Spectral Intensity Research Articles

Effects of H2/CO ratio and CO2 dilution on the explosion behavior and flame evolution of syngas/air mixtures

Syngas, which is used as both a fuel and a raw material, presents numerous safety risks during its utilization. In this paper, the effects of H2/CO ratio (R = 0%, 30%, 50%, 70% and 100%) and CO2 dilution ([CO2] = 0%–20%) on the explosion behaviors of syngas/air mixtures with different equivalence ratios (φ = 0.8, 1.0, 1.6, and 2.5) are examined via experiments and numerical calculations. The results show that the peaks of explosion pressure and OH* spectral intensity are significantly influenced by the H2 percentage in the syngas. The introduction of CO2 notably decreases these parameters in oxygen-rich and severe oxygen-poor states. Additionally, the flame propagation speed noticeably increases with increasing H2 proportion in the syngas. The influence of CO2 on the buoyancy instability of the spherical flame of syngas is considered negligible when compared to that of other combustible gases, such as CH4. Nevertheless, the presence of CO2 decreases the density ratio between the unburned gas and burned gas and increases the flame thickness of syngas/air mixtures. This trend reduces the cellular instability of the spherical flame, particularly in severe oxygen-poor states. Notably, there is a definite correlation between the formation of cellular flames and the rise rate of explosion pressure. The cellular structure on the flame surface becomes more pronounced when the rise rate of explosion pressure increases. Conversely, it develops later or is noticeably inhibited. Furthermore, the main reaction pathways are H2 → ∙OH → H2O2 → H2O and H2 → ∙OH → CO → CO2 during the syngas explosion process. ∙OH is critical in the entire chain reaction because it promotes the conversion of H2 to H2O and acts as a vital connection between the reactants H2 and CO in the explosion process.

Effect of single and double pulse laser-induced breakdown spectroscopy towards steel alloy in different gaseous media

In recent years, laser-induced breakdown spectroscopy (LIBS) technique showed its promising for material analysis, quality control, forensic science, and environmental safety monitoring. Besides, due to its rapid measurement capability and minimal sample-preparation requirements, it is a widely recognized elemental analysis approach used in a variety of study domains. The current work compares spatially resolved single (SP) and double pulse (DP), especially orthogonal DP, setups for LIBS to examine and enhance the limit of detection (LOD) in duplex stainless steels alloy. Using the Boltzmann plot method and the Stark broadening mechanism, the electron temperature and density of local arc plasma are highlighted due to single pulse and orthogonal pre-pulse double pulse configurations. With delay time, the circumstances of arc plasma's local thermodynamic equilibrium (LTE) of emission spectral lines are investigated. The experimental results show that there are significant increases in the spectral intensities of the Fe lines in double pulse LIBS under Ar-gas compared to those under He-gas. Internal standardization was used to create calibration curves for the spectral lines of Cr, Cu, and Mn in the alloy sample for the both studied media. It demonstrated that argon outperforms helium in determining the elemental composition of steel alloys. Furthermore, LOD for SP-LIBS and DP-LIBS in both media were calculated, which showed a higher detection LOD for DP-LIBS in the presence of Ar-gas compared to that in He-gas.

On-line quantitative analysis of major elements in phosphate slurry using LIBS assisted by plasma information from orthogonal directions imaging

To solve the lag of chemical element information feedback and realize the on-line analysis of phosphorite flotation, LIBS was used to measure major elements in the phosphate slurry. Plasma images in two orthogonal directions were captured synchronously to describe the irregular plasma. The plasma image projections in different directions were calculated as the features. A spectral correction method with these features was proposed to solve the problems of large signal fluctuations and poor measurement accuracy. The proposed method was first applied in the filed application of phosphate slurry on-line analysis. The RSDs of the characteristic spectral intensity decreased from more than 20 % to less than 3 %, improving the stability of spectra significantly. The method was further combined with the calibration of the concentrations of P2O5, MgO and SiO2. The R2 and MAE of P2O5 were 0.99 and 0.5 %, respectively, meeting the accuracy requirements of online measurements.

