representation is critical for data learning, particularly in learning spectroscopic data. Machine learning (ML) and deep learning (DL) models learn Raman spectra for rapid, nondestructive, and label-free cell phenotype identification, which facilitate diagnostic, therapeutic, forensic, and microbiological applications. But these are challenged by high-dimensional, unordered, and low-sample spectroscopic data. Here, we introduced novel 2D image-like dual signal and component aggregated representations by restructuring Raman spectra and principal components, which enables spectroscopic DL for enhanced cell phenotype and signature identification. New ConvNet models DSCARNets significantly outperformed the state-of-the-art (SOTA) ML and DL models on six benchmark datasets, mostly with >2% improvement over the SOTA performance of 85-97% accuracies. DSCARNets also performed well on four additional datasets against SOTA models of extremely high performances (>98%) and two datasets without a published supervised phenotype classification model. Explainable DSCARNets identified Raman signatures consistent with experimental indications.

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We investigated the formation of nitrosamines from urine during electrochemical chlorination (EC) using dimensionally stable anodes. Short-term electrolysis (< 1 h) of urine at 25 mA cm-2 generated seven nitrosamines (0.1-7.4 µg L-1), where N-nitrosodimethylamine, N-nitrosomethylethylamine, and N-nitrosodiethylamine were predominant with concentrations ranging from 1.2 to 7.4 µg L-1. Mechanistic studies showed that the formation kinetics of nitrosamines was influenced by urine aging and composition, with fresh urine generating the highest levels (0.9-5.8 µg L-1) compared with aged, centrifuged, or filtered urine (0.2-4.1 µg L-1). Concurrently, studies on urine pretreatment through filtration and centrifugation underscored the significance of nitrogenous metabolites (such as protein-like products and urinary amino acids) and particle-associated humic fractions in nitrosamine formation during EC of urine. This finding was confirmed through chromatographic and spectroscopic studies utilizing LCOCD, Raman spectra, and 3DEEM fluorescence spectra. Parametric studies demonstrated that the ultimate [nitrosamines] increased at a pH range of 4.5-6.2, and with increasing [bromide], [ammonium], and current density. Conversely, sulfate and carbonate ions inhibited nitrosamine formation. Moreover, the implications of EC in urine-containing source waters were evaluated. The results indicate that regardless of the urine source (individual volunteers, septic tank, swimming pool, untreated municipal wastewater), high levels of nitrosamines (0.1-17.6 µg L-1) were generated, surpassing the potable reuse guideline of 10 ng L-1. Overall, this study provides insights to elucidate the mechanisms underlying nitrosamine formation and optimize the operating conditions. Such insights facilitate suppressing the generation of nitrosamine byproducts during electrochemical treatment of urine-containing wastewater.

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The influence of the pH of the reaction medium on the structural characteristics of hydrothermally reduced graphene oxide, synthesized by the tour method, has been investigated. Varying the pH of the reaction medium within the range of 8.0, 10.0 and 12.0 (adjusted with NaOH) has revealed distinct effects on the morphology and properties of the resulting reduced graphene oxide. At a pH of 8.0 the hydrothermal treatment yielded reduced graphene oxide comprising of two particle fractions with a thickness equivalent to 4-5 graphitic layers each. In contrast, pH of 10.0 resulted in two particle fractions corresponding to 2-3 and 4 layers, respectively, while pH of 12.0 produced a single fraction with a particle thickness of 0.70 nm, encompassing 3 graphitic layers. Increasing the pH led to a decrease in the average lateral size of reduced graphene oxide particles to about 8 nm. All rGOs had micro- and mesopores with a specific surface area up to 226 m2g-1, showing a proportional increase in mesopores with increasing pH. Analysis of slit-like micropores revealed a minimum fractal dimension (D= 2.18) at pH = 8.0. The obtained results provide valuable insights into tailoring the structural properties of hydrothermally reduced graphene oxide by controlling the pH of the reaction medium, offering potential applications in various fields, including nanotechnology and materials science.

