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Tailoring the coordination environment of Co-Sn active sites via zinc aluminate spinel support for highly chemoselective hydrogenation of methyl oleate
(2027-01-01)
Sooknoi, Tawan
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Chanakha, Vichuda
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Wattanakul, Titiporn
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Ausavasukhi, Artit
The chemoselective hydrogenation of methyl oleate to oleyl alcohol was investigated over Co-Sn catalysts supported on zinc aluminate (ZnAl<inf>2</inf>O<inf>4</inf>). The ZA-M support, synthesized via a methanol-mediated solvothermal route, provided a high specific surface area (296.8 m2/g) and an optimized mesoporous structure. Sequential NaBH<inf>4</inf> and H<inf>2</inf> reduction finely tuned the coordination of active sites, enabling the optimized 2Co4SnBH/ZA-M catalyst to achieve a superior oleyl alcohol yield (33.58%) and a high selectivity (65.39%) at a conversion level of 51.36% via a direct hydrogenation pathway. Based on bulk and surface characterizations, a fraction of cobalt was found to remain in a cationic state, stabilized within a network of interfacial Co-O-Sn complexes and framework CoAl<inf>2</inf>O<inf>4</inf>. These species are proposed to function as bifunctional active centers, where the Snn+/Sn0 species and neighboring Co2+ sites cooperatively enhance the chemoselectivity toward C=O reduction. Furthermore, the optimized catalyst demonstrated reasonable structural stability and reusability over four cycles, maintaining its catalytic viability despite a minor extent of metal leaching. These findings underscore the efficacy of spinel-supported ionic-metallic ensembles for the highly chemoselective and efficient hydrogenation of long-chain fatty acid methyl esters.
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Development of BaO-containing radiation-shielding glass using natural dolomite as a raw-material component
(2027-01-01)
Cheewasukhanont, W.
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Kothan, S.
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Tungjai, M.
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Intachai, N.
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Ruangtaweep, Y.
This study investigates the incorporation of dolomite (CaMg(CO<inf>3</inf>)<inf>2</inf>) as a natural substitute for synthetic CaO in the fabrication of radiation shielding glass (RSG). Dolomite samples from Kanchanaburi, Thailand, were characterized using X-ray fluorescence (XRF) to determine their chemical composition, revealing CaO (76.59-79.84 wt%) and MgO (19.13-21.54 wt%) as the primary components. These dolomites were used to synthesize borosilicate-based host glasses, where density remained stable (∼2.5 g/cm3), ensuring structural integrity. Optical transmittance measurements showed an average 80% transparency in the visible range. To enhance radiation attenuation, BaO was incorporated into the glass matrix at varying concentrations (5-35 mol%). Increasing BaO content increased the density of the glass samples, which contributed to improved radiation attenuation performance. The radiation-shielding properties, predicted using WinXCom over a wide photon energy range and experimentally evaluated at 0.662 MeV, improved with increasing BaO content. The HVL, Pb-equivalent thickness, and EABF results further supported the enhanced attenuation performance of the developed glasses, indicating that higher BaO content reduced the contribution of scattered photons to absorbed energy buildup in the intermediate-energy region. In addition, preliminary glass-forming tests of the B4 composition demonstrated the feasibility of preparing a larger glass sheet under similar melting and annealing conditions, although further optimization is still required. These findings suggest that natural dolomite can serve as a useful raw-material component for developing BaO-containing radiation-shielding glass with balanced optical transparency, density, and attenuation performance.
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A smartwatch-based holistic health management model for elderly care in Thailand: Development and pilot feasibility evaluation study
(2027-01-01)
Phromsen, Methee
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Sukkamart, Aukkapong
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Siripongdee, Surapong
Thailand's rapidly aging population presents major challenges for sustainable healthcare. Smartwatch-based gerontechnology offers potential for holistic health management (HHM), but culturally adapted models for Thai elderly care are lacking. This pilot study aimed to develop a smartwatch-based HHM model for Thai elderly care and to explore caregivers' perceived acceptability and feasibility. A four-component model (health data management, integrated physical–mental–social monitoring, healthcare service linkage, and senior-friendly design) was developed based on a literature review and the Diffusion of Innovations theory. Forty caregivers and healthcare providers from Chiang Rai Province, Thailand, were purposively sampled. They completed a validated 16-item questionnaire assessing four innovation attributes: comparative advantage, compatibility, trialability, and observability. Descriptive statistics and exploratory group comparisons (t-tests, ANOVA) were used for analysis. All four innovation attributes received high perceived effectiveness ratings (overall mean = 4.75/5.00, SD = 0.14). Compatibility scored the highest (mean = 4.80, SD = 0.25). No significant differences were found by gender, age, or occupation (all p > .05), suggesting broad stakeholder acceptability across diverse caregiver roles. The proposed model was rated as highly acceptable by Thai caregivers. These preliminary findings provide a basis for larger-scale implementation research that examines clinical outcomes, elderly user experiences, and real-world effectiveness. Key limitations include the small purposive sample, the lack of direct input from elderly end users, and the absence of hands-on device trials. Elderly end-users—the ultimate beneficiaries—did not provide direct input. Their technology acceptance, digital literacy, and actual usage patterns may differ substantially from caregiver perceptions. User-centered design requires direct elderly involvement, which future studies must prioritize.
