UMass Amherst’s Yingjie Hang Wins ECS Student Research Award for High-Sensitivity POC Diagnostic PLFS Combining Plasmonic Nanoparticles and ML

July 5, 2026

UMass Amherst USA

Overview

Yingjie Hang, a PhD graduate from UMass Amherst, won the ECS Sensor Division Student Research Award for her work on enhancing the sensitivity of paper lateral flow test strips (PLFS) for point-of-care (POC) diagnostics using plasmonic nanoparticles and machine learning (ML). Her research focused on improving the detection sensitivity for low-abundance antigens, particularly in blood samples, and designing PLFS for SARS-CoV-2 and HIV biomarker detection. This marks a significant step towards realizing next-generation portable biosensors.

In Depth

Key Findings

Yingjie Hang, a PhD graduate from UMass Amherst, has been awarded the ECS Sensor Division Student Research Award for her innovative research on enhancing the sensitivity of paper lateral flow test strips (PLFS) for point-of-care (POC) diagnostics. Her work successfully combined plasmonic nanoparticles with machine learning (ML) to dramatically improve the detection capability of low-abundance antigens, particularly in blood samples.

Technical/Clinical Details

Hang’s research involved integrating plasmonic nanoparticles into PLFS design to leverage optical signal enhancement effects, thereby improving sensitivity. This makes it possible to efficiently detect antigens present in minute quantities in blood, such as SARS-CoV-2 and HIV biomarkers. Furthermore, she utilized machine learning algorithms to screen plasmon-enhanced near-infrared fluorescent probes, identifying optimal sensor designs. This approach overcomes the detection limit challenges faced by conventional PLFS, enabling more reliable early diagnostics. PLFS are crucial tools in POC diagnostics due to their simplicity, low cost, and rapid results, but their sensitivity has been a limitation. Hang’s technology addresses this critical performance gap.

Background & Context

Point-of-care diagnostics are indispensable tools for public health, especially in responding to infectious disease pandemics, as they allow rapid diagnosis even in settings without hospital or specialized lab facilities. While paper lateral flow test strips are widely adopted for their ease of use, they have been limited by insufficient sensitivity, particularly for early infections or when disease markers are at low concentrations. Hang’s research addresses this unmet need in the field, providing more effective screening and diagnostic tools.

Strategic Significance & Outlook

Yingjie Hang’s research represents a significant step towards the development of next-generation portable biosensors. The integration of plasmonic nanoparticles and machine learning holds the potential to improve the performance of POC diagnostics in a wide range of applications, including early detection of infectious diseases, cancer, and chronic conditions. In the future, these technologies are expected to be incorporated into smaller, more user-friendly devices, making significant contributions to strengthening global health security and advancing personalized medicine. This award highlights how innovative contributions from young researchers are shaping the future of diagnostic technology.

Source: https://www.umass.edu/engineering/news/yingjie-hang-award

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