PFAS Decomposition & Remediation
Design catalytic, electrochemical, plasma, and thermal pathways to break persistent C–F bonds and transform PFAS into safer products.
Application / Environmental Solutions
AI-guided materials and catalysis for cleaner water, safer coatings, and circular chemistry.
DeepChemIQ helps partners design environmental technologies for PFAS replacement or decomposition, nitrate reduction, water treatment, depolymerization, and sustainable material discovery.
H₂O
AI
Environmental Design EngineMD → ML → Catalysis → Treatment Digital Twin
Solution areas
From persistent pollutants to sustainable alternatives.
We combine molecular simulation, active learning, catalyst modeling, and process-scale analysis to design technologies that reduce environmental impact while preserving performance.
Non-PFAS Coating Replacement
Use MD simulation, wetting models, and active learning to discover fluorine-free or reduced-fluorine coatings for water and oil repellency.
Nitrate Reduction in Water
Develop catalyst and reactor strategies for converting nitrate-contaminated water into benign or valuable nitrogen-containing products.
Water Treatment Materials
Screen porous materials, membranes, adsorbents, and catalytic surfaces for pollutant capture, degradation, and selective conversion.
Polymer Depolymerization
Model catalytic pathways for polymer breakdown, monomer recovery, waste valorization, and circular chemical manufacturing.
Environmental Digital Twins
Combine molecular simulation, ML, kinetic modeling, and CFD to predict treatment performance from material scale to process scale.
Study case
Active learning for PFAS-coated textile omniphobicity.
Our PFAS coating study used molecular dynamics and active learning to predict water and oil contact angles across PFAS head groups, chain lengths, packing densities, and branched structures. This provides a data-driven foundation for designing environmentally benign, non-PFAS liquid-repellent coatings.
<3°contact-angle MAE for linear PFAS models
<5°prediction error for branched PFAS after active learning
C1–C7PFAS chain lengths explored
Water + Oildual repellency prediction
Workflow
A closed loop for environmental materials innovation.
01
Define
Identify pollutant chemistry, target performance, operating conditions, and environmental constraints.
02
Model
Use DFT, MD, kinetic modeling, and transport simulation to understand molecular and process mechanisms.
03
Learn
Apply active learning and interpretable ML to reduce simulation cost and prioritize promising materials.
04
Design
Propose catalysts, coatings, adsorbents, membranes, or reactor conditions with improved sustainability.
05
Scale
Build digital twins and CFD models to evaluate process performance, efficiency, and deployment pathways.
Technology platform
Materials discovery, degradation chemistry, and treatment process design.
DeepChemIQ can support environmental R&D using DFT for reaction mechanisms, molecular dynamics for interfaces and coatings, machine learning for inverse and forward design, microkinetic modeling for catalytic pathways, and CFD for water-treatment or reactor-scale digital twins.
PFAS-free coating discoveryPFAS decomposition pathway screeningNitrate-to-benign product conversionPollutant adsorption and transportPolymer depolymerization chemistryTreatment process digital twins
Build environmental solutions with molecular intelligence.
Partner with DeepChemIQ to design safer materials, cleaner water-treatment technologies, and scalable pollutant-conversion processes.
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