Carbon Nanotubes for Sensing Applications

Koçak, Çağrı Ceylan; Karabiberoğlu, Şükriye Ulubay; Dursun, Zekerya

doi:10.1007/978-3-030-91346-5_31

Cited by 1 publication

(1 citation statement)

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“…These qualities make CNTs ideal candidates for the production of chemical and biological sensors, as they are capable of absorbing a considerable number of molecules onto their surfaces through electronic interactions. Additionally, CNTs exhibit exceptional electrical properties, including high carrier mobility, near-perfect quantum efficiency, and ultrathin network [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Development of poly(safranine-co-phenosafranine)/GNPs/MWCNTs nanocomposites for quartz crystal microbalance sensor detection of arsenic (III) ions

Al-Sodies,

Asiri,

Ismail

et al. 2024

Mater. Res. Express

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\Contamination of drinking water by heavy metals is extremely dangerous to human health. The formation of a quartz crystal microbalance (QCM) sensor for the rapid and portable detection of harmful heavy metals, such as arsenic (As) ions, in water samples is detailed in this work. Equimolar ratio of safranine (SF) and phenosafranine (Ph) copolymers (PSF-Ph) were synthesized using a chemical oxidative polymerization approach. The copolymer was modified with multi-wall carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) in different percentages (1, 3, 5, and 10%) to formed nanocomposites of PSF-Ph/MWCNTs/GNPs. The thermal study of the nanocomposites displayed the final polymer decomposition temperatures (PDTfinal) values fell between 619 to 630°C and the nanocomposite with 10% loading exhibited the highest decomposition temperatures for T10, T30, and T50. Nanohybrids QCM sensor detected As(III) down to parts-per-billion levels based on the change in oscillation frequency. The sensor was tested on spiked water samples with different concentrations of As(III) (0-20 ppb). A strong linear correlation (R2≈0.99) of the frequency shift versus concentration with low detection limit (0.1 ppb) validated the quantitative detection capability of the sensor. This QCM platform with an optimal recognition ligand is a promising field-deployable tool for on-site arsenic analysis in water.

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Section: Introductionmentioning

confidence: 99%