Tryptophan, uric acid, and dopamine detected simultaneously using an accurate and affordable electrochemical sensor based on Co9S8-supported electrochemically reduced graphene oxide

A cost-effective and simple electrochemical approach was developed for the simultaneous detection of tryptophan (Trp), uric acid (UA) and dopamine (DA). Through hydrothermal synthesis, Co 9 S 8 was combined with graphene oxide (GO) using physical ultrasound. Subsequently, the Co 9 S 8 -RGO/GCE was created by electrochemically reducing a Co 9 S 8 -GO dispersion after drop-coating it onto the GCE surface. The amalgamation of Co 9 S 8 with RGO conferred the sensor with remarkable sensitivity, outstanding selectivity, and robust catalytic prowess towards DA, UA and Trp. The ternary mixture exhibited notable peak separations of up to 160 mV, 278 mV, and 438 mV between DA–UA, UA–Trp, and DA–Trp, respectively. The linear calibration ranges spanned 0.1–2.0 μM and 2.0–10 μM for DA, 0.06–10 μM for UA, and 0.02–10 μM for Trp, with matching 0.04 µM for DA, 0.02 µM for UA, and 6.0 nM for Trp detection limits (S/ N = 3). Moreover, the sensor's effective use in the identification of DA, UA and Trp in authentic samples yielded satisfactory results. Comparison of analytical performance of different modified electrodes for simultaneous detection of DA, UA and Trp

Authors

Institutions

Publication Details

Journal
Results in Engineering
Published
2025-05-01
DOI
https://doi.org/10.1016/j.rineng.2025.105171
Citations
7
Primary Topic
Electrochemical sensors and biosensors
Type
article
Field-Weighted Citation Impact
5.11
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Tryptophan, uric acid, and dopamine detected simultaneously using an accurate and affordable electrochemical sensor based on Co9S8-supported electrochemically reduced graphene oxide

Quanguo He, Chuanqin Zhou, Shuting Shi, Peihong Deng et al.
7 citations
Results in Engineering
Electrochemical sensors and biosensors
5.11
article

Tryptophan, uric acid, and dopamine detected simultaneously using an accurate and affordable electrochemical sensor based on Co9S8-supported electrochemically reduced graphene oxide

Quanguo He, Chuanqin Zhou, Shuting Shi, Peihong Deng, Nana Tang, Aiting Chen
article en
7 citations

Abstract

A cost-effective and simple electrochemical approach was developed for the simultaneous detection of tryptophan (Trp), uric acid (UA) and dopamine (DA). Through hydrothermal synthesis, Co 9 S 8 was combined with graphene oxide (GO) using physical ultrasound. Subsequently, the Co 9 S 8 -RGO/GCE was created by electrochemically reducing a Co 9 S 8 -GO dispersion after drop-coating it onto the GCE surface. The amalgamation of Co 9 S 8 with RGO conferred the sensor with remarkable sensitivity, outstanding selectivity, and robust catalytic prowess towards DA, UA and Trp. The ternary mixture exhibited notable peak separations of up to 160 mV, 278 mV, and 438 mV between DA–UA, UA–Trp, and DA–Trp, respectively. The linear calibration ranges spanned 0.1–2.0 μM and 2.0–10 μM for DA, 0.06–10 μM for UA, and 0.02–10 μM for Trp, with matching 0.04 µM for DA, 0.02 µM for UA, and 6.0 nM for Trp detection limits (S/ N = 3). Moreover, the sensor's effective use in the identification of DA, UA and Trp in authentic samples yielded satisfactory results. Comparison of analytical performance of different modified electrodes for simultaneous detection of DA, UA and Trp

Results in EngineeringVol. 26
Hengyang Normal University (CN), Hunan University of Technology (CN)
Affordable and clean energy
Openalex Percentile: Top 5%
Electrochemical sensors and biosensors
5.11
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.