Comprehensive Investigation of Various Structural Features of Bituminous Coals Using Advanced Analytical Techniques
Bibliographic record
Abstract
An in-depth investigation was carried out on five Chinese coals using a range of advanced analytical techniques focused specifically on extracting structural parameters. Detailed investigations were carried out using Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and X-ray diffraction followed by peak deconvolution and data analysis. Correlations were established for parameters determined from different techniques. The FTIR data showed good linear relationships between the apparent aromaticity ( f a(FTIR) ) and ( R / C ) u with the H/C atomic ratio for all coals under investigation. These results indicate that FTIR spectroscopy coupled with appropriate data analysis can be successfully used to determine aromaticity and the coal rank. Raman spectroscopy data showed a negative linear relationship between the G L fraction and H/C ratio; no well-defined relationship was observed between other band fractions and the H/C ratio. The decrease of A D / A G with increasing H/C ratio indicates the growth of aromatic rings; i.e., the structure of the sample was closer to that of graphite. This result is in good agreement with the decrease of apparent aromaticity ( f a(FTIR) ) as determined by the FTIR spectroscopy. A good linear relationship was observed between the structural parameters ( f a(X-ray) and R X-ray ) determined with X-ray and coal rank (represented by the H/C ratio). Even though the correlations among parameters derived from three techniques showed a similar trend and were consistent with each other, FTIR, and X-ray diffraction techniques were found to be better than Raman spectra to characterize coal maturity. These findings have led to a simplified coal model based on the complementary information from different techniques on various aspects of the coal structure.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".