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Perfluorooctanoic acid (PFOA) has received increasing concerns in recent years due to its wide distribution and potential toxicity. Existing detection techniques of PFOA require complex pre-treatment, therefore often taking several hours. Here, we developed a rapid PFOA detection mode to detect approximate concentrations of PFOA (ranging from 10-15 to 10-3 mol/L) in deionized water, and detecting one sample takes only 20 min. The detection mode was achieved using a deep learning model trained by a large surface enhanced Raman spectra dataset, based on the agglomeration of PFOA with crystal violet. In addition, transfer learning approach was used to fine tune the model, the fine-tuned model was generalizable across water samples with different impurities and environments to determine whether meet the safety standards of PFOA, the accuracy was 96.25 % and 94.67 % for tap water and lake water samples, respectively. The mechanism and specificity of the detection mode were further confirmed by molecular dynamics simulation. Our work provides a promising solution for PFOA detection, especially in the context of the increasingly widespread application of PFOA.

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Explainable artificial intelligence (XAI) has gained significant attention in various domains, including natural and medical image analysis. However, its application in spectroscopy remains relatively unexplored. This systematic review aims to fill this gap by providing a comprehensive overview of the current landscape of XAI in spectroscopy and identifying potential benefits and challenges associated with its implementation. Following the PRISMA guideline 2020, we conducted a systematic search across major journal databases, resulting in 259 initial search results. After removing duplicates and applying inclusion and exclusion criteria, 21 scientific studies were included in this review. Notably, most of the studies focused on using XAI methods for spectral data analysis, emphasizing identifying significant spectral bands rather than specific intensity peaks. Among the most utilized AI techniques were SHapley Additive exPlanations (SHAP), masking methods inspired by Local Interpretable Model-agnostic Explanations (LIME), and Class Activation Mapping (CAM). These methods were favored due to their model-agnostic nature and ease of use, enabling interpretable explanations without modifying the original models. Future research should propose new methods and explore the adaptation of other XAI employed in other domains to better suit the unique characteristics of spectroscopic data.

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Epitaxial bilayer graphene, grown by chemical vapor deposition on SiC substrates without silicon sublimation, is crucial material for graphene field effect transistors (GFETs). Rigorous characterization methods, such as atomic force microscopy and Raman spectroscopy, confirm the exceptional quality of this graphene. Post-nanofabrication, extensive evaluation of DC and high-frequency properties enable the extraction of critical parameters such as the current gain (fmax) and cut-off frequency (ft) of hundred transistors. The Raman spectra analysis provides insights into material property, which correlate with Hall mobilities, carrier densities, contact resistance and sheet resistance and highlights graphene's intrinsic properties. The GFETs' performance displays dispersion, as confirmed through the characterization of multiple transistors. Since the Raman analysis shows relatively homogeneous surface, the variation in Hall mobility, carrier densities and contact resistance cross the wafer suggest that the dispersion of GFET transistor's performance could be related to the process of fabrication. Such insights are especially critical in integrated circuits, where consistent transistor performance is vital due to the presence of circuit elements like inductance, capacitance and coplanar waveguides often distributed across the same wafer.

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We introduce a deep neural network (DNN) framework called theReal-spaceAtomicDecompositionNETwork (radnet), which is capable of making accurate predictions of polarization and of electronic dielectric permittivity tensors in solids and aims to address limitations of previously available machine learning models for Raman predictions in periodic systems. This framework builds on previous, atom-centered approaches while utilizing deep convolutional neural networks. We report excellent accuracies on direct predictions for two prototypical examples: GaAs and BN. We then use automatic differentiation to efficiently calculate the Born-effective charges, longitudinal optical-transverse optical (LO-TO) splitting frequencies, and Raman tensors of these materials. We compute the Raman spectra, and find agreement withab initioresults. Lastly, we explore ways to generalize the predictions of polarization while taking into account periodic boundary conditions and symmetries.

