Nukunudompanich, Methawee

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Nukunudompanich, Meethawee

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School of Engineering

methawee.nu@kmitl.ac.th

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Storage conditions, energy consumption, and food safety implications of domestic refrigerators in Bangkok
(2025-12-01)
Chaomuang, Nattawut
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Potisuwan, Cheeraphat
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Duret, Steven
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Derens-Bertheau, Evelyne
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Paviet-Salomon, Yvanne
Domestic refrigeration plays a critical role in food preservation and safety by maintaining optimal storage conditions for perishable items. This study investigated the temperature and humidity conditions, as well as the energy consumption, of domestic refrigerators in Bangkok. Data were collected through on-site investigations of 123 refrigerators using data loggers to record temperature, humidity, and electrical consumption over a five-day period. In addition, a predictive microbiological model was used to assess the potential for microbial growth under the investigated storage conditions, specifically of Listeria monocytogenes and lactic acid bacteria (LAB). Results indicated that although the overall mean temperatures of chilling compartments (5.0 ± 2.5 °C) were within the recommended range for domestic refrigeration, only about 30–40 % of individual refrigerators consistently maintained temperatures at or below 5 °C on the top and middle shelves throughout the five-day monitoring period, where fresh and ready-to-eat products are typically stored. Electrical consumption analysis revealed significant differences across refrigerator types; appliances with two doors and bottom freezers exhibited the highest annual electrical consumption, while single door refrigerators recorded the lowest. However, the specific energy consumption (SEC) did not differ significantly among refrigerator types. The microbial exposure assessment based on a test product highlighted that for over 50 % of refrigerators, L. monocytogenes load exceeds the regulation safety limits (> 102 CFU/g) within five days. These findings emphasize the need for improved temperature control, appliance design, and consumer education to ensure food safety and energy efficiency in domestic refrigeration.
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Systematic improvement of source separation designs: the effects of visual prompts and trash bin combinations in enhancing waste separation behavior
(2026-07-01)
Soralump, Cheema
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Phyo Ei, Nine Yawai
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Nukunudompanich, Methawee
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Nakaroengrit, Supawan
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Asingsamanunt, Jarudej
Source separation is essential for effective waste management, yet research on its efficiency remains limited. This study explores the effects of visual prompt design and bin combinations on waste separation behavior through a two-cycle experimental framework aimed at systematic improvement. In Cycle 1, a survey identified that environmental gain framing combined with numerical data was the most preferred design among all designed visual prompts. Implementation of this design significantly improved recyclable waste separation, with the effective capture rate (effCR) reaching 63.36%. In Cycle 2, while users preferred having more bin categories, the effCR for recyclables peaked at 79.76% in a 3-bin setup and declined to 40.74% in a 5-bin setup. This trend highlights how cognitive overload and choice paralysis negatively impact sorting accuracy as system complexity increases. Additionally, bin transparency proved critical, as the effCR for PET bottles dropped significantly from 85.65% to 35.05% when using untransparent bins. The findings indicate that while numerical environmental prompts drive initial engagement, bin transparency is essential for maintaining material-specific accuracy. However, the success of these interventions depends on keeping bin categories manageable to prevent cognitive overload. Ultimately, this integrated approach provides practical guidance for optimizing separation systems and advancing sustainable waste management practices.
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Machine learning of properties of lead-free perovskites with a neural network with additive kernel regression-based neuron activation functions
(2024-07-01)
Nukunudompanich, Methawee
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Yoon, Heejoo
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Hyojae, Lee
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Kameda, Keisuke
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Ihara, Manabu
Machine learning (ML) of properties of perovskite materials, in particular of the bandgap of perovskites used in optoelectronic applications, has recently attracted increasing attention. Typically, off-the-shelf ML methods such as neural networks (NN), kernel methods or tree-based methods are used. We employ the recently proposed type of NN that uses additive Gaussian process regression to construct optimal neuron activation functions and avoids non-linear optimization to machine learn the band gap and heat of formation of lead-free inorganic halide double perovskites for solar cell applications. The method combines the high expressive power of an NN with the robustness of a linear regression. We show that better prediction quality can be obtained, in particular in the visible region relevant for most applications, compared to previous results using standard methods. Most important variables and the importance of coupling among features, in particular for bandgap prediction, can also be identified with the new method. Graphical abstract: (Figure presented.)
