FeNi bimetallic functionalized lignin catalyst for sustainable oxidation processes
Bibliographic record
Abstract
The advancement of sustainable and efficient catalytic procedures is crucial in tackling the continuous environmental and industrial challenges, with research being inherently focused on sustainable chemical science to exploit the possibilities of cost-effective bio-based materials for practical applications. Considerably, this investigation delves into the synthesis, characterization, and use of Fe Ni bimetallic functionalized lignin (FeNi@Lig) catalysts using lignin extracted from spent coffee grounds, an underutilized agro-industrial waste. This eco-friendly approach emphasizes the valorization of non-traditional biomass while reducing waste streams. FeNi@Lig was used for oxidation processes, concentrating on the oxidation of bromothymol blue and cellulose for environmental remediation and the production of valuable chemicals. By capitalizing on the multifaceted attributes of lignin, FeNi@Lig catalysts were produced and examined using several techniques, uncovering an effective dispersion of Fe and Ni nanoparticles on the lignin support. The catalysts displayed remarkable efficiency and selectivity in oxidative processes, notably boosting reaction speeds and diminishing the creation of unwanted side products. The oxidation of bromothymol blue (BB) was carried out with a 2 % catalyst, yielding a conversion efficiency of 99.35 % in just 180 s. Likewise, the optimal cellulose oxidation exhibited an oxidation degree of 91.11 % with a 5 % catalyst. The outcomes emphasize the promise of catalysts derived from biomass in industrial settings, advocating for sustainable methodologies and propelling the realm of eco-friendly chemistry. • Fe Ni bimetallic lignin catalyst achieves 99.35 % dye oxidation in 3 min. • Efficient cellulose oxidation reaches 91.11 % using 5 % in 60 min at 60 °C. • High recyclability demonstrated, maintaining superior efficiency over multiple cycles. • Functionalized catalyst combines lignin biopolymer with metal nanoparticle synergy. • Sustainable solution for environmental cleanup and chemical transformations.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".