Chemometrics & spectroscopy
Explore variation in your spectra, separate overlapping contributions and resolve energy-dependent distributions.
DataChem / Scientific data analysis
Scientific and experimental data analysis
DataChem combines scientific expertise with Python tools tailored to your experimental data.
- Analyse and interpret your measurements
- Develop or adapt your analysis tools
Experimental data → Analysis → Model → Scientific insight
Experimental measurements
Fitted model
Scientific insightCharacteristic responseModel parameters
Fit assessment
Illustrative model · not experimental results
Methods suited to your measurements
Adsorption & kinetics
Fit experimental curves and estimate parameters with explicit model assumptions.
Scientific tools & automation
Develop or adapt Python tools to your data to run processing steps, visualise results and reproduce analyses.
Scientific case studies
The figures show the analysis. Each study explains what it can reveal and its limitations.
01 / Spectroscopy & adsorption
From experimental data to adsorption insight
Combining FTIR analysis, PCA and MCR-ALS with equilibrium isotherm modelling to examine how CO₂ adsorption differs across zeolite samples.
Explore the study →
02 / Dynamic adsorption · Python tool
From breakthrough curves to mass-transfer parameters
A DataChem tool developed in Python to process experimental data, fit models and extract the associated parameters.
Data → Model → Parameters → Report
Explore the tool and study →A four-step approach
Define the question
Clarify the objective, available data and expected output.
Review the measurements
Check units, experimental conditions, quality and preprocessing.
Analyse and assess
Choose a suitable method, inspect residuals and discuss limitations.
Deliver clear results
Provide figures, indicators and a summary your team can use.
Your contact
Abdelhafid Ait Blal
PhD in chemistry, specialising in adsorption, spectroscopy and experimental-data analysis. An approach connecting numerical analysis with the measurements behind it.
Contact
Let’s discuss your data.
Describe your question briefly, without including confidential data in this first enquiry.
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