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In this paper, the crystal geometry, electronic structure, lattice vibration, Infrared and Raman spectra of ternary layered borides M3AlB2 (M = Ti, Zr, Hf, Ta) are studied by using first principles calculation method based on the density functional theory. The electronic structure of M3AlB2 indicates that they are all electrical conductors, and the d orbitals of Ti, Zr, Hf, and Ta occupy most of the bottom of the conduction band and most of the top of the valence band. Al and B have lower contributions near their Fermi level. The lightweight and stronger chemical bonds of atom B are important factors that correspond to higher levels of peak positions in the Infrared and Raman spectra. However, the vibration frequencies, phonon density of states, and peak positions of Infrared and Raman spectra are significantly lower because of heavier masses and weaker chemical bonds for M and Al atoms. And, there are 6 Infrared active modes A2u and E1u, and 7 Raman active modes, namely A1g, E2g, and E1g corresponding to different vibration frequencies in M3AlB2. Furthermore, the Infrared and Raman spectra of M3AlB2 were obtained respectively, which intuitively provided a reliable Infrared and Raman vibration position and intensity theoretical basis for the experimental study.

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Raman spectroscopy is a rapid method for analysing the molecular composition of biological material. However, noise contamination in the spectral data necessitates careful pre-processing prior to analysis. Here we propose an end-to-end Convolutional Neural Network to automatically learn an optimal combination of pre-processing strategies, for the classification of Raman spectra of superficial and deep layers of cartilage harvested from 45 Osteoarthritis and 19 Osteoporosis (Healthy controls) patients. Using 6-fold cross-validation, the Multi-Convolutional Neural Network achieves comparable or improved classification accuracy against the best-performing Convolutional Neural Network applied to either the raw or pre-processed spectra. We utilised Integrated Gradients to identify the contributing features (Raman signatures) in the network decision process, showing they are biologically relevant. Using these features, we compared Artificial Neural Networks, Decision Trees and Support Vector Machines for the feature selection task. Results show that training on fewer than 3 and 300 features, respectively, for the disease classification and layer assignment task provide performance comparable to the best-performing CNN-based network applied to the full dataset. Our approach, incorporating multi-channel input and Integrated Gradients, can potentially facilitate the clinical translation of Raman spectroscopy-based diagnosis without the need for laborious manual pre-processing and feature selection.

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Negative thermal expansion (NTE) compounds provide a solution for the mismatch of coefficients of thermal expansion in highly integrated device design. However, the current NTE compounds are rare, and how to effectively design new NTE compounds is still challenging. Here, a new concept is proposed to design NTE compounds, that is, to increase the flexibility of framework structure by expanding the space in framework structure compounds. Taking the parent compound NaZr2(PO4)3 as a case, a new NTE system AIBIICIII(MoO4)3 (A = Li, Na, K, and Rb; B = Mg and Mn; C = Sc, In, and Lu) is designed. In these compounds, the large volume of MoO4 tetrahedron is used to replace the small volume of PO4 tetrahedron in NaZr2(PO4)3 to enhance structural space and NTE performance. Simultaneously, a joint study of temperature-dependent X-ray diffraction, Raman spectroscopy, and the first principles calculation reveals that the NTE in AIBIICIII(MoO4)3 series compounds arise from the coupled oscillation of polyhedral. Large-radius ions are conducive to enhancing the space and softening the framework structure to achieve the enhancement of NTE. The current strategy for designing NTE compounds is expected to be adopted in other compounds to obtain more NTE compounds.

