Nanomolar electrochemiluminescence via smartphone: Image encoding matters.

Area of Science:

• Analytical Chemistry
• Biomedical Engineering
• Imaging Science

Background:

• Smartphone electrochemiluminescence (ECL) offers potential for decentralized diagnostics.
• Existing methods are limited by unaddressed error sources: acquisition parameters, gamma encoding, and JPEG compression.
• These limitations hinder sensitivity and reliable quantification in smartphone-based ECL detection.

Purpose of the Study:

• To develop and validate a quantitative framework for smartphone ECL imaging.
• To address key error sources limiting sensitivity in smartphone-based ECL detection.
• To enable reliable, on-device, decentralized diagnostics using mobile devices.

Main Methods:

• Optimized image acquisition using long-exposure RAW images under fixed conditions.
• Developed a processing pipeline (ECLiCare app) involving full-histogram integration and inverse sRGB transfer function.
• Utilized screen-printed glassy carbon electrodes with a ruthenium(II)/tripropylamine system for ECL generation.

Main Results:

• Achieved nanomolar detection limits (16 nM) and quantification limits (53 nM) in solution, surpassing previous smartphone-based systems.
• Demonstrated the critical importance of lossless image formats, as JPEG compression yields misleadingly low detection limits.
• Validated the workflow on a sandwich immunoassay, achieving a detection limit of 2.31 μg/mL for epidermal growth factor receptor.

Conclusions:

• A comprehensive workflow, ECLiCare, enables sensitive smartphone-based ECL detection by addressing critical error sources.
• The developed method provides a reliable, on-device quantitative framework for field and point-of-care deployment.
• This advancement paves the way for accessible, decentralized diagnostic tools using ubiquitous smartphone technology.