Publications by authors named "N Murmu"

Integration of different active sites by heterostructure engineering is pivotal to optimize the intrinsic activities of an oxygen electrocatalyst and much needed to enhance the performance of rechargeable Zn-air batteries (ZABs). Herein, a biphasic nanoarchitecture encased in in situ grown N-doped graphitic carbon (MnO/Co-NGC) with heterointerfacial sites are constructed. The density functional theory model reveals formation of lattice oxygen bridged heterostructure with pyridinic nitrogen atoms anchored Co species, which facilitate adsorption of oxygen intermediates.

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Complete eradication of aggressive head and neck squamous cell carcinoma (HNSCC) still remains a major challenging problem due to numerous resistance properties of cancer stem cells (CSC) which is crucially responsible for tumor recurrence and metastasis. This challenge causes a high demand for the emergence of novel targeted treatment modalities for improved therapeutic efficacies. Phytochemicals derived from plants proves to be a wide reservoir of important drug candidates which have the potential to impede multiple aspects of malignant growth and progression.

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Rechargeable zinc-air batteries (ZABs) with high-performance and stability is desirable for encouraging the transition of the technology from academia to industries. However, achieving this balance remains a formidable challenge, primarily due to the requirement of robust, earth-abundant reversible oxygen electrocatalyst. The present study introduces a simple strategy to synthesize Co-N rich nanoalloy with N-doped porous carbon tubes (NiCo@NPCTs).

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Article Synopsis
  • Effective segmentation of biomedical images requires a balance between understanding both global context and local details, but existing transformer models often struggle with accuracy and computational limits.
  • The proposed solution in the paper is a multi-scale dual-channel decoder that utilizes two parallel encoders and a hierarchical Attention-gated Swin Transformer decoder to enhance feature extraction while minimizing computational demands.
  • Evaluation of the model on various datasets, including public and private ones, shows that it surpasses existing models in segmentation accuracy for liver tumors and spleen images, maintaining a manageable computational cost.
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Colon cancer is on the rise in younger adults. Despite multimodal treatment strategies, clinical outcomes in advanced stage colon cancer patients remain poor. Neoadjuvant/adjuvant chemotherapy efficacy is limited due to chemoresistance, toxicity, and negative side effects.

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