Simple Strategies for Enhancement of the Strength of Lignin‐Based Nanofibrous Aerogels
Bibliographic record
Abstract
Abstract Solvent fractionated lignin and cellulose nanocrystals (CNCs) are used to create highly resilient nanofiber‐based aerogel materials. Two fractions of softwood kraft lignin (SKL) are combined and subsequently electrospun into nanofibers composed of 99% lignin. Additionally, 5 wt.% of CNCs is added into the fiber, based on the solid lignin weight, to enhance the physical properties of the nanofiber materials. The manufacturing process involves dispersing the fibers in water followed by freeze‐drying and subsequent heat treatment. The heat treatment process, with carefully chosen blends of fractionated lignin with specific glass transition temperatures, provides an initial thermoplastic behavior that results in the physical cross‐linking of entangled fibers upon heat treatment. This tailored morphology shows four times higher compressive strength compared to lignin nanofiber materials that only contain high molecular weight fractions. Moreover, CNC is a critical additive that helps maintain fiber geometry by reducing significant softening of lignin under elevated temperatures. Therefore, the fibers with CNC additives ensure the 3D shape after heat treatment, resulting in enhanced physical connections at fiber junctions. As a result, lignin/CNC nanofibers are transformed into 3D structured, lightweight materials that can undergo near full recovery after repeated compressive strain matching the performance of some carbonized analogs.
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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 | 0.000 | 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) | 0.001 | 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".