Evidence map›Paper›PMID 40831982›Full record

ArticleThe journal of physical chemistry. C, Nanomaterials and interfaces2025

Optimization of Carbon Nanotube Percolation Networks for Broad Dynamic Range Detection of Permanganate in Water.

Md Ali Akbar, Ponnambalam Ravi Selvaganapathy, Peter Kruse

Abstract read
In one paragraph

Article in The journal of physical chemistry. C, Nanomaterials and interfaces, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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

3 authors.

Md Ali AkbarDepartment of Chemistry and Chemical Biology, McMaster University, 1280 Main St. W., Hamilton, Ontario L8S 4M1, Canada.ORCID https://orcid.org/0000-0002-1809-8232
Ponnambalam Ravi SelvaganapathyDepartment of Mechanical Engineering, McMaster University, 1280 Main St. W., Hamilton, Ontario L8S 4L7, Canada.ORCID https://orcid.org/0000-0003-2041-7180
Peter KruseDepartment of Chemistry and Chemical Biology, McMaster University, 1280 Main St. W., Hamilton, Ontario L8S 4M1, Canada.ORCID https://orcid.org/0000-0003-4051-4375

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Percolation networks of single-walled carbon nanotubes (SWCNTs) are widely used in chemiresistive sensors due to their tunable electronic properties, which control the sensitivity of the devices. This study explores the tunability of the electronic properties of SWCNT-based chemiresistive films by controlling the percolation network density. We demonstrate that simple spectroscopic measurements can ensure repeatability in the sensor fabrication process and that optimizing the percolation network can expand the useful detection range for the desired analyte. We show that percolation networks within three resistance ranges can be utilized to detect a disinfectant (permanganate), which has applications over a wide concentration range. Devices fabricated with the highest resistance (∼25 kΩ) were shown to detect lower range concentrations of permanganate (0.01-0.1 mg/L, limit of detection, LoD 0.0001 mg/L), medium resistance devices (∼15 kΩ) can detect midrange concentrations (0.2-1.6 mg/L, LoD 0.002 mg/L), and low-resistance devices (∼5 kΩ) can detect high-range concentrations (1- 8 mg/L, LoD 0.03 mg/L), depending on the specific application. SWCNT networks can also be further functionalized to enhance the sensitivity, selectivity, and stability of the devices. This study highlights the potential of SWCNT percolation networks for customizable chemiresistive sensing in environmental monitoring and industrial applications.

Identifiers

PMID40831982
PMCPMC12360326

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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.