Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 177

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Mechanistic Challenges and Advantages of Biosensor Miniaturization into the Nanoscale

Soleymani, Leyla, Li, Feng·2017·ACS Sensors

Abstract

Over the past few decades, there has been tremendous interest in developing biosensing systems that combine high sensitivity and specificity with rapid sample-to-answer times, portability, low-cost operation, and ease-of-use. Miniaturizing the biosensor dimensions into the nanoscale has been identified as a strategy for addressing the functional requirements of point-of-care and wearable biosensors. However, it is important to consider that decreasing the critical dimensions of biosensing elements impacts the two most important performance metrics of biosensors: limit-of-detection and response time. Miniaturization into the nanoscale enhances signal-to-noise-ratio by increasing the signal density (signal/geometric surface area) and reducing background signals. However, there is a trade-off between the enhanced signal transduction efficiency and the longer time it takes to collect target analytes on sensor surfaces due to the increase in mass transport times. By carefully considering the signal transduction mechanisms and reaction-transport kinetics governing different classes of biosensors, it is possible to develop structure-level and device-level strategies for leveraging miniaturization toward creating biosensors that combine low limit-of-detection with rapid response times.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1021/acssensors.7b00069

Cites

5 of 5 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 5 references

Cited by

See all 177

Metadata

Authors
Soleymani, L., & Li, F.
Venue
ACS Sensors
Published
2017-04-03
Volume
2
Issue
4
Pages
458-467
ISSN
2379-3694 (ISSN-L)
OpenAlex
W2601832233
Cited by
177
References
5
FWCI
4.25
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: Mechanistic Challenges and Advantages of Biosensor Miniaturization into the Nanoscale. https://app.cochat.ai/papers/10.1021/acssensors.7b00069
The paper
Soleymani, L., & Li, F. (2017). Mechanistic Challenges and Advantages of Biosensor Miniaturization into the Nanoscale. ACS Sensors. https://doi.org/10.1021/acssensors.7b00069

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.