Quantitative and qualitative analysis of nitrogen species in carbon at the ppm level
Bibliographic record
Abstract
Advanced carbon materials used for energy-related applications often contain nitrogen as a heteroatom, which can substantially influence their physical, chemical, and electronic properties. However, conventional analytical techniques for nitrogen environments provide limited compositional and structural information in high sensitivity, which significantly restricts rationalized materials design. Herein, we present the advanced temperature-programmed desorption (TPD) technique up to 2,100°C as a comprehensive analytical tool for chemical speciation in bulk nitrogen-doped carbon materials with record-high sensitivity. Employing complementary X-ray photoelectron spectroscopy, elemental analysis, and computational modeling, we discovered that the gas emission patterns can provide both compositional and structural information regarding nitrogen environments. Importantly, TPD enables the bulk quantification of nitrogen species at 10 ppm levels, which is two orders of magnitude more sensitive than conventional methods. Such an advanced characterization method provides a foundation for next-generation research, focusing on the structural design at the ppm level, and offers significant potential for industrial applications.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".