Invertebrate vision relies on bistable visual pigments flipping upon photon absorption between rhodopsin and metarhodopsin states. In living butterflies, the UV-VIS absorption spectra of rhodopsin and metarhodopsin, respectively with 11-cis and all-trans isomers of 3-hydroxy-retinal (A3) chromophore, can be conveniently recorded from the eyeshine, the light reflected from the compound eye after passing twice through the light-guiding rhabdoms. * Here, a comprehensive pipeline for the analysis of eyeshine reflection spectra is presented. A microscope coupled with a broadband LED source and a microspectrometer was used to record the photorelaxations. Fitting temporal exponential relaxations to log-reflectance arrays yielded transient and baseline spectra that are analogous to absorbance difference and sum, respectively. Both types of spectra were subjected to singular value decomposition and to fitting of templated visual pigment absorption spectra. * The compound eye of the high brown fritillary Fabriciana adippe (Nympahlidae) was exposed to a series of second-long broadband light pulses, causing photorelaxations with time constants between 40 and 120 ms that led to 80% metarhodopsin in equilibrium. The transient and baseline spectra were fitted with pigment templates, estimating the alpha peak wavelength 547-552 nm for rhodopsin and 496-501 nm for metarhodopsin. The metarhodopsin to rhodopsin alpha peak absorbance ratio 1.25-1.35 is consistent with the isosbestic wavelength at 530 nm. The second isosbestic wavelength indicates that rhodopsin beta (UV) peak absorbs more strongly than metarhodopsin below 405 nm. * Baseline spectra, which were not explicitly analysed in previous studies, enable concatenation of exposures, monitor long-term changes of pigment, and enhance the estimation of beta peak parameters. * The method can be directly used in many butterflies and could be adapted to other insects, particularly fruitflies, facilitating studies of the relation between the visual pigment spectra and the opsin sequences. Spectroscopic results can be complemented with physiologically measured photoreceptor spectral sensitivity datasets and analysed with the same global fitting procedure.

Research on sonoluminescence spectroscopy was continued with a focus on determining the content of the main substance in natural underground brines and technological solutions with high salt concentrations (350-1000 g·dm[-][3]) using high-frequency ultrasound (44-50 MHz) to initiate sonoluminescence. The influence of the initiating ultrasound frequency on the sonoluminescence intensity of CsCl, LiCl, and NaCl was determined. The maximum sonoluminescence intensity for alkali metal chlorides was achieved at an ultrasound frequency of 48-49 MHz and an intensity of 32 W·cm[-][2].A method for determining Cs, Li, and Na content in highly concentrated technological solutions using high-frequency ultrasound to initiate sonoluminescence was developed. The method's validity was verified by analyzing the same samples using sonoluminescence spectroscopy with low- and mid-frequency ultrasound initiation.The simultaneous effect of high-frequency (45-50 MHz) and low-frequency (18-23 kHz) ultrasound in determining macroimpurities (KCl and LiCl) in NaCl brines using sonoluminescence spectroscopy was studied.When using the simultaneous action of high-frequency (44-50 MHz) and low-frequency ultrasound (18-23 kHz), the lower limit of determination of KCl and LiCl content in brines decreases by 12-15 times compared to the use of ultrahigh and low-frequency ultrasound and amounts to 0.007 and 0.008 g·dm[-3], respectively. The best results were obtained with the simultaneous effect of high-frequency ultrasound of 47-48 MHz with an intensity of 29-30 W cm[-2] and low-frequency ultrasound of 19-22 kHz with an intensity of 1.3-1.5 W cm[-2].

This review presents recent advances in integrated vibrational and X-ray spectroscopic approaches for the characterization of cultural heritage materials. Vibrational techniques, including Raman, Fourier transform infrared (FT-IR), and optical photothermal infrared (O-PTIR) spectroscopy, together with X-ray fluorescence, diffraction, and absorption methods, provide complementary molecular, elemental, and structural information for the analysis of pigments, binders, and degradation products. Emphasis is placed on analytical capabilities, limitations, and the synergistic use of these techniques within multimodal workflows.Vibrational spectroscopy plays a central role in molecular identification and monitoring chemical transformations, whereas X-ray methods provide insight into elemental composition, crystallography, and pigment alteration mechanisms. Their integration establishes a robust framework for materials identification, stratigraphic interpretation, and investigation of degradation pathways in historical objects.Representative case studies discussed in this review illustrate how integrated vibrational and X-ray spectroscopic strategies address key analytical challenges in cultural heritage research, including the characterization of lead-based materials in works attributed to Leonardo da Vinci, compositional layering in paintings by Pablo Picasso, and cadmium yellow degradation in masterpieces by Edvard Munch and Vincent van Gogh. O-PTIR is highlighted as an emerging tool for submicron analysis, while combined Raman and X-Ray spectoscopic methods resolve environmentally driven pigment transformations.This review outlines current advances, practical challenges, and future directions in spectroscopy-driven research on complex materials.

