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Record W4306778373 · doi:10.18280/i2m.210403

Deposition-Pellet Preparation Technique for Powder Samples Measurement Using Laser-Induced Breakdown Spectroscopy

2022· article· en· W4306778373 on OpenAlexvenueno aff
Hery Suyanto, Aulia Nasution, Ni Luh Putu Trisnawati, Iryanti Eka Suprihatin

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2022
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsnot available
FundersUniversitas Udayana
KeywordsPelletsLaser-induced breakdown spectroscopyPelletMaterials scienceDeposition (geology)SpectroscopyAnalytical Chemistry (journal)Energy-dispersive X-ray spectroscopySample preparationScanning electron microscopeChemistryComposite materialChromatography

Abstract

fetched live from OpenAlex

Laser-Induced Breakdown Spectroscopy (LIBS) is a spectroscopy-based measurement technique that is capable of rapid and accurate qualitative, as well as quantitative, analysis of elemental ingredients either in solid (either for organic or inorganic compounds), liquid, or gaseous samples. Unfortunately, this is not the case for powdered samples, where the focused laser beam will disperse the powder. This can be overcome by making the powder into pellets. But it has an inherent drawback, i.e., the minimum amount of powder is about 0.2 g to obtain a good detectable signal. To cope with this unfavorable condition, especially for the amount of powder less than 0.1 mg, we proposed a sub-target deposition method to make the pellets in this reported work. Using this method, the analyzed powder was deposited into an indented hole on a pellet substrate of KBr with the following optimum conditions, i.e., pellet's pressing pressure of 400 kPa, laser energy of 120 mJ, and a sample's heating temperature of 70℃. Microanalyses of standard powdered samples of PbO, CuO, and ZnO have been carried out with estimated detection limits of 4.7 μg, 4.6 μg, and 3.9 μg, respectively. So, this method can be used to analyze small amounts (in the microgram range) of powdered samples.

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)0.001
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 score0.072
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.050
GPT teacher head0.297
Teacher spread0.247 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueInstrumentation Mesure MétrologieSame topicLaser-induced spectroscopy and plasmaFrench-language works237,207