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Cleaner and sustainable water production lead to the development of environmentally friendly adsorbent materials for energy-efficient, cost-effective, and cleaner water production. In this study, the biochar derived from the rice husk, wheat straw, and corncob has been used for the adsorptive removal of heavy metals, including the lead (Pb+2) and cadmium (Cd+2). The synthesised biochar was characterised by a different structural and analytical approach. The characterisation of biochar revealed the existence of the combined redox, i.e. reductive and oxidative surface functional groups along with some inert functional groups which play a significant role in donating or accepting an electron to degrade the pollutants in the wastewater. The biochar synthesised in this study was found to be amorphous, and some negligible disorders and defects have been observed in the structure of biochar. The biochar has been highly stable under harsh thermal conditions by sustaining significant weight over a temperature of 700 ​°C and also be hygroscopic. The biochar rice husk, wheat straw, and corncob demonstrated the lead (Pb+2) adsorption capacity of 96.41%, 95.38%, and 96.92%, while for cadmium (Cd+2), the uptake capacity was found to be 94.73%, 93.68%, and 95.78%. The reported biochar is a cleaner, environmentally friendly, economical, and sustainable alternative to conventional adsorbent materials.

The municipal solid waste (MSW) is widely considered as a heterogeneous source of energy, with numerous characteristics varying depending on its geographical region or origin from where it is collected. The development of correlations for the prediction of the higher heating value (HHV) of MSW is a sustainable and cleaner way based on data of specific region from where MSW is obtained. In this research, a new approach reported for the development of correlations to predict the energy content of MSW accurately. The developed correlations lie within the all parameter affecting the HHV of MSW. The models presented can predict the HHV of MSW accurately (R2 > 0.8) compared to other models reported in the literature. However, model 13 developed on the basis of ultimate analysis demonstrated maximum accuracy (R2 = 0.919) among all 7 developed models. The aim of developing the prediction model for the HHV of different MSW samples was to reliably predict using proximate or ultimate analytical data to save experimentation costs and provide theoretical foundations for modelling MSW combustion, pyrolysis processes and thermal conversion for the energy security.

Biochar is considered to be the cost-effective, environmentally-friendly and sustainable sorbent that has an extraordinary potential to efficiently remove toxic elements, including arsenic (As) from water. Until now, no review has focused to understand various important and intriguing aspects on biochar use as a sorbent for As removal from water, either pristine or modified. This review discusses various factors governing As removal potential of biochars (e.g., pH, biochar dose and physico-chemical properties of biochar), sequestration mechanisms, fate of sorbed As on biochar and the redox-mediated interactions between biochar and As. The significance of biochar-derived materials for the treatment of As-contaminated drinking water/wastewater and their potential regeneration ability is also critically discussed, which has not been previously elaborated. This comprehensive review article could be greatly valuable for scientists, policymakers, water treatment industries, environmentalists and graduate students, who are involved in biochar-As research. The review covers some new overarching and key scientific opportunities for the remediation of As-contaminated water using biochar-based materials, which is a potential health risk to millions of people worldwide.

Despite the significant potential of nanofluids in energy storage applications, the experimental determination of thermophysical properties is relatively costly and time-consuming. Therefore, the modeling techniques can be used for the accurate estimation of thermo-physical behavior. The predictive models are useful for the understanding of thermo-physical behavior. Due to restrictions on classical models, there is a need to develop more reliable models to simulate the thermophysical behavior of nanofluids. This paper deals with the synthesis and experimental thermal conductivity measurement of the multi-walled carbon nanotubes (MWCNTs)-Kapok seed oil nanofluid. Additionally, two new correlations based on multiple non-linear regression analysis along well as dimensionless analysis are proposed to estimate thermal conductivity with high precision compared to the classical models. Based on the statistical analysis, the prediction accuracy of the proposed model was ranked. Finally, the sensitivity analysis has been carried out in combination with the residual analysis to assure the accuracy of the model parameters of both proposed models and adequacy of estimated values of model parameters, respectively. The results revealed the global minimum values for all parameters at 0% perturbations indicating that the model parameters were estimated with high accuracy and adequacy.