Adaptive Shadow Compensation Method in Hyperspectral Images via Multi-Exposure Fusion and Edge Fusion

Shadows in hyperspectral images lead to reduced spectral intensity and changes in spectral characteristics, significantly hindering analysis and applications. However, current shadow compensation methods face the issue of nonlinear attenuation at different wavelengths and unnatural transitions at the shadow boundary. To address these challenges, we propose a two-stage shadow compensation method based on multi-exposure fusion and edge fusion. Initially, shadow regions are identified through color space conversion and an adaptive threshold. The first stage utilizes multi-exposure, generating a series of exposure images through adaptive exposure coefficients that reflect spatial shadow intensity variations. Fusion weights for exposure images are determined based on exposure, contrast, and spectral variance. Then, the exposure sequence and fusion weights are constructed as Laplacian pyramids and Gaussian pyramids, respectively, to obtain a weighted fused exposure sequence. In the second stage, the previously identified shadow regions are smoothly reintegrated into the original image using edge fusion based on the p-Laplacian operator. To further validate the effectiveness and spectral fidelity of our method, we introduce a new hyperspectral image dataset. Experimental results on the public dataset and proposed dataset demonstrate that our method surpasses other mainstream shadow compensation methods.

Direct comparison of different protocols to obtain surface enhanced Raman spectra of human serum

Label-free Surface Enhanced Raman Spectroscopy (SERS) is a rapid technique that has been extensively applied in clinical diagnosis and biomedicine for the analysis of biofluids. The purpose of this approach relies on the ability to detect specific “metabolic fingerprints” of complex biological samples, but the full potential of this technique in diagnostics is yet to be exploited, mainly because of the lack of common analytical protocols for sample preparation and analysis. Variation of experimental parameters, such as substrate type, laser wavelength and sample processing can greatly influence spectral patterns, making results from different research groups difficult to compare. This study aims at making a step toward a standardization of the protocols in the analysis of human serum samples with Ag nanoparticles, by directly comparing the SERS spectra obtained from five different methods in which parameters like laser power, nanoparticle concentration, incubation/deproteinization steps and type of substrate used vary. Two protocols are the most used in the literature, and the other three are “in-house” protocols proposed by our group; all of them are employed to analyze the same human serum sample. The experimental results show that all protocols yield spectra that share the same overall spectral pattern, conveying the same biochemical information, but they significantly differ in terms of overall spectral intensity, repeatability, and preparation steps of the sample. A Principal Component Analysis (PCA) was performed revealing that protocol 3 and protocol 1 have the least variability in the dataset, while protocol 2 and 4 are the least repeatable.

A liquid lever sensor based on an abrupt taper and micro‐arc Michelson interferometer in single‐mode fiber

AbstractA novel two‐parameter sensor is proposed based on the abrupt taper and micro‐arc Michelson interferometer (MI) in single‐mode fiber. When there is a regular change in the liquid level or temperature, the phase difference changes, and the spectral intensity or wavelength of the interfering valleys of this sensor is directionally shifted. This study enriches the construction of MI and provides a new perspective on liquid level measurement. We experimentally explored the sensor's sensing performance over temperature and liquid level: this sensor's temperature sensitivity and liquid level sensitivity were up to 62.9 pm/°C and 0.2896 dB/mm, respectively. In addition, this sensor is characterized by compactness, simplicity, low cost, and high reliability, and it is suitable for measurement of liquid level and temperature, expanding the application field of MI.