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The symbiotic relationship between corals and their associated microorganisms is crucial for the health of coral reef eco-environmental systems. Recently, there has been a growing interest in unraveling how the manipulation of symbiont nutrient cycling affects the stress tolerance in the holobiont of coral reefs. However, most studies have primarily focused on coral-Symbiodiniaceae-bacterial interactions as a whole, neglecting the interactions between Symbiodiniaceae and bacteria, which remain largely unexplored. In this study, we proposed a hypothesis that there exists an inner symbiotic loop of Symbiodiniaceae and bacteria within the coral symbiotic loop. We conducted experiments to demonstrate how metabolic exchanges between Symbiodiniaceae and bacteria facilitate the nutritional supply necessary for cellular growth. It was seen that the beneficial bacterium, Ruegeria sp., supplied a nitrogen source to the Symbiodiniaceae strain Durusdinium sp., allowing this dinoflagellate to thrive in a nitrogen-free medium. The Ruegeria sp.-Durusdinium sp. interaction was confirmed through 15N-stable isotope probing-single cell Raman spectroscopy, in which 15N infiltrated into the bacterial cells for intracellular metabolism, and eventually the labeled nitrogen source was traced within the macromolecules of Symbiodiniaceae cells. The investigation into Symbiodiniaceae loop interactions validates our hypothesis and contributes to a comprehensive understanding of the intricate coral holobiont. These findings have the potential to enhance the health of coral reefs in the face of global climate change.

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Cangjie Temple was built to commemorate Cangjie, the legendary inventor of Chinese characters. It stands as one of the few remaining temples in China dedicated to the invention and creation of writing. In this study, the material properties of wooden paintings from the Cangjie temple were characterized using Polarized Light Microscopy (PLM), Scanning Electron Microscopy coupled with Energy Dispersive X-ray Spectroscopy (SEM-EDS), Micro-confocal Raman Spectroscopy, X-ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and Pyrolysis-Gas Chromatography-Mass Spectrometry (Py-GC/MS). It was confirmed that the pigments of the paintings included cinnabar, lapis lazuli, lead white, Paris green, and carbon black. The proteinaceous glue was used as an adhesive in the pigment samples, with tung oil likely being utilized as a primer for the wooden structures before painting. This study not only provides valuable data support for the conservation and restoration of the architectural features of Cangjie Temple but also provides useful reference for the maintenance and inheritance of similar ancient buildings.

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This study presents a quantum chemical investigation into the structural analysis and calculated Raman spectra of modeled amylose with varying units of linked glucose molecules. We systematically examined the rotation of hydroxymethyl groups and intramolecular hydrogen bonds within these amylose models. Our study found that as the number of linked glucose units increases, the linear structure becomes more complex, resulting in curled, cyclic, or helical structures facilitated by establishing various intramolecular interactions. The hydroxymethyl groups were confirmed to form interactions with oxygen atoms and with hydroxymethyl and hydroxyl groups from adjacent rings in the molecular structures. We identified distinct peaks and selected specific bands applicable in various analytical contexts by comparing their calculated Raman spectra. Representative vibrational modes within selected regions were identified across the different lengths of amylose models, serving as characteristic signatures for linear and more coiled structural conformations. Our findings contribute to a deeper understanding of amylose structures and spectroscopic signatures, with implications for theoretical studies and potential applications. This work provides valuable reference points for the detailed assignment of Raman peaks of amylose structure, facilitating their application in broader research on carbohydrate structures and their associated spectroscopic properties.

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The Raman spectra, x-ray photoelectron spectroscopy (XPS), and x-ray excited luminescence spectra of crystalline quartz samples subjected to different pressure levels through detonation were compared with the spectra of the original samples. In the samples under study, the luminescence of a self-trapped exciton (STE) was analyzed, which, when excited by x-rays, has a high energy yield (∼20%) in crystallineα-quartz not treated by detonation. The deviations of the luminescence spectrum are small in the pressure range from 9 to 27 GPa relative to untreated samples, which means the presence of crystalline quartz grains. A sharp change in the spectrum occurs for the sample subjected to a pressure of 34 GPa. The STE band practically disappeared and a band appeared at 350 nm. This band appears in thermally stimulated luminescence (in contrast to the STE band, which is not observed at all in thermally stimulated luminescence) and, therefore, can be attributed to some defects arising due to high pressure. This luminescence is not similar to the luminescence of a stishovite single crystal, but analysis of the XPS spectra suggests the formation of non-crystalline stishovite in detonated samples. In the Raman scattering spectra, a single sharp line at 465 cm-1, characteristic ofα-quartz, was observed in the samples after detonation pressure for the remaining small crystal grains. This line decreased greatly for the sample subjected to a pressure of 34 GPa. Against the structureless background of exposed samples of 'poor' optical quality, other Raman bands did not appear. It can be assumed that there are very broad bands of Raman scattering caused by the amorphization of stishovite under high detonation pressure. Amorphization explains the absence of luminescence, similar to the stishovite crystal.

