Behrouzi Kamyar, Khodabakhshi Fard Zahra, Chen Chun-Ming, He Peisheng, Teng Megan, Lin Liwei
Department of Mechanical Engineering, University of California, Berkeley, CA, USA.
Berkeley Sensor and Actuator Center (BSAC), Berkeley, CA, USA.
Nat Commun. 2025 May 17;16(1):4597. doi: 10.1038/s41467-025-59868-y.
A major challenge in addressing global health issues is developing simple, affordable biosensors with high sensitivity and specificity. Significant progress has been made in at-home medical detection kits, especially during the COVID-19 pandemic. Here, we demonstrated a coffee-ring biosensor with ultrahigh sensitivity, utilizing the evaporation of two sessile droplets and the formation of coffee-rings with asymmetric nanoplasmonic patterns to detect disease-relevant proteins as low as 3 pg/ml, under 12 min. Experimentally, a protein-laden droplet dries on a nanofibrous membrane, pre-concentrating biomarkers at the coffee ring. A second plasmonic droplet with functionalized gold nanoshells is then deposited at an overlapping spot and dried, forming a visible asymmetric plasmonic pattern due to distinct aggregation mechanisms. To enhance detection sensitivity, a deep neural model integrating generative and convolutional networks was used to enable quantitative biomarker diagnosis from smartphone photos. We tested four different proteins, Procalcitonin (PCT) for sepsis, SARS-CoV-2 Nucleocapsid (N) protein for COVID-19, Carcinoembryonic antigen (CEA) and Prostate-specific antigen (PSA) for cancer diagnosis, showing a working concentration range over five orders of magnitude. Sensitivities surpass equivalent lateral flow immunoassays by over two orders of magnitude using human saliva samples. The detection principle, along with the device, and materials can be further advanced for early disease diagnostics.
应对全球健康问题的一个主要挑战是开发出简单、经济实惠且具有高灵敏度和特异性的生物传感器。家用医疗检测试剂盒已取得重大进展,尤其是在新冠疫情期间。在此,我们展示了一种具有超高灵敏度的咖啡环生物传感器,它利用两个固定液滴的蒸发以及形成具有不对称纳米等离子体图案的咖啡环,在12分钟内检测低至3 pg/ml的疾病相关蛋白质。实验中,一个载有蛋白质的液滴在纳米纤维膜上干燥,在咖啡环处预浓缩生物标志物。然后将第二个带有功能化金纳米壳的等离子体液滴沉积在重叠点并干燥,由于不同的聚集机制形成可见的不对称等离子体图案。为提高检测灵敏度,使用了一个整合生成网络和卷积网络的深度神经模型,以便从智能手机照片中进行生物标志物的定量诊断。我们测试了四种不同的蛋白质,用于败血症诊断的降钙素原(PCT)、用于新冠诊断的新冠病毒核衣壳(N)蛋白、用于癌症诊断的癌胚抗原(CEA)和前列腺特异性抗原(PSA),显示出超过五个数量级的工作浓度范围。使用人类唾液样本时,其灵敏度比等效的侧向流动免疫测定法高出两个多数量级。该检测原理以及设备和材料可进一步改进用于早期疾病诊断。