Preparation and Study of Structural, Optical and Electrical Properties of the Composite Films [PVA-PEO-MnCl2.4H2O

In the present paper, pure polymeric blend film [PVA-PEO] and the polymeric blend films reinforced by MnCl2.4H2O salt with different weight ratios (10, 20, 30, 40, 50 wt %) have been prepared by using the solution casting method. The effects of weight ratio of salt on structural properties for all reinforced polymeric blends films were studied. FTIR spectra were used to establish the interaction between the polymer (PVA) and the polymer (PEO) reinforced by MnCl2.4H2O salt, which causes some changes in the vibration modes and the position of the bonds. All inrestigated samples exhibited broad bonds at around (3310 cm-1). The results showed the asymmetric vibration of O-H group in the polymers matrix. The peaks around (2880 cm-1) can be attributed to the presence of C- H stretching vibration group. The C=C stretching vibration appears in the bonds at (1955 cm-1). The peak at (1096 cm-1) for all reinforced films is attributed to the stretching mode of C-O-C stretching. There is a shift of out-of-plane rings C-H bending vibration from 961.2 to 955 cm-1. These indicate the chemical interactions of MnCl2.4H2O salt with the polymeric blend [PVA-PEO]. The two strong bonds observed around (1471 cm-1 and 844 cm-1) are assigned to the bending and stretching vibration of the CH2 group, respectively. The change in the spectral intensities of polymeric blend [PVA-PEO] involves a shift in some bonds due to the MnCl2.4H2O salt addition. The effects of weight ratio of salt on optical properties for all reinforced polymeric blends films were studied, as the transmittance and absorbance spectra were recorded within the range of wavelengths (190-1100) nm, and it was found that the energy gap value decreases with an increase of the added salt’s weight ratio. Results showed that the indirect electronic transitions are allowed. Effects of salt’s weight ratio on electrical (dielectrical) properties for all reinforced polymeric blends films were studied, and the practical results showed a decrease in the dielectric constant (ε΄) with an increase in frequency for all polymeric blends films, while the practical results also showed an increase in the alternating electrical conductivity (σa.c) with increasing frequency for all polymeric blends films, as well as an increase in both the dielectric constant and alternating electrical conductivity with an increase in the added salt’s weight ratio at same frequency.

Combining LiDAR and Spaceborne Multispectral Data for Mapping Successional Forest Stages in Subtropical Forests

The Brazilian Atlantic Rainforest presents great diversity of flora and stand structures, making it difficult for traditional forest inventories to collect reliable and recurrent information to classify forest succession stages. In recent years, remote sensing data have been explored to save time and effort in classifying successional forest stages. However, there is a need to understand if any of these sensors stand out for this purpose. Here, we evaluate the use of multispectral satellite data from four different platforms (CBERS-4A, Landsat-8/OLI, PlanetScope, and Sentinel-2) and airborne light detection and ranging (LiDAR) to classify three forest succession stages in a subtropical ombrophilous mixed forest located in southern Brazil. Different features extracted from multispectral and LiDAR data, such as spectral bands, vegetation indices, texture features, and the canopy height model (CHM) and LiDAR intensity, were explored using two conventional machine learning methods such as random trees (RT) and support vector machine (SVM). The statistically based maximum likelihood (MLC) algorithm was also compared. The classification accuracy was evaluated by generating a confusion matrix and calculating the kappa index and standard deviation based on field measurements and unmanned aerial vehicle (UAV) data. Our results show that the kappa index ranged from 0.48 to 0.95, depending on the chosen dataset and method. The best result was obtained using the SVM algorithm associated with spectral bands, CHM, LiDAR intensity, and vegetation indices, regardless of the sensor. Datasets with Landsat-8 or Sentinel-2 information performed better results than other optical sensors, which may be due to the higher intraclass variability and less spectral bands in CBERS-4A and PlanetScope data. We found that the height information derived from airborne LiDAR and its intensity combined with the multispectral data increased the classification accuracy. However, the results were also satisfactory when using only multispectral data. These results highlight the potential of using freely available satellite information and open-source software to optimize forest inventories and monitoring, enabling a better understanding of forest structure and potentially supporting forest management initiatives and environmental licensing programs.