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Comparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema
(2026-12-01)
Yodrabum, Nutcha
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Wongpraparut, Chanisada
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Titijaroonroj, Taravichet
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Chularojanamontri, Leena
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Bunyaratavej, Sumanas
Accurately differentiating scaly erythematous rashes among psoriasis, eczema, and dermatophytosis remains a clinical challenge, particularly for non-dermatologists. This study aimed to develop and evaluate deep learning models using macroscopic clinical images to classify these conditions and compare their performance with that of non-specialists. A total of 2940 images were sourced from public datasets, the Siriraj Dermatology databank, and newly collected images from Thai participants. Among sixteen evaluated models, the Swin demonstrated the best performance and interpretability. Gradient-weighted Class Activation Mapping (Grad-CAM) visualizations confirmed that the model focused on clinically relevant lesion features. Most importantly, in a pilot comparison, the Swin outperformed non-specialists in diagnostic accuracy. However, given the limited sample size of 30 images and 30 evaluators, these results should be interpreted as exploratory. Future studies with larger datasets and diverse clinician cohorts are warranted to confirm these findings and to support clinical integration.
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Purification, characterization, and structural insights of Exo-1,4-β-D-glucosaminidase from Amycolatopsis sp. KLSc63: predictive modeling with glucosamine and N-acetyl glucosamine dimers
(2026-12-01)
Kornrawudaphikasama, Yosita
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Maneeruttanarungroj, Cherdsak
Chitin, a long-chain polysaccharide, is a major component in the exoskeletons of arthropods and the cell walls of fungi. Its derivative, chitosan, is widely used in various fields due to its solubility and versatility. This study focuses on the purification and biochemical characterization of exo-1,4-β-D-glucosaminidase from Amycolatopsis sp. KLSc63, an enzyme crucial for the degradation of chitin and chitosan. The enzyme was purified using anion exchange chromatography and characterized for its activity on colloidal chitin and chitosan solutions. Optimal activity was observed at pH 5.0 and temperatures of 40–45 °C for colloidal chitin and pH 4.0–6.0 at 55 °C for chitosan solution. The enzyme’s molecular weight was approximately 94 kDa. Various metal ions and surfactants significantly influenced enzyme activity, with Mn²⁺ at 1 mM concentration notably enhancing both activities. Structural modeling and docking studies confirmed the enzyme’s substrate specificity and binding interactions. These findings highlight the potential applications of exo-1,4-β-D-glucosaminidase in industrial processes, waste management, and the production of bioactive compounds.
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Deep learning-based object detection of restorative dental instruments with potential implications for workflow automation and infection control in dental supply units
(2026-12-01)
Poomrittigul, Suvit
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Mittong, Sirawit
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Thanathornwong, Bhornsawan
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Suebnukarn, Siriwan
This study presents a proof-of-concept deep learning approach for automated detection and classification of restorative dental instruments on standardized trays, aiming to support workflow automation and infection control in dental supply units. A dataset comprising 1,000 images and 14,000 annotated instances of restorative dental instruments across 14 categories was developed. The YOLOv8 model was trained and evaluated on this dataset using standard object detection metrics, including precision, recall, and mean average precision at IoU thresholds 0.5 (mAP@0.5) and 0.5:0.95 (mAP@[0.5:0.95]). To assess model advancement, YOLOv8 performance was compared against its predecessors, YOLOv5, YOLOv6, and YOLOv7, under identical experimental settings. A session-level data split was implemented as the primary evaluation to minimize data leakage and provide a realistic estimate of generalization across unseen tray configurations. The YOLOv8 model achieved highest mean average precision mAP@0.5 of 95.9% and mAP@[0.5:0.95] of 80.9%, demonstrating robust detection capability under both standard and stringent evaluation thresholds. Across instrument categories, YOLOv8 demonstrated precision ranging from 90.3% to 100% and recall from 80.6 to 98.5%. The findings demonstrate the feasibility of using YOLOv8 for automated restorative dental instrument detection as an early-stage tool for improving supply unit efficiency. While results indicate high detection accuracy and robustness, further validation in diverse clinical environments is needed. Future deployment should incorporate human-in-the-loop verification, audit trails, and error escalation mechanisms to ensure safe and accountable AI-assisted workflows.
