Research on a deconvolution algorithm for laser-induced fluorescence diagnosis based on the maximum entropy principle

Laser-induced fluorescence(LIF)spectroscopy is employed for plasma diagnosis,necessitating the utilization of deconvolution algorithms to isolate the Doppler effect from the raw spectral signal.However,direct deconvolution becomes invalid in the presence of noise as it leads to infinite amplification of high-frequency noise components.To address this issue,we propose a deconvolution algorithm based on the maximum entropy principle.We validate the effectiveness of the proposed algorithm by utilizing simulated LIF spectra at various noise levels(signal-to-noise ratio,SNR=20–80 d B)and measured LIF spectra with Xe as the working fluid.In the typical measured spectrum(SNR=26.23 d B)experiment,compared with the Gaussian filter and the Richardson–Lucy(R-L)algorithm,the proposed algorithm demonstrates an increase in SNR of 1.39 d B and 4.66 d B,respectively,along with a reduction in the root-meansquare error(RMSE)of 35%and 64%,respectively.Additionally,there is a decrease in the spectral angle(SA)of 0.05 and 0.11,respectively.In the high-quality spectrum(SNR=43.96 d B)experiment,the results show that the running time of the proposed algorithm is reduced by about98%compared with the R-L iterative algorithm.Moreover,the maximum entropy algorithm avoids parameter optimization settings and is more suitable for automatic implementation.In conclusion,the proposed algorithm can accurately resolve Doppler spectrum details while effectively suppressing noise,thus highlighting its advantage in LIF spectral deconvolution applications.

Ultrafast dynamics of femtosecond laser-induced high spatial frequency periodic structures on silicon surfaces

Femtosecond laser-induced periodic surface structures(LIPSS)have been extensively studied over the past few decades.In particular,the period and groove width of high-spatial-frequency LIPSS(HSFL)is much smaller than the diffraction limit,making it a useful method for efficient nanomanufacturing.However,compared with the low-spatial-frequency LIPSS(LSFL),the structure size of the HSFL is smaller,and it is more easily submerged.Therefore,the formation mechanism of HSFL is complex and has always been a research hotspot in this field.In this study,regular LSFL with a period of 760 nm was fabricated in advance on a silicon surface with two-beam interference using an 800 nm,50 fs femtosecond laser.The ultrafast dynamics of HSFL formation on the silicon surface of prefabricated LSFL under single femtosecond laser pulse irradiation were observed and analyzed for the first time using collinear pump-probe imaging method.In general,the evolution of the surface structure undergoes five sequential stages:the LSFL begins to split,becomes uniform HSFL,degenerates into an irregular LSFL,undergoes secondary splitting into a weakly uniform HSFL,and evolves into an irregular LSFL or is submerged.The results indicate that the local enhancement of the submerged nanocavity,or the nanoplasma,in the prefabricated LSFL ridge led to the splitting of the LSFL,and the thermodynamic effect drove the homogenization of the splitting LSFL,which evolved into HSFL.

Femtosecond laser-induced nanoparticle implantation into flexible substrate for sensitive and reusable microfluidics SERS detection

Surface-enhanced Raman spectroscopy(SERS)microfluidic system,which enables rapid detection of chemical and biological analytes,offers an effective platform to monitor various food contaminants and disease diagnoses.The efficacy of SERS microfluidic systems is greatly dependent on the sensitivity and reusability of SERS detection substrates to ensure repeated use for prolonged periods.This study proposed a novel process of femtosecond laser nanoparticle array(NPA)implantation to achieve homogeneous forward transfer of gold NPA on a flexible polymer film and accurately integrated it within microfluidic chips for SERS detection.The implanted Au-NPA strips show a remarkable electromagnetic field enhancement with the factor of 9×108 during SERS detection of malachite green(MG)solution,achieving a detection limit lower than 10 ppt,far better than most laser-prepared SERS substrates.Furthermore,Au-NPA strips show excellent reusability after several physical and chemical cleaning,because of the robust embedment of laser-implanted NPA in flexible substrates.To demonstrate the performance of Au-NPA,a SERS microfluidic system is built to monitor the online oxidation reaction between MG/NaClO reactants,which helps infer the reaction path.The proposed method of nanoparticle implantation is more effective than the direct laser structuring technique.It provides better performance for SERS detection,robustness of detection,and substrate flexibility and has a wider range of applications for microfluidic systems without any negative impact.

