Biochar and materials discovery

Biochar, a biotechnology, is a C based material produced via pyrolysis of biomass (with a limited and controlled Oxygen supply) with an enormous surface area from the nanoscale (pores) and larger that can be functionalised, determined by: the feedstock type, the pyrolysis technology and operation, biological inoculation, combination with other elements or compounds or even bio-composite requirements. In other words - 'designer biochars' are possible that can be matched to the application, possibly using a combination of an interface/autonomous lab with machine learning (ML) software and rapid material prototyping then all the way to field trials and commercialisation at different scales of manufacturing.

LEARNING ISSUES
- What desirable properties (biological, chemical, physical) does the Biochar material need for the application eg. A specific plant/crop?
- what material/resource can be replaced with a new Biochar material?

• Could break down every material in every materials-based application/product (a gigantic task) and look for Biochar material bio-substitutes. Maybe start with plastics.

• How can non-C based materials be mimicked by a designer eg. a doped Biochar material with similar/same properties?
• What is the effect of pyrolysis on sub-cellular organelle surface biochemistry?
• Convergence between the chemical engineer 'programmer' and the ML materials discovery interface/'autonomous lab'

The Mother of all biochar chemical engineering questions:

• How can anyone program and test biochar with ML when every biochar particle has a unique 3D matrix with a diaspora of chemical binding and bonding sites?
• • Maybe needs an error corrected Quantum computer with 10s of thousands of Qubits, 3D vector+raster graphics modelling on the nanoscale (eg. Nvidia graphics cards) with a cutting edge AI platform....but I'm just speculating. Maybe start with 'Deep Forest'.
  • Somehow cause and effect would need to be measurable
  • A lot of dead ends and a lot of new possibilities
  • Could be used to discover new biochar-based materials for solid state batteries and more
  BUT the 'Biochar Matrix Disclaimer': Do I need to know everything about the 3D biochar matrix, which might be random, before using biochar in a given application? I imagine that as the 3D matrix models become clearer and cheaper over time, the specificity of biochar materials will  improve. I don't think we have the time to wait for a much deeper understanding of matrix surface biochemistry given the urgency of the climate emergency and the need to rapidly remove C from the atmosphere!

I can think of a number of applications where an autonomous lab might not be very useful:

eg1. in the case of gardening, horticulture, agriculture, agroforestry et al - biochar compost (alchemy)

    The sky is the limit for how much chemistry research could be done around this but field trials with different biochar composts combined with different ingredients eg.manures, sea kelp etc. for different plants in different soils in different climates would probably be a better strategy. A lot of research has already been done in this area but there's probably a lot more to do as every growing system using biochar compost would be variable for the best results

eg2. biochar (80% w/w) combined with 'no-bake binders' (according to the 'Composite Materials Consultancy' in the UK) to produce biochar bricks. I should mention too they seem to be fire retardant.

   Although understanding the chemistry is important, testing mechanical properties are presumably more important for building material research.

REFERENCES

The 3D biochar matrix

• https://www.doublehelixoptics.com/
• • • not a bad start, down to 20nm resolution - needs to get down to <5nm
    • check out the video..verrry nice
  • ->
  • https://deepforest.readthedocs.io/en/latest/landing.html
  • ->
  • https://www.intechopen.com/chapters/84407
  • ->

    Autonomous/ML labs

  • https://www.nature.com/articles/d41586-023-03745-5
  • https://news.ncsu.edu/2023/11/smart-dope-autonomous-lab/
  • https://www.rmit.edu.au/news/all-news/2023/nov/machine-learning-solar