Evidence map›Paper›PMID 32143406›Full record

ArticleBiosensors2020

Molecularly Imprinted Nanoparticles Based Sensor for Cocaine Detection.

Roberta D'Aurelio, Iva Chianella, Jack A Goode, Ibtisam E Tothill

Open access · goldAbstract read
In one paragraph

Article in Biosensors, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
3.4field-weighted citation impact, top 7% 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

11 citing papers in PubMed, 57 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. Review
  5. Review
  6. Interpol Review of Drug Analysis 2019-2022.Forensic science international. Synergy · 2023
    Review
  7. Article
  8. Review
  9. A Comparison of EIS and QCM NanoMIP-Based Sensors for Morphine.Nanomaterials (Basel, Switzerland) · 2021
    Article
  10. Article
  11. Review
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 at 2 institutions in 1 country.

Roberta D'AurelioAdvanced Diagnostics and Sensors Group, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK.
Iva ChianellaAdvanced Diagnostics and Sensors Group, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK.
Jack A GoodeSchool of Biomedical Sciences, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, UK..
Ibtisam E TothillAdvanced Diagnostics and Sensors Group, Cranfield University, Cranfield, Bedfordshire MK43 0AL, UK.
Cranfield University · GBUniversity of Leeds · GB

Funding

Horizon 2020 Framework Programme 653839
6 · The paper itself

Abstract

The development of a sensor based on molecularly imprinted polymer nanoparticles (nanoMIPs) and electrochemical impedance spectroscopy (EIS) for the detection of trace levels of cocaine is described in this paper. NanoMIPs for cocaine detection, synthesized using a solid phase, were applied as the sensing element. The nanoMIPs were first characterized by Transmission Electron Microscopy (TEM) and Dynamic Light Scattering and found to be ~148.35 ± 24.69 nm in size, using TEM. The nanoMIPs were then covalently attached to gold screen-printed electrodes and a cocaine direct binding assay was developed and optimized, using EIS as the sensing principle. EIS was recorded at a potential of 0.12 V over the frequency range from 0.1 Hz to 50 kHz, with a modulation voltage of 10 mV. The nanoMIPs sensor was able to detect cocaine in a linear range between 100 pg mL

Indexed as

Biosensing TechniquesCocaineElectrochemical TechniquesHumansMolecular ImprintingNanoparticlesCocainecocainedrugs of abuseEIS sensorelectrochemical impedance spectroscopymolecularly imprinted polymer nanoparticles

Identifiers

PMID32143406
PMCPMC7146329
OpenAlexW3010311814

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.