Physico-chemical characterisation of corn cob biochar produced through slow pyrolysis in a low-cost metallic kiln - Haseena, S; Sainath, N; Guttikonda, V; Eligeti, R; Srilaxmi, B (Plant Science Today, 2026)
Biochar production represents a sustainable approach to biomass management and soil fertility improvement. This study synthesised biochar from corn cob residue through slow pyrolysis using a low-cost, custom-designed metallic kiln and evaluated its key physico-chemical properties. The produced biochar exhibited an alkaline pH (7.53), moderate electrical conductivity (1.35 dS m-1) and a high total organic carbon content (70.1 %). Nutrient analysis revealed appreciable levels of macro- and micronutrients (N: 1.48 %, P: 0.42 %, K: 2.30 %, S: 0.27 %, Zn: 5.3 ppm and Cu: 5.3 ppm), along with a favorable C:N ratio (47:36). Its high porosity (73 %) and substantial surface area (926 m² g-1) suggest strong potential for improving soil structure, nutrient retention and long-term carbon sequestration. The pyrolysis process achieved a biochar recovery of 47 %, indicating efficient carbon retention and minimal biomass loss. The study hypothesised that controlled slow pyrolysis of corn cob residue using an affordable, decentralised kiln could produce biochar with desirable agronomic properties and economic viability. This is particularly important because corn cob waste is abundant and often burned, contributing to pollution, while escalating fertiliser costs demand cost-effective nutrient alternatives. The results confirm that simple, low-cost reactor systems can convert agricultural waste into high-quality biochar with physicochemical properties comparable to those produced using advanced technologies. The findings highlight opportunities for decentralised biochar production, low-cost fertiliser substitution and carbon-credit generation. With the global biochar market valued at USD 0.76–2.2 billion, corn cob-based biochar holds strong commercial potential. Future work should include field-scale evaluation, quality standardisation and techno-economic assessments utilising digital twins and AI-based modeling to optimise its agricultural and environmental applications.