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In this research work, the ZIF-67-coupled plasmonic-gold-incorporated porous g-C3N4 (ZIF/Au-PCN) nanocomposites have been successfully synthesized and utilized for the conversion of CO2 into useful products and Bisphenol A (BPA) decontamination. Compared to pristine PCN, the photocatalytic activities of the most active 3ZIF/1.5Au-PCN nanocomposite are enhanced by 8.0-fold for the conversion of CO2 and by 2.5-fold for BPA degradation. On the basis of our experimental results, it is verified that the porous nature increases the surface area of g-C3N4. Remarkably, the incorporation of Au exceptionally adjusts the band gap of g-C3N4 from 2.7 to 2.48 eV via the surface plasmon resonance (SPR) effect, while the coupling of a metal–organic framework (MOF; ZIF-67) not only enhances the surface area but also prominently enhances the charge separation of g-C3N4 via a photoelectron modulation mechanism. In addition, transmission electron microscopy, scanning electron microscopy, photocurrent action spectroscopy, electrochemical impedance spectroscopy, time-resolved photoluminescence, fluorescence spectroscopy linked with •OH amount, Fourier transform infrared, Brunauer–Emmett–Teller, etc., confirmed that the insertion of a noble-metal Au atom and the fabrication of a MOF offered a suitable energy platform and improved the photocatalytic activities for BPA decontamination and CO2 conversion into valuable products. Moreover, on the basis of thermogravimetric analysis and stability tests, it is proven that the as-synthesized samples are highly stable and no morphological and physiochemical changes are observed before and after various analyses and photocatalytic reactions. Hence, our present research work will manifestly open an innovative gateway and feasible strategy to prepare MOF-supported and plasmonic-assisted g-C3N4-based porous and highly efficient photocatalysts for CO2 conversion and environmental protection.

This paper presents an efficient Optical Character Recognition (OCR) system for offline isolated Pashto characters recognition. Developing an OCR system for handwritten character recognition is a challenging task because of the handwritten characters vary both in shape and in style and most of the time the handwritten characters also vary among the individuals. The identification of the inscribed Pashto letters becomes even palling due to the unavailability of a standard handwritten Pashto characters database. For experimental and simulation purposes a handwritten Pashto characters database is developed by collecting handwritten samples from the students of the university on A4 sized page. These collected samples are then scanned, stemmed and preprocessed to form a medium sized database that encompasses 14784 handwritten Pashto character images (336 distinguishing handwritten samples for each 44 characters in Pashto script). Furthermore, the Zernike moments are considered as a feature extractor tool for the proposed OCR system to extract features of each individual character. Linear Discriminant Analysis (LDA) is followed as a recognition tool for the proposed recognition system based on the calculated features map using Zernike moments. Applicability of the proposed system is tested by validating it with 10-fold cross-validation method and an overall accuracy of 63.71% is obtained for the handwritten Pashto isolated characters using the proposed OCR system.

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