Publications by authors named "K M Babar Pal"

Novel studies on typical synthesized magnetite nanoparticles were encapsulated into a poly (butylene succinate)/poly (ethylene glycol) copolymer (PBS-PEG). PBS was chosen because of its biocompatibility characteristics necessary for biomedical applications. PEG, as part of the macromolecular structure, increases the hybrid system's solubility in an aqueous environment, increasing the circulation time of the material in the bloodstream.

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Many micro-particles including pathogens strongly adhere to hosts. It remains elusive how macrophages detach these surface-bound particles during phagocytosis. We show that, rather than binding directly to these particles, macrophages form unique β integrin-mediated adhesion structures at the cell-substrate interfaces, specifically encircling the surface-bound particles.

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BiTe, a member of the (Bi)(BiTe) homologous series, possesses natural van der Waals-like heterostructure with a Bi bilayer sandwiched between the two [Te-Bi-Te-Bi-Te] quintuple layers. BiTe exhibits both the quantum states of weak topological and topological crystalline insulators, making it a dual topological insulator and a suitable candidate for spintronics, quantum computing and thermoelectrics. Herein, we demonstrate that the chemical bonding in BiTe is to be metavalent, which plays a significant role in the pressure dependent change in the topology of the electronic structure Fermi surface.

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The mediastinal vasculature can be affected by various etiologies in cancer patients. Both direct and indirect sequela of cancer may result in life-threatening clinical presentations. Tumor growth may cause vessel narrowing and decreased blood flow from either extrinsic mass effect, invasion into the vascular wall, or tumor thrombus within the lumen.

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Article Synopsis
  • The use of high-throughput density functional theory (DFT) accelerates the search for new stable inorganic compounds, but the process remains costly due to the extensive search space.
  • To enhance these searches, recommendation engines based on elemental substitution, data mining, and neural networks have been developed and compared, with neural networks proving to be the most effective for identifying stable Heusler compounds.
  • Improved recommendation engines have led to the discovery of tens of thousands of stable compounds at zero temperature and pressure, contributing to the Open Quantum Materials Database and highlighting applications in thermoelectricity and solar thermochemical fuel production.
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