Photodegradation of lignin biowaste catalyzed by biosynthesized zinc oxide nanoparticles using the leaf extract of Aristotelia chilensis
Bibliographic record
Abstract
• Biogenic ZnO–B nanoparticles achieved up to 90.57% lignin degradation. • First study evaluating biogenic ZnO for photocatalytic lignin degradation. • Photocatalytic lignin conversion into high-value chemicals with diverse uses. This study evaluated the photocatalytic activity of zinc oxide nanoparticles (ZnO-B) synthesized using a leaf extract of Aristotelia chilensis and the effect of calcination at different temperatures (400, 600, and 800 °C) on their properties and performance. The photocatalytic degradation of lignin was compared among biogenic ZnO-B, chemically synthesized ZnO (ZnO–Ch), and commercial ZnO (ZnO–C). The lignin degradation rates after 24 h were ZnO–B_400 (60.8%), ZnO–B_600 (90.57%), ZnO–B_800 (27.83%), ZnO–Ch (23.2%), and ZnO–C (80.7%). The nanoparticles were characterized by TEM, XRD, FTIR, and UV–vis spectroscopy. The physicochemical properties and photocatalytic efficiency of ZnO–B were significantly influenced by calcination temperature, with ZnO–B_600 demonstrating superior photocatalytic activity under UV-A and simulated sunlight. GC–MS analysis of lignin degradation products revealed the transformation of lignin into high-value chemicals, including 2,3-hexanediol, 1,2-benzenedicarboxylic acid diethyl ester, phthalic acid cyclobutyl isobutyl ester, 2-(1-oxopropyl)-benzoic acid, and 4-hydroxy-2-butanone. These findings highlight the potential of biogenic ZnO-B nanoparticles in photocatalytic processes for the valorization of Kraft lignin into value-added compounds of interest to the chemical, cosmetic, and pharmaceutical industries.
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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) | 0.000 | 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 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".