简介：Nuclearfusionhasenormouspotentialtogreatlyaffectglobalenergyproduction.Thenext-generationtokamakITER,whichisaimedatdemonstratingthefeasibilityofenergyproductionfromfusiononacommercialscale,isunderconstruction.Wallerosion,materialtransport,andfuelretentionareknownfactorsthatshortenthelifetimeofITERduringtokamakoperationandgiverisetosafetyissues.Thesefactors,whichmustbeunderstoodandsolvedearlyintheprocessoffusionreactordesignanddevelopment,areamongthemostimportantconcernsforthecommunityofplasma-wallinteractionresearchers.Todate,lasertechniquesareamongthemostpromisingmethodsthatcansolvetheseopenITERissues,andlaser-inducedbreakdownspectroscopy（LIBS）isanidealcandidateforonlinemonitoringofthewallsofcurrentandnext-generation（suchasITER）fusiondevices.LIBSisawidelyusedtechniqueforvariousapplications.Ithasbeenconsideredrecentlyasapromisingtoolforanalyzingplasma-facingcomponentsinfusiondevicesinsitu.ThisarticlereviewstheexperimentsthathavebeenperformedbymanyresearchgroupstoassessthefeasibilityofLIBSforthispurpose.

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简介：Laser-inducedbreakdownspectroscopy(LIBS)isapromisinganalyticalspectroscopytechnologybasedonspectroscopicanalysisoftheradiationemittedbylaser-producedplasma.However,forquantitativeanalysisbyLIBS,theso-calledself-absorptioneffectsonthespectrallines,whichaffectplasmacharacteristics,emissionlineshapes,calibrationcurves,etc,cannolongerbeneglected.Hence,understandinganddeterminingtheself-absorptioneffectsareofutmostimportancetoLIBSresearch.Thepurposeofthisreviewistoprovideaglobaloverviewofself-absorptioninLIBSontheissuesofexperimentalobservationsandadverseeffects,physicalmechanisms,correctionoreliminationapproaches,andutilizationsinthepastcentury.Webelievethatbetterunderstandingandeffectivesolvingtheself-absorptioneffectwillfurtherenhancethedevelopmentandmaturityofLIBS.

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简介：在这份报纸，结果在导致激光的故障光谱学(解放)的第一应用上被介绍为在alpha石蜡的Mg集中的量的决心的技术。这种技术在与传统的方法的那些可比较的Mg集中给精确性，这被表明，作为ICP-OES，在短时间并且没有对样品准备的需要。为500ppb的顺序的Mg的察觉的限制被表明。而且，关于合适的刻度曲线和察觉(LOD)的限制的定义的实现的简短讨论被报导。CLC数字TP274

简介：Laser-inducedbreakdownspectroscopy(LIBS)hasbeenextensivelyappliedduetothecapabilitiesofreal-time,multi-phaseandmulti-elementanalysis.Withtheadvantages,portableLIBSisinrapiddevelopmentwithgreatpotentialoffieldmeasurements.Inthiswork,aportableLIBSwasdevelopedbya50μJlevellaserforexcitationandanobjectivelensforfocusingorimaging.Elementdetectionwassuccessfullyachievedbythissetup,andalsothemicroscopicobservationcouldbeobtainedwiththemagnificationof350×.ApreliminaryinvestigationwascarriedoutformineralrecognitionbythedevelopedportableLIBS.Theobtainedresultsindicatethattheelementdetectioncombinedwithmicroobservationwasaneffectivewayforrecognizingminerals,especiallyforsimilarmineralidentificationordiscrimination.Itissuggestedthatthemicro-joulelasercouldbealternativeforthecommercialportableLIBS,andtheon-sitemineralorrockrecognitionisapromisingfieldfortheapplication.

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简介：摘 要：本研究基于激光诱导击穿光谱（LIBS）法，探讨了铁矿石中硅（SiO2）、铝（Al2O3）、钙（CaO）和镁（MgO）含量的测定方法。采集了来自澳大利亚、南非、哈萨克斯坦和智利四个国家的244个铁矿石样品，代表了12个不同种类。通过LIBS测量，获取了这些样品的光谱数据，涵盖了180～970nm波长范围内的12814个数据点。在实验中，使用商用的LIBS仪器进行测量，通过调Q的Nd:YAG激光器，波长为1064nm，激光能量设置为30mJ，延迟时间为1μs，频率为5Hz。

简介：Theaccuracyoflaser-inducedbreakdownspectroscopy(LIBS)quantitativemethodisgreatlydependentontheamountofcertifiedstandardsamplesusedfortraining.However,inpracticalapplications,onlylimitedstandardsampleswithlabeledcertifiedconcentrationsareavailable.Anovelsemi-supervisedLIBSquantitativeanalysismethodisproposed,basedonco-trainingregressionmodelwithselectionofeffectiveunlabeledsamples.Themainideaoftheproposedmethodistoobtainbetterregressionperformancebyaddingeffectiveunlabeledsamplesinsemi-supervisedlearning.First,effectiveunlabeledsamplesareselectedaccordingtothetestingsamplesbyEuclideanmetric.Twooriginalregressionmodelsbasedonleastsquaressupportvectormachinewithdifferentparametersaretrainedbythelabeledsamplesseparately,andthentheeffectiveunlabeledsamplespredictedbythetwomodelsareusedtoenlargethetrainingdatasetbasedonlabelingconfidenceestimation.Thefinalpredictionsoftheproposedmethodonthetestingsampleswillbedeterminedbyweightedcombinationsofthepredictionsoftwoupdatedregressionmodels.Chromiumconcentrationanalysisexperimentsof23certifiedstandardhigh-alloysteelsampleswerecarriedout,inwhich5sampleswithlabeledconcentrationsand11unlabeledsampleswereusedtotraintheregressionmodelsandtheremaining7sampleswereusedfortesting.Withthenumbersofeffectiveunlabeledsamplesincreasing,therootmeansquareerroroftheproposedmethodwentdownfrom1.80%to0.84%andtherelativepredictionerrorwasreducedfrom9.15%to4.04%.