Mixed-Anion Band Engineered Cobalt Oxynitride for Efficient Solar Water Splitting and Nitrogen Fixation
Bibliographic record
Abstract
This study presents a strategic band structure engineering approach for synthesizing of cobalt oxynitride (CON) using cobalt nitride (CN) as a precursor. Comprehensive structural characterization, employing X-ray diffraction with Rietveld refinement, micro-Raman spectroscopy, and high-resolution transmission electron microscopy, confirmed the presence of Co–O and Co–N bonding within the single-phase CON structure, highlighting the integrated coordination environment of cobalt in the system. Notably, DFT calculations reveal that body-diagonal positioning of nitrogen atoms in oxynitride structure offers superior thermodynamic stability due to minimized anion repulsion, symmetric Co–N bonding, and reduced lattice strain. Optical and physicochemical analyses revealed enhanced light absorption and a reduced bandgap of 2.41 eV for CON compared to 2.62 eV for the cobalt oxide (CO) system. This improvement arises from the effective hybridization of O 2p and N 2p orbitals, leading to a hybrid phase that denotes a solid-solution-type Co–O–N lattice with local bonding heterogeneity, rather than a separate multiphase structure. The CON system demonstrated superior photocatalytic performance, achieving a dye degradation efficiency of ∼92%, a hydrogen (H 2 ) evolution rate of 1748.6 μmol g –1 h –1, and an ammonia (NH 3 ) production rate of 411.2 μmol g –1 h –1, significantly outperforming both oxide (CO: ∼62%, 512.5, 187.3 μmol g –1 h –1 ) and nitride (CN: ∼39%, 152.4, 78.6 μmol g –1 h –1 ) systems. These enhancements arise from synergistic interactions within the mixed-anion oxynitride lattice, which optimize charge separation and transfer dynamics during the photocatalytic reactions. This work highlights cobalt oxynitrides as a promising class of materials for energy conversion and environmental remediation, offering a new paradigm in the functional tuning of conventional metal oxides.
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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".