Siritaratiwat, Apirat

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Siritaratiwat, Apirat
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Siritaratiwat, A.
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apirat.si@kmitl.ac.th

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    A Self-Powered and Chemically Responsive Triboelectric Nanogenerator Based on Surface Protonation in SrO2Nanopowder/Graphene Oxide/epoxy Composite for pH Sensing
    (2025-12-05)
    Saengpoe, Prasert
    ;
    Supasai, Wisut
    ;
    Amorntep, Narong
    ;
    Nilnumpetch, Chatree
    ;
    Nokkaew, Manussawee
    Practical implementation of triboelectric nanogenerators (TENGs) in autonomous systems is frequently impeded by their inadequate durability in chemically harsh environments. To address this limitation, we present a durable TENG utilizing a strontium dioxide nanopowders/graphene oxide/epoxy resin (SrO<inf>2</inf>NPOs/GO/ER) composite, positioning SrO<inf>2</inf>NPOs as an innovative, high-permittivity filler for triboelectric applications. By synergistically integrating the elevated dielectric constant of SrO<inf>2</inf>NPOs with the interfacial polarization of GO NPOs, our optimized composite achieves an outstanding output of approximately 136 V and 2.3 μA/cm<sup>2</sup>under a 100 N force, exceeding the performance of numerous advanced TENGs. Significantly, we convert a common degradation mechanism, i.e., surface protonation, into a functional sensing approach. The device leverages reversible protonation–deprotonation dynamics to convert environmental pH into distinct electrical signals, enabling self-powered, real-time pH sensing. The sensor exhibits excellent linearity (R<sup>2</sup>> 0.97) across three distinct operational regions (pH 1–12), demonstrating high sensitivity to acidity changes. The device has demonstrated remarkable durability, completing approximately 11,000 mechanical cycles. Also, the proposed device serves high chemical durability, maintaining stable performance (up to 6000 cycles) after 24 h immersion in neutral and alkaline solutions. Our work establishes a resilient, multifunctional platform that simultaneously harvests energy and senses its chemical surroundings by reframing protonation as a design principle. This breakthrough paves the way for next-generation TENGs for use in environmental monitoring, resilient IoT networks, and adaptive self-powered electronics that can function under conditions where the chemical environment changes.
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    Aspect-Level Sentiment Analysis Using WangchanBERTa for Fine-Grained Service Insight Extraction in Hotel Reviews
    (2026-06-01)
    Suwan, Thanachok
    ;
    Nokkaew, Manussawee
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    Surawanitkun, Chayada
    ;
    Sorn-In, Kanda
    ;
    Mueanrit, Nongram
    Online booking site reviews substantially influence Thai SME hotel reputations and consumer decisions. Hotels should readily extract useful information from unstructured Thai-language ratings. WangchanBERTa, a Thai deep learning model, automates hotel sentiment analysis and strategic insight development in this study. System is two-stage. Phase 1 divides 10,040 Thai hotel reviews from Agoda, Booking.com, Traveloka, and Trip.com into good and negative attitudes and determines price, service quality, and cleanliness. Phase 2 extracts aspect-level information across 11 service characteristics to discover complex trends like consumers being satisfied with service but unhappy with cost. The sentiment categorization model performed well with 91.63% accuracy and 89.69% macro F1-score in experiments. The aspect-based sentiment analysis system achieved 91.63% accuracy, 91.55% macro precision, 91.63% recall, 91.52% F1-score, and real-world insight extraction. This methodology helps hoteliers listen to customers, integrate data into business ideas, and compete in Thailand’s tourism market.
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    Mining User Mobility Insights from Public Wi-Fi Data Using Association Rules in Urban Riverfront Areas
    (2026-07-01)
    Matarat, Korakot
    ;
    Surawanitkun, Chayada
    ;
    Wongsinlatam, Wullapa
    ;
    Remsungnen, Tawun
    ;
    Nokkaew, Manussawee
    Understanding user mobility patterns is essential for effective urban planning and resource management. This study employs Association Rule Mining to analyze public Wi-Fi data collected from 11 access points along the Mekong River in Sri Chiang Mai, Thailand, from May 2022 to December 2023. By examining co-occurring movement patterns in over 73.7 million connection records, the research uncovers key insights into human behavior in the area. The findings highlight the area in front of the Fresh Market as a central destination, with confidence values exceeding 0.99 for related movement rules. The results reveal pronounced temporal variations in movement patterns, with transitions from commercial areas in the mornings to leisure spaces in the afternoons and evenings. Weekday patterns differ notably from weekend behaviors, reflecting how time influences urban space utilization. These insights provide urban planners and policymakers with data-driven evidence to optimize infrastructure development, enhance public spaces, and improve resource allocation. Although this study is limited to Wi-Fi data, it provides significant contributions to the development of smart cities that are more responsive and sustainable, paving the way for improved urban living experiences and more efficient resource management. Future work could integrate multiple data sources to enable more comprehensive mobility analysis and advance sustainable urban development goals.