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Remodelling hierarchical NiCo2O4@ZnS nanorods with multi-walled carbon nanotubes as a counter electrode for dye-sensitized solar cell applications
(2026-12-01)
Nukunudompanich, Methawee
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Nachaithong, Theeranuch
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Phumuen, Phatcharin
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Wannabut, Wassana
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Kunbuala, Neeraphat
A hierarchical NiCo<inf>2</inf>O<inf>4</inf>@ZnS/MWCNT (NCO@Z-MWCNTs) nanocomposite was synthesized to serve as a platinum-free counter electrode for dye-sensitized solar cells (DSSCs). The nanocomposite comprised spinel NiCo<inf>2</inf>O<inf>4</inf> nanorods, ZnS associated with the surface of the nanorods, and an interconnected multi-walled carbon nanotube (MWCNT) network, and it was synthesized via a low-temperature solution-based hydrothermal method. XRD confirmed the presence of cubic NiCo<inf>2</inf>O<inf>4</inf> and zinc blende ZnS phases, while FESEM–EDS and XPS analyses verified the incorporation of ZnS and the formation of a conductive carbon framework interconnecting adjacent nanorods. ZnS, rather than acting as an isolated catalytic component, was considered to contribute additional sulfide-related surface sites and to modulate the interfacial electronic environment of the NiCo<inf>2</inf>O<inf>4</inf> nanorods, which likely facilitated redox reactions involving the I−/I<inf>3</inf>− couple. Meanwhile, the MWCNT network established continuous electron transport pathways, effectively reducing interfacial resistance and enhancing charge-transfer efficiency. Thermogravimetric and electrochemical analyses revealed enhanced thermal stability, improved redox kinetics, and a significant reduction in charge-transfer resistance compared with pristine NiCo<inf>2</inf>O<inf>4</inf>.The optimized NCO@Z–MWCNT 9wt% counter electrode achieved a power conversion efficiency of 10.03% under AM 1.5 G illumination, exceeding that of the Pt reference device (9.6%). Overall, the improved performance was attributed to the combined contributions of ZnS surface modification and the conductive MWCNT network, which together enhanced charge transport and electrocatalytic activity. This work demonstrates a scalable strategy for developing cost-effective, durable, and high-performance counter electrodes for dye-sensitized solar cells.
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The Perceptive Classification of the Wastes for Recycling and Composting for Sustainable Waste Management
(2024-01-01)
Leeabai, Nattapon
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Nukunudompanich, Methawee
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Areeprasert, Chinnathan
The problem with municipal solid waste (MSW) in Thailand necessitates the well-planned MSW management is important. The perceptive classification of waste is the effective tool to design MSW management and waste separation campaign. It was found that the perceptive classification of the waste was significantly graded in order to the characteristics of the waste. The certain numbers of the wastes were high perceptive classification to the target category. Thus, the perceptual classification of these wastes did not prevent their separation into the designated waste bin. On the other hand, careful attention was required to increase the perception classification of aluminum foil as recyclable waste. The perceptive classification of recyclable waste on aluminum foil was too low, and it was around 37.66%. Misclassification may result from a misunderstanding of the waste type. In addition, some wastes were hardly segregated to a specific category, and there were depending on the implementation process in the MSW management system. For example, the perceptive classification of tissue paper as compostable wastes was low. To design the composting process including the input of tissue paper, the campaign to improve the perceptive classification on tissue papers was recommended. In contrast, the implementation of composting without the input of tissue paper did not need the campaign to modify the perceptive classification on tissue papers. Therefore, the significant findings of this study will be the designing tools to enable the effective and sustainability MSW management.
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Synergistic Ni–Cu/char bimetallic catalysts for enhanced hydrogen production from corn stover bio-oil via steam reforming
(2026-01-01)
Wongcharee, Surachai
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Suriyachai, Nopparat
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Kreetachat, Torpong
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Nukunudompanich, Methawee
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Jadsadajerm, Supachai
Catalytic steam reforming of biomass-derived bio-oil offers a promising route for renewable hydrogen production, yet catalyst deactivation and coke formation limit its practical application, particularly for complex whole bio-oils. Herein, hydrogen production from corn stover-derived whole bio-oil was investigated via an integrated fast pyrolysis-steam reforming process using char-supported Ni–Cu bimetallic catalysts. The optimized Ni–Cu composition exhibited enhanced hydrogen yield (∼53%) and feedstock conversion (∼78%), with low carbon deposition compared to monometallic counterparts. Elevated reforming temperatures promoted hydrocarbon cracking and suppressed coke formation. Long-term stability tests demonstrated sustained catalytic performance under steam oxygen reforming conditions. Structural characterization confirmed uniform metal dispersion and preserved catalyst porosity after reaction. The improved performance is attributed to the synergistic interaction between Ni, facilitating C–C bond cleavage, and Cu, enhancing water–gas shift activity and mitigating carbon deposition. These findings highlight the potential of char-supported Ni–Cu catalysts as a robust and coke-resistant system for scalable hydrogen production from real biomass-derived bio-oil.
