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Visible light- and dark-driven degradation of palm oil mill effluent (POME) over g-C3N4 and photo-rechargeable WO3

Razali NAM, Salleh WNW, Mohamed MA, Aziz F, Jye LW, Yusof N, et al.

Environ Sci Pollut Res Int, 2024 Jul 03.
PMID: 38958863 DOI: 10.1007/s11356-024-34081-4

The investigations of real industrial wastewater, such as palm oil mill effluent (POME), as a recalcitrant pollutant remain a subject of global water pollution concern. Thus, this work introduced the preparation and modification of g-C3N4 and WO3 at optimum calcination temperature, where they were used as potent visible light-driven photocatalysts in the degradation of POME under visible light irradiation. Herein, g-C3N4-derived melamine and WO3 photocatalyst were obtained at different calcination temperatures in order to tune their light absorption ability and optoelectronics properties. Both photocatalysts were proven to have their distinct phases, crystallinity levels, and elements with increasing temperature, as demonstrated by the ultraviolet-visible spectroscopy (UV-Vis), X-ray diffraction analysis (XRD), thermogravimetric analysis (TGA), Fourier-transform infrared spectroscopy (FTIR), Brunauer-Emmett-Teller (BET), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS) results. Significantly, g-C3N4 (580 °C) and WO3 (450 °C) unitary photocatalysts exhibited the highest removal efficiency of POME without dilution due to good crystallinity, extended light absorption, high separation, and less recombination efficiency of electron-hole pairs. Furthermore, surprisingly, the superior energy storage photocatalytic performance with outstanding stability by WO3 achieved an approximately 10% increment during darkness, compared with g-C3N4 under visible light irradiation. Moreover, it has been proven that the WO3 and g-C3N4 photocatalysts are desirable photocatalysts for various pollutant degradations, with excellent visible-light utilization and favorable energy storage application.
Fulltext Opinion mining for national security: techniques, domain applications, challenges and research opportunities

Razali NAM, Malizan NA, Hasbullah NA, Wook M, Zainuddin NM, Ishak KK, et al.

J Big Data, 2021;8(1):150.
PMID: 34900516 DOI: 10.1186/s40537-021-00536-5

Background: Opinion mining, or sentiment analysis, is a field in Natural Language Processing (NLP). It extracts people's thoughts, including assessments, attitudes, and emotions toward individuals, topics, and events. The task is technically challenging but incredibly useful. With the explosive growth of the digital platform in cyberspace, such as blogs and social networks, individuals and organisations are increasingly utilising public opinion for their decision-making. In recent years, significant research concerning mining people's sentiments based on text in cyberspace using opinion mining has been explored. Researchers have applied numerous opinions mining techniques, including machine learning and lexicon-based approach to analyse and classify people's sentiments based on a text and discuss the existing gap. Thus, it creates a research opportunity for other researchers to investigate and propose improved methods and new domain applications to fill the gap.
Methods: In this paper, a structured literature review has been done by considering 122 articles to examine all relevant research accomplished in the field of opinion mining application and the suggested Kansei approach to solve the challenges that occur in mining sentiments based on text in cyberspace. Five different platforms database were systematically searched between 2015 and 2021: ACM (Association for Computing Machinery), IEEE (Advancing Technology for Humanity), SCIENCE DIRECT, SpringerLink, and SCOPUS.
Results: This study analyses various techniques of opinion mining as well as the Kansei approach that will help to enhance techniques in mining people's sentiment and emotion in cyberspace. Most of the study addressed methods including machine learning, lexicon-based approach, hybrid approach, and Kansei approach in mining the sentiment and emotion based on text. The possible societal impacts of the current opinion mining technique, including machine learning and the Kansei approach, along with major trends and challenges, are highlighted.
Conclusion: Various applications of opinion mining techniques in mining people's sentiment and emotion according to the objective of the research, used method, dataset, summarized in this study. This study serves as a theoretical analysis of the opinion mining method complemented by the Kansei approach in classifying people's sentiments based on text in cyberspace. Kansei approach can measure people's impressions using artefacts based on senses including sight, feeling and cognition reported precise results for the assessment of human emotion. Therefore, this research suggests that the Kansei approach should be a complementary factor including in the development of a dictionary focusing on emotion in the national security domain. Also, this theoretical analysis will act as a reference to researchers regarding the Kansei approach as one of the techniques to improve hybrid approaches in opinion mining.
Fulltext Comparative Study on Extraction of Cellulose Fiber from Rice Straw Waste from Chemo-Mechanical and Pulping Method

Razali NAM, Mohd Sohaimi R, Othman RNIR, Abdullah N, Demon SZN, Jasmani L, et al.

Polymers (Basel), 2022 Jan 19;14(3).
PMID: 35160377 DOI: 10.3390/polym14030387

Inspired by nature, cellulose extracted from plant wastes has been explored, due to its great potential as an alternative for synthetic fiber and filler that contributes to structural performance. The drive of this study was to extract, treat, and evaluate the characteristics of rice straw (RS) (Oryza sativa L.) cellulose as a biodegradable reinforcement to be utilized in polymer base materials. Two routes of extraction and treatment were performed via the pulping (Route 1) and chemo-mechanical methods (Route 2), in order to discover comparative characteristics of the synthesized cellulose fiber. Comprehensive characterization of RS cellulose was carried out to determine crystallinity, surface morphology, and chemical bonding properties, using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and Fourier transform infra-red (FTIR), respectively. The XRD test results showed that the crystallinity index (CI) of cellulose powder (CP) decreased after the surface modification treatment, Route 2, from 64.50 to 50.10% CI for modified cellulose powder (MCP), due to the surface alteration of cellulose structure. From Route 1, the crystallinity of the fibers decreased up to 33.5% (dissolve cellulose, DC) after the pulp went through the surface modification and dissolution processes, resulting from the transformation of cellulose phase into para-crystalline structure. FESEM micrographs displayed a significant reduction of raw RS diameter from 7.78 µm to 3.34 µm (treated by Route 1) and 1.06 µm (treated by Route 2). The extracted and treated cellulose via both routes, which was considerably dominated by cellulose II because of the high percentage of alkaline used, include the dissolve cellulose (DC). The dissolution process, using NMMO solvent, was performed on the pulp fiber produced by Route 1. The fiber change from cellulose I to cellulose II after undergoes the process. Thus, the dissolution process maintains cellulose II but turned the pulp to the cellulose solution. The acquired characteristics of cellulose from RS waste, extracted by the employed methods, have a considerably greater potential for further application in numerous industries. It was concluded that the great achievement of extracted RS is obtained the nanosized fibers after surface modification treatment, which is very useful for filler in structural composite applications.

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