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Record W4409467065 · doi:10.1002/eem2.70014

Harnessing Nickel‐Based Photocatalysts for <scp>CO</scp><sub>2</sub> Conversion and Hydrogen Production – A Review

2025· review· en· W4409467065 on OpenAlexaff
N. Zahir, Rajangam Vinodh, Shankara S. Kalanur, Sergey I. Nikitenko, Bruno G. Pollet

Bibliographic record

VenueEnergy & environment materials · 2025
Typereview
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsNickelHydrogen productionHydrogenProduction (economics)Materials scienceCatalysisChemistryChemical engineeringMetallurgyEngineeringBiochemistryEconomicsOrganic chemistry

Abstract

fetched live from OpenAlex

Photocatalysis offers a sustainable solution to two pressing global issues: greenhouse gas mitigation and clean energy generation. By harnessing light energy, photocatalytic processes enable water splitting for hydrogen production and CO 2 conversion into value‐added products. Among the materials explored for photocatalysis, nickel‐based photocatalysts have emerged as highly promising due to their low cost, abundance, stability, and efficiency. This review summarizes recent advancements in Ni‐based photocatalysts, highlighting their role in improving photocatalytic performance by enhancing light absorption, charge separation, and reducing charge recombination. Key challenges and future directions for optimizing these materials are also discussed, offering insights into their potential for advancing clean energy technologies.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.742
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.276
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations12
Published2025
Admission routes1
Has abstractyes

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