Inline Analysis of the Dry Reforming Process through Fourier Transform Infrared Spectroscopy and Use of Nitrogen as an Internal Standard for Online Gas Chromatography Analysis
Bibliographic record
Abstract
In this work, Fourier transform infrared spectroscopy (FTIR) was used as a process analytical tool (PAT), allowing for quantification of the output gas from a dry reforming of methane (DRM) reaction. This PAT was correlated with gas chromatography (GC) analysis, where nitrogen was used as an internal standard. Because nitrogen does not react under dry reforming (DR) conditions (1 atm and 700–950 °C), it was found to be reliable and accurate, thus allowing for an easy mass balance of the reaction. N 2 as an internal standard allowed for a more streamlined determination of the effect of the temperature and flow rate on conversion of CO 2 and CH 4 . The DRM reaction is an example of thermal processes where FTIR could be used as an inline process analytical tool, allowing for fast carbon balance determination (quantification of CO 2, CH 4, and CO). Such a PAT could downstream be adapted to process as gasification, pyrolysis, and most of the methane-reforming processes.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".