Isolation of lignin‐containing cellulose nanocrystals: life‐cycle environmental impacts and opportunities for improvement
Bibliographic record
Abstract
Abstract Deep eutectic solvent (DES) has recently been attracting great interest for its role in isolating nanocellulose owing to its distinct advantages of biodegradability, low toxicity, and recyclability. Lignin‐containing cellulose nanocrystals (LCNCs) obtained using DES pretreatment has led to an improvement in the production of nanomaterials. Understanding the potential environmental impacts of this novel technology at the laboratory scale provides important insights to improve its sustainability at full scale in the future. This study evaluates the environmental impacts of the production of LCNCs from thermomechanical pulp (TMP) following acidic DES pretreatment (using a binary system of ‘choline chloride – oxalic acid dihydrate’ or a ternary system of ‘choline chloride – oxalic acid dihydrate – p‐toluenesulfonic acid’) based on various laboratory trials. The evaluation was conducted through a cradle‐to‐gate life‐cycle assessment for global warming potential (GWP), acidification potential (AP) and the cumulative energy demand (MJ). The average GWP, AP, and energy use were 39 kg CO2‐eq, 0.17 kg SO2‐eq, and 995 MJ per kg LCNCs, respectively. The sensitivity analysis showed that different degrees of reduction in environmental impact could be achieved by varying the input volume and/or reuse frequency of DES solutions. The largest reductions in GWP, AP, and energy use were achieved by reducing the input volume of DES solutions to 20% of its default value. The results of this LCA study illustrate the direction for future research and development (R&D) to further improve the sustainability of this DES‐mediated LCNC production technology. Through comparisons with the existing literature, this study also confirms the predominant contribution of chemical manufacturing to the overall environmental impacts of nanocellulose isolation technologies in general. © 2021 Society of Chemical Industry and John Wiley & Sons, Ltd
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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".