The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Highly Anisotropic, Highly Transparent Wood Composites
Abstract
For the first time, two types of highly anisotropic, highly transparent wood composites are demonstrated by taking advantage of the macro-structures in original wood. These wood composites are highly transparent with a total transmittance up to 90% but exhibit dramatically different optical and mechanical properties.
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
Cites
Transparent and high gas barrier films of cellulose nanofibers prepared by TEMPO-mediated oxidation
Fukuzumi, H., Saito, T., Iwata, T., Kumamoto, Y., & Isogai, A. (2009) · Biomacromolecules · doi:10.1021/bm801065u · Cited by 1,271
Cited by
Wood-Derived Materials for Green Electronics, Biological Devices, and Energy Applications
Zhu, H., Luo, W., Ciesielski, P. N., Fang, Z., Zhu, J. Y., Henriksson, G., Himmel, M. E., & Hu, L. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00225 · Cited by 1,532
Self-citationWood-based nanotechnologies toward sustainability
Jiang, F., Li, T., Li, Y., Zhang, Y., Gong, A., Dai, J., Hitz, E., Luo, W., & Hu, L. (2018) · Advanced Materials · doi:10.1002/adma.201703453 · Cited by 599
Self-citationZeng, X., Sun, J., Yao, Y., Sun, R., Xu, J.-B., & Wong, C.-P. (2017) · ACS Nano · doi:10.1021/acsnano.7b02359 · Cited by 523
Self-citationBioinspired wood nanotechnology for functional materials
Berglund, L. A., & Burgert, I. (2018) · Advanced Materials · doi:10.1002/adma.201704285 · Cited by 518
Chen, F., Gong, A. S., Zhu, M., Chen, G., Lacey, S. D., Jiang, F., Li, Y., Wang, Y., Dai, J., Yao, Y., et al. (2017) · ACS Nano · doi:10.1021/acsnano.7b01350 · Cited by 486
Self-citation
Metadata
- Authors
- Zhu, M., Song, J., Li, T., Gong, A., Wang, Y., Dai, J., Yao, Y., Luo, W., Henderson, D., & Hu, L.
- Venue
- Advanced Materials
- Publisher
- Wiley
- Published
- 2016-05-04
- Volume
- 28
- Issue
- 26
- Pages
- 5181-5187
- ISSN
- 0935-9648 (ISSN-L)
- 1521-4095
- OpenAlex
- W4251495297
- Cited by
- 744
- References
- 1
- FWCI
- 51.57
- 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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Highly Anisotropic, Highly Transparent Wood Composites. https://app.cochat.ai/papers/10.1002/adma.201600427
- The paper
- Zhu, M., Song, J., Li, T., Gong, A., Wang, Y., Dai, J., Yao, Y., Luo, W., Henderson, D., & Hu, L. (2016). Highly Anisotropic, Highly Transparent Wood Composites. Advanced Materials. https://doi.org/10.1002/adma.201600427
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.