## 1 Introduction  
Dopamine (DA) and uric acid (UA) often coexist in the biological fluids, such as blood serum, urine, and extracellular fluids, which play a vitally significant role on the regulation of human physiological functions and metabolic activities [1]. As an essential catecholamine neurotransmitter, DA plays a pivotal role in regulating the functions of cardiovascular and central nervous systems, adjusting emotions, and maintaining hormonal balances [2]. The dysfunction of DA possibly causes many neurological disorders like Parkinson's syndrome, Alzheimer's diseases, and schizophrenia [3; 4; 5]. For a healthy individual, the DA levels in biological matrixes generally vary from 0.01 uM to 1 uM. The response signals of DA are often susceptible to interferences from endogenous biomolecules i.e.,ascorbic acid (AA) and UA. Therefore, it remains a great challenge for the fast and precise detection of DA. As another critical biomolecule in human body, UA is commonly regarded as the metabolic product of purine [6]. Generally, the UA level is \(4.1\pm 8.8\) mg/100 mL for a healthy individual [7]. The abnormal concentration of UA in physiological fluids likely leads to several disorders including pneumonia, hyperuricemia, and gout [8]. Thus, the levels of DA and UA in physiological fluids have become important indicators or biomarkers for healthcare and clinical diagnosis. Therefore, it is extremely necessary to propose some efficient and reliable approaches toward the simultaneous determination of DA and UA. Up until now, various detection approaches have been reported for detecting DA and UA, such as chemiluminescent [9], HPLC [10, 11], fluorometry [12], spectrophotometry [13], and surface plasmon resonance [14]. These techniques are very reliable, but they often involve cumbersome and time-consuming procedures that require large instruments, experienced technicians, and even a large amount of poisonous solvents [15]. Recently, electrochemical approaches have drawn growing attention for the determination of bioactive compounds, food dyes, and pollutants, owing to their considerable advantages such as being inexpensive, facial operation, high efficiency, good selectivity, and sensitivity [16; 17; 18; 19; 20]. In addition, DA and UA are highly electroactive biomolecules, which are more suitable for electrochemical detection. However, bare electrodes often suffer from electrode fouling and cross-interference issues, which result in poor sensitivity and reproducibility [7; 21]. To address the issues, various nanomaterials were developed to construct electrochemical sensors. As a versatile transition metal oxide, MnO\({}_{2}\) has been intensively utilized in energy storage, catalysis, and sensors because of its peculiar properties including low-cost, more abundance, high-catalytic activity, and environmental friendliness. Until now, a variety of nanostructured MnO\({}_{2}\) such as nanowires [16; 22], nanorods [17; 23; 24], nanotubes [25; 26], microspheres [27; 28], and nanoflowers [29; 30] have been prepared, characterized, and even used in electrochemical determination. Among these morphologies, MnO\({}_{2}\) nanoflowers (MnO\({}_{2}\)NFs) have drawn considerable attention, attributing to their pore structure and large specific surface area. As sensing materials, MnO\({}_{2}\) nanoflowers have been used for the detection of lead ion [29], ractopamine [30], salbutamol [30], guaiacol [31], vanillin [31], hydrogen peroxide [32], and DA [33]. These studies demonstrate that MnO\({}_{2}\) nanoflowers improve the electrochemical performances significantly. But their poor dispersibility and electrical conductivity have impeded widespread applications in electrochemical sensors. To resolve this problem, an effective strategy is to composite nanostructured MnO\({}_{2}\) with graphene materials, which not only effectively improve the dispersibility, but also endow a synergistic effect towards sensing target analytes. However, the electrical conductivity of graphene cannot be fully controlled due to the lack of bandgap [34].