Fabrication of hemicellulose-derived supercapacitor and Pd@lignin nanosphere catalyst by targeted lignocellulose fractionation
Bibliographic record
Abstract
Upgrading lignocellulose resources into value-added products is charming but encounters challenges due to their structural heterogeneity and product complexity. A tandem fractionation technique combining hydrothermal pretreatment (HTP) and deep eutectic solvent (DES) extraction was tailored to separate lignocellulosic three components selectively. HTP achieved almost 100 % hemicellulose dissolution into the aqueous stream, while subsequent DES extraction enabled efficient lignin removal (29–68 %) with high cellulose retention (>80 %). The water-soluble hemicellulose fraction was converted into activated nanocarbons (ANCs) via in-situ hydrothermal carbonization and KOH activation, yielding hierarchical porous materials with specific surface areas up to 2782 m² g⁻¹ . When applied in supercapacitors, rice straw-derived ANC exhibited a high specific capacitance of 284 F g⁻¹ at 0.5 A g⁻¹ and exceptional cycling stability (99 % retention after 10,000 cycles). Simultaneously, DES-extracted lignin riched in phenolic hydroxyl groups and condensed structure after tandem fractionation, was self-assembled into lignin nanospheres (LNSs) serving as supports for Pd nanoparticles (Pd@LNSs). The resulting catalyst demonstrated outstanding catalytic activity for toxic Cr (VI) reduction to Cr (III) under solar irradiation, achieving complete conversion within 8 min and maintaining > 90 % efficiency over eight cycles. This work establishes a closed-loop pathway for lignocellulose valorization, transforming underutilized hemicellulose and lignin into high-performance energy storage materials and robust catalysts, thereby enhancing the economic viability of biorefineries.
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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".