Absorbance spectroelectrochemistry is a powerful in situ technique utilized to investigate redox changes and charge carrier properties in electronic materials, such as mixed ionic-electronic conducting polymers. In this method, the absorption spectrum of the material is obtained as a function of applied potential, and the results are used to understand its electrochemical behavior. The standard approach uses chronoamperometry, in which a constant potential is applied, and an absorption spectrum is measured at electrochemical equilibrium for a series of potentials selected across the electrochemical window of the sample. However, this method is limited by slow, labor-intensive data acquisition and lack of time-resolved absorbance information. Herein, we describe an automated absorbance spectroelectrochemical method that couples a high-speed, broadband optical absorbance instrument with normal pulse voltammetry to capture full absorbance spectroelectrochemistry spectra in a single measurement. The instrument measures absorption spectra over a 400 - 2500 nm window at a maximum spectral acquisition rate of 200 Hz (5 ms response time). We demonstrated the capability of this spectroscopy technique to automatically obtain a full set of potential-dependent absorbance spectra and absorbance transients by applying it to study the electrochemical charging behavior of a polythiophene electrode, which was time-resolved at 1 spectrum/s.

Q355C steel is a critical structural material widely used in offshore wind power facilities, including steel piles and tower cylinders, corrosion caused by marine salt spray poses a serious threat to long term structural integrity. In this study, laser-induced breakdown spectroscopy (LIBS) was employed to assess the corroded characteristics of Q355C steel. A field-deployable LIBS system based on two-dimensional scanning was developed for surface analysis. A quantitative calibration curve was established between the surface salt density of uncorroded Q355C steel and the sodium spectral intensity, achieving a high coefficient of determination (R[2]=0.94). Subsequently, LIBS measurements were performed on simulated corrosion specimens, revealing variations in elemental distribution within the corrosion zone with depth. Since surface corrosion products primarily consist of iron oxide whose thickness varies with corrosion severity, the ratio of manganese to iron spectral intensities (IMn/IFe) was identified as a potential indicator for distinguishing corrosion severity. By exploiting the spectral differences between genuine corrosion areas and pseudo-corrosion areas (areas covered by flow rust), accurate differentiation between the two was achieved. The accuracy of the LIBS binarization method was approximately 24% higher than that of traditional visual methods.

This work utilized terahertz time-domain spectroscopy (THz-TDS) and Fourier Transform Infrared (FTIR) spectroscopy to characterize epoxy blends. The epoxy blends were prepared by blending a virgin DER-332/LC-100 epoxy system with Recovered Epoxy Polymer (REP) at 1-10 wt%, and were compared with references of virgin epoxy and REP. The REP was obtained via peracetic acid digestion of Carbon Fibre Reinforced Polymer (CFRP), followed by purification. Using THz-TDS amplitude-phase analysis, five optical parameters were obtained: refractive index (n), extinction coefficient (κ), absorption coefficient (α), and the real and imaginary of the dielectric constant (ε' and ε″). Under ambient conditions, these parameters extracted from THz-TDS were unable to distinguish low REP content 0-10% REP but were able to clearly differentiate 100% REP from all other epoxy blends. Compared to the other blends, 100% REP shows markedly increased n, κ, α, ε', and ε″, attributed to oxidation, formation of polar groups, chain scission and microvoid-induced scattering. To distinguish low REP-content blends using THz-TDS, external perturbation was applied. Under perturbation, linear concentration-dependent trends in Δn, Δκ, Δα, Δε' and Δε'' were observed. The external perturbation enhanced the interaction between the samples and the THz wave, likely due to amplification of dipole relaxation. Additionally, the transmission spectra (3500-7500 cm[-][1]) obtained from FTIR increased as the REP content increased from 0-10%. This concentration dependent trend can be attributed to partial network dilution and reduced overtone absorption. However, the transmission of 100% REP showed strong attenuation due to structural disorder and enhanced scattering.

