电化学免疫传感器超越了传统的诊断技术，在癌症生物标志物检测方面表现出巨大的潜力。然而，在信号灵敏度和操作稳定性之间实现微妙的平衡，特别是在异质结构界面处，对于实用的免疫传感器至关重要。在此，多孔碳（PC）与Ti3C2Tx-MXene（MX）和金纳米颗粒（Au NP）的整合构建了一个多功能的免疫传感平台，用于检测细胞外基质蛋白1（ECM1），一种乳腺癌相关的生物标志物。 PC 的加入提供了坚固的结构支撑，通过扩大表面积增强电解扩散，同时协同促进与 Ti3C2TX 的电荷转移。使用 1.0 mg PC 优化的生物传感器利用基于抑制的 ECM1 检测策略，对表面结合的硫堇 (th) 氧化还原探针表现出强大的电化学氧化还原反应。 PC 集成 Ti3C2Tx-Au NP 平台 (MX-Au-C-1) 上强大的抗体-抗原相互作用可实现 0.1-7.5 nM 范围内的强大 ECM1 检测，检测下限 (LOD) 为 0.012 nM。构建的生物传感器显示出更高的操作稳定性，1 小时内电流保留率为 98.6%，超过了 MXene 集成 (MX-Au) 和原始 Au NP 电极（分别为 63.2% 和 44.3%）。此外，成功采用人工神经网络 (ANN) 模型对生成的 DPV 数据进行预测分析，进一步验证了生物传感器的准确性，有望在人工智能驱动的远程健康监测中得到应用。版权所有 © 2024。由 Elsevier B.V. 出版。

Electrochemical immunosensors, surpassing conventional diagnostics, exhibit significant potential for cancer biomarker detection. However, achieving a delicate balance between signal sensitivity and operational stability, especially at the heterostructure interface, is crucial for practical immunosensors. Herein, porous carbon (PC) integration with Ti3C2Tx-MXene (MX) and gold nanoparticles (Au NPs) constructs a versatile immunosensing platform for detecting extracellular matrix protein-1 (ECM1), a breast cancer-associated biomarker. The inclusion of PC provided robust structural support, enhancing electrolytic diffusion with an expansive surface area while synergistically facilitating charge transfer with Ti3C2Tx. The biosensor optimized with 1.0 mg PC demonstrates a robust electrochemical redox response to the surface-bound thionine (th) redox probe, utilizing an inhibition-based strategy for ECM1 detection. The robust antibody-antigen interactions across the PC-integrated Ti3C2Tx-Au NPs platform (MX-Au-C-1) enabled robust ECM1 detection within 0.1-7.5 nM, with a low limit of detection (LOD) of 0.012 nM. The constructed biosensor shows improved operational stability with a 98.6 % current retention over 1 h, surpassing MXene-integrated (MX-Au) and pristine Au NPs (63.2 % and 44.3 %, respectively) electrodes. Moreover, the successful adaptation of the artificial neural network (ANN) model for predictive analysis of the generated DPV data further validates the accuracy of the biosensor, promising its future application in AI-powered remote health monitoring.Copyright © 2024. Published by Elsevier B.V.