Estimating the yield stress of soft materials via laser-induced breakdown spectroscopy

Taking three typical soft samples prepared respectively by loose packings of 77-,95-,and 109-μm copper grains as examples,we perform an experiment to investigate the energy-dependent laser-induced breakdown spectroscopy(LIBS)of soft materials.We discovered a reversal phenomenon in the trend of energy dependence of plasma emission intensity:increasing initially and then decreasing separated by a well-defined critical energy.The trend reversal is attributed to the laser-induced recoil pressure at the critical energy just matching the sample's yield strength.As a result,a one-to-one correspondence can be well established between the samples'yield stress and the critical energy that is easily obtainable from LIBS measurements.This allows us to propose an innovative method for estimating the yield stress of soft materials via LIBS with attractive advantages including in-situ remote detection,real-time data collection,and minimal destructive to sample.

Theoretical simulation study of laser-induced plasma bombardment on bacteria

With the rapid advancement of laser decontamination technology and growing awareness of microbial hazards,it becomes crucial to employ theoretical model to simulate and evaluate decontamination processes by laser-induced plasma.This study employs a two-dimensional axisymmetric fluid dynamics model to simulate the power density of plasma bombardment on bacteria and access its decontamination effects.The model considers the transport processes of vapor plasma and background gas molecules.Based on the destructive impact of high-speed moving particles in the plasma on bacteria,we investigate the bombardment power density under various conditions,including different laser spot sizes,wavelengths,plate's tilt angles,and plate-target spacing.The results reveal that the bombardment power density increases with a decrease in laser spot size and wavelength.For instance,when the plate is parallel to the target surface with a 1 mm spacing,the bombardment power density triples as the laser spot size decreases from 0.8 mm to 0.5 mm and quadruples as the wavelength decreases from 1064 nm to 266 nm.Notably,when the plate is parallel to the target with a relatively close spacing of 0.5 mm,the bombardment power density at 0°inclination increases sevenfold compared to 45°.This simulation study is essential for optimizing optical parameters and designing component layouts in decontamination devices using laser-induced plasma.The reduction of laser spot size,wavelength,plate-target spacing and aligning the plate parallel to the target,collectively contribute to achieving precise and effective decontamination.

Effect of matrix thermal properties on laser-induced plasma

The matrix thermal properties have an important impact on laser-induced plasma,as the thermal effect dominates the interaction between ns-pulsed laser and matter,especially in metals.We used a series of pure metals and aluminum alloys to measure plasma temperature and electron density through laser-induced breakdown spectroscopy,in order to investigate the effect of matrix thermal properties on laser-induced plasma.In pure metals,a significant negative linear correlation was observed between the matrix thermal storage coefficient and plasma temperature,while a weak correlation was observed with electron density.The results indicate that metals with low thermal conductivity or specific heat capacity require less laser energy for thermal diffusion or melting and evaporation,resulting in higher ablation rates and higher plasma temperatures.However,considering ionization energy,thermal effects may be a secondary factor affecting electron density.The experiment of aluminum alloy further confirms the influence of thermal conductivity on plasma temperature and its mechanism explanation.

