In situ measurement of the specific surface area of reduced graphene oxide using time-resolved laser induced incandescence
Bibliographic record
Abstract
<title>Abstract</title> Reduced graphene oxide (rGO) produced by the rapid thermal expansion of graphite oxide is a promising alternative to graphene for applications requiring large volumes and some chemical functionality on the graphene, since this approach offers potentially high yields and throughput. However, the surface area and single layer dispersibility of the resulting rGO powder depends heavily on precursor moisture content, chemistry and morphology, as well as and process parameters that include residence time and quenching rates. While large-scale commercial deployments have been demonstrated, there are currently no real-time diagnostics capable of assessing inline material quality. In this study, we propose time-resolved laser-induced incandescence (TiRe-LII) as a means to derive the real-time relative specific surface area (SSA) of rGO particles which is expected to be a strong indicator of single sheet dispersibility. The TiRe-LII derived SSA were found to be consistent in both magnitude and trend to those found through batch gas adsorption measurements analyzed by fitting the Brunauer-Emmett-Teller (BET) isotherm.
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 | 0.000 | 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 | 0.001 | 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".