2018
DOI: 10.1038/s41598-018-34287-w
|View full text |Cite
|
Sign up to set email alerts
|

Bio-inspired Z-scheme g-C3N4/Ag2CrO4 for efficient visible-light photocatalytic hydrogen generation

Abstract: Due to low charge separation efficiency and poor stability, it is usually difficult for single-component photocatalysts such as graphitic carbon nitride (g-C3N4) and silver chromate (Ag2CrO4) to fulfill photocatalytic hydrogen production efficiently. Z-scheme charge transport mechanism that mimics the photosynthesis in nature is an effective way to solve the above problems. Inspired by photosynthesis, we report Ag2CrO4 nanoparticles-decorated g-C3N4 nanosheet as an efficient photocatalyst for hydrogen evolutio… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

3
39
0

Year Published

2019
2019
2022
2022

Publication Types

Select...
8
1

Relationship

1
8

Authors

Journals

citations
Cited by 72 publications
(42 citation statements)
references
References 82 publications
3
39
0
Order By: Relevance
“…Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks have been shown to achieve stateof-the-art performance in many benchmark time-series and sequential data applications (Bahdanau et al 2014;Sutskever et al 2014;Che et al 2018). Its success in these applications is due to its ability to retain an internal memory of previous data, and hence capture long-term temporal dependencies of variable-length observations in sequential data.…”
Section: Recurrent Neural Network Architecturementioning
confidence: 99%
“…Recurrent Neural Networks (RNNs), such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks have been shown to achieve stateof-the-art performance in many benchmark time-series and sequential data applications (Bahdanau et al 2014;Sutskever et al 2014;Che et al 2018). Its success in these applications is due to its ability to retain an internal memory of previous data, and hence capture long-term temporal dependencies of variable-length observations in sequential data.…”
Section: Recurrent Neural Network Architecturementioning
confidence: 99%
“…Mainly, the individual component of the pure g‐C 3 N 4 catalyst quickly suffered with recombination of photo induced electron/hole pairs as only a minor fraction of them actively contributed in water splitting reaction, and thus showed low photocatalytic activity . As a result of experiments and characterization, a plausible photocatalytic reaction mechanism was proposed for g‐C 3 N 4 with Nb 2 O 5 heterostructure as shown in Figure .…”
Section: Resultsmentioning
confidence: 99%
“…This result further manifests that photoexcited PAE–BTz needs more time to arrive in the balance than pure PAE. 50…”
Section: Results and Discussionmentioning
confidence: 99%