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Sharma, Mrityunjay K.

Publications and source records attributed to Sharma, Mrityunjay K..

The crystal orientation of THF clathrates in nano-confinement by in situ polarized Raman spectroscopy

Gas hydrates form at high pressure and low temperatures in marine sediments and permafrost regions of the earth. Despite forming in nanoporous structures, gas hydrates have been extensively studied only in bulk. Understanding nucleation and growth of gas hydrates in nonporous confinement can help create ways for storage and utilization as a future energy source. Herein, we introduce a new method for studying crystal orientation/tilt during tetrahydrofuran (THF) hydrate crystallization under the influence of nano-confinement using polarized Raman spectroscopy. Uniform cylindrical nanometer size pores of anodic aluminum oxide (AAO) are used as a model nano-confinement, and hydrate experiments are performed in a glass microsystem for control of the flash hydrate nucleation kinetics and analysis via in situ polarized Raman spectroscopy. The average THF hydrate crystal tilt of 56 ± 1° and 30.5 ± 0.5° were observed for the 20 nm and 40 nm diameter pores, respectively. Crystal tilt observed in 20 and 40-nanometer-size pores was proportional to the pore diameter, resulting in lower tilt relative to the axis of the confinement at larger diameter pores. Finally, the results indicate that the hydrates nucleation and growth mechanism can depend on the nanoconfinement size. A 1.6 ± 0.01 °C to 1.8 ± 0.01 °C depression in melting point compared to the bulk is predicted using the Gibbs–Thomson equation as a direct effect of nucleation in confinement on the hydrate properties.

25 ENERGY STORAGE↗

Perspectives on Polyolefin Catalysis in Microfluidics for High-Throughput Screening: A Minireview

Polyolefins are the largest produced plastics in the world which traditionally employ continuous stirred tank reactors and fluidized bed reactors for commercial production. The operating condition, reaction kinetics, and molecular interactions inside the reactor strongly affect the polyolefin properties, which require stringent process control in conventional procedures. Understanding the catalytic pathway, behavior of polymer particles and effect of reactor conditions are essential for designing specific polymer properties, namely the molecular weight, chain length, polydispersity, etc. Microfluidics can play a significant role in designing polymers tailored to the user needs. Smaller channel dimensions help obtain uniform reaction conditions over the length of the microfluidic reactor in a controlled environment. With real-time monitoring techniques in microfluidics, even single particle growth of polymer can be studied to understand the parameters affecting the polymer properties. High throughput microfluidics can help catalyst screening in a short duration with less consumption of reagents generating less waste. When supplemented with efficient machine learning algorithms, automated high throughput microfluidics has the potential to rapidly optimize the process and develop new knowledge even with a limited data set. When trained on data sets generated using microfluidic experiments that are designed efficiently with working knowledge of the process, machine learning algorithms can provide the relationship between the multivariable parameters space and polymer properties, which is not possible with the traditional statistical methods and interpolation techniques. Here, the rise in the utilization of microfluidics, with the advancement of machine learning algorithms, for polyolefin catalysis, highlights the importance of microfluidics for catalyst discovery, parameter optimization, and understanding reaction pathway for producing polymers with specific properties for specialized applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Perspectives on Microfluidics for the Study of Asphaltenes in Upstream Hydrocarbon Production: A Minireview

The utilization of microfluidics has generated deep insights into asphaltene precipitation mechanisms and oil–water emulsion stabilization. Agglomeration and precipitation of asphaltenes can cause flow assurance problems during the extraction and transportation of crude oil. Change in temperature, pressure, reservoir conditions, and solvents can change the local environment, leading to asphaltene precipitation. Understanding asphaltene properties and precipitation pathways becomes critical in devising mitigation methods, demulsifiers, and suitable conditions during hydrocarbon processing. Microfluidics has helped in high throughput measurement studies, understanding critical processing conditions, fast demulsifier screening, and the effect of solvent concentration on deposition, generating useful information for utilization at the point of resource extraction facilitating improved resource management. It has become possible to capture the porous, complex nature of reservoir formations and the interaction of chemicals during precipitation through integrated analytics and visualization studies available only through microfluidics. The use of droplet microfluidics, with optical microscopy and high-speed imaging to study the oil–water interface, has resulted in greater understanding of the role of asphaltenes in interfacial properties and emulsion stabilization. Here, this minireview highlights the crucial aspects of microfluidics that have been used to understand physicochemical behavior and dynamics of asphaltene deposition. Some of the unique devices have been presented focusing on the key elements of microfluidics design, fabrication, and analysis, as the insight obtained from microfluidics strongly depends on the device design and the controllability of the experimental parameters. Successful implementation of microfluidics for efficient and controlled experiments, short analysis time scales and rapid screening, and generation of high-quality, reliable data that convey asphaltene deposition issues and interface behavior in emulsions shows the importance of microsystems for advancing knowledge in hydrocarbon production and processing.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