AI-Powered Sustainable Environmental Practices using Laser-Induced Breakdown Spectroscopy (LIBS)

Technological advancements in artificial intelligence (AI) have the potential to lead the way in promoting sustainable environmental practices as well as support environmental sciences and biodiversity field studies and research. The research will focus on designing an innovative system architecture that integrates laser-induced breakdown spectroscopy (LIBS) with a robust machine learning (ML) framework, significantly advancing sustainable environmental practices, especially since LIBS offers rapid and precise multi-elemental analysis, while AI enhances data processing and predictive capabilities. As technological innovations advance, the integration of the suggested LIBS system and advanced AI will be pivotal in addressing environmental challenges and promoting sustainability. This paper presents LIBS analytical data used to qualitatively assess soil constituents as a case study.

Authors

Publication Details

Journal
Springer Link (Chiba Institute of Technology)
Published
2025-01-22
DOI
https://doi.org/10.1051/e3sconf/202560805013/pdf
Primary Topic
Laser-induced spectroscopy and plasma
Type
article
Field-Weighted Citation Impact
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article

AI-Powered Sustainable Environmental Practices using Laser-Induced Breakdown Spectroscopy (LIBS)

Muhammad Sufi bin Roslan, Haider M Al Juboori, Syed Zuhaib H. Rizvi, Josephine Y. Liew
Springer Link (Chiba Institute of Technology)
Laser-induced spectroscopy and plasma
article

AI-Powered Sustainable Environmental Practices using Laser-Induced Breakdown Spectroscopy (LIBS)

Muhammad Sufi bin Roslan, Haider M Al Juboori, Syed Zuhaib H. Rizvi, Josephine Y. Liew
article en

Abstract

Technological advancements in artificial intelligence (AI) have the potential to lead the way in promoting sustainable environmental practices as well as support environmental sciences and biodiversity field studies and research. The research will focus on designing an innovative system architecture that integrates laser-induced breakdown spectroscopy (LIBS) with a robust machine learning (ML) framework, significantly advancing sustainable environmental practices, especially since LIBS offers rapid and precise multi-elemental analysis, while AI enhances data processing and predictive capabilities. As technological innovations advance, the integration of the suggested LIBS system and advanced AI will be pivotal in addressing environmental challenges and promoting sustainability. This paper presents LIBS analytical data used to qualitatively assess soil constituents as a case study.

Springer Link (Chiba Institute of Technology)
Life in Land
Openalex Percentile: Top 38%
Laser-induced spectroscopy and plasma
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