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Record W4414558899 · doi:10.1021/acsaem.5c01671

Multivalent Rh@RhO<sub><i>x</i></sub> and MnO<sub><i>x</i></sub>-Modified CdS for Robust and Efficient Photocatalytic Hydrogen Evolution

2025· article· en· W4414558899 on OpenAlexaff
Yi Zheng, C.C. Zhang, Qiaoling Chen, Shuaishuai Zhu, Wen Li, Rong Ma, Chunlei Li

Bibliographic record

VenueACS Applied Energy Materials · 2025
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsNickel Institute
FundersScience and Technology Program of Gansu ProvinceLanzhou University of TechnologyNational Natural Science Foundation of China
KeywordsOxidizing agentHydrogenPhotocatalysisOxideAdsorptionRadicalWater splitting

Abstract

fetched live from OpenAlex

Rh is considered an effective HER active site, but the impact of the oxide layer formed by exposure to air on HER has been ignored. Here, Rh@RhO x and MnO x are used to enhance the hydrogen evolution reaction (HER) activity of CdS by vacuum photodeposition method. We leverage the built-in electric field generated between the supporter and cocatalysts, as well as the valence state transitions of multivalent cocatalysts, to achieve maximum photocarrier separation. Through in situ XPS, H 2 O-TPD, ESR, LSV, and TPV, the roles of Rh@RhO x and MnO x were thoroughly investigated. First, Rh@RhO x and MnO x act as reducing and oxidizing cocatalysts, respectively. Second, water (H 2 O) adsorption and activation are accomplished on their surfaces. Third, RhO x facilitates the formation of adsorbed H 2 (H ads ), while MnO x efficiently consumes photogenerated holes (h + ) through the generation of hydroxyl radicals (·OH). At their maximum effective concentrations, photogenerated electrons interact with H ads and H 2 O to generate H 2 . The hydrogen evolution rate of the optimal sample (R 0.7 CM 1.5 ) was 78.95 mmol·g –1 ·h –1, which was 415 times higher than that of CdS. This study provides a convenient and feasible modification scheme for commercially available HER photocatalysts.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.010
GPT teacher head0.233
Teacher spread0.223 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations3
Published2025
Admission routes1
Has abstractyes

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