Binary Iron‐Manganese Cocatalyst for Simultaneous Activation of C−C and C−O Bonds to Maximally Utilize Lignin for Syngas Generation over InGaN
Bibliographic record
Abstract
Abstract Solar‐powered lignin reforming offers a carbon‐neutral route for syngas production. This study explores a dual non‐precious iron‐manganese cocatalyst to simultaneously activate both C−C and C−O bonds for maximizing the utilization of various substituents of native lignin to yield syngas. The cocatalyst, integrated with InGaN nanowires on a Si wafer, affords a measurable syngas evolution rate of 42.4 mol gcat−1 h−1 from native lignin in distilled water with a high selectivity of 93 % and tunable H2/CO ratios under concentrated light, leading to a considerable light‐to‐fuel efficiency of 11.8 %. The high FeMn atom efficiency arising from the 1‐dimensional nanostructure of InGaN enables the achievement of a high turnover frequency (TOF) of 220896 mol syngas per mol FeMn per hour. Combined experimental and theoretical investigations reveal that the synergetic iron‐manganese cocatalyst supported by InGaN nanowires enables simultaneous activation of C−C and C−O bonds with comparable minimized dissociation energies, thus promising to maximally utilize different substituents of −OCH3, and −CH2CH2CH3 in lignin for syngas production. Moreover, the dual Fe‐Mn cocatalyst demonstrates a most energetically favorable route for the consecutive release of hydrogen from •CH3 and •OH by the oxidative holes while inhibiting the reversion of hydrogen and hydroxyl into water.
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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".