CNT-doped transition metal carbide enables sensitive organic electrochemical transistor based carcinoembryonic antigen aptasensor towards precise lung cancer diagnosis - Zhang, Yunzeng; Shen, Changming; Tang, Yan; Li, Xiaofeng; Liu, Dawei; Wei, Ailin; Wang, Cheng; Zhao, Xiaogang (Frontiers in Materials, 2024)
Effective detection of carcinoembryonic antigen (CEA) plays an important role in the diagnosis of lung cancer. Given the challenges posed by the low abundance and complexity of biosamples, it is urgent to develop sensitive, cost-effective and fast detection strategies. In this paper, a novel platform is developed using doped transition metal carbides as semiconductor materials for organic electrochemical transistor (OECT) aptamer-based sensors to satisfy sensitivity, specificity, rapidity, and low cost. A new material, CNT-doped MXene, was synthesized and utilized in the fabrication of CM-OECATs. The morphology and doping of CNT-doped MXene were validated effectively. 2.0 wt% CNT achieved maximum doping efficiency at transconductance (Gm) of 0.801 ms. Through systematic optimization of temperature, pH, aptamer concentration and incubation time, a wide detection range ranging from 0.1 pg/mL to 100 ng/mL was achieved, and the lower limit was 0.051 pg/mL. Favorable stability (0.819% decline), specificity and repeatability (RSD = 2.05%) were demonstrated. CM-OECATs effectively distinguished between 11 biosamples of lung cancer from 12 healthy controls (AUC = 0.9748, specificity = 0.9565, sensitivity = 0.9978) for the clinics. The test carried out in two batches gave p-values <0.05, indicating the effectiveness of the CM-OECATs in discriminating effectively. In addition, CM-OECATs demonstrated a favourable correlation in 25 clinical samples (y = 0.9782x + 0.7532, R2 = 0.9723). To sum up, an organic electrochemical transistor aptamer-based sensor based on CNT-doped MXene (CM-OECATs) is promising for future real-time monitoring in clinical settings, paving the way for an efficient, cost-effective and highly sensitive detection strategy.