Evidence map›Paper›PMID 33226776›Full record

ArticleACS nano2020

Going beyond the Debye Length: Overcoming Charge Screening Limitations in Next-Generation Bioelectronic Sensors.

Vladimir Kesler, Boris Murmann, H Tom Soh

Open access · hybridAbstract read
In one paragraph

Article in ACS nano, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers.

0numbers the graph read from it
0cells of the map it votes in
69citing papers in PubMed
0.2field-weighted citation impact, top 48% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

69 citing papers in PubMed, 1 citations in OpenAlex.

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9 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

Vladimir KeslerDepartment of Electrical Engineering, Stanford University, Stanford, California 94305, United States.
Boris MurmannDepartment of Electrical Engineering, Stanford University, Stanford, California 94305, United States.ORCID 0000-0003-3417-8782
H Tom SohDepartment of Electrical Engineering, Stanford University, Stanford, California 94305, United States.ORCID 0000-0001-9443-857X
Stanford University · USChan Zuckerberg Initiative (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electronic biosensors are a natural fit for field-deployable diagnostic devices because they can be miniaturized, mass produced, and integrated with circuitry. Unfortunately, progress in the development of such platforms has been hindered by the fact that mobile ions present in biological samples screen charges from the target molecule, greatly reducing sensor sensitivity. Under physiological conditions, the thickness of the resulting electric double layer is less than 1 nm, and it has generally been assumed that electronic detection beyond this distance is virtually impossible. However, a few recently described sensor design strategies seem to defy this conventional wisdom, exploiting the physics of electrical double layers in ways that traditional models do not capture. In the first strategy, charge screening is decreased by constraining the space in which double layers can form. The second strategy uses external stimuli to prevent double layers from reaching equilibrium, thereby effectively reducing charge screening. In this Perspective, we describe these relatively new concepts and offer theoretical insights into mechanisms that may enable electronic biosensing beyond the Debye length. If these concepts can be further developed and translated into practical electronic biosensors, we foresee exciting opportunities for the next generation of diagnostic technologies.

Indexed as

Biosensing TechniquesElectronicsIonsIons

Identifiers

PMID33226776
PMCPMC7761593
OpenAlexW3045290641

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.