Rapid visual detection of silver ions using nitrogen-doped carbon dots derived from cotton stalk
Year: 2026
Type: article
Abstract: Silver ions (Ag + ), as a biologically toxic heavy metal pollutant, are difficult to naturally degrade in the environment and can readily bioaccumulate through the food chain, posing a serious threat to ecosystems and human health. Therefore, the development of efficient, sensitive, and operationally simple detection methods is of great significance. Carbon dots (CDs) have become a research focus in recent years due to their advantages such as simple preparation, rapid response, and low cost. However, most existing CDs-based probes rely on small-molecule chemical precursors of high purity, which leads to high costs, and some systems exhibit insufficient stability under harsh conditions or limited performance in visual detection. In this study, blue-emitting nitrogen-doped CDs with a quantum yield of 4.90% were successfully synthesized by employing environmentally friendly and renewable cotton stalks as the carbon precursor and low-cost, amino group-rich l -arginine as the nitrogen dopant. The synthesized l -arginine-doped cotton stalk carbon dots (ACS-CDs) were uniform in size (approximately 4.17 ± 1.12 nm) and based on a static quenching mechanism, exhibited high selectivity and a rapid fluorescence quenching response toward Ag + , with a detection limit as low as 4.30 μM. Notably, the quenching process was accompanied by a visible color change of the solution from colorless to dark brown, enabling instrument-free and rapid visual detection of Ag + , which was likely attributed to static quenching via the formation of Ag 2 O. This work not only provided a novel technique for the rapid and cost-effective detection of Ag + but also opened a green pathway for the high-value resource utilization of cotton stalk.
Institution: Shihezi University
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $1,600
Award: G1398031019
OpenAlex ID: W7160909846
