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Record W2990043573 · doi:10.1002/er.5014

Constructing 1D/2D heterojunction photocatalyst from FeSe <sub>2</sub> nanorods and MoSe <sub>2</sub> nanoplates with high photocatalytic and photoelectrochemical performance

2019· article· en· W2990043573 on OpenAlexaff
Zhe Kong, Jun Zhang, Hongbo Wang, Bo Cui, Jinghui Zeng, Xuan Chen, Penghui Tian, Guozhou Huang, Junhua Xi, Zhenguo Ji

Bibliographic record

VenueInternational Journal of Energy Research · 2019
Typearticle
Languageen
FieldEnergy
TopicAdvanced Photocatalysis Techniques
Canadian institutionsUniversity of Waterloo
FundersZhejiang Xinmiao Talents ProgramNational Natural Science Foundation of China
KeywordsNanorodPhotocatalysisHeterojunctionMaterials scienceNanotechnologyVisible spectrumOptoelectronicsChemical engineeringChemistryCatalysis

Abstract

fetched live from OpenAlex

Although the traditional metal oxide catalyst has high activity and strong degradation ability, the forbidden bandgap is generally larger, and the utilization rate of sunlight is much low. Moreover, the high internal resistance inhibits carrier transfer, so the photoelectrochemical performance needs to be improved. Selenides with narrow bandgap and low internal resistance are promising candidates for photocatalysts. A new type of 1D/2D selenide heterojunction was constructed by compositing MoSe2 and FeSe2, two kinds of narrow bandgap metal selenides. In this 1D/2D heterojunction, MoSe2 presents a three-dimensional network structure, which can effectively collect and transport optical carriers, and it is an ideal heterostructure as a substrate loaded with 1D FeSe2 nanorods. Moreover, this heterojunction has good light absorption characteristics and can achieve full spectrum absorption of ultraviolet and visible light. This FeSe2/MoSe2 composite has photocatalytic performance more than 3.4 times that of MoSe2, and its photoelectrochemical performance is more than 2 times. The experimental results show that FeSe2/MoSe2 is an ideal composite system with great potential in photocatalysis.

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 score0.023
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.012
GPT teacher head0.271
Teacher spread0.259 · 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

Citations40
Published2019
Admission routes1
Has abstractyes

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