Statistical analysis of the effects of carbonization parameters on the structure of carbonized electrospun organosolv lignin fibers
Bibliographic record
Abstract
ABSTRACT Organosolv process is among the top choices for pretreatment of lignocellulosic biomass in cellulosic ethanol production. The lignin obtained as coproduct of this process is a low molecular weight biopolymer. Development of high‐value application for this material enhances the economic viability of the bioethanol production process. In this study, aqueous sodium hydroxide solution of organosolv lignin/poly(ethylene oxide) (PEO) blend with 95/5 wt % ratio was electrospun. The fiber morphology and thermal properties were compared with organosolv lignin/PEO fibers electrospun from N , N ‐dimethylformamide (DMF) solution. Although organosolv lignin powder and fibers spun from DMF had a low T g (∼100 °C), the fibers from alkaline aqueous solutions did not exhibit a glass transition point and could be carbonized without thermostabilization. The effects of carbonization heating rate, temperature, and time on the average fiber diameter, Raman peaks, and X‐ray diffraction results of the carbonized electrospun fibers were statistically analyzed by using a two‐level factorial design of experiments. Carbonization temperature, time, and interaction of these two parameters have the most significant effects. Formation of graphitized structures in the carbonized fibers was confirmed by transmission electron microscopy (TEM). The alkaline aqueous electrospinning of organosolv lignin has the advantages of using a green solvent, ability to increase the lignin content of fibers to more than 95 wt %, and removing the time and energy intensive step of thermostabilization before carbonization. © 2016 The Authors. Journal of Applied Polymer Science published by Wiley Periodicals LLC. J. Appl. Polym. Sci. 2016 , 133 , 44005.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".