A theoretical study of the signal enhancement mechanism of coaxial DP-LIBS

In the field of dual-pulse laser-induced breakdown spectroscopy (DP-LIBS) research, the pursuit of methods for determining pulse intervals and other parameters quickly and conveniently in order to achieve optimal spectral signal enhancement is paramount. To aid researchers in identification of optimal signal enhancement conditions and more accurate interpretation of the underlying signal enhancement mechanisms, theoretical simulations of the spatiotemporal processes of coaxial DP-LIBS-induced plasma have been established in this work. Using a model based on laser ablation and two-dimensional axisymmetric fluid dynamics, plasma evolutions during aluminum–magnesium alloy laser ablation under single-pulse and coaxial dual-pulse excitations have been simulated. The influences of factors, such as delay time, laser fluence, plasma temperature, and particle number density, on the DP-LIBS spectral signals are investigated. Under pulse intervals ranging from 50 to 1500 ns, the time evolutions of spectral line intensity, dual-pulse emission enhancement relative to the single-pulse results, laser irradiance, spatial distribution of plasma temperature and species number density, as well as laser irradiance shielded by plasma have been obtained. The study indicates that the main reason behind the radiation signal enhancement in coaxial DP-LIBS-induced plasma is attributed to the increased species number density and plasma temperature caused by the second laser, and it is inferred that the shielding effect of the plasma mainly occurs in the boundary layer of the stagnation point flow over the target surface. This research provides a theoretical basis for experimental research, parameter optimization, and signal enhancement tracing in DP-LIBS.

In situ monitoring of loading and elution processes of cryoprotectants at the single-cell level with Raman spectroscopy

BackgroundThe analysis of cell membrane permeability plays a crucial role in improving the procedures of cell cryopreservation, which will affect the specific parameter settings in loading, removal and cooling processes. However, existing studies have mostly focused on deriving permeability parameters through osmotic theoretical models and cell volume response analysis, and there is still a lack of the direct experimental evidence and analysis at the single-cell level regarding the migration of cryoprotectants. ResultsIn this work, a side perfusion microfluidics chips combined with Raman spectroscopy system was built to monitor in situ the Raman spectroscopy of extracellular and intracellular solution during loading and elution process with different cryoprotectant solution systems (single and dual component). And it was found that loading a high concentration cryoprotectant solution system through a single elution cycle may result in significant residual protective agent, which can be mitigated by employing a multi-component formula but multiple elution operations are still necessary. Furthermore, the collected spectral signals were marked and analyzed to was perform preliminary relative quantitative analysis. The results showed that the intracellular concentration changes can be accurately quantified by the Raman spectrum and are closely related to the extracellular solution concentration changes. Significance and noveltyBy using the method of small flow perfusion (≤20 μL/min) in the side microfluidic chip after the gravity sedimentation of cells, the continuous loading and elution process of different cryoprotectants on chip and the spectral acquisition can be realized. The intracellular and extracellular concentrations can be quantified in situ based on the ratio of spectral peak intensities. These results indicate that spectroscopic analysis can be used to effectively monitor intracellular cryoprotectant residues.

Large-cage occupation and quantum dynamics of hydrogen molecules in sII clathrate hydrates.

Hydrogen clathrate hydrates are ice-like crystalline substances in which hydrogen molecules are trapped inside polyhedral cages formed by the water molecules. Small cages can host only a single H2 molecule, while each large cage can be occupied by up to four H2 molecules. Here, we present a neutron scattering study on the structure of the sII hydrogen clathrate hydrate and on the low-temperature dynamics of the hydrogen molecules trapped in its large cages, as a function of the gas content in the samples. We observe spectral features at low energy transfer (between 1 and 3meV), and we show that they can be successfully assigned to the rattling motion of a single hydrogen molecule occupying a large water cage. These inelastic bands remarkably lose their intensity with increasing the hydrogen filling, consistently with the fact that the probability of single occupation (as opposed to multiple occupation) increases as the hydrogen content in the sample gets lower. The spectral intensity of the H2 rattling bands is studied as a function of the momentum transfer for partially emptied samples and compared with three distinct quantum models for a single H2 molecule in a large cage: (i) the exact solution of the Schrödinger equation for a well-assessed semiempirical force field, (ii) a particle trapped in a rigid sphere, and (iii) an isotropic three-dimensional harmonic oscillator. The first model provides good agreement between calculations and experimental data, while the last two only reproduce their qualitative trend. Finally, the radial wavefunctions of the three aforementioned models, as well as their potential surfaces, are presented and discussed.

