br Experimental Section br Results and Discussion br Conclus

Experimental Section
Results and Discussion
Conclusions The advantages of the novel enzymo-chemical method for Mn and Co assay are simplicity of analytical procedure and economic effect due to the usage of only one enzyme in the form of apo-enzyme. An effectiveness of the apo-arginase-based analytical method was proved on the real samples of wastewater. The obtained results proved to be in a good correlation with atomic Bryostatin 1 method. Due to a high stability of apo-arginase, the development and manufacture of enzymatic kits for metallic ions assay based on the proposed method is very prospective. Being sensitive, valid, low-cost, highly selective, the proposed analytical method would be promising in different fields of fundamental and practical science, including environmental chemistry, plant and animal biochemistry, nutrition, and medicine.
Ethical Statement
Conflict of Interest Disclosure
Acknowledgments This work was financially supported by National Academy of Sciences (NAS) of Ukraine (program “Sensors for Medical, Environmental, Industrial, and Technological Needs: Metrological Support and Trial Operation”, Project 13-2017), by the Ministry of Education and Science of Ukraine (project #0116U004737), by NATO (Project CBP. NUKR.SFP 984173) and by fellowship of President of Ukraine for Young Scientists in 2017 (N. Stasyuk). We acknowledge the funding through the European Union's Horizon 2020 research and Programme entitled HEARTEN under grant agreement No 643694.
Introduction L-arginine ((S)-amino-5-guanidinopentanoic acid) is the most basic amino acid and occurs widely in living organisms. In the human body, it has significant biological functions in the cardiovascular, immune and endocrine system (Guoyao, 2009, Popolo et al., 2014, Tong and Barbul, 2004). The monitoring of L-arginine in physiological fluids is therefore of great interest in clinical diagnostics, research and therapy. Furthermore, it is relevant in industrial settings, such as quality control in food and beverages (Stasyuk et al., 2016, Valle-Vega et al., 1980, Verma et al., 2015) and optimization of microbial biosynthesis in industrial L-arginine production (Ginesy et al., 2015). To date, detection of L-arginine in health-related studies is mostly done by high-performance liquid chromatography (HPLC) (Bode-Böger et al., 1998, Hong et al., 2010, Van Waardenburg et al., 2007) and liquid chromatography-tandem mass spectrometry (LC-MS) (Luneburg et al., 2011, Németh et al., 2016), both of which require specialized equipment and laborious sample preparation and analysis. Optical detection methods may rely on enzymatic reactions (Alonso et al., 1995, Stasyuk et al., 2017, Stasyuk et al., 2016), fluorescent probes (Lu et al., 2017) or functionalized nanoparticles (Pu et al., 2013, Velugula and Chinta, 2017). Albeit enabling highly sensitive and precise detection, these methods frequently fail to meet many other desirable features such as easy handling, real time response, electronic read-out and cost-efficiency. As a cutting-edge approach, biosensors based on nanomaterial field-effect transistors (FET) have gained increasing attention in clinical diagnostics because of their following attractive features (Yan et al., 2014): Label-free, real-time response, operation in aqueous solutions at very low voltages (less than 1 V, which is elemental for biological sensing), and inherent amplification property (Zhang et al., 2015). Graphene, a two-dimensional zero band gap semiconducting material, has remarkable electronic, chemical and mechanical properties (Balasubramanian and Kern, 2014, Nehra and Pal Singh, 2015). Its high carrier mobility and ambipolar field effect together with a great interface sensitivity make graphene a unique sensing material. Graphene-based FETs (gFETs) have been applied for detection of pH (Ohno et al., 2009, Reiner-Rozman et al., 2015), DNA (Dong et al., 2010) and proteins (Kim et al., 2013), as well as for the enzymatic detection of small molecules like urea or glucose (Piccinini et al., 2017, Zhang et al., 2015) and other analytes (Fu et al., 2017).