Harnessing Nickel-based Photocatalysts for CO2 Conversion and Hydrogen Production -- A Review
Bibliographic record
Abstract
Photocatalytic processes of carbon dioxide (CO2) conversion and molecular hydrogen (H2) production could potentially address two major global challenges: greenhouse gas (GHG) mitigation and clean energy generation, respectively. During photocatalysis, the energy harvested via the light absorption in photosensitizers drives the important chemical reactions using the photogenerated charges to convert water into H2 or CO2 into value-added products. Recent decades have witnessed the widespread popularity of photocatalytic technology owing to its sustainable, renewable, and greener pathway to produce fuels and chemicals. Given this, a wide range of materials have been explored for photocatalytic applications. Among the developed materials, Ni-based photocatalysts have received considerable attention due to their distinct properties including low cost, stability, abundance, and high activity. This review addresses recent developments concerning nickel (Ni)-based photocatalysts used in photocatalytic CO2 conversion and H2 production. The use of Ni-materials plays a crucial role in enhancing photocatalytic activity through improved light-absorption, charge-separation, along with suppressed charge recombination to enhance the efficiency of hydrogen evolution and CO2 conversion. The performance of nickel-based photocatalysts during CO2 reduction and water splitting reactions is summarized, which provide a comprehensive overview of Ni-based photocatalyst efficiency and selectivity. Finally, challenges and future prospects are examined in detail for further optimization of Ni-based photocatalysts. This review also provides an update on the studies that have been conducted on Ni-based materials for H2 generation and CO2 reduction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".