A Facile Preparation of Super Long‐Term Stable Lignin Nanoparticles from Black Liquor
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract With the increase in lignin availability, materials derived from micro‐ and nano‐scale lignin developed rapidly. However, the tedious processes of raw‐lignin pretreatment and poor stability seriously restrict the large‐scale applications of lignin nanoparticles. To overcome these problems, in this work, a novel green and simple approach was developed to produce super long‐term stable lignin nanoparticles (LTSL NPs). The LTSL NPs were prepared directly from black liquor via an acid precipitation method. The obtained LTSL NPs exhibited well uniformity, excellent dispersibility, controllable size, and super long‐term stability in aqueous media (no apparent size increase or any particle precipitation for 90 days in neutral water medium). Analyses of nuclear magnetic resonance and ion chromatography revealed that LTSL NPs exhibited higher S/G ratio and higher content of hemicellulose comparing with the lignin nanoparticles which obtained from traditional solvent exchange (ethanol/H 2 O) method. In addition, the size of the LTSL NPs can be well controlled by tuning the degree of acid precipitation (the final pH value). This work not only can promote the development of lignin‐based nanomaterials, but also provides a promising utilization pathway for black liquor that simultaneously achieves the fabrication of lignin materials and hemicellulose application.
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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 | 0.000 |
| 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) | 0.000 | 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 it