Recent experimental progresses regarding broadband laser-plasma instabilities(LPIs)show that a 0.6%laser bandwidth can reduce backscatters of the stimulated Brillouin scattering(SBS)and the stimulated Raman scattering...Recent experimental progresses regarding broadband laser-plasma instabilities(LPIs)show that a 0.6%laser bandwidth can reduce backscatters of the stimulated Brillouin scattering(SBS)and the stimulated Raman scattering(SRS)at normal incidence[Phys.Rev.Lett.132035102(2024)].In this paper,we present a further discussion of the spectral distributions of the scatters developed by broadband LPIs,in addition to a brief validation of the effectiveness of bandwidth on LPIs mitigation at oblique incidence.SBS backscatter has a small redshift in the broadband case contrary to the blueshift with narrowband laser,which may be explained by the self-cross beam energy transfer between the various frequency components within the bandwidth.SRS backscatter spectrum presents a peak at a longer wavelength in the broadband case compared to the short one in the narrowband case,which is possibly attributed to the mitigation effect of bandwidth on filaments at underdense plasmas.The three-halves harmonic emission(3ω/2)has a one-peak spectral distribution under the broadband condition,which is different from the two-peak distribution under the narrowband condition,and may be related to the spectral mixing of different frequency components within the bandwidth if the main sources of the two are both two-plasmon decays.展开更多
Discharge plasma parameter measurement is a key focus in low-temperature plasma research.Traditional diagnostics often require costly equipment,whereas electro-acoustic signals provide a rich,non-invasive,and less com...Discharge plasma parameter measurement is a key focus in low-temperature plasma research.Traditional diagnostics often require costly equipment,whereas electro-acoustic signals provide a rich,non-invasive,and less complex source of discharge information.This study harnesses machine learning to decode these signals.It establishes links between electro-acoustic signals and gas discharge parameters,such as power and distance,thus streamlining the prediction process.By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques,the Mel-Frequency Cepstral Coefficients(MFCCs)of the acoustic signals are extracted to construct the predictors.Three machine learning models(Linear Regression,k-Nearest Neighbors,and Random Forest)are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance.All models display impressive performance in prediction precision and fitting abilities.Among them,the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error(MSE=0.00571)and the highest R-squared value(R^(2)=0.93877).The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm,which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.展开更多
Eukaryotes package their DNA into chromatin with histones. An ever-growing number of histone post-translational modifications have been discovered, which facilitate and modulate chromatinbased fundamental processes, s...Eukaryotes package their DNA into chromatin with histones. An ever-growing number of histone post-translational modifications have been discovered, which facilitate and modulate chromatinbased fundamental processes, such as transcription, DNA damage repair, and DNA replication. Lysine residues are the most modified histone residues. Acetylation and methylation on lysine residues are the two most studied histone modifications, and they differ greatly in one biophysical property.展开更多
基金Project supported by the National Natural Science Foundation of China (Grant No.11905280)。
文摘Recent experimental progresses regarding broadband laser-plasma instabilities(LPIs)show that a 0.6%laser bandwidth can reduce backscatters of the stimulated Brillouin scattering(SBS)and the stimulated Raman scattering(SRS)at normal incidence[Phys.Rev.Lett.132035102(2024)].In this paper,we present a further discussion of the spectral distributions of the scatters developed by broadband LPIs,in addition to a brief validation of the effectiveness of bandwidth on LPIs mitigation at oblique incidence.SBS backscatter has a small redshift in the broadband case contrary to the blueshift with narrowband laser,which may be explained by the self-cross beam energy transfer between the various frequency components within the bandwidth.SRS backscatter spectrum presents a peak at a longer wavelength in the broadband case compared to the short one in the narrowband case,which is possibly attributed to the mitigation effect of bandwidth on filaments at underdense plasmas.The three-halves harmonic emission(3ω/2)has a one-peak spectral distribution under the broadband condition,which is different from the two-peak distribution under the narrowband condition,and may be related to the spectral mixing of different frequency components within the bandwidth if the main sources of the two are both two-plasmon decays.
基金partially supported by National Natural Science Foundation of China(No.52377155)the State Key Laboratory of Reliability and Intelligence of Electrical Equipment(No.EERI-KF2021001)Hebei University of Technology。
文摘Discharge plasma parameter measurement is a key focus in low-temperature plasma research.Traditional diagnostics often require costly equipment,whereas electro-acoustic signals provide a rich,non-invasive,and less complex source of discharge information.This study harnesses machine learning to decode these signals.It establishes links between electro-acoustic signals and gas discharge parameters,such as power and distance,thus streamlining the prediction process.By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques,the Mel-Frequency Cepstral Coefficients(MFCCs)of the acoustic signals are extracted to construct the predictors.Three machine learning models(Linear Regression,k-Nearest Neighbors,and Random Forest)are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance.All models display impressive performance in prediction precision and fitting abilities.Among them,the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error(MSE=0.00571)and the highest R-squared value(R^(2)=0.93877).The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm,which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.
基金supported by the National Natural Science Foundation of China (32288102)the K.C.Wong Educational Foundation (GJTD-2020-06)the Youth Innovation Promotion Association (2020097) of the Chinese Academy of Sciences。
文摘Eukaryotes package their DNA into chromatin with histones. An ever-growing number of histone post-translational modifications have been discovered, which facilitate and modulate chromatinbased fundamental processes, such as transcription, DNA damage repair, and DNA replication. Lysine residues are the most modified histone residues. Acetylation and methylation on lysine residues are the two most studied histone modifications, and they differ greatly in one biophysical property.
文摘为了实现激光诱导击穿光谱(LIBS)技术对自然水体中痕量铬(Cr)元素的快速检测,实验以蒙脱石为吸附基底,对溶液中的Cr元素富集后进行LIBS测量。通过分析样品的发射光谱特性,确定了425.4 nm的特征谱线为Cr的分析线;并基于不同参数条件下分析线的强度和信噪比,得出最佳激光光斑尺寸为50μm,最优的激光能量为40.8 m J,最佳延迟时间为5μs。在最佳实验参数条件下,建立了水体中Cr元素的定标曲线,拟合结果显示,线性相关系数为0.989,检测限达到0.33 mg/L。研究结果为水体中重金属元素的LIBS测量提供了一种新途径。
