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Related Experiment Video

Updated: Apr 4, 2026

Author Spotlight: Development of a Smartphone-Enhanced Paper-Based Device for Rapid Dengue NS1 Detection

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Smartphone based visual and quantitative assays on upconversional paper sensor.

Qingsong Mei¹, Huarong Jing¹, You Li¹

¹School of Medical Engineering, Hefei University of Technology, Tunxi road 193, Hefei, Anhui 230009, PR China.

Biosensors & Bioelectronics

|September 11, 2015

Summary

A 3D-printed device enables smartphone-based detection of the pesticide thiram using upconversion paper sensors. This innovation allows for rapid, quantitative analysis of luminescence variations, offering a new tool for point-of-care diagnostics.

Keywords:

3D printing technology Pesticide thiram Smartphone Test paper Upconversion nanosensor

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Published on: November 9, 2018

Area of Science:

Nanotechnology and Materials Science
Analytical Chemistry
Biomedical Engineering

Background:

Smartphone-based sensors offer potential for rapid, quantitative analysis.
Effective methods for acquiring luminescence signals from paper sensors are lacking.
Upconversion nanoparticles provide sensitive luminescence detection.

Purpose of the Study:

To develop a smartphone-based system for quantitative analysis of pesticide thiram.
To overcome limitations in acquiring luminescence signals from paper sensors.
To demonstrate a point-of-care detection platform using upconversion nanosensors.

Main Methods:

Fabrication of test paper using copper ion-decorated NaYF4:Yb/Tm upconversion nanoparticles on filter paper.
Development of a 3D-printed auxiliary device integrating a mini-laser, optical filter, and mini-cavity.
Quantitative analysis of thiram by monitoring blue luminescence quenching via smartphone camera and a custom Android application.

Main Results:

The system successfully quantified thiram through luminescence resonance energy transfer (LRET) based quenching.
A reliable and accurate detection limit of 0.1 μM for thiram was achieved.
Demonstrated the feasibility of digital imaging of luminescence variations on test paper.

Conclusions:

Integrated upconversion nanosensors with smartphone digital imaging for paper-based analysis.
Provided an effective method for acquiring luminescence signals on test paper using a 3D-printed device.
Established a reliable and accurate platform for point-of-care pesticide detection.