Michel Gad

Research Focus

Advancing understanding of environmental processes through analytical chemistry

Nanoparticle Fate & Behavior

Investigating the transformation and environmental impact of nanoparticles in surface waters using advanced mass spectrometry techniques.

FT-ICR MS Eco-corona TiO₂ NPs

Surface Water Analysis

Characterizing dissolved organic matter composition and its interaction with environmental factors across diverse aquatic systems.

DOM Analysis Machine Learning Environmental Drivers

Predictive Modeling

Developing multivariate random forest models to predict molecular composition and environmental behavior patterns.

Random Forest Python & R HPC Computing

Featured Project

Advancing research data management through innovative tools

Research Data Management

SciTrace - Research Data Lineage Platform

A comprehensive research data lineage platform that integrates with DataLad functionalities and adds interactive visualizations for the complete data lifecycle. SciTrace helps researchers track data transformations, execute code seamlessly, and maintain complete trails of their research data workflows.

Data Lineage Tracking

Complete traceability of data transformations and processing steps

Seamless Code Execution

Integrated code execution within the data management workflow

Interactive Visualizations

Visual representation of data flows and dependencies

FAIR Data Principles

Ensures Findable, Accessible, Interoperable, and Reusable data

Python DataLad Interactive Visualization Data Lineage Research Workflows

View on GitHub Learn More

SciTrace

Raw Data

→

Processing

→

Results

10:30 Data imported

10:35 Preprocessing applied

10:40 Analysis completed

Publications

Recent scientific contributions to environmental chemistry

Environmental drivers of dissolved organic matter composition across central European aquatic systems

Water Research

2025

Novel correlation-based machine learning model integrating FT-ICR MS data to predict DOM composition and identify key environmental drivers.

Dissolved Organic Matter FT-ICR MS Machine Learning Environmental Drivers

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Measuring and predicting the ζ-potential of anthropogenic TiO₂-nanoparticles in surface waters

Environmental Science: Nano

2025

Novel approach for determining and predicting the ζ-potential of nanoparticles in surface waters using XGBoost models and in-situ exposure methods.

TiO₂ Nanoparticles ζ-potential XGBoost Surface Waters

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Bioimaging of Keratan Sulfate in Ocular Tissue Sections

ACS Applied Bio Materials

2022

Advanced bioimaging workflow using immunofluorescence microscopy and LA-ICP-MS for spatial resolution of biomolecules.

Bioimaging LA-ICP-MS Tissue Analysis

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Research Experience

Academic journey in environmental analytical chemistry

Helmholtz Centre for Environmental Research (UFZ)

Feb 2022 - Present

Ph.D. Candidate in Environmental Chemistry

Leading research on nanoparticle behavior in aquatic environments using advanced mass spectrometry and machine learning approaches.

Developed novel multivariate random forest models for DOM prediction

Implemented reproducible data pipelines with KNIME

Deployed workflows on HPC cluster for large-scale analysis

FT-ICR MS Python & R Machine Learning HPC Computing

Federal Institute for Materials Research and Testing (BAM)

Aug 2020 - Feb 2021

Student Research Assistant

Collaborative research with Charité applying advanced tissue imaging for early cancer detection and biomarker analysis.

Performed bioimaging using LA-ICP-MS

Managed high-resolution tissue imaging data

LA-ICP-MS Bioimaging Data Processing

Get in Touch

Interested in collaboration or research opportunities?

Email

michel.gad@outlook.com

Institution

Helmholtz Centre for Environmental Research (UFZ)

Location

Leipzig, Germany