Results 111 to 120 of about 1,936 (180)

Applying LIBS, SEM/EDX, and FTIR spectroscopic analysis for the conservation of cairene architectural heritage. [PDF]

Sci Rep
El-Saeid RH, Mahran A, Ali MF, Abdel-Harith M.   +3 more
europepmc   +1 more source

Study of Bending Strength Detection Method for SMC Composites Based on Laser-Induced Breakdown Spectroscopy. [PDF]

Materials (Basel)
Wang H, Gao M, Qiao Z, Li J, Cui X, Wang X.   +5 more
europepmc   +1 more source

Self-absorption correction in calibration-free laser-induced breakdown spectroscopy for quantitative elemental profiling and chemometric classification of <i>Spinacia oleracea</i>. [PDF]

RSC Adv
Aslam A, Ghuffar A, Farooq Z.
europepmc   +1 more source

Reflection-enhanced LIF for improved label-free classification of blood inflammation with complementary LIBS validation. [PDF]

Anal Bioanal Chem
Abdelazeem RM, Abdel-Salam Z, Abdel-Harith M.   +2 more
europepmc   +1 more source

Real-time P[Formula: see text]O[Formula: see text] quantification in industrial phosphoric acid using laser-induced breakdown spectroscopy: a paradigm shift in process analytical chemistry. [PDF]

Sci Rep
El Kohen M, El Asri M, El Idrissi H, Mounaam A, Griguer H, El Alami R.   +5 more
europepmc   +1 more source

Improvement of SNR in laser-induced breakdown spectroscopy using microwave and multifiber synergy. [PDF]

Sci Rep
Ikeda Y.
europepmc   +1 more source

High-Accuracy and Efficient Classification of Uranium Slag by Origin and Category via LIBS Integrated with Hybrid Machine Learning. [PDF]

Sensors (Basel)
Zhang M, Li H, Deng L, Hua R, Zhang X, Wu D, Wang X, Liu X, Liu Z, Liu X.   +9 more
europepmc   +1 more source

Detection of Pollutants in Soil Using Laser-Induced Breakdown Spectroscopy (LIBS)

Al-Bāhir
Ekhlas Jabir Mahmood, Mithaq Muter Mehdy Al- Sultani   +1 more
doaj   +1 more source

Comparative Evaluation of Spectroscopic Sensor Modalities (LIBS, MIRS, and VNIR-SWIR Hyperspectral Imaging) for the Quantification of Calcium Carbonate. [PDF]

Sensors (Basel)
Kanaan A, El Haddad J, Bouchard P, Padioleau C, Vanier F, Harhira A, Vidal F.   +6 more
europepmc   +1 more source

Machine learning for quantitative LIBS analysis of aluminum alloys: a comparison of random forest, gradient boosting, and extremely randomized trees. [PDF]

Sci Rep
Capella AG, López MM, de Damborenea González JJ, Arenas MÁ, Diego IG.   +4 more
europepmc   +1 more source