ISSN: 2651-3080
e-ISSN: 2651-3080
Founded: 2018
Publisher: Niyazi BULUT
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Journal of Physical Chemistry and Functional Materials (JPCFM) is a peer-reviewed journal and publishes the latest findings in analytical methods, astrochemistry, biochemistry, composite materials, computational methods, energy production, gas-phase reaction, green chemistry, kinetics, and dynamics, material science, mathematical physics, medical physics, molecular structure, nanoscience, nanostructures, natural science, nuclear structure and models, optical materials, optoelectronic applications, optoelectronic materials, organic semiconductors, polymer science, quantum chemistry, spectroscopy, structural materials, surface and interface, surface chemistry, thermodynamics, and theoretical developments areas twice a year, in June and December.

Quality and effect are the criteria for publication. In addition, experimental results published in the journal have direct implications for theory, and theoretical advancements or non-routine calculations have direct implications for experiments. These conditions must be met, and manuscripts should not be modest extensions of existing work. The obtained and presented data must be unique and saturated to fill the existing gaps in the relevant area.

        JPCFM publishes:

  • Communications
  • Research papers
  • Reviews











2024 - Volume: 7 Issue: 1

Research Article

Investigation of phytochemical contents and anticancer, antioxidant, antimicrobial activities of Cucurbita pepo leaves

Research Article

EPILEPTIC SEIZURE DETECTION FROM EEG SIGNALS WITH RECURRENT NEURAL NETWORKS BASED CLASSIFICATION MODEL

Research Article

A New Hybrid Method for Classification of Rice Leaf Diseases: SVM+NCA+Resnet50

Research Article

Image fire detection module for automatic fire extinguishing system with unmanned ground vehicles

Research Article

Extracting Meaningful Information from Turkish Chemistry and Physics Texts with Machine Learning

Research Article

Examining Thyroid Cancer Diagnosis: Harnessing Machine Learning for Early Risk Detection