Correction of self-absorption effect in laser-induced breakdown spectroscopy based on temperature iteration

Laser-induced breakdown spectroscopy (LIBS) is an ideal real-time on-line method for the detection of minor elements in alloy steel, but it needs to solve the undesired self-absorption effect in plasma. Since the plasma electron temperature (T), radiation particle number density and absorption path length (Nl) determine the degree of self-absorption and affect the corrected spectral line intensity, a new temperature iterative self-absorption correction method based on the plasma thermal equilibrium radiation model is proposed to continuously calculate and correct these two parameters. Compared with generally applied self-absorption correction methods, this method has obvious advantages of simple programming, high computational efficiency, and independence of the availability or accuracy of Stark broadening coefficients. The quantitative analysis results of Cu show that the linearity of calibration curves and the measurement accuracy of elemental content are significantly improved with the self-absorption correction. In addition, this method can directly obtain the accurate radiation particle number density and absorption path length, which benefits the plasma diagnostics and quantitative analysis.

Theoretical-Numerical Investigation of a New Approach to Reconstruct the Temperature Field in PBF-LB/M Using Multispectral Process Monitoring

The monitoring of additive manufacturing processes such as powder bed fusion enables the detection of several process quantities important to the quality of the built part. In this context, radiation-based monitoring techniques have been used to obtain information about the melt pool and the general temperature distribution on the surface of the powder bed. High temporal and spatial resolution have been achieved at the cost of large storage requirements. This contribution aims to offer an alternative strategy of gaining information about the powder bed’s temperature field with sufficient resolution but with an economical amount of data. The investigated measurement setup uses a spectrometer to detect the spectral radiation intensities emitted by an area enclosing the melt pool and part of its surroundings. An analytical description of this process is presented, which shows that the measured spectral entities can be reconstructed by the Ritz method. It is also shown that the corresponding weighting factors can be physically interpreted as subdomains of constant temperature within the measurement area. Two different test cases are numerically analyzed, showing that the methodology allows for an approximation of the melt pool size while further assumptions remain necessary to reconstruct the actual temperature distribution.

Multiparameter gas sensor based on UV-SFR combined with wavelet transform-neural network at ppb level

Abnormal concentrations of nitric oxide (NO), ammonia (NH3), carbon disulfide (CS2) and sulphur dioxide (SO2) in exhaled breath are closely associated with diseases in specific organs of the body. Meanwhile, the above gases are also common pollutants in the atmosphere, and effective online detection of them plays a key role in disease prevention and environmental pollution prevention. This study reports a multiparameter gas sensor system based on the combination of ultraviolet segmented fitting reduction (UV-SFR) and wavelet transform-neural network (WT-NN) model for simultaneous online detection of the concentrations of NO, NH3, CS2 and SO2. Firstly, to avoid the occurrence of spectral baseline drift and absorption peak intensity reduction, a UV-SFR method is proposed for the discrete single peak type absorption spectra contained in the UV spectra. Our method can remove the slow change absorption induced by other factors, and ultimately obtain the differential absorption spectra of the gas mixtures. Then, the WT is used to denoise the differential absorption spectra of the gas mixtures. Next, for the presence of severe spectral line overlap in gas mixtures, the NN model is built based on the denoised spectral data. Finally, transfer learning is introduced into NN to improve the detection accuracy. The experimental results show that the sensor system can reliably achieve simultaneous online detection of NO, NH3, CS2, and SO2 at ppb level, and the average absolute errors of their measurements are 0.96 ppb, 1.27 ppb, 0.76 ppb and 0.97 ppb, with measurement accuracies of 3.5 %, 0.8 %, 0.5 % and 0.9 %, respectively.

