Fast identification of mural pigments at Mogao Grottoes using a LIBS-based spectral matching algorithm

Zhang, Yiming; Sun, Duixiong; Yin, Yaopeng; Yu, Zongren; Su, Bomin; Dong, Chenzhong; Su, Maogen

doi:10.1088/2058-6272/ac5afa

Cited by 6 publications

(4 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MATLAB code used for simulation, spectral matching, and plastic analysis; raw imaging data have been deposited in GitHub and Figshare ( https://github.com/qnxcarnation/SRS-tailored-Spectral-Matching-algorithm-for-plastic-identification.git ( 83 ); and https://doi.org/10.6084/m9.figshare.24635793.v2 ) ( 84 ). All other data are included in the manuscript and/or SI Appendix .…”

Section: Data Materials and Software Availabilitymentioning

confidence: 99%

Rapid single-particle chemical imaging of nanoplastics by SRS microscopy

Qian,

Gao,

Lang

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

105

View full text Add to dashboard Cite

Plastics are now omnipresent in our daily lives. The existence of microplastics (1 µm to 5 mm in length) and possibly even nanoplastics (<1 μm) has recently raised health concerns. In particular, nanoplastics are believed to be more toxic since their smaller size renders them much more amenable, compared to microplastics, to enter the human body. However, detecting nanoplastics imposes tremendous analytical challenges on both the nano-level sensitivity and the plastic-identifying specificity, leading to a knowledge gap in this mysterious nanoworld surrounding us. To address these challenges, we developed a hyperspectral stimulated Raman scattering (SRS) imaging platform with an automated plastic identification algorithm that allows micro-nano plastic analysis at the single-particle level with high chemical specificity and throughput. We first validated the sensitivity enhancement of the narrow band of SRS to enable high-speed single nanoplastic detection below 100 nm. We then devised a data-driven spectral matching algorithm to address spectral identification challenges imposed by sensitive narrow-band hyperspectral imaging and achieve robust determination of common plastic polymers. With the established technique, we studied the micro-nano plastics from bottled water as a model system. We successfully detected and identified nanoplastics from major plastic types. Micro-nano plastics concentrations were estimated to be about 2.4 ± 1.3 × 10 5 particles per liter of bottled water, about 90% of which are nanoplastics. This is orders of magnitude more than the microplastic abundance reported previously in bottled water. High-throughput single-particle counting revealed extraordinary particle heterogeneity and nonorthogonality between plastic composition and morphologies; the resulting multidimensional profiling sheds light on the science of nanoplastics.

show abstract

Section: Data Materials and Software Availabilitymentioning

confidence: 99%

Rapid single-particle chemical imaging of nanoplastics by SRS microscopy

Qian,

Gao,

Lang

et al. 2024

Proc. Natl. Acad. Sci. U.S.A.

105

View full text Add to dashboard Cite

show abstract

“…In 2022, Feng et al [15] used LIBS technology and machine learning methods to quickly distinguish the pollution level of metal elements in atmospheric deposition particles. To quickly identify the mineral pigments in the Dunhuang murals, Zhang et al [16] combined SMA with LIBS for the first time. Cherni et al [17] proposed a new LIBS-based technique for rapid screening for type 2 diabetes using the minerals calcium, sodium, magnesium, and zinc contained in hair as biomarkers.…”

Section: Introductionmentioning

confidence: 99%

Rapid detection and recognition of phosphors using laser-induced breakdown spectroscopy and principal component analysis method—back propagation neural network algorithm

Shen,

Tian,

Yao

et al. 2024

Laser Phys.

View full text Add to dashboard Cite

Rapid detection and quality monitoring of phosphor materials have always been a difficult problem in phosphor materials market. In this work, an independently proposed method based on principal component analysis method—error back propagation neural network algorithm—laser induced breakdown spectroscopy (PCA-BPNN-LIBS) was used for the detection and recognition of phosphors. Firstly, spectroscopic study was carried out on phosphor material samples, and the composition of phosphor elements was analyzed according to the full emission spectrum. Spectral data with different element characteristics detected by LIBS were used as training data sets for further identification. Then PCA method and BPNN algorithm were applied to identify 4 types phosphor samples (P11, P20, P43, P46). A very clear distinction graph was obtained, and the classification accuracy of 99.93% was verified. Allresults show that the proposed PCA-BPNN-LIBS method is an effective method for rapid analysis and recognition of phosphors.

show abstract

“…This special issue is to memorialize the 4th ASLIBS and the ten-year anniversary of CSLIBS, and to mark a starting point for the next stage of growth of ACLIBS and CSLIBS. It collected nine research articles covering fundamental [5], instrumentation [6], data processing [7,8], and application [9][10][11][12][13] studies. The effects of lens-to-sample distance on plasma morphology and spectrum using a flat-top laser beam were investigated [5]; a LIBS-assisted glow discharge method for liquid samples was developed [6]; a support vector machine combined with restricted Boltzmann machine was proposed for steel classification [7]; and plasma images were utilized to reduce the spectral fluctuation in combustion environments [8].…”

mentioning

confidence: 99%

“…The effects of lens-to-sample distance on plasma morphology and spectrum using a flat-top laser beam were investigated [5]; a LIBS-assisted glow discharge method for liquid samples was developed [6]; a support vector machine combined with restricted Boltzmann machine was proposed for steel classification [7]; and plasma images were utilized to reduce the spectral fluctuation in combustion environments [8]. The other five articles focused on the applications, including the quantitative analysis of ceramic raw materials [9], the identification of mural pigments [10] and volatile organic compounds [11], the determination of aqueous ruthenium by microwave-assisted LIBS [12], and the quantitative analysis of coal by X-ray fluorescence (XRF) assisted LIBS [13].…”

mentioning

confidence: 99%