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Updated: May 5, 2026

Diffuse Reflectance Infrared Spectroscopic Identification of Dispersant/Particle Bonding Mechanisms in Functional Inks

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Published on: May 8, 2015

Image-Based Machine Learning Using Inkjet-Printed Chemicals: Mixing Ratio Prediction and Metal Ion Detection.

Taichi Sano¹, Yuki Terauchi¹, Yuki Ide²

¹Division of Applied Chemistry, Faculty of Engineering, Hokkaido University, Kita 13, Nishi 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan.

Organic Letters

|July 12, 2025

Summary

This summary is machine-generated.

Inkjet printing combined with machine learning (ML) offers a fast, affordable way to predict mixture ratios using color. This versatile technique also enables sensitive detection of metal ions without specialized equipment.

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Area of Science:

Analytical Chemistry
Materials Science
Machine Learning

Background:

Image-based machine learning (ML) requires extensive training data.
Traditional methods for chemical analysis can be time-consuming and expensive.

Purpose of the Study:

To develop a rapid, low-cost method for generating training images for ML-based mixture analysis.
To demonstrate the versatility of inkjet printing and ML in chemical sensing.

Main Methods:

Inkjet printing of π-conjugated organic compounds to create training images.
Development of ML models for predicting mixing ratios.
Adaptation of the printing surface to accommodate colorless compounds.
Application of the method for spatial mapping of Zn²⁺ ions using colorimetric sensing.

Main Results:

Achieved ML models with mean absolute errors below 4% within hours.
Successfully analyzed dyes with subtle color differences.
Extended the method to colorless compounds, including isomers and macrocyclic systems.
Enabled detection and spatial mapping of sub-microgram levels of Zn²⁺ ions without a spectrometer.

Conclusions:

Inkjet printing provides a simple and versatile strategy for integrating π-conjugated materials with ML.
This approach significantly accelerates the development of ML models for colorimetric sensing and mixture analysis.
The method offers a cost-effective and accessible alternative for various chemical sensing applications.