Deep learning regression for quantitative LIBS analysis-Reference-Cited by-同舟云学术

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Deep learning regression for quantitative LIBS analysis

Published:2023-04 Issue: Volume:202 Page:106634
ISSN:0584-8547
Container-title:Spectrochimica Acta Part B: Atomic Spectroscopy
language:en
Short-container-title:Spectrochimica Acta Part B: Atomic Spectroscopy

Author:

Van den Eynde Simon,Díaz-Romero Dillam Jossue,Zaplana Isiah,Peeters Jef

Publisher

Elsevier BV

Subject

Spectroscopy,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Reference82 articles.

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2. Laser-induced breakdown spectroscopy for human and animal health: a review;Gaudiuso;Spectrochim. Acta B At. Spectrosc.,2019

3. CHAPTER 4. Laser-Induced Breakdown Spectroscopy (LIBS) in Forensic Sensing;Naozuka,2019

4. The ChemCam instrument suite on the Mars Science laboratory (MSL) rover: Science objectives and mast unit description;Maurice;Space Sci. Rev.,2012

5. The SuperCam instrument for the Mars 2020 rover;Nelson,2020

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2. recent advances in machine learning methodologies for LIBS quantitative analysis;Pulsed Laser Processing of Materials;2024-04-09

3. Comparative Life Cycle Assessment of Aluminium Scrap Treatment Strategies;Procedia CIRP;2024

4. Multidimensional characterization of Ni-Zn ferrite films based on laser-induced breakdown spectroscopy technology;Thin Solid Films;2023-11

5. Quantification of alloying elements in steel targets: The LIBS 2022 regression contest;Spectrochimica Acta Part B: Atomic Spectroscopy;2023-08

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