AI for Carbon Capture and Molecular Separation
Identify adsorbents and operating conditions that separate the right molecule under realistic process constraints. CD ComputaBio combines molecular simulation, AI-guided materials screening, adsorption analysis, and regeneration-aware ranking for carbon capture, gas purification, hydrocarbon separation, pollutant removal, and adsorbent lifecycle studies.
The highest uptake material is not necessarily the best separator
Separation performance emerges from several coupled properties. Stronger adsorption can increase capacity but make regeneration harder. Narrow pores can improve selectivity while slowing diffusion. A promising dry-gas material may lose performance in humid or contaminated streams.
Our screening logic therefore ranks materials against the complete operating objective rather than a single adsorption value.
Working Capacity
Usable loading difference across adsorption and desorption conditions, not only maximum uptake.
Selectivity
Preference for the target component in a mixture at relevant composition, pressure, and temperature.
Transport and Kinetics
Diffusion, pore accessibility, particle effects, and the time required to approach useful separation.
Regeneration Burden
Energy, vacuum, purge, temperature, solvent, or pressure change required to restore capacity.
Five research routes defined by feed stream and process objective
Select the route that matches the molecules being separated, the required product specification, and the conditions the material must withstand.
Carbon Capture Materials
Screen and optimize materials for post-combustion, pre-combustion, biogas, natural-gas, or direct-air capture scenarios.
Hydrogen Purification and Storage
Evaluate porous materials for hydrogen recovery, impurity removal, pressure-swing purification, and reversible storage.
Light Hydrocarbon Separation
Target difficult mixtures whose components have similar sizes, boiling points, or physicochemical properties.
PFAS and Pollutant Removal
Prioritize adsorbents for trace contaminant capture in water or complex environmental matrices.
Adsorbent Stability and Regeneration
Examine how water, impurities, temperature, cycling, and regeneration conditions change structure and performance.
Evaluate performance at three connected scales
A material can rank highly at the molecular level yet fail after shaping, under mixed feeds, or during repeated regeneration. The workflow connects these scales before final prioritization.
Molecular Recognition
How pore size, topology, charge, functional groups, and guest interactions control adsorption and selectivity.
Binding sites · isotherms · diffusionMaterial Performance
How defects, flexibility, moisture, mixture competition, particle form, and shaping affect usable behavior.
Working capacity · kinetics · stabilityProcess Relevance
How pressure, temperature, cycle design, purity, recovery, productivity, and regeneration change the ranking.
PSA · TSA · VSA · cyclic operationScreen the material under the stream it will actually see
Idealized single-component data are useful for interpretation, but material selection should reflect mixture composition, trace contaminants, humidity, pressure, temperature, and regeneration conditions.
Spend high-fidelity calculations on the candidates that survive
The first screening layer removes obvious mismatches quickly. More demanding simulations and mechanistic analyses are then focused on candidates with credible pore accessibility, material stability, and process relevance.
Deliverables built for material selection and validation planning
The final package connects performance metrics with operating assumptions, stability risks, and the experiments required to distinguish leading candidates.
Material and Process Shortlist
Candidate recommendations, operating conditions, mechanistic interpretation, and remaining uncertainty in one package.
Candidate materials compared across capacity, selectivity, kinetics, stability, and regeneration.
Pressure, temperature, mixture, humidity, and cycle assumptions tied to each ranking.
Binding sites, pore occupancy, molecular orientation, diffusion, and competitive adsorption.
Recommended isotherms, mixture tests, breakthrough, cycling, stability, and regeneration studies.
Planning a molecular separation project
What information is needed to begin a separation-materials project?
Useful inputs include the feed composition, target product or contaminant, pressure and temperature range, humidity, required purity or removal level, regeneration method, known materials, and available adsorption or cycling data. The study can also begin from a target mixture and an open materials search.
Can MOFs, COFs, zeolites, activated carbons, and porous polymers be compared together?
Yes, provided the comparison uses consistent process-relevant metrics and acknowledges differences in stability, shaping, density, pore accessibility, cost-related constraints, and data quality. The goal is to identify credible material classes and candidates rather than force unlike materials into one simplistic score.
Can the model account for humidity and mixed-gas competition?
Yes. Depending on available structures, force fields, data, and project scope, humidity and mixture effects can be addressed using competitive adsorption models, molecular simulation, data-driven corrections, or targeted experimental-data integration.
How is regeneration included in material ranking?
Ranking can include working capacity, adsorption strength, desorption conditions, heat of adsorption, pressure or temperature swing, irreversible binding risk, and predicted capacity retention. The appropriate regeneration metric depends on the intended process.
Can proprietary adsorption isotherms or breakthrough data be used?
Yes. Client-provided isotherms, kinetic measurements, breakthrough curves, cycling results, stability data, and negative findings can be incorporated into project-specific models and candidate comparisons under the agreed confidentiality framework.
Define the separation before selecting the material
Share the feed mixture, target purity or removal goal, operating conditions, candidate material space, and available data. Our scientists will help design a screening and validation strategy around the real process decision.
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