This review showcases a comprehensive analysis of studies that highlight the different conversion procedures attempted across the globe. The resources of biogas production along with treatment methods are presented. The effect of different governing parameters like feedstock types, pretreatment approaches, process development, and yield to enhance the biogas productivity is highlighted. Biogas applications, for example, in heating, electricity production, and transportation with their global share based on national and international statistics are emphasized. Reviewing the world research progress in the past 10 years shows an increase of ~ 90% in biogas industry (120 GW in 2019 compared to 65 GW in 2010). Europe (e.g., in 2017) contributed to over 70% of the world biogas generation representing 64 TWh. Finally, different regulations that manage the biogas market are presented. Management of biogas market includes the processes of exploration, production, treatment, and environmental impact assessment, till the marketing and safe disposal of wastes associated with biogas handling. A brief overview of some safety rules and proposed policy based on the world regulations is provided. The effect of these regulations and policies on marketing and promoting biogas is highlighted for different countries. The results from such studies show that Europe has the highest promotion rate, while nowadays in China and India the consumption rate is maximum as a result of applying up-to-date policies and procedures.

The adhesion of carbon nanotube (CNT) forests to their growth substrate is a critical concern for many applications. Here, we measured the delamination force of CNT forest micropillars using in situ scanning electron microscopy (SEM) tensile testing. A flat tip with epoxy adhesive first established contact with the top surface of freestanding CNT pillars and then pulled the pillars in displacement-controlled tension until delamination was observed. An average delamination stress of 6.1 MPa was measured, based on the full pillar cross-sectional area, and detachment was observed to occur between catalyst particles and the growth substrate. Finite element simulations of CNT forest delamination show that force and strain are heterogeneously distributed among CNTs during tensile loading and that CNTs progressively lose adhesion with increased displacement. Based on combined experiments and simulations, an adhesion strength of approximately 350 MPa was estimated between each CNT and the substrate. These findings provide important insight into CNT applications such as thermal interfaces, mechanical sensors, and structural composites while also suggesting a potential upper limit of tensile forces allowed during CNT forest synthesis.

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A time-resolved two-dimensional finite element simulation is developed to model the forces generated during the self-assembly of actively growing CNT populations with distributed properties and growth characteristics. CNTs are simulated as interconnected frame elements that undergo the base growth mechanism. Mechanical equilibrium at each computational node is determined at each time step using the Updated Lagrangian method. Emphasis is placed on the transmission of force to the growth substrate, where catalyst particles reside. The simulated CNT forest structural morphology is similar to that of physical CNT forests, and results indicate that stresses on the order of GPa are transmitted to catalyst particles. The force transmitted to a given catalyst particle is correlated to the rate at which the CNT grows relative to the population averaged growth rate. The effect of diameter-dependent CNT growth rates and the persistence of vdW bonds are also examined relative to the forces generated during forest self-assembly. Results from this study may be applied to the study of CNT forest self-assembly, resultant ensemble forest properties, and force-modulated CNT growth kinetics.

The parameter space of CNT forest synthesis is vastand multidimensional, making experimental and/or numericalexploration of the synthesis prohibitive. We propose a morepractical approach to explore the synthesis-process relationshipsof CNT forests using machine learning (ML) algorithms toinfer the underlying complex physical processes. Currently, nosuch ML model linking CNT forest morphology to synthesisparameters has been demonstrated. In the current work, weuse a physics-based numerical model to generate CNT forestmorphology images with known synthesis parameters to trainsuch a ML algorithm. The CNT forest synthesis variablesof CNT diameter and CNT number densities are varied togenerate a total of 12 distinct CNT forest classes. Images of theresultant CNT forests at different time steps during the growthand self-assembly process are then used as the training dataset.Based on the CNT forest structural morphology, multiplesingle and combined histogram-based texture descriptors areused as features to build a random forest (RF) classifier topredict class labels based on correlation of CNT forest physicalattributes with the growth parameters. The machine learningmodel achieved an accuracy of up to 83.5% on predicting thesynthesis conditions of CNT number density and diameter.These results are the first step towards rapidly characterizingCNT forest attributes using machine learning. Identifying therelevant process-structure interactions for the CNT forests usingphysics-based simulations and machine learning could rapidlyadvance the design, development, and adoption of CNT forestapplications with varied morphologies and properties.