Laser-induced breakdown spectroscopy as a method for millimeter-scale inspection of surface flatness

A non-contact method for millimeter-scale inspection of material surface flatness via Laser-Induced Breakdown Spectroscopy(LIBS)is investigated experimentally.The experiment is performed using a planished surface of an alloy steel sample to simulate its various flatness,ranging from 0 to 4.4 mm,by adjusting the laser focal plane to the surface distance with a step length of 0.2 mm.It is found that LIBS measurements are successful in inspecting the flatness differences among these simulated cases,implying that the method investigated here is feasible.It is also found that,for achieving the inspection of surface flatness within such a wide range,when univariate analysis is applied,a piecewise calibration model must be constructed.This is due to the complex dependence of plasma formation conditions on the surface flatness,which inevitably complicates the inspection procedure.To solve the problem,a multivariate analysis with the help of Back-Propagation Neural Network(BPNN)algorithms is applied to further construct the calibration model.By detailed analysis of the model performance,we demonstrate that a unified calibration model can be well established based on BPNN algorithms for unambiguous millimeter-scale range inspection of surface flatness with a resolution of about 0.2 mm.

Estimating the grain size of microgranular material using laser-induced breakdown spectroscopy combined with machine learning algorithms

Recent work has validated a new method for estimating the grain size of microgranular materials in the range of tens to hundreds of micrometers using laser-induced breakdown spectroscopy(LIBS).In this situation,a piecewise univariate model must be constructed to estimate grain size due to the complex dependence of the plasma formation environment on grain size.In the present work,we tentatively construct a unified calibration model suitable for LIBS-based estimation of those grain sizes.Specifically,two unified multivariate calibration models are constructed based on back-propagation neural network(BPNN)algorithms using feature selection strategies with and without considering prior information.By detailed analysis of the performances of the two multivariate models,it was found that a unified calibration model can be successfully constructed based on BPNN algorithms for estimating the grain size in the range of tens to hundreds of micrometers.It was also found that the model constructed with a priorguided feature selection strategy had better prediction performance.This study has practical significance in developing the technology for material analysis using LIBS,especially when the LIBS signal exhibits a complex dependence on the material parameter to be estimated.

Characteristics of laser-induced breakdown spectroscopy of liquid slag

Rapid online analysis of liquid slag is essential for optimizing the quality and energy efficiency of steel production. To investigate the key factors that affect the online measurement of refined slag using laser-induced breakdown spectroscopy(LIBS), this study examined the effects of slag composition and temperature on the intensity and stability of the LIBS spectra. The experimental temperature was controlled at three levels: 1350℃, 1400℃, and 1450℃. The results showed that slag composition and temperature significantly affected the intensity and stability of the LIBS spectra. Increasing the Fe content and temperature in the slag reduces its viscosity, resulting in an enhanced intensity and stability of the LIBS spectra. Additionally, 42 refined slag samples were quantitatively analyzed for Fe, Si, Ca, Mg, Al, and Mn at 1350℃, 1400℃, and 1450℃.The normalized full spectrum combined with partial least squares(PLS) quantification modeling was used, using the Ca Ⅱ 317.91 nm spectral line as an internal standard. The results show that using the internal standard normalization method can significantly reduce the influence of spectral fluctuations. Meanwhile, a temperature of 1450℃ has been found to yield superior results compared to both 1350℃ and 1400℃, and it is advantageous to conduct a quantitative analysis of the slag when it is in a “water-like” state with low viscosity.

Towards a new avenue for rapid synthesis of electrocatalytic electrodes via laser-induced hydrothermal reaction for water splitting

Electrochemical production of hydrogen from water requires the development ofelectrocatalysts that are active,stable,and low-cost for water splitting.To address these challenges,researchers are increasingly exploring binder-free electrocatalytic integratedelectrodes (IEs) as an alternative to conventional powder-based electrode preparation methods,for the former is highly desirable to improve the catalytic activity and long-term stability for large-scale applications of electrocatalysts.Herein,we demonstrate a laser-inducedhydrothermal reaction (LIHR) technique to grow NiMoO4nanosheets on nickel foam,which is then calcined under H2/Ar mixed gases to prepare the IE IE-NiMo-LR.This electrode exhibits superior hydrogen evolution reaction performance,requiring overpotentials of 59,116 and143 mV to achieve current densities of 100,500 and 1000 mA·cm-2.During the 350 h chronopotentiometry test at current densities of 100 and 500 m A·cm-2,the overpotentialremains essentially unchanged.In addition,NiFe-layered double hydroxide grown on Ni foam is also fabricated with the same LIHR method and coupled with IE-NiMo-IR to achieve water splitting.This combination exhibits excellent durability under industrial current density.The energy consumption and production efficiency of the LIHR method are systematicallycompared with the conventional hydrothermal method.The LIHR method significantly improves the production rate by over 19 times,while consuming only 27.78%of the total energy required by conventional hydrothermal methods to achieve the same production.

