Green Solvents for the Liquid-Phase Exfoliation of Biochars
Bibliographic record
Abstract
Exfoliation can be used to weaken and break van der Waals interactions within layered materials and produce small monolayered counterparts with remarkable properties. In the current study, liquid-phase exfoliation (LPE) using ultrasound and organic solvents is explored as a method to break down layered structures in biochars. Unfortunately, preferred solvents that can effectively disperse and stabilize the sheets produced during exfoliation often possess several health risks. In this work, we show that LPE in greener solvents can be used to access nanostructures of biochars to further improve the applications of this biobased material. Herein, pristine and oxidized biochars are exfoliated in a range of solvents to allow the identification of benign alternatives, which have been classified as nonhazardous or less hazardous by various solvent guides. The majority of biochar nanostructures produced consists of stacked nanosheets containing between two and eight layers with 15 nm thickness in average. Correlations between the LPE of biochars and different solvent parameters are established, and surface modification of biochars has potential to increase their exfoliation in more benign solvents. The LPE of oxidized biochars is more efficient in hydrogen-bond-accepting solvents due to the increased concentration of functional groups on their surface. Dispersions containing 0.20–0.75 mg/mL exfoliated oxidized biochars were obtained in solvents such as polyethylene glycols and ε-caprolactone. The LPE of pristine biochars in dimethyl carbonate and ethyl acetate gives similar yields to the most commonly used solvent for this process, N -methyl-2-pyrrolidone.
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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".