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Data · dataset · 2026

Designing highly sensitive and selective colorimetric sensing of dopamine by ionic liquid functionalized cobalt doped zinc oxide/activated carbon nanocomposite

Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.6084/m9.figshare.34041668.v1

<p>It is a well-known fact that dopamine is a key neurotransmitter found in bodily fluids and neurological system mostly helps in the maintenance of neural-communication, cardiovascular activity and renal function.

Description

Whereas, its abnormal level mostly causes various chronic diseases like psychosocial stress, Parkinson’s disease and Alzheimer’s disease. Therefore, it is urgently needed to sensitively detect it by a portable and economically viable sensing method for early diagnosis of diseases.

For this purpose, herein we have explicitly studied a highly rapid, cheap and on-site detection platform known as a colorimetric sensing method based on the fabrication of a high-surface-area nanocomposite structure (NCs) called ionic liquid-coated cobalt-doped zinc oxide/activated carbon (Co-ZnO/C/IL). In detail, the suggested sensor exhibited a strong, wide, and linear range of 3×10<sup>-9</sup> - 3.63×10<sup>-7</sup> M with R<sup>2</sup> value of 0.9995 and a lower limit of detection (LOD) value of 0.9×10<sup>-9</sup> M and a limit of quantification (LOQ) of 2.5×10<sup>-9</sup> M in a shorter time of 2.5 min.

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Moreover, it also demonstrated high selectivity towards dopamine, which increases its effectiveness in complex environments. In addition, the sensor also demonstrates strong practicality for the detection of dopamine in real samples (blood serum samples). These outstanding sensing performances towards dopamine were mainly ascribed to strong synergistic integration of cobalt-doped ZnO, activated carbon, and ionic liquid which ultimately enhances the catalytic activity, electron-transfer properties, and exposure of active-sites.

Thus, this work will improve for providence of reliable pathways to screen potential-patients for neurological diseases.</p>

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Inferred from text
Disease 75%
Provenance · 3 source records, 21 field assertions
SourceKeyLast seenRaw
ZivaHuboai:figshare.com:article/3404166813 d agoJSON v1
Deakin Research Onlineoai:figshare.com:article/3404166813 d agoJSON v1
DMU Figshareoai:figshare.com:article/3404166813 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · zivahub uct ac zaconnector:zivahub_uct_ac_za@1.0.0
concepts[disease].local:disease:diseaseenrichment · zivahub uct ac zakeyword-concept-rules@1.0.0title+description (75%)
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