The DOI matches its Crossref record.
Facile Fabrication of MoS2-Modified SnO2 Hybrid Nanocomposite for Ultrasensitive Humidity Sensing
Abstract
An ultrasensitive humidity sensor based on molybdenum-disulfide- (MoS2)-modified tin oxide (SnO2) nanocomposite has been demonstrated in this work. The nanostructural, morphological, and compositional properties of an as-prepared MoS2/SnO2 nanocomposite were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), energy dispersive spectrometry (EDS), nitrogen sorption analysis, and Raman spectroscopy, which confirmed its successful preparation and rationality. The sensing characteristics of the MoS2/SnO2 hybrid film device against relative humidity (RH) were investigated at room temperature. The RH sensing results revealed an unprecedented response, ultrafast response/recovery behaviors, and outstanding repeatability. To our knowledge, the sensor response yielded in this work was tens of times higher than that of the existing humidity sensors. Moreover, the MoS2/SnO2 hybrid nanocomposite film sensor exhibited great enhancement in humidity sensing performances as compared to the pure MoS2, SnO2, and graphene counterparts. Furthermore, complex impedance spectroscopy and bode plots were employed to understand the underlying sensing mechanisms of the MoS2/SnO2 nanocomposite toward humidity. The synthesized MoS2/SnO2 hybrid composite was proved to be an excellent candidate for constructing ultrahigh-performance humidity sensor toward various applications.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Chemical sensing with 2D materials
Anichini, C., Czepa, W., Pakulski, D., Aliprandi, A., Ciesielski, A., & Samorì, P. (2018) · Chemical Society Reviews · doi:10.1039/c8cs00417j · Cited by 756
Contact-Based Methods for Measuring Respiratory Rate
Massaroni, C., Nicolò, A., Lo Presti, D., Sacchetti, M., Silvestri, S., & Schena, E. (2019) · Sensors · doi:10.3390/s19040908 · Cited by 426
Zhang, D., Wu, J., Li, P., & Cao, Y. (2017) · Journal of Materials Chemistry A · doi:10.1039/c7ta07001b · Cited by 425
Self-citationEvolution of breath analysis based on humidity and gas sensors: Potential and challenges
Tai, H., Wang, S., Duan, Z., & Jiang, Y. (2020) · Sensors and Actuators B: Chemical · doi:10.1016/j.snb.2020.128104 · Cited by 412
Functional gas sensing nanomaterials: A panoramic view
Malik, R., Tomer, V. K., Mishra, Y. K., & Lin, L. (2020) · Applied Physics Reviews · doi:10.1063/1.5123479 · Cited by 410
Samadi, M., Sarikhani, N., Zirak, M., Zhang, H., Zhang, H.-L., & Moshfegh, A. Z. (2018) · Nanoscale Horizons · doi:10.1039/c7nh00137a · Cited by 408
Wearable humidity sensor based on porous graphene network for respiration monitoring
Pang, Y., Jian, J., Tu, T., Yang, Z., Ling, J., Li, Y., Wang, X., Qiao, Y., Tian, H., Yang, Y., et al. (2018) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2018.05.038 · Cited by 369
MoS2‐Based Nanomaterials for Room‐Temperature Gas Sensors
Kumar, R., Zheng, W., Liu, X., Zhang, J., & Kumar, M. (2020) · Advanced Materials Technologies · doi:10.1002/admt.201901062 · Cited by 287
Carbon Nanocoil-Based Fast-Response and Flexible Humidity Sensor for Multifunctional Applications
Wu, J., Sun, Y.-M., Wu, Z., Li, X., Wang, N., Tao, K., & Wang, G. P. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.8b18599 · Cited by 264
Self-citationLead‐Free Cs 2 BiAgBr 6 Double Perovskite‐Based Humidity Sensor with Superfast Recovery Time
Weng, Z., Qin, J., Umar, A. A., Wang, J., Zhang, X., Wang, H., Cui, X., Li, X., Zheng, L., & Zhan, Y. (2019) · Advanced Functional Materials · doi:10.1002/adfm.201902234 · Cited by 236
Peng, B., Zhao, F., Ping, J., & Ying, Y. (2020) · Small · doi:10.1002/smll.202002681 · Cited by 224
Cho, M., Kim, S., Kim, I., Kim, E., Wang, Z., Kim, N., Kim, S., & Oh, J. (2020) · Advanced Functional Materials · doi:10.1002/adfm.201907449 · Cited by 169
Wu, Z., Yang, J., Sun, X., Wu, Y., Wang, L., Meng, G., Kuang, D., Guo, X., Qu, W., Du, B., et al. (2021) · Sensors and Actuators B: Chemical · doi:10.1016/j.snb.2021.129772 · Cited by 142
Placencia-Gómez, E., Kerisit, S. N., Mehta, H. S., Qafoku, O., Thompson, C. J., Graham, T. R., Ilton, E. S., & Loring, J. S. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.0c00897 · Cited by 57
Metadata
- Authors
- Zhang, D., Sun, Y., Li, P., & Zhang, Y.
- Venue
- ACS Applied Materials & Interfaces
- Publisher
- American Chemical Society (ACS)
- Published
- 2016-05-24
- Volume
- 8
- Issue
- 22
- Pages
- 14142-14149
- ISSN
- 1944-8244 (ISSN-L)
- 1944-8252
- OpenAlex
- W2395594249
- Cited by
- 515
- References
- 0
- FWCI
- 30.49
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