A research on the effect of plasma spectrum collection device on LIBS spectral intensity

Only a small amount of spectral information is collected because the collection solid angle of the optical fiber probe and lens is very limited when collecting spectral information. To overcome this limitation, this study presents a novel method for acquiring plasma spectral information from various spatial directions. A parabolic-shaped plasma spectral collection device (PSCD) is employed to effectively collect more spectral information into the spectrometer, thereby enhancing the overall spectral intensity. The research objects in this study were soil samples containing different concentrations of heavy metals Pb, Cr, and Cd. The results indicate that the PSCD significantly enhances the spectral signal, with an enhancement rate of up to 45%. Moreover, the signal-to-noise ratio also increases by as much as 36%. Simultaneously, when compared to the absence of a device, it is found that there is no significant variation in plasma temperature when the PSCD is utilized. This observation eliminates the impact of the spatial effect caused by the PSCD on the spectral intensity. Consequently, a concentration-spectral intensity relationship curve is established under the PSCD. The results revealed that the linear fitting R 2 for Pb, Cr, and Cd increased by 0.011, 0.001, and 0.054, respectively. Additionally, the limit of detection (LOD) decreased by 0.361 ppm, 0.901 ppm, and 0.602 ppm, respectively. These findings indicate that the spectral enhancement rate elevates with the increase in heavy metal concentration. Hence, the PSCD can effectively enhance the spectral intensity and reduce the detection limit of heavy metals in soil.

A strategy to reduce spectral intensity uncertainty and predicted content uncertainty of low and medium alloy steel elements

This work presents a correction method for reducing the uncertainty in the spectral intensity and quantitative prediction model by incorporating plasma acoustic pressure parameters (First peak value and First attenuation slope) and validated it in order to reduce the uncertainty in the quantitative analysis of laser-induced breakdown spectroscopy (LIBS). Mo, Al, Mn, and V elements in low and medium alloy steels were analyzed using a simultaneous plasma spectroscopy and acoustic pressure signal acquisition system. The relationship between the spectra and the acoustic pressure parameters was then examined in order to analyze the spectral intensities and the uncertainty of the quantitative prediction model after incorporating the acoustic pressure parameters. The findings indicate that, in comparison to the original spectra, the average relative standard deviation (ARSD%) of the spectral intensities of the elements Mo, Al, Mn, and V incorporated in the first peak of the sound pressure were reduced by approximately 50.32%, 44.33%, 52.74%, and 41.57%, respectively, and that of the elements incorporated in the first attenuation slopes were reduced by approximately 35.82%, 29.99%, 38.38%, and 24.66%, respectively. We discovered that the quantitative laser-induced breakdown spectroscopy model based on the plasma acoustic pressure parameter correction may also successfully lower the uncertainty of the projected elemental content values through prediction model cross-validation studies.

Impact of human upper dermis tissue on the spectral intensity of a pulsed chirped general model vortex higher-order cosh-Gaussian beam

Impact of human upper dermis tissue on the spectral intensity of a pulsed chirped general model vortex higher-order cosh-Gaussian beam

The XMM-Newton Line Emission Analysis Program (X-LEAP). I. Emission-line Survey of O vii, O viii, and Fe L-shell Transitions

The XMM-Newton Line Emission Analysis Program (X-LEAP) is designed to study diffuse X-ray emissions from the Milky Way (MW) hot gas, as well as emissions from the foreground solar wind charge exchange (SWCX). This paper presents an all-sky survey of spectral feature intensities corresponding to the O vii, O viii, and iron L-shell (Fe-L) emissions. These intensities are derived from 5418 selected XMM-Newton observations with long exposure times and minimal contamination from point or extended sources. For 90% of the measured intensities, the values are within ≈2–18 photons cm−2 s−1 sr−1 (line unit (LU)), ≈0–8 LU, and ≈0–9 LU, respectively. We report long-term variations in O vii and O viii intensities over 22 yr, closely correlating with the solar cycle and attributed to SWCX emissions. These variations contribute ∼30% and ∼20% to the observed intensities on average and peak at ≈4 and ≈1 LU during solar maxima. We also find evidence of short-term and spatial variations in SWCX, indicating the need for a more refined SWCX model in future studies. In addition, we present SWCX- and absorption-corrected all-sky maps for a better view of the MW hot gas emission. These maps show a gradual decrease in oxygen intensity moving away from the Galactic center and a concentration of Fe-L intensity in the Galactic bubbles and disk.

