Residues of pharmaceuticals in wastewater impose severe environmental and human health related problems. To tackle these challenges, we will develop in LAFAST3 a 3D architecture consisting of nanocellulose and nanocarbon materials, integrated together to form a tunable membrane structure capable of detecting drug residues from effluents at superior sensitivity and selectivity. By tailoring the basic building blocks of our hybrid material, we can optimize solvent flow and electroanalytical properties separately. We will use different machine learning approaches such as spectral analysis, for the electrochemical data. The main outcomes are (i) physicochemical and electrochemical information about the hybrid materials, (ii) associations between these structures and desired properties and (iii) specifications for a proof-of-concept detection device for wastewater analytics.
We have demonstrated the potential of nanocellulose/multiwalled carbon nanotube hybrids for electrochemical sensing systems.
Choice of (nano)cellulose material governs the morphology, swelling and analyte diffusion, as well as fouling properties of the hybrid electrode
Testing of various grades of cellulose reveals that presence of either fibrillar nanocellulose or carboxymethyl cellulose enables optimal electroanalytical performance for the electrode
Controlled deposition by slow spin coating procedure results in a uniform hybrid film of <400 nm with optimized electroanalytical performance, superior to either ultrathin (<50 nm) or micron-scale layers