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On the Sufficiency of a Single Hidden Layer in Feed-Forward Neural Networks Used for Machine Learning of Materials Properties
(2025-03-01)
Thant, Ye Min
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Manzhos, Sergei
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Ihara, Manabu
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Nukunudompanich, Methawee
Feed-forward neural networks (NNs) are widely used for the machine learning of properties of materials and molecules from descriptors of their composition and structure (materials informatics) as well as in other physics and chemistry applications. Often, multilayer (so-called “deep”) NNs are used. Considering that universal approximator properties hold for single-hidden-layer NNs, we compare here the performance of single-hidden-layer NNs (SLNN) with that of multilayer NNs (MLNN), including those previously reported in different applications. We consider three representative cases: the prediction of the band gaps of two-dimensional materials, prediction of the reorganization energies of oligomers, and prediction of the formation energies of polyaromatic hydrocarbons. In all cases, results as good as or better than those obtained with an MLNN could be obtained with an SLNN, and with a much smaller number of neurons. As SLNNs offer a number of advantages (including ease of construction and use, more favorable scaling of the number of nonlinear parameters, and ease of the modulation of properties of the NN model by the choice of the neuron activation function), we hope that this work will entice researchers to have a closer look at when an MLNN is genuinely needed and when an SLNN could be sufficient.
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Dataset of temperature and relative humidity measurements in domestic refrigerators in Bangkok Thailand
(2026-08-01)
Paviet-Salomon, Yvanne
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Chaomuang, Nattawut
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Potisuwan, Cheeraphat
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Derens-Bertheau, Evelyne
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Nukunudompanich, Methawee
This dataset provides detailed measurements of internal air temperature, product temperature, and relative humidity in domestic refrigerators in households in Bangkok, Thailand. Data were collected from 123 households using Freshliance TagPlus-TH data loggers installed both inside and outside the refrigerators. For each refrigerator, air temperature and relative humidity were monitored at five internal locations: top shelf, middle shelf, vegetable drawer, door shelf, and freezer compartment. In addition, the temperature of a meat-like test product placed on the middle shelf was recorded. Ambient air conditions surrounding each refrigerator were also measured to characterize the external thermal environment. Prior to deployment, all sensors were prepared and activated by the research team, with time zero (T0) defined at this stage. Participants were only required to place the sensors according to the provided instructions while minimizing disturbances to their usual refrigerator usage habits. The sensors remained inside the refrigerators for seven consecutive days, recording measurements every 5 minutes before being removed. Data collected during the first and seventh days of monitoring were excluded from the analysis to account for the uncertainty regarding the exact times at which consumers placed and removed the sensors. This exclusion also helped eliminate transient effects caused by the introduction of heat loads during sensor installation and removal, which are not representative of normal refrigerator operating conditions. Consequently, only the five-day dataset corresponding to stabilized operating conditions is presented. The dataset is provided as tab-delimited.txt files. File names identify both the refrigerator number (1–123) and refrigerator configuration, including single-door, two-door top-freezer, and two-door bottom-freezer models. An accompanying R script enables the generation of temperature and humidity profiles for each refrigerator over the 5-day monitoring period. A README document is also included and provides detailed descriptions of column names, sensor locations, and metadata associated with each measurement campaign. Temperature sensors have a resolution of 0.1°C over a range of −30°C to +70°C, and an expanded uncertainty of ±0.5°C (k = 2). Relative humidity sensors have a resolution of 0.1 % and an expanded uncertainty of ±3 % (k = 2). These uncertainty values are based on the manufacturer’s specifications, additional calibration could not be performed within the scope of this study due to the lack of suitable calibration equipment. This high-resolution dataset provides valuable insights into the thermal and hygrometric conditions of domestic refrigeration systems under real household usage conditions. The data may support a wide range of applications, including the assessment and optimization of refrigeration performance through analysis of temperature heterogeneity and thermal behavior inside refrigerating compartments, the evaluation of food safety and risks associated with domestic storage temperatures, the identification of potential temperature abuse conditions in challenge-test studies, the development of consumer recommendations, the development and validation of simplified thermal models and CFD simulations for predicting airflow and heat transfer within domestic refrigerators, studies related to energy consumption and food preservation, and the optimization of domestic refrigerator design parameters using CFD and artificial neural network approaches. The dataset is expected to provide valuable insights for researchers, stakeholders, and policymakers working on refrigeration technologies, food safety, and cold-chain management.