Emulating the interstellar medium chemistry with neural operators

Context. The study of galaxy formation and evolution critically depends on our understanding of the complex photo-chemical processes that govern the evolution and thermodynamics of the interstellar medium (ISM). In a computational sense, resolving the chemistry is among the weightiest tasks in cosmological and astrophysical simulations. Aims. Astrophysical simulations can include photo-chemical models that allow for a wide range of densities (n), abundances of different species (ni/n) and temperature (T), and plausible evolution scenarios of the ISM under the action of a radiation field (F) with different spectral shapes and intensities. The evolution of such a non-equilibrium photo-chemical network relies on implicit, precise, computationally costly, ordinary differential equations (ODE) solvers. Here, we aim to substitute such procedural solvers with fast, pre-trained emulators based on neural operators. Methods. We emulated a non-equilibrium chemical network up to H2 formation (9 species, 52 reactions) by adopting the DeepONet formalism, namely: by splitting the ODE solver operator that maps the initial conditions and time evolution into a tensor product of two neural networks (named branch and trunk). We used KROME to generate a training set, spanning −2 < log(n/cm−3) ≤ 3.5, log(20) ≤ log(T/K) ≤ 5.5, −6 ≤ log(ni/n) < 0, and adopting an incident radiation field, F, sampled in 10 energy bins with a continuity prior. We separately trained the solver for T and each ni for ≃4.34 GPUhrs. Results. Compared with the reference solutions obtained by KROME for single-zone models, the typical precision obtained is of the order of 10−2, that is, it is 10 times better when using a training that is 40 times less costly, with respect to previous emulators that only considered a fixed F. DeepONet also performs well for T and ni outside the range of the training sample. Furthermore, the emulator aptly reproduces the ion and temperature profiles of photo dissociation regions as well; namely, by giving errors that are comparable to the typical difference between various photo-ionization codes. The present model achieves a speed-up of a factor of 128× with respect to stiff ODE solvers. Conclusions. Our neural emulator represents a significant leap forward in the modelling of ISM chemistry, offering a good balance of precision, versatility, and computational efficiency. Nevertheless, further work is required to address the challenges represented by the extrapolation beyond the training time domain and the removal of potential outliers.

A rapid in-situ hardness detection method for steel rails based on LIBS and machine learning

The railway, as a public transportation system, has played a significant role in global economic development. However, the pursuit of high speed and heavy load in trains has brought significant bending and shear stresses on the rails, making the performance of steel rails a notable focal point. The properties of steel rails are crucial factors determining the operational safety of high-speed trains, while the surface hardness is regarded as a key mechanical characteristic. This study is to develop a new rapid in-situ method to measure the hardness of steel rails, by employing Laser-Induced Breakdown Spectroscopy (LIBS) and specified analysis technology. Three distinct methods, including spectral line intensity ratios, plasma excitation temperature and machine learning, have been compared and analysed. Particularly, a multivariate model is established and optimized using the machine learning methods for U71Mn steel rail hardness analysis. In the machine learning algorithms, variance normalization is utilized, resulted in a significant improvement for the information retention during the data dimensionality reduction process. Subsequently, twelve algorithm combinations were explored, revealing that the Particle Swarm Optimization employed in Support Vector Regression (PSO-SVR) yielded the lowest mean squared error (MSE). Further refinement of the PSO-SVR was achieved through the incorporation of adaptive stochastic weights, resulting in an elevated coefficient of determination (R2) to 0.9876. Finally, the performance of the model on the new five samples was validated with an R2 of 0.9864. Otherwise, its potential applicability may be extended to broader domains, providing robust support for enhancing the precision and reliability of LIBS technology in surface hardness quantitative analysis.