Isolated Iridium Sites on Potassium‐Doped Carbon‐nitride wrapped Tellurium Nanostructures for Enhanced Glycerol Photooxidation
Bibliographic record
Abstract
Abstract Many industrial processes such transesterification of fatty acid for biodiesel production, soap manufacturing and biosynthesis of ethanol generate glycerol as a major by‐product that can be used to produce commodity chemicals. Photocatalytic transformation of glycerol is an enticing approach that can exclude the need of harsh oxidants and extraneous thermal energy. However, the product yield and selectivity remain poor due to low absorption and unsymmetrical site distribution on the catalyst surface. Herein, tellurium (Te) nanorods/nanosheets (TeNRs/NSs) wrapped potassium‐doped carbon nitride (KCN) van der Waal (vdW) heterojunction (TeKCN) is designed to enhance charge separation and visible‐NIR absorption. The iridium (Ir) single atom sites decoration on the TeKCN core‐shell structure (TeKCNIr) promotes selective oxidation of glycerol to glyceraldehyde with a conversion of 45.6% and selectivity of 61.6% under AM1.5G irradiation. The catalytic selectivity can reach up to 88% under 450 nm monochromatic light. X‐ray absorption spectroscopy (XAS) demonstrates the presence of undercoordinated IrN2O2 sites which improved catalytic selectivity for glycol oxidation. Band energies and computational calculations reveal faile charge transfer in the TeKCNIr heterostructure. EPR and scavenger tests discern that superoxide (O2•−) and hydroxyl (•OH) radicals are prime components driving glycerol oxidation.
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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".