Miscanthus Biomass for the Sustainable Fractionation of Ethanol–Water Mixtures
Bibliographic record
Abstract
Miscanthus is a rich source of lignocellulosic biomass with low mineral content suitable for applications that range from biofuel production to value-added biomass-derived products including a sustainable biosorbent. Herein, Miscanthus and its modified forms were used for the fractionation of water (W) and ethanol (E) mixtures that were analyzed by an in situ analytical method, referred to as quantitative NMR (qNMR) spectroscopy. Miscanthus was pretreated by hydrolysis and subsequent grinding to yield materials with variable biopolymer content (cellulose and lignins) and particle size. The Miscanthus materials were evaluated as sorbents in binary water–ethanol (W-E) mixtures. The maximum biomass adsorption capacity ( Q m; g g –1 ) with water ( Q m,W ) and ethanol ( Q m,E ) fractions were determined by the best-fit Sips model parameters listed in parentheses: raw Miscanthus biomass ( Q m,W = 8.93 and Q m,E = 4.15) and pretreated Miscanthus biomass ( Q m,W = 4.73 and Q m,E = 3.22, g g –1 ). The fractionation properties of Miscanthus and its biopolymer constituents show molecular selectivity [ R selectivity = Q m,W / Q m,E ] between W and E. The R selectivity values are given in parentheses, as follows: untreated Miscanthus (3:1), pretreated Miscanthus (1.5:1), and lignins (1:5.4). The pretreated Miscanthus was prepared by acid and base hydrolysis for the removal of hemicellulose and lignins, respectively, leading to cellulose enrichment. The raw and pretreated Miscanthus have preferential water uptake properties that relate to the relative biopolymer composition. To test the reusability and regeneration of Miscanthus, the biosorbent was tested over four adsorption–desorption cycles. This work contributes to a greater understanding of chemical treatment effects on biomass adsorption properties and evaluation of the adsorptive contributions of biopolymer components for the fractionation of water–ethanol mixtures.
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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) | 0.001 | 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".