DOI: 10.5281/zenodo.15662176
title: Dataset for "Low-cost CMOS-based luminescence lifetime imaging of oxygen, temperature or pH"

description: Dataset Description

This dataset contains the experimental data supporting the conclusions presented in "Low-cost CMOS-based luminescence lifetime imaging of oxygen, temperature or pH" by Gauthier Rousseau, Marianne Pons, Hessel Adelerhof, Nathan Pellerin, Mart Giesbergen, Bastien Carde, Martin Wolff, Koen Blanckaert, Sergey M. Borisov, and Benoit Fond.

The data were generated using the CLICC (Cumulative Lifetime Imaging on CMOS-Camera) platform for luminescence lifetime imaging across three applications: oxygen sensing, temperature mapping, and pH imaging. CLICC platform control code available at: https://gitlab.com/groussea/clicc

Dataset Contents

Oxygen Sensing Data



Calibration data: Lifetime measurements vs. oxygen partial pressure (pO₂) for PtTPTBPF-based planar optodes

Time-domain reference measurements for validation

RLD imaging data with different window durations (Δt = 15 μs and 25 μs)

Dynamic response data from flow-through cell experiments

Spatial gradient imaging data from sediment simulation experiments

Nitrogen jet response measurements


Temperature Imaging Data



Calibration data: TADF lifetime vs. temperature for Zr(MesIPDP^t-Bu Ph)₂ nanoparticles

Time-domain reference decay measurements

RLD temperature imaging data from fluid injection experiments


pH Imaging Data



DLR calibration data: Ratio response vs. pH using aza-BODIPY indicator and Egyptian blue reference

pH gradient imaging data from buffer experiments

Sigmoidal curve fitting parameters


File Formats and Structure

Raw Image Data: Stored in HDF5 format with application-specific structures:



High-resolution images (2056 × 2464 pixels, float64): background correction images and multiple reference images (REF1, REF2)

Experimental parameters vary by application:



Oxygen sensing: oxygen concentration, LED timing, exposure parameters

Temperature imaging: temperature values, Laser timing, exposure parameters

pH imaging: pH values, LED timing, exposure parameters



Total file sizes: ~30 MB per HDF5 file


Additional Data: Calibration curves (CSV) and analysis scripts (Python)

Reading HDF5 Data

The HDF5 files can be accessed using standard libraries:

Python:

import h5py
with h5py.File('filename.h5', 'r') as f:
    background = f['data/background'][:]
    image_ref1 = f['data/image_REF1'][:]
    image_ref2 = f['data/image_REF2'][:]
    # Access experimental parameters
    oxygen = f['data/Oxygen'][0]  # for oxygen sensing data


MATLAB:

background = h5read('filename.h5', '/data/background');
image_ref1 = h5read('filename.h5', '/data/image_REF1');
experimental_param = h5read('filename.h5', '/data/Oxygen');


The provided algorithms enable direct calculation of ratios and subsequent lifetime determination from the raw HDF5 image data across all three applications (oxygen, temperature, and pH). Please note that the way the HDF5 files are structured can vary from one file to another, you can use the python files to identify which treatment you need to apply to a specific file.

Acquisition Parameters

All data acquired using Ximea MC050MG-SY-UB camera with SONY IMX250 sensor (2056 × 2464 resolution), synchronized with LED/laser excitation sources via Arduino R3 controller. Experimental conditions and parameters specific to each application (oxygen, temperature, or pH) are embedded within the respective HDF5 files.