As a traditional Chinese medicinal material, the quality of Poria cocos (PC) significantly affects its pharmacological efficacy. However, adulteration of PC in the market has been frequently observed. Adulterated PC not only compromises its medicinal value but also poses potential risks to public health. Conventional identification methods are often labor-intensive, destructive, and insufficiently sensitive to complex adulteration scenarios. In this study, a rapid and nondestructive strategy for both qualitative and quantitative analysis of PC adulteration is developed by integrating near-infrared (NIR) two-dimensional correlation spectroscopy (2DCOS) with deep learning algorithms. NIR spectra in the range of 960-1600 nm are collected from pure PC and samples adulterated with 5 common adulterants at concentration gradients of 5%. Specifically, to enhance spectral resolution, a systematic 2DCOS dataset is established by combining 5 representative spectral preprocessing methods with 2 types of 2DCOS. Furthermore, a bidirectional long short-term memory network with an attention mechanism (BiLSTM-Attn) is proposed to fully exploit the sequential dependency of correlation features embedded in 2DCOS maps. Compared with experiments involving convolutional neural network (CNN), gated recurrent unit (GRU), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) models, the proposed BiLSTM-Attn achieved 100% classification accuracy and superior regression performance, with a mean coefficient of determination (R[2]) of 0.9920, a low root mean squared error (RMSE) of 2.41, and a high mean residual predictive deviation (RPD) of 12.85. Extended experiments demonstrate that the combination of NIR 2DCOS and deep learning algorithms enables accurate and robust detection of PC adulteration. The proposed framework offers valuable support for quality control in traditional Chinese medicine, and has a wide range of applications.

A highly sensitive method has been developed for the determination of bisphenol A (BPA) in beverages (juice, beer, carbonated soft drinks) using surface-enhanced Raman spectroscopy (SERS) coupled with solid-phase extraction (SPE), in situ synthesis of silver nanoparticles (AgNPs), and surface modification with tetraoctylammonium bromide (TNOAB). SPE showed high BPA recovery rates (>90%) from complex food matrices. The crucial factor for achieving maximum sensitivity was the in situ synthesis of AgNPs directly in the presence of the analyte, as the use of pre-synthesized nanoparticles led to an approximately 100-fold decrease in signal intensity. Investigation of cationic modifiers with different structures revealed that the efficiency of SERS signal enhancement correlates with alkyl chain length. TNOAB (C8) provided a 7-fold increase in signal compared to the unmodified system, significantly outperforming the effects of tetrabutylammonium bromide (C4, 1.7-fold enhancement) and triethylamine (C2, 1.3-fold enhancement). The limit of detection for BPA was 0.077 ppb with a signal enhancement factor of 1.87×10[8]. For quantitative analysis, a PLS calibration model was applied in the range of 1-100 ppb (R[2] = 0.9986, RMSEC = 1.75 ppb, RMSEP = 1.95 ppb). The simplicity of the developed approach makes it suitable for routine monitoring of BPA content in beverages.

Near-Infrared Spectroscopy (NIRS) can directly provide product quality information at the molecular level, such as concentration, viscosity, etc. However, NIRS is overly sensitive to environmental noise. Therefore, how to make full use of the information of process variables to enhance the robustness of NIR modeling is the key. For this purpose, this paper presents a multi-level fusion model based on Attention Mechanism (AM). First, two-branch Convolutional Neural Network (CNN) is introduced to extract the feature of NIRS. Then, a fusion block based on AM is designed, which consists a cross-attention operation, a residual connection, and a Multi-layer Perceptron (MLP). The residual connection weights and integrates features of NIRS and process variables according to the cross-attention distribution, while the MLP further combines the features in the feature dimension. Additionally, considering different information exchanged ways of fused features at each level of two-stream CNN, two different fusion schemes are designed namely central and shared strategies. Experiments on the 2,6-dimethylphenol distillation process datasets demonstrate that proposed method outperforms some existing methods.

The X-ray fluorescence (XRF) spectroscopy is widely used in mining for elemental analysis, but it cannot detect lithium (Li) due to its low atomic number. The low sensitivity and high detection limits of conventional laser-induced breakdown spectroscopy (LIBS) also present serious challenges for analyzing lithium ore. This work demonstrates improved analytical capabilities of LIBS by using an external electric field to detect Li and other trace elements in Li ore. A notable increase in emission intensity and detection sensitivity for all trace elements has been observed in electric field-assisted LIBS (EF-LIBS) compared to conventional LIBS. Significant changes in plasma parameters, such as stabilized electron density and an extended plasma lifespan (∼10 μs), were achieved by applying the electric field, resulting in better spectral signals. The most impressive improvement was an eightfold increase in the detection limit for lithium, which decreased from 46.07 ppm (traditional LIBS) to 5.93 ppm (EF-LIBS). Increased excitation efficiency under the electric field and a longer plasma plume lifetime are responsible for this enhancement. For lithium exploration and mining, EF-LIBS offers a better alternative to traditional methods due to its field-deployable design and low sample requirements, providing increased sensitivity and operational advantages without the need for additional complex equipment.