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Azospirillum brasilense is a non-photosynthetic α-Proteobacteria, belongs to the family of Rhodospirillaceae and produces carotenoids to protect itself from photooxidative stress. In this study, we have used Resonance Raman Spectra to show similarity of bacterioruberins of Halobacterium salinarum to that of A. brasilense Cd. To navigate the role of genes involved in carotenoid biosynthesis, we used mutational analysis to inactivate putative genes predicted to be involved in carotenoid biosynthesis in A. brasilense Cd. We have shown that HpnCED enzymes are involved in the biosynthesis of squalene (C30), which is required for the synthesis of carotenoids in A. brasilense Cd. We also found that CrtI and CrtP desaturases were involved in the transformation of colorless squalene into the pink-pigmented carotenoids. This study elucidates role of some genes which constitute very pivotal role in biosynthetic pathway of carotenoid in A. brasilense Cd.

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The identification of mixed solutions is a challenging and important subject in chemical analysis. In this paper, we propose a novel workflow that enables rapid qualitative and quantitative detection of mixed solutions. We use a methanol-ethanol mixed solution as an example to demonstrate the superiority of this workflow. The workflow includes the following steps: (1) converting Raman spectra into Raman images through CWT; (2) using MobileNetV3 as the backbone network, improved multi-label and multi-channel synchronization enables simultaneous prediction of multiple mixture concentrations; and (3) using transfer learning and multi-stage training strategies for training to achieve accurate quantitative analysis. We compare six traditional machine learning algorithms and two deep learning models to evaluate the performance of our new method. The experimental results show that our model has achieved good prediction results when predicting the concentration of methanol and ethanol, and the coefficient of determination R2 is greater than 0.999. At different concentrations, both MAPE and RSD outperform other models, which demonstrates that our workflow has outstanding analytical capabilities. Importantly, we have solved the problem that current quantitative analysis algorithms for Raman spectroscopy are almost unable to accurately predict the concentration of multiple substances simultaneously. In conclusion, it is foreseeable that this non-destructive, automated, and highly accurate workflow can further advance Raman spectroscopy.

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The tunability of the transition metal dichalcogenide properties has gained attention from numerous researchers due to their wide application in various fields including quantum technology. In the present work, WS2has been deposited on fluorine doped tin oxide substrate and its properties have been studied systematically. These samples were irradiated using gamma radiation for various doses, and the effect on structural, morphological, optical and electrical properties has been reported. The crystallinity of the material is observed to be decreased, and the results are well supported by x-ray diffraction, Raman spectroscopy techniques. The increase in grain boundaries has been supported by the agglomeration observed in the scanning electron microscopy micrographs. The XPS results of WS2after gamma irradiation show evolution of oxygen, carbon, C=O, W-O and SO4-2peaks, confirming the addition of impurities and formation of point defect. The gamma irradiation creates point defects, and their density increases considerably with increasing gamma dosage. These defects crucially altered the structural, optical and electrical properties of the material. The reduction in the optical band gap with increased gamma irradiation is evident from the absorption spectra and respective Tauc plots. TheI-Vgraphs show a 1000-fold increase in the saturation current after 100 kGy gamma irradiation dose. This work has explored the gamma irradiation effect on the WS2and suggests substantial modification in the material and enhancement in electrical properties.