Evidence map›Paper›PMID 41970862›Full record

ArticleACS omega2026

Miniaturizing Sensor Active Areas to Enhance Analyte Surface Densities by Increasing "Effective" Analyte Concentrations.

Aruna Chandra Singh, D Balakrishnan, P Grysan, Sivashankar Krishnamoorthy

Abstract read
In one paragraph

Article in ACS omega, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Aruna Chandra SinghLuxembourg Institute of Science and Technology (LIST), 41, Rue du Brill, Belvaux L-4422, Luxembourg.ORCID https://orcid.org/0000-0002-6219-0204
D BalakrishnanLuxembourg Institute of Science and Technology (LIST), 41, Rue du Brill, Belvaux L-4422, Luxembourg.ORCID https://orcid.org/0000-0001-5540-4633
P GrysanLuxembourg Institute of Science and Technology (LIST), 41, Rue du Brill, Belvaux L-4422, Luxembourg.ORCID https://orcid.org/0000-0003-2081-8267
Sivashankar KrishnamoorthyLuxembourg Institute of Science and Technology (LIST), 41, Rue du Brill, Belvaux L-4422, Luxembourg.ORCID https://orcid.org/0000-0003-0410-1696

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The drive to miniaturize sensing footprints to the micro- and nanoscale has been motivated by reduced sensor real estate, lower sample consumption, and quick response times. However, there remains a limited understanding of how reducing sensor active areas impacts analyte-sensor interactions at fixed analyte concentrations. Using gold nanoparticles as a model analyte, we demonstrate that diminishing sensor active areas are associated with a nonlinear enhancement of analyte surface densities without changing the solution concentration. This behavior is rationalized by correlating the reduced sensor dimensions to an increase in the "effective" analyte availability per surface site. Consequently, sensors with reduced footprints would require substantially fewer analyte molecules to achieve surface densities comparable to those of macroscopic sensors. The resulting increase in nanoparticle surface density further translates into enhanced signal intensities in surface-enhanced Raman detection, arising from the higher density of nanoparticle-substrate plasmonic hotspots within a fixed optical measurement footprint. Overall, the work highlights how micro- and nanoscale sensors with active areas approaching the dimensions of the measurement footprint of highly sensitive transducers (e.g., nanowires or plasmonic hotspots) maximize the benefits of sensor miniaturization.

Identifiers

PMID41970862
PMCPMC13063087

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.