The combination of laser-induced breakdown spectroscopy (LIBS) and machine learning provides a rapid approach for coal ash quantification. However, in practical coal preparation plants, calibration samples are often limited, and ash contents are usually confined to a narrow range, making reliable quantitative modeling difficult. To address this issue, we propose a structured spectral in-context learning framework for small-sample coal ash quantification. Unlike conventional methods that rely on task-specific iterative parameter optimization, the proposed framework performs downstream inference by conditioning on a limited set of calibration samples using a pre-trained tabular foundation model. To adapt continuous high-dimensional LIBS spectra to this framework, segmented statistical descriptors and multi-scale Haar-like features were constructed to capture subtle local spectral variations associated with narrow ash-content changes. The experimental results show that the proposed SS-ICL framework achieves robust predictive performance under restricted conditions, with an R[2] of 0.8880 and an RMSE of 0.1953 in a narrow-range washed-coal scenario. These results demonstrate that the proposed framework provides a practical and robust strategy for on-site LIBS-based coal quality analysis in restricted industrial environments.

Layered mineral composites may exhibit incidence-side-dependent terahertz responses because interfacial reflection and unequal optical paths modify wave propagation through the structure. In this study, terahertz time-domain spectroscopy (THz-TDS) was employed to investigate the effective refractive index response of opal-chlorite bilayer composites. The results show that the refractive index difference between opposite incidence sides decreases and gradually approaches a stable level with increasing chlorite thickness. TMM inversion retrieved the chlorite refractive index spectrum in good agreement with the directly measured result. Proton irradiation applied from the opal side enhanced the asymmetry mainly in the thinnest sample, whereas this effect was rapidly suppressed as the chlorite thickness increased. In addition, the measurements under the investigated environmental conditions showed that the refractive index difference remained comparatively stable. These results provide an experimental basis for analyzing incidence-side-dependent terahertz responses in layered mineral bilayers.

A divide-and-conquer approach is presented that helps undergraduate students interpret organic infrared-spectra. In this method, DivnConqIR, students deal with the confusion of a complicated spectrum by focusing first on the carbonyl band at ∼1720 cm[-1] near the middle of the spectrum. When present, its position and related bands can be analyzed to determine the specific functional group to which the carbonyl belongs; in its absence, students eliminate many functional groups. In either case, students begin spectral interpretation with significant accomplishment. This strategy divides the spectrum again, now at 3000 cm[-1], to define the backbone of the organic molecule and determine the possible presence of alkenes, alcohols, amines, or terminal alkynes. Students divide the spectrum again, now at 1375 cm[-1], to confirm the presence of methyl group(s) with the methyl umbrella band or, if present, isopropyl or tert-butyl doublet bands for further connectivity. Students divide the spectrum again, now at 1000 cm[-1], to search for the presence of one or more C-O stretches and to confirm the presence of alcohols, carboxylic acids, esters, or anhydrides, or to discover ethers. Lastly, students use DivnConqIR to examine unaccounted bands to identify other useful functional groups that may be present. Students interpret IR spectra without confusion and determine functional groups effectively; they integrate functional groups into possible complete structures. Qualitative and quantitative student performance data are offered to suggest the effectiveness of this interpretive approach, and DivnConqIR is compared to other IR interpretation methods to show that it is unique and most comprehensive. Many worked examples are presented, and additional resources are provided.

This review is concerned with our recent four papers on vibrational spectroscopy studies of water. The four papers had dual purposes. One was to deepen the understanding of IR, Raman and NIR spectra of water including the low-frequency regions of IR (far-infrared; FIR) and Raman spectra, and another was to explore the structure of water and water hydrogen-bonding networks. In this review we put emphasis on the former. Particularly, the importance of 2D-COS in the spectral analysis was explained in detail. In the studies of low-frequency region of IR spectra, 2D-COS revealed that there are two or three bands assignable to libration L2 modes of tetrahedron structure (THS) and pseudo-tetrahedron structure (PTHS). Bast on these observations we concluded that there are two or three water hydrogen bonding networks with THS or PTHS species. In the NIR study of water 2D-COS was used to construct the relations between PTHS and THS bands in the NIR spectra an those in the IR spectra. 2D-COS was also useful to determine the sequence order of the events occurring during temperature increase.