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Comparative analysis of anti-soiling coatings for PV modules in a tropical climate
(2025-11-01)
Sakarapunthip, Nattakarn
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Nukunudompanich, Meethawee
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Sittipunsakda, Oranoot
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Sangpongsanont, Yaowanee
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Chuangchote, Surawut
The accumulation of dust on photovoltaic (PV) modules significantly reduces their efficiency, making anti-soiling coatings (ASCs) an essential solution. However, comprehensive comparisons of different ASC technologies under real-world conditions remain limited. This study systematically evaluates four ASC types (surfactant, hydrophilic-photocatalyst, hydrophilic-antistatic, and hydrophobic coatings) to assess their adhesion strength, durability, anti-soiling performance, and impact on PV energy output in a tropical environment. Surface morphologies and elemental compositions of ASCs were analyzed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS), while adhesion performance was examined through tape tests and contact angle measurements. The coatings were field-tested on PV modules in two distinct environments: a rooftop PV system near a mangrove area and a PV power plant near an agricultural zone, with data collected over both dry and rainy seasons. Results indicate that hydrophobic coatings exhibited the highest durability and anti-soiling efficiency, followed by hydrophilic-antistatic, hydrophilic-photocatalyst, and surfactant coatings. The hydrophobic coating also demonstrated the most significant improvement in PV energy output, making it the most effective long-term solution. Additionally, this study evaluates the environmental and economic feasibility of ASC application, highlighting the trade-offs between coating effectiveness and maintenance costs. These findings provide critical insights into selecting optimal ASC technologies for PV systems in high-soiling regions, bridging the gap between laboratory research and real-world performance.
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Morphological, TGA, and FTIR on Rigid Polyurethane Composite Laminated with Untreated and Treated Bamboo Fiber Roof Insulation
(2024-05-01)
Roseli, Adyla Illyana
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Hassan, Nik Normunira Mat
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Leman, Abdul Mutalib
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Latif, Najibah Abdul
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Aminanda, Yulfian
The performance of roof insulation such as polyurethane decreased due to problems such as insufficient absorption and poor thermal insulation performance, especially during rainstorms. The aims of this study are to investigate the physical property and its potential reinforced material such as rigid polyurethane doped with treated and untreated bamboo fiber composite (RPU-BF) at different ratios of 0, 25, 30, 35, and 40% of bamboo fibers as an insulation material for roof applications. The bamboo fibers were treated by using silane coupling agent treatment. The rigid polyurethane composite samples were prepared and then laminated bamboo fiber to overcome the sound problem in roofs. The physical characterization was investigated by Water Contact Angle (WCA), the morphological by Scanning Electron Microscopy (SEM), Thermo-gravimetric Analysis (TGA), and Fourier Transform Infrared Spectroscopy (FTIR) Analysis. The results showed that the treated bamboo fiber had a 192.5° water contact angle as a super hydrophobic property due to the presence of the chemical bonds Si-O-Si and Si-O-C in the silane coupling agent treatment. The morphology showed that 30% ratios of RPU-BF-T30 give the smallest pore diameter size. The peak of thermal degradation temperature of untreated and treated bamboo fiber was increased from 320°C to 350 °C with a weight loss of 80% to 50%. The treated bamboo fiber exhibited peaks at 3010–3040 cm-1 were associated with stronger Si-O-Si bonding, indicating the formation of new chemical bonds between bamboo fiber and silane coupling agent due to the ester bond from the cellulose, lignin, and hemicellulose. Thus, there was a similar trend peak in the functional chemical group in the FTIR spectrum of the RPU-BF composite. This result shows that RPU-BF composite had the potential of the optimum ratio of bamboo fiber as an insulation material for local communities and beneficial to the bamboo industry.

Nukunudompanich, Methawee

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