Incorporating empirical knowledge into data-driven variable selection for quantitative analysis of coal ash content by laser-induced breakdown spectroscopy

Laser-induced breakdown spectroscopy(LIBS)has become a widely used atomic spectroscopic technique for rapid coal analysis.However,the vast amount of spectral information in LIBS contains signal uncertainty,which can affect its quantification performance.In this work,we propose a hybrid variable selection method to improve the performance of LIBS quantification.Important variables are first identified using Pearson's correlation coefficient,mutual information,least absolute shrinkage and selection operator(LASSO)and random forest,and then filtered and combined with empirical variables related to fingerprint elements of coal ash content.Subsequently,these variables are fed into a partial least squares regression(PLSR).Additionally,in some models,certain variables unrelated to ash content are removed manually to study the impact of variable deselection on model performance.The proposed hybrid strategy was tested on three LIBS datasets for quantitative analysis of coal ash content and compared with the corresponding data-driven baseline method.It is significantly better than the variable selection only method based on empirical knowledge and in most cases outperforms the baseline method.The results showed that on all three datasets the hybrid strategy for variable selection combining empirical knowledge and data-driven algorithms achieved the lowest root mean square error of prediction(RMSEP)values of 1.605,3.478 and 1.647,respectively,which were significantly lower than those obtained from multiple linear regression using only 12 empirical variables,which are 1.959,3.718 and 2.181,respectively.The LASSO-PLSR model with empirical support and 20 selected variables exhibited a significantly improved performance after variable deselection,with RMSEP values dropping from 1.635,3.962 and 1.647 to 1.483,3.086 and 1.567,respectively.Such results demonstrate that using empirical knowledge as a support for datadriven variable selection can be a viable approach to improve the accuracy and reliability of LIBS quantification.

Two-dimensional investigation of characteristic parameters and their gradients for the self-generated electric and magnetic fields of laser-induced zirconium plasma

Two-dimensional diagnosis of laser-induced zirconium(Zr)plasma has been experimentally performed using the time-of-flight method by employing Faraday cups in addition to electric and magnetic probes.The characteristic parameters of laser-induced Zr plasma have been evaluated as a function of different laser irradiances ranging from 4.5 to 11.7 GW cm-2 at different axial positions of 1–4 cm with a fixed radial distance of 2 cm.A well-supporting correlation between the plume parameters and the laser-plasma-produced spontaneous electric and magnetic(E and B)fields was established.The measurements of the characteristic parameters and spontaneously induced fields were observed to have an increasing trend with the increasing laser irradiance.However,when increasing the spatial distance in both the axial and radial directions,the plasma parameters(electron/ion number density,temperature and kinetic energy)did not show either continuously increasing or decreasing trends due to various kinetic and dynamic processes during the spatial evolution of the plume.However,the E and B fields were observed to be always diffusing away from the target.The radial component of electron number densities remained higher than the axial number density component,whereas the axial ion number density at all laser irradiances and axial distances remained higher than the radial ion number density.The higher axial self-generated electric field(SGEF)values than radial SGEF values are correlated with the effective charge-separation mechanism of electrons and ions.The generation of a self-generated magnetic field is observed dominantly in the radial direction at increasing laser irradiance as compared to the axial one due to the deflection of fast-moving electrons and the persistence of two-electron temperature on the radial axis.

