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Record W2997737937 · doi:10.5539/ijc.v12n1p89

Determination of Elements in Acid Leaching of Graphite Using Instrumental Neutron Activation Analysis

2019· article· en· W2997737937 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Chemistry · 2019
Typearticle
Languageen
FieldMaterials Science
TopicGraphite, nuclear technology, radiation studies
Canadian institutionsnot available
Fundersnot available
KeywordsGraphiteChemistryImpurityNeutron activation analysisReagentNeutron activationCertified reference materialsRadiochemistryAnalytical Chemistry (journal)Nuclear chemistryNeutronDetection limitChromatography

Abstract

fetched live from OpenAlex

Graphite material is extremely undissolvable to be turned into chemical solutions, therefore sample preparation is a serious problem faced in the determination of elemental impurity content in a graphite material. In this work, The nondestructive approach of instrumental neutron activation analysis (INAA) is applied to determine the concentration of multi-element in a graphite material, by employing both the forth floating process and the acid treatment method to the local Indonesian graphite. The sample was irradiated in the Rabbit system of G.A. Sywabessy Multi-Purpose Reactor at Serpong, Indonesia. The precision of the analysis was evaluated using certified reference materials which were obtained good performance with the most of concentration value in the range of 3 < zheta score < -3. Eleven elemental (Al, Sb, Co, Cu, La, Mn, Sc, Na, W, V, and Zn) concentration were determined in the forth floating process of the graphite. The Cu elemental is the most content with the value of 60,8 mg/kg or about 90% of total concentration content in graphite. Followed by the Sb content with a value of 5,5 mg/kg (about 8% of total impurities content in graphite). The remaining 2% includes the intermediate and the minor content of other impurity elements. After the acid treatment, the total concentration of impurities contained in the graphite material drastically decreases from 6.7% w/w to about 0,1; 0.6; and 0.59 % w/w for treatment employing the HF,  HNO3+H2SO4,and HF+HCl+H2SO4 acid reagent, respectively. Cu element makes the largest contribution to reduce the concentration of impurities in graphite which decreased from 60,675 mg/kg to 1,088 mg/kg; 925 mg/kg and 835 mg/kg for HF, HNO3+H2SO4 and HF+HCl+H2SO4 acid reagent, respectively. In addition, Sb element concentration dropped dramatically from 5,514 mg/kg to 93 mg/kg using HF reagents. The other trace elements (As, Ba, Ca, Ce, Eu, Fe, Mg, Sm, and Th) were also identified in the acid reagent treated graphite sample which are suspected to derivates from the impurity reagent and or from contamination during the sample preparation. The treated HF for graphite was obtained the low purity grades approach for nuclear graphite.

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.

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 categoriesnone
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 score0.109
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.277
Teacher spread0.267 · 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