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Record W3022600154 · doi:10.1364/ao.385932

Elemental analysis of sage (herb) using calibration-free laser-induced breakdown spectroscopy

2020· article· en· W3022600154 on OpenAlexaff
Javed Iqbal, Haroon Asghar, Syed Khurrum Hussain Shah, M. Naeem, Shahab Ahmed Abbasi, Raja Hussain Ali

Bibliographic record

VenueApplied Optics · 2020
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsLaser-induced breakdown spectroscopyAnalytical Chemistry (journal)SpectroscopyMaterials scienceLaserEmission spectrumSpectrometerAtomic emission spectroscopySpectral lineCalibrationPlasmaOpticsChemistryInductively coupled plasmaPhysics

Abstract

fetched live from OpenAlex

In this work, laser-induced breakdown spectroscopy (LIBS) has been used for the quantitative and qualitative analysis of the sage sample using the calibration-free LIBS (CF-LIBS) technique. The sage plasma is generated by focusing the second harmonics (532 nm) of a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>Q</mml:mi> </mml:math> -switched Nd:YAG laser with a repetition rate of 10 Hz and pulse duration of 5 ns. The emission spectra are recorded using a LIBS 2000 detection system spectrometer consisting of five high-resolution spectrometers covering a wavelength range from 200 to 720 nm. The optical emission spectra of the sage sample reveal the spectral lines of Fe, Ca, Ti, Co, Mn, Ni, and Cr. The plasma temperature and electron number density of the neutral spectral lines of the pertinent elements have been deduced using the Boltzmann plot and Stark-broadening line profile method, with average values <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>8855</mml:mn> </mml:mrow> <mml:mo>±</mml:mo> <mml:mn>885</mml:mn> <mml:mspace width="thickmathspace"/> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">K</mml:mi> </mml:mrow> </mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>3.89</mml:mn> </mml:mrow> <mml:mo>×</mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>16</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> <mml:mspace width="thickmathspace"/> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">c</mml:mi> <mml:mi mathvariant="normal">m</mml:mi> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mo>−</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> , respectively. The average values of the plasma parameters were used for the quantification of the detected elements in the sample. Based on the calibration-free method, the measured results demonstrate that Fe is the major constituent in the sample, having a percentage concentration of 48.1%, while the remaining elements are Ca, Ti, Co, Mn, Ni, and Cr, with percentage concentrations 0.7%, 5.3%, 8%, 11%, 12.3%, and 14.6%, respectively. This study demonstrates the feasibility of LIBS for the compositional analysis of major and trace elements present in the plant samples and its further applications in medicine.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.309
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.230
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2020
Admission routes1
Has abstractyes

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