高职院校学生ARCS学习动机模型的探索与实践

本研究利用发放调查问卷、查阅文献和现场访谈等方法,结合ARCS学习动机模型,对现阶段高职院校学生学习现状进行调查研究。结合调查分析结果,借助ARCS学习动机模型进行教学设计并实施教学,调动学生学习的积极性,激发学生的学习动机,提高了学生的学习兴趣和学习能力。

OBE-ARCS导向的化学工程专业学位研究生课程体系建设和课堂教学模式创新

立足学校理工交叉学科特色,化学工程专业以OBE理念为指导,融合产学研用多要素,对专业学位研究生的核心课程体系进行重构;基于ARCS动机模型,创新研究生课程教学模式;协同专业、课程和教材思政,构建一体化育人机制;依托工程实践基地建设,强化理论与工程实践的结合。基于以上举措构建的“四维一体”育人模式将研究生的知识学习、能力培养、意识强化、专业素养提升紧密融合,在培养高质量应用型人才方面取得了显著成效。

基于ARCS模型的翻转课堂教学设计

以“取样定理”为例,提出采用ARCS模型进行翻转课堂教学设计的案例。教学内容设计注重核心价值观的传播,从取样定理形成的历史脉络引出课程思政;运用多种教学手段,提升学生注意力;重视学习内容的关联设计,学生端倚重信息技术应用,及时反馈,提升学生自信度;学习评价注重应用性,提升学习满意度。教学实施结果表明,该教学设计能调动学生的课堂热情,师生满意度高。

ARCS动机模型混合学习造纸英语听力App设计与开发

ARCS动机模型是1987年美国学者研究并提出的。信息技术的迅速发展,促使该动机模型在多媒体产品开发、课件设计及教育App领域得以广泛应用,并以此来调动学习者的积极性。依托ARCS动机模型对造纸英语听力App进行设计的主要目的是提升学习者对时间的利用率和学习满意度。基于此,针对用户对造纸英语听力App的学习需求,与现有技术相结合,对当前只限于“速记”“跟读”的学习英语听力App进行创新,提供新的研发思路与方向。

基于ARCS激励模型的老年健步走运动产品设计研究

为提高老年群体参与健步走运动锻炼的积极性,文章从运动激励的角度出发开展老年健步走运动相关的产品设计研究,并利用计算机化行为研究的E-Prime软件,建立SAM情绪测试实验程序。同时,通过对实验所得的情感体验数据进行单因素方差分析,证实引入ARCS模型的健步走运动对于激发老年人积极情绪价值的有效性,并得出ARCS模型激励要素的优先性,基于此提出老年健步走产品设计的策略,以期为老年运动相关产品设计提供新的思路。

ARCS模型视角下高职英语教学资源建设与应用研究

基于ARCS模型视角的高职英语教学资源平台建设和应用,坚持以学习者为中心,以整体化、系统化、标准化、科学化、合作性、共享性、循环性改进为原则,围绕高职学生岗位英语语言技能和职业素养目标,构建高职英语课程教学资源库和教学资源共享平台,完善学习成果考核评价体系,建立质量评估和诊改机制,多举措提升教改能力,促进数字化技术与教学改革实践深度融合与双向赋能。

The recent progress of laser-induced graphene based device applications

Laser writing is a fast and efficient technology that can produce graphene with a high surface area,whereas laser-induced graphene(LIG)has been widely used in both physics and chemical device application.It is necessary to update this important progress because it may provide a clue to consider the current challenges and possible future directions.In this review,the basic principles of LIG fabrication are first briefly described for a detailed understanding of the lasing process.Sub-sequently,we summarize the physical device applications of LIGs and describe their advantages,including flexible electronics and energy harvesting.Then,chemical device applications are categorized into chemical sensors,supercapacitors,batteries,and electrocatalysis,and a detailed interpretation is provided.Finally,we present our vision of future developments and challenges in this exciting research field.

基于ARCS模型设计的VR实训课程教学效果分析

以基站运行与维护实训课程为例,基于ARCS模型进行教学设计,关注学生学习感受的四个方面:注意、关联、信心、满足感。同时结合情境认知理论和建构主义理论,通过VR实训课程教学,激发学生学习的兴趣和动机,分析实训课程教学设计,增强课程教学效果。