Photodegradation of lignin biowaste catalyzed by biosynthesized zinc oxide nanoparticles using the leaf extract of Aristotelia chilensis
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it