Renewable Carbon Material Production in Continuous Flow

Invention Reference Number

202405612

Male scientist adjusting switch for fume hood in lab cleanroom. Image from Envato.

This technology addresses limitations in producing biobased carbon materials using conventional batch-based, pressurized systems. It introduces a continuous-flow approach for forming uniform, submicron amorphous carbon materials while enabling real-time process observation. By replacing closed, batch reactors with a flow-based method, the technology improves process control, scalability, tunability of properties based and safety. The approach is applicable to organizations seeking consistent carbon material production with enhanced quality oversight and opportunities for material customization during manufacture for specific applications of broad range of starting materials.

Description

Conventional preparation of biobased amorphous carbon materials typically relies on sealed, high-pressure batch reactors, which limit production continuity and prevent direct observation of material formation. This technology describes a method for producing amorphous carbon materials using a continuous-flow system that supports elevated temperature and pressure conditions without reliance on closed, batch pressurized vessels processing.

The approach allows renewable precursor materials to be introduced and processed within a flowing environment, enabling uniform product material formation and improved operational oversight. The system architecture centric feature is integrated monitoring and diagnostic capabilities, offering insight into dynamic of material evolution during production rather than single point analysis after process completion.

Additionally, the reactor design enables in-process modification of the substrate or resulting materials through controlled introduction of additional reactive or additive inputs lines during operation or assembly of mixed processing units which combine benefits of batch and flow processing in parallel or serial connectivity. The quality control units incorporated on each processing element of the architecture enables process tunability to accommodate for the different original materials. This flexibility supports material tailoring and quality control while maintaining a continuous production mode. The method provides a safer and more adaptable alternative to traditional batch-based processes while remaining compatible with industrial manufacturing environments and high-volume production.

Benefits

Enables continuous production of uniform amorphous carbon materials on bench and industrial scales
Improves process monitoring and quality control during material formation
Reduces reliance on closed, batch pressurized systems
Uses active material quality control allowing for adjustment of processing parameters in situ based on the developed correlational models acting as agents and physics-based models of batch and flow reactions acting as process digital twin of each processing unit
Incorporated advanced artificial intelligence/ machine learning algorithm which combines Retrieval-Augmented Generation framework that enhances Large Language Models (RAG-LLMs) connected to external knowledge bases for retrieval real-time, specific, data for modeling and answering process specific questions with improved accuracy
Expandable nature of AI/ML algorithms enables correlation between the original biomaterials, reaction condition and product for selection optimal (from techno economic assessment consideration) process and product
Expandable knowledge of in depth multiparameter correlations which is expandable with each experiment

Applications and Industries

Advanced materials manufacturing (including nano, micron and macro) with controlled architecture
Industrial carbon materials processing
Autonomous and semi- autonomous chemical and materials research operations
Production of pre-packed (high surface area, tunable functionality and catalytic activity) materials in the columns for integration in synthesis, water desalination, flow synthesis (including catalytic)
Production of materials with the surface attached fertilizers, growth factors, anti-fungal, antibacterial compounds for agricultural applications including but not limited to advanced controlled release, soil recovery and stabilization)
Agentic AI based on digital twin process with the natural language processing interface for direct interaction with the process for product and process tunability
Accelerated material, process development and continuous system knowledge update based on shared transferable knowledge and AI/ML models
Integration with the rapid computation systems for rapid modeling of transition state and reaction path based on accelerate modeling workflow which combines Density Function Theory, Molecular dynamic with Machine Learning interatomic potential and Kinetic Monte Carlo simulation