Impact of Thermal Oxidative Stabilization on the Performance of Lignin-Based Carbon Nanofiber Mats
Bibliographic record
Abstract
Abstract Lignin is a renewable biopolymer considered as a potential precursor for low-cost carbon materials. Thermal oxidative stabilization (TOS) is an important processing step to maintain fiber geometry during carbonization, yet the impact of TOS on the properties of lignin-based carbon materials has not been clearly identified in the literature. Yield, change in fiber diameter/distribution, elemental composition, and mechanical properties were explored for both stabilized and carbonized lignin fibers. Vibrational spectroscopy and solid-state 13C nuclear magnetic resonance spectroscopy were used to analyze the changes in lignin molecular structure after exposure to various heating conditions during the TOS steps. Further, studies were focused on the effects of TOS conditions on the resulting carbon structure of fiber mats through Raman spectroscopy measurements and electrical conductivity analysis. Although TOS conditions influenced the properties of the oxidized lignin fiber mats, properties of the carbonized samples were invariant to the TOS procedures used in this study over most of the conditions. As a result, there was flexibility for the parameters (time and temperature) in the TOS process when conditioning softwood lignin materials for carbon fibers.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".