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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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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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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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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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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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Neural networks for neurocomputing circuits: A computational study of tolerance to noise and activation function non-uniformity when machine learning materials properties
(2025-12-01)
Thant, Ye min
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Nukunudompanich, Methawee
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Chueh, Chu Chen
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Ihara, Manabu
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Manzhos, Sergei
Dedicated analog neurocomputing circuits are promising for high-throughput, low power consumption applications of machine learning (ML) and for applications where implementing a digital computer is unwieldy (remote locations; small, mobile, and autonomous devices, extreme conditions, etc.). Neural networks (NN) implemented in such circuits, however, must contend with circuit noise and the non-uniform shapes of the neuron activation function (NAF) due to the dispersion of performance characteristics of circuit elements (such as transistors or diodes implementing the neurons). We present a computational study of the impact of circuit noise and NAF inhomogeneity in regression problems as a function of NN architecture and training regimes. We focus on one application that requires high-throughput ML: materials informatics, using as representative problem ML of formation energies vs. lowest-energy isomer of peri-condensed hydrocarbons, formation energies and band gaps of double perovskites, and zero point vibrational energies of molecules from QM9 dataset. We show that in these applications, NNs generally possess low noise tolerance with the model accuracy rapidly degrading with noise level. Single-hidden layer NNs, and NNs with larger-than-optimal sizes are somewhat more noise-tolerant. Models that show less overfitting (not necessarily the lowest test set error) are more noise-tolerant. Importantly, we demonstrate that the effect of activation function inhomogeneity can be palliated by retraining the NN using practically realized shapes of NAFs.
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Integrated Assessment of Rooftop Photovoltaic Systems and Carbon Footprint for Organization: A Case Study of an Educational Facility in Thailand
(2025-05-01)
Leeabai, Nattapon
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Sakaraphantip, Natthakarn
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Kunbuala, Neeraphat
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Roongrueng, Kamonchanok
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Nukunudompanich, Methawee
This study presents an integrated methodology to assess and reduce greenhouse gas (GHG) emissions in institutional buildings by combining organizational carbon footprint (CFO) analysis with rooftop photovoltaic (PV) system simulation. The HM Building at King Mongkut’s Institute of Technology Ladkrabang (KMITL), Thailand, was selected as a case study to evaluate carbon emissions and the feasibility of solar-based mitigation strategies. The CFO assessment, conducted in accordance with ISO 14064-1:2018 and the Thailand Greenhouse Gas Management Organization (TGO) guidelines, identified total emissions of 1841.04 tCO<inf>2</inf>e/year, with Scope 2 electricity-related emissions accounting for 442.00 tCO<inf>2</inf>e/year. Appliance-level audits revealed that classroom activities represent 36.7% of the building’s electricity demand. These findings were validated using utility data totaling 850,000 kWh/year. A rooftop PV system with a capacity of 207 kWp was simulated using PVsyst software (version 7.1), incorporating site-specific solar irradiance and technical loss parameters. Monocrystalline modules produced the highest energy output of 292,000 kWh/year, capable of offsetting 151.84 tCO<inf>2</inf>e/year, equivalent to 34.4% of Scope 2 emissions. Economic evaluation indicated a 7.4-year payback period, with a net present value (NPV) of THB 12.49 million and an internal rate of return (IRR) of 12.79%. The integration of verified CFO data with empirical load modeling and derated PV performance projections provides a robust, scalable framework for institutional carbon mitigation. This approach supports data-driven Net Zero campus planning aligned with Thailand’s Nationally Determined Contributions (NDCs) and carbon neutrality policies.
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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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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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Electrospinning of SnO2-TiO2nanofiber/nanorod composites for uses as electron transport layers in flexible perovskite solar cells
(2024-05-17)
Nukunudompanich, Methawee
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Roongraung, Kamonchanok
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Sanglee, Kanyanee
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Lekkla, Wassana
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Chuangchote, Surawut
In perovskite solar cells (PSCs), the most commonly used electron transport layers (ETLs) are titanium dioxide (TiO2) and tin oxide (SnO2). The problem with SnO2 is that its conduction band does not match that of perovskites, while TiO2's photocatalytic nature can destroy perovskite materials. Additionally, these ETLs are typically applied in the form of nanoparticles. Electrospinning was used to produce composite nanofibers or nanorods of SnO2-TiO2 to improve the photovoltaic performance of flexible PSCs, which are required for flexible electronic devices. SnO2-TiO2 nanofibers or NRs as ETLs assist perovskites in harvesting light, separating excitons, extracting and collecting electrons, blocking holes, and preventing perovskites from decomposing and forming defects. PSCs containing SnO2-TiO2 nanoparticles have been produced. From the J-V characteristics of flexible-PSCs, the use of SnO2-TiO2 nanofibers improved the power conversion efficiency of the solar cells. A higher current density was obtained. This occurs because the 1D structure allows for more freely moving electrons. Comparing SnO2 nanoparticles and TiO2 nanofibers, an SnO2-TiO2 layer provides superior charge mobility and helps improve strength of the bonding at the perovskite/ITO interface. It is superior to SnO2 NPs and TiO2 nanofibers in reducing surface recombination.

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