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Characterization of dust particles generated in Thailand Tokamak-1
(2027-01-01)
Nilgumhang, Kewalee
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Chokchaiworadilok, Sirat
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Poolyarat, Nopporn
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Dangtip, Somsak
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Limsuwan, Pichet
Thailand Tokamak-1 (TT-1) is the first magnetic confinement fusion device in Thailand and the ASEAN region. During plasma operation, plasma–wall interactions (PWI) inevitably lead to erosion of the 316L stainless-steel vacuum vessel and the boronized first wall, resulting in dust generation and plasma contamination. This work investigates the morphology and elemental composition of dust particles collected from the TT-1 vacuum chamber after plasma campaigns. In this experiment, we carried out a total of 604 discharge operation shots over a period of three months, using 99.999% purity H<inf>2</inf> gas as the fuel gas and 99.999% purity He gas for Glow Discharge Cleaning (GDC). Dust was sampled from the inner surface of the vacuum vessel using carbon tape and analyzed by field-emission scanning electron microscopy (FE-SEM) coupled with energy-dispersive X-ray spectroscopy (EDS). Four primary dust morphologies were identified: (i) smooth flake-like particles, (ii) grain-crack particles with surface cracking, (iii) granular particles with fine grains distributed across the surface, and (iv) cauliflower-like structures attributed to repeated thermal cycling. EDS analysis revealed that Fe, Cr, and Ni, originating from the 316L stainless-steel wall, are the dominant metallic constituents, together with light elements such as B, C, and O associated with the boronization layer and subsequent oxidation. The presence of B- and C-rich granular dust confirms successful deposition and erosion of the boron coating used for wall conditioning. These results demonstrate that dust generated in TT-1 is a mixture of wall and boronization-layer fragments, which poses potential theoretical risks of plasma impurity accumulation, enhanced radiative losses, and compromised plasma ignition and stability in future high-performance campaigns. The present characterization provides a basis for future studies on dust transport, retention, and mitigation strategies in TT-1 and similar medium-size tokamak devices.
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Extraction techniques, structural features, and functional properties of collagenous derivatives from unconventional animal sources: a review
(2026-12-01)
Indriani, Sylvia
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Petcharat, Tanyamon
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Andriani, Cynthia
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Benjakul, Soottawat
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Nalinanon, Sitthipong
Collagenous derivatives (collagen, gelatin, and collagenous hydrolysate (CH)) are extensively used across the food, biomedical, and pharmaceutical industries. Traditionally, these have been sourced from porcine, bovine, and fish due to their ready availability and biocompatibility. However, conventional collagenous derivatives face ongoing challenges regarding sustainability, resource intensity, and socio-cultural perceptions. This has led to the exploration of alternative collagenous derivatives from unconventional sources, with a primary focus on evaluating their potential for yields, extractability, and functional properties, all of which are fundamental for future scale-up and alternative applications. This review summarizes alternative collagenous derivatives from unconventional animals, including amphibians, mollusks, echinoderms, insects, unconventional fish and byproducts, and reptiles. Their structures, extraction techniques, functional properties, and potential applications are comprehensively summarized, showcasing their ability to complement or even surpass conventional sources in specific uses. Additionally, the challenges and prospects for industrial application, emphasizing the sustainability of meeting growing collagen demand and encouraging further research into these promising alternative sources, were discussed. Unconventional collagenous derivatives demonstrate excellent and unique characteristics as alternatives to conventional ones. Type I collagen from amphibians, reptiles, and mollusks had superior thermal stability. Unconventional gelatin and CH also possess various bio-functionalities that can enhance their potential applications. The relatively low extraction yield could be addressed by increasing the concentration of chemicals or extraction time and incorporating green technology without causing an adverse impact on the quality. These findings indicate the potential applications of unconventional collagenous derivatives as food ingredients and supplements.
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A machine learning approach for predicting osmotic coefficients and deriving activity coefficients in alkyl ammonium salts
(2026-12-01)
Chawuthai, R.
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Murathathunyaluk, S.
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Saengsuradech, S.
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Nukaew, A.
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Simasatitkul, L.
Quaternary Ammonium Salts (Quats) have diverse applications across various domains. They are extensively used as phase-transfer catalysts (PTCs) in chemical reactions, facilitating the transfer of reactants between aqueous and organic phases. Their unique structure enables the formation of ion pairs, enhancing reaction rates at phase boundaries. This research develops a novel method for predicting Quats’ osmotic coefficients using Simplified Molecular Input Line Entry System (SMILES) notation and supervised machine learning. A comprehensive dataset of 1,654 data points from 52 distinct Quats was compiled. The structural characteristics were encoded using SMILES notation. The data was evaluated using random splitting and Leave-One-Group-Out (LOGO) validation to train seven machine learning algorithms. Gaussian Process (GP) emerged as the optimal algorithm. The GP model achieved a mean absolute percentage error (MAPE) of 5.29% and root mean square error (RMSE) of 0.034. Comparisons with Electrolyte-NRTL and Extended UNIQUAC models demonstrate that this data-driven approach offers competitive accuracy while enabling generalization to structurally similar compounds. This work marks a significant starting point for the machine learning-enhanced prediction of activity coefficients, with considerable potential for future refinement and application.
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An innovative chitosan-coated aquatic feed pellets production from coastal waste using top-spray fluidized bed drying
(2026-12-01)
Maikaew, Jatuphat
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Srisang, Naruebodee
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Tambunlertchai, Supreeda
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Srisang, Siriwan
