Synthesis of Graphene 3D Graphene Foams

Lead Research Organisation: Durham University
Department Name: Chemistry

Abstract

Graphene has attracted much interest since its discovery in 2004 due to a host of exceptional properties. These properties include a high mechanical strength, high electrical conductivity, high thermal conductivity, and large surface area. More recently, the concept of macroporous graphenes, more specifically monoliths with pores sizes > 1 micron, has begun to emerge with the prospect of developing three principal application areas namely: electrodes conducting frameworks for polymer thermosets, and filtration/pollution control. These applications all have a shared requirement for easily accessible pores of the type inherent to macroporous structures. In applications where electrical conductivity is needed, such as electrode materials, the easily accessible pores and continuous electrically conducting structure of macroporous graphene can improve electron transport and electrolyte diffusion compared to discontinuous powder electrodes.
This project will look at new methods to produce macroporous 3D graphene foams using a variety of templating methods. The material produced will be investigated in energy storage applications.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/N509462/1 01/10/2016 30/09/2021
1743232 Studentship EP/N509462/1 01/10/2016 31/12/2019 Stuart Goldie
 
Description Out understanding of the formation of graphene in 3D structures has been advanced, potentially allowing greater control over the final properties of such materials. High temperature processing of carbon based structures with catalysts present can produce very porous materials with promising properties for a variety of applications, many already detailed in the abstract. By altering the catalyst precursor used it is possible to exercise more control over the final material and tailor properties for different applications.
In addition to graphene foam production from solid carbon starting materials, investigations were also made into the growth of graphene layers on porous metal templates; this approach has the advantage of producing materials with better properties for energy storage applications like conductivity however the low volume of material produced and relative complexity and cost of the processing makes scale up and impossibility with current methods. The obvious question that remains is how to marry the excellent properties produced by direct graphene production on metal templates with the scale achievable using solid carbon foams.
Exploitation Route The understanding gained in the behaviour of metal nanoparticles when embedded in carbon at high temperature may be highly relevant to industrial processes that use metal catalysts; either embedded in porous supports or else to catalyse chemical reactions of carbon species which often become permanently bonded to the metals during the processing.
In addition, a new method for data analysis of these and other graphene based materials was developed to overcome limitations in the existing computational methods; and this will shortly be made available to researchers in the field for routine analysis.
Sectors Chemicals,Energy,Environment

 
Title Automated Analysis of Large Graphene Raman Datasets 
Description Analysing Graphene and related materials requires a range of different techniques. One very powerful method is Raman spectroscopy that can provide information about graphene sheet thickness, size and the presence of defects and other forms of carbon. This algorithm allows easy and accurate analysis of large data sets generated by instruments capable of automatically collecting multiple spectra from different points on a sample surface. In contrast to existing commercial software this Python script validates each peak is present before returning the parameters from all valid peaks in a simple database and graphical output that can be used to comment on the nature of the graphene sample. 
Type Of Material Computer model/algorithm 
Year Produced 2018 
Provided To Others? No  
Impact Accurate and fast analysis of large Raman data-sets allows materials to be more completely characterised and inhomogeneous elements detected more rapidly. Within our research group the algorithm has been widely employed when analysing a range of samples. Whilst not currently in the public domain the script will shortly be made available alongside a research paper in progress that discusses the statistical significant of this technique in more detail.