Transition Metal‐Based Perovskite Derivatives for Selective CO <sub>2</sub> Photoreduction: Role of Orbital Occupancy
Bibliographic record
Abstract
Abstract Transition metals are renowned for their effective catalytic properties. Incorporating transition metals into halide perovskite derivatives is a key strategy for tuning the properties of perovskites to enhance their photocatalytic performance. Understanding the d‐orbital occupancy and spin activity of these transition metals in the CO 2 photoreduction process is essential for fully realizing the photocatalytic potential of these materials. In this study, layered perovskite derivatives are synthesized using cobalt (Co) and copper (Cu) as transition metal components. We observed that Cu and Co exhibit complementary absorption properties attributed to their d‐orbital configuration. Additionally, (DMAP) 2 CuCl 4 (DMAP = 4‐Dimethylaminopyridine) exhibited the highest performance in CO 2 photoreduction with remarkable selectivity for CH 4 formation (≈97%). Pressure‐dependent experiments showed that higher pressures enhance catalytic activity by improving CO 2 saturation and adsorption, accelerating the reaction rate and boosting product yield. The ferromagnetism, hysteresis, and strong spin species detection of (DMAP) 2 CuCl 4 enhance carrier separation and charge availability, boosting CO 2 conversion efficiency. Further, the first‐principles‐based atomistic computations reveal that a more delocalized conduction band edge makes mobile electrons available for CO 2 reduction in (DMAP) 2 CuX 4 . These findings guide the design of selective CO 2 reduction photocatalysts and highlight layered perovskite derivatives for sustainable energy solutions.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".