Introducing the Novel Mixed Gaussian-Lorentzian Lineshape in the Analysis of the Raman Signal of Biochar
Bibliographic record
Abstract
In this research, an innovative procedure is proposed to elaborate Raman spectra obtained from nanostructured and disordered solids. As a challenging case study, biochar, a bio-derived carbon based material, was selected. The complex structure of biochar (i.e., channeled surface, inorganic content) represents a serious challenge for Raman characterization. As widely reported, the Raman spectra are closely linked to thermal treatments of carbon material. The individual contributions to the Raman spectra are difficult to identify due to the numerous peaks that contribute to the spectra. To tackle this problem, we propose a brand new approach based on the introduction, on sound theoretical grounds, of a mixed Gaussian--Lorentzian lineshape. As per the experimental part, biochar samples were carbonized in an inert atmosphere at various temperatures and their respective spectra were successfully decomposed using the new lineshape. The evolution of the structure with carbonization temperature was investigated by Raman and XRD analysis. The results of the two techniques fairly well agree. Compared to other approaches commonly reported in the literature this method (i) gives a sounder basis to the lineshape used in disordered materials, and (ii) appears to reduce the number of components, leading to an easier understanding of their origin.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".