Evidence map›Paper›PMID 39196483›Full record

ArticleDiscover nano2024

Key role of adsorption site abundance in the direct electrochemical co-detection of estradiol and dopamine.

Naela Delmo, Ishan Pande, Emilia Peltola

Abstract read
In one paragraph

Article in Discover nano, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Naela DelmoDepartment of Mechanical and Materials Engineering, Faculty of Technology, University of Turku, 20500, Turku, Finland.
Ishan PandeDepartment of Electrical Engineering and Automation, School of Electrical Engineering, Aalto University, 00076, Aalto, Finland.
Emilia PeltolaDepartment of Mechanical and Materials Engineering, Faculty of Technology, University of Turku, 20500, Turku, Finland. emilia.peltola@utu.fi.

Funding

Research Council of Finland #328854, #347021, #352421
6 · The paper itself

Abstract

Estradiol (E2) is a hormone that influences various aspects of women's health. Beyond its reproductive functions, E2 impacts neurotransmitter systems such as dopamine (DA). Vertically aligned carbon nanofibers (VACNFs) have shown good sensitivity, selectivity against ascorbic acid (AA) and uric acid (UA), biocompatibility, and reduced fouling in DA sensing. In this study, we explore the use of Ti-Ni-CNF electrodes with CNFs grown for 5 min and 30 min for the direct electrochemical co-detection of E2 and DA. The longer growth time led to a 142% increase in average CNF length and a 36% larger electroactive surface area. In E2 detection, the electrodes demonstrate a wide linear range of 0.05-10 µM and sensitivity of 0.016 and 0.020 µA/µM for Ti-Ni-CNF-5 min and Ti-Ni-CNF-30 min, respectively. The sensor performance remains largely unaffected even in the presence of other steroid hormones such as progesterone and testosterone. Co-detection of equimolar E2 and DA shows promising peak separation of 0.34 ± 0.01 V and repeatability after 10 measurements. A notable improvement in the E2/DA peak current ratio, from 0.53 ± 0.07 to 0.81 ± 0.16, was achieved with the increased CNF length. Our results demonstrate the influence of adsorption sites in electrochemical detection, especially for analytes such as E2 and DA that both rely on adsorption for oxidation. While detecting small and fluctuating physiological concentrations remains a challenge, these findings can be used in choosing and fabricating electrode materials for more accurate and accessible continuous hormone measurements, including the possibility of multianalyte sensing platforms.

Identifiers

PMID39196483
PMCPMC11358574

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Registered trials

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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.