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Record W3185008295 · doi:10.1002/er.7111

C‐doped <scp>SnO<sub>2</sub></scp> nanostructure/<scp>MoS<sub>2</sub></scp>/<scp>p‐Si</scp> electrodes for visible light‐driven photoelectrochemical hydrogen evolution reaction

2021· article· en· W3185008295 on OpenAlexaff
K. Mallikarjuna, Mahider Tekalgne, Amirhossein Hasani, Sung Hyun Hong, ‪Sang Hyun Ahn, Soo Young Kim, Haekyoung Kim

Bibliographic record

VenueInternational Journal of Energy Research · 2021
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsSimon Fraser University
FundersNational Research Foundation of KoreaChung-Ang University
KeywordsWater splittingMaterials scienceHydrogen productionHydrogenNanostructureChemical engineeringContext (archaeology)ElectrochemistryDopingSubstrate (aquarium)NanotechnologyCarbon fibersElectrodeOptoelectronicsPhotocatalysisChemistryCatalysis

Abstract

fetched live from OpenAlex

Summary Recently, the increase in the CO 2 content in the Earth's atmosphere causes global warming and the rapid consumption of fossil fuel resources such as coal and oil. Therefore, effort is required to create clean and sustainable energy resources to address these environmental issues. In this context, hydrogen evolution from water splitting‐based photoelectrochemical technologies plays a significant role as a zero CO 2 emission fuel. Here, we design and prepare carbon‐doped SnO 2 nanostructures by a simple single‐step thermal decomposition method and coated on a MoS 2 /p‐Si substrate for hydrogen evolution by photoelectrochemical water splitting. The C‐doped SnO 2 /MoS 2 /p‐Si shows enhanced activity in the hydrogen evolution reaction, with an onset potential of −0.17 V at 2.73 mA/cm 2 , and high stability for over 45 hours. In addition, the doping of carbon influences the shape of the nanostructures, inducing their transformation from cubical rods to polyhedral structures. This study provides a promising method for the fabrication of heterogeneous photoelectrocatalysts for overall water splitting.

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)0.002
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-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 score0.083
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.002
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 head0.309
Teacher spread0.292 · 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

Citations7
Published2021
Admission routes1
Has abstractyes

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