Girish Chandra Sharma

ORCID: 0009-0004-0708-1779
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About
Contact & Profiles
Research Areas
  • Synthesis and biological activity
  • Metal complexes synthesis and properties
  • Multicomponent Synthesis of Heterocycles
  • Synthesis and Characterization of Heterocyclic Compounds
  • Metal-Organic Frameworks: Synthesis and Applications
  • MicroRNA in disease regulation
  • Nanoparticle-Based Drug Delivery
  • Catalytic Processes in Materials Science
  • Spectroscopy Techniques in Biomedical and Chemical Research
  • Advanced Photocatalysis Techniques
  • Catalytic Cross-Coupling Reactions
  • Advanced biosensing and bioanalysis techniques
  • Computational Drug Discovery Methods
  • Catalytic C–H Functionalization Methods
  • Corporate Taxation and Avoidance
  • Spectroscopy and Chemometric Analyses
  • melanin and skin pigmentation
  • Synthesis and Biological Evaluation
  • Carbon Dioxide Capture Technologies
  • RNA Interference and Gene Delivery
  • Nonlinear Optical Materials Research
  • Advanced Nanomaterials in Catalysis
  • Ferrocene Chemistry and Applications
  • AI in cancer detection
  • Protein Interaction Studies and Fluorescence Analysis

NIMS University
2024-2025

Applied Sciences (United States)
2025

Aligarh Muslim University
2010-2021

Jaypee University of Information Technology
2021

Abstract In this work, a triple-junction tandem solar cell (TSC) has been designed in order to increase the photovoltaic (PV) performance through utilizing maximum light photons. To create three junctions work subcells have and optimized at its best PV performance. The optimization of all done various variations absorber layer like thickness bulk defect density (BDD). It seen that parameters top middle bottom are high low BDD. For designing triple junction configuration, two filtered...

10.1007/s40243-024-00291-6 article EN cc-by Materials for Renewable and Sustainable Energy 2025-01-21

This study proposes an advanced machine learning (ML) framework for breast cancer diagnostics by integrating transcriptomic profiling with optimized feature selection and classification techniques. A dataset of 1759 samples (987 patients, 772 healthy controls) was analyzed using Recursive Feature Elimination, Boruta, ElasticNet selection. Dimensionality reduction techniques, including Non-Negative Matrix Factorization (NMF), Autoencoders, transformer-based embeddings (BioBERT, DNABERT), were...

10.1007/s12672-025-02111-3 article EN cc-by-nc-nd Discover Oncology 2025-03-17
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