The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Progress of new label-free techniques for biosensors: a review
Abstract
The detection techniques used in biosensors can be broadly classified into label-based and label-free. Label-based detection relies on the specific properties of labels for detecting a particular target. In contrast, label-free detection is suitable for the target molecules that are not labeled or the screening of analytes which are not easy to tag. Also, more types of label-free biosensors have emerged with developments in biotechnology. The latest developed techniques in label-free biosensors, such as field-effect transistors-based biosensors including carbon nanotube field-effect transistor biosensors, graphene field-effect transistor biosensors and silicon nanowire field-effect transistor biosensors, magnetoelastic biosensors, optical-based biosensors, surface stress-based biosensors and other type of biosensors based on the nanotechnology are discussed. The sensing principles, configurations, sensing performance, applications, advantages and restriction of different label-free based biosensors are considered and discussed in this review. Most concepts included in this survey could certainly be applied to the development of this kind of biosensor in the future.
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
Two-dimensional gas of massless Dirac fermions in graphene
Novoselov, K. S., Geim, A. K., Morozov, S. V., Jiang, D., Katsnelson, M. I., Grigorieva, I. V., Dubonos, S. V., & Firsov, A. A. (2005) · Nature · doi:10.1038/nature04233 · Cited by 21,611
Highly Stretchable and Sensitive Strain Sensor Based on Silver Nanowire–Elastomer Nanocomposite
Amjadi, M., Pichitpajongkit, A., Lee, S., Ryu, S., & Park, I. (2014) · ACS Nano · doi:10.1021/nn501204t · Cited by 2,350
Electrochemical Biosensors - Sensor Principles and Architectures
Grieshaber, D., MacKenzie, R., Vörös, J., & Reimhult, E. (2008) · Sensors · doi:10.3390/s80314000 · Cited by 2,329
Importance of the Debye Screening Length on Nanowire Field Effect Transistor Sensors
Stern, E., Wagner, R., Sigworth, F. J., Breaker, R., Fahmy, T. M., & Reed, M. A. (2007) · Nano Letters · doi:10.1021/nl071792z · Cited by 806
Glucose sensor based on organic thin film transistor using glucose oxidase and conducting polymer
Liu, J., Agarwal, M., & Varahramyan, K. (2008) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2008.08.009 · Cited by 183
Cited by
Recent Advances in Biosensor Technology for Potential Applications – An Overview
Vigneshvar, S., Sudhakumari, C. C., Senthilkumaran, B., & Prakash, H. (2016) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2016.00011 · Cited by 674
“…materials, such as graphite, grapheme, and carbon nanotubes, are used for developing biosensor immobilization (Li et al., 2011; Zhou et al., 2012; Guo, 2013; Ko et al., 2013; Senveli and Tigli, 2013; Valentini et al., 2013; Lamprecht et al., 2014; Shen et al., 2014; Sang et al., 2015).”
BackgroundMetasurface Micro/Nano-Optical Sensors: Principles and Applications
Qin, J., Jiang, S., Wang, Z., Cheng, X., Li, B., Shi, Y., Tsai, D. P., Liu, A. Q., Huang, W., & Zhu, W. (2022) · ACS Nano · doi:10.1021/acsnano.2c03310 · Cited by 359
Optical biosensors: A decade in review
Singh, A. K., Mittal, S., Das, M., Saharia, A., & Tiwari, M. (2023) · Alexandria Engineering Journal · doi:10.1016/j.aej.2022.12.040 · Cited by 292
Zhou, Z., Shi, Z., Cai, X., Zhang, S., Corder, S. G., Li, X., Zhang, Y., Zhang, G., Chen, L., Liu, M., et al. (2017) · Advanced Materials · doi:10.1002/adma.201605471 · Cited by 148
Futuristic biosensors for cardiac health care: an artificial intelligence approach
Vashistha, R., Dangi, A. K., Kumar, A., Chhabra, D., & Shukla, P. (2018) · 3 Biotech · doi:10.1007/s13205-018-1368-y · Cited by 113
Metadata
- Authors
- Sang, S., Wang, Y., Feng, Q., Wei, Y., Ji, J., & Zhang, W.
- Venue
- Critical Reviews in Biotechnology
- Publisher
- Informa UK Limited
- Published
- 2015-01-22
- Volume
- 36
- Issue
- 3
- Pages
- 1-17
- ISSN
- 0738-8551 (ISSN-L)
- 1549-7801
- OpenAlex
- W2163618583
- Cited by
- 272
- References
- 5
- FWCI
- 6.74
- 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: Progress of new label-free techniques for biosensors: a review. https://app.cochat.ai/papers/10.3109/07388551.2014.991270
- The paper
- Sang, S., Wang, Y., Feng, Q., Wei, Y., Ji, J., & Zhang, W. (2015). Progress of new label-free techniques for biosensors: a review. Critical Reviews in Biotechnology. https://doi.org/10.3109/07388551.2014.991270
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.
