Purification of Indonesian Natural Graphite by Acid Leaching Method as Nuclear Fuel Matrix: Physical Characterization
Bibliographic record
Abstract
Graphite matrix in Pebble Bed Reactor (PBR) fuel has an important role not only as neutron moderator and structural material to protect nuclear fuel, but also as heat transfer media. Therefore, the graphite matrix must meet the criteria of physical and chemical properties specified for PBR fuel. This paper focuses on the purification of the Indonesian natural graphite by using hydrometallurgy method with acid treatments. The characteristic of the purified graphite was studied for its specification compliance as a candidate of fuel matrix for PBR type of High Temperature Gas Cooled Reactor (HTGR). Acid and acid mixtures such as HF, HNO3+H2SO4 and HF+HCl+H2SO4 were used for the purification process. Crystal structure examination by X-Ray Diffraction indicates that the graphite sample was 2H poly type with hexagonal crystal structure and lattice group of P 63 m c space group. It was observed that the graphite sample purified by HNO3+H2SO4 mixture had the closest resemblance to single crystalline graphite with a deviation of 0.94 when compared to perfect graphite crystal. The density of graphite decreases from 2.3273 g/cm3 (before acid treatment) to 2.1808; 2.2203 and 2.2752 g/cm3 after treatment with HF, HNO3+H2SO4 and HF+HCl+H2SO4, respectively. These results are close to the theoretical density value of 2.26 g/cm3. The surface area decreases from 10.346 m2/g to 6.177; 5.831 and 7.63 m2/g for the treated graphite with HF, HNO3+H2SO4 and HF+HCl+H2SO4 respectively. However, these values are still higher than that of nuclear grade graphite (i.e. between 4.80 and 5.55 m2/g). The average diameter size of graphite decreased from 29.65 μm (before treated acid) into 23.12 μm (after treated acid). The Indonesian natural graphite obtained from acid purification treatment is potential to be used as matrix material for PBR - HTGR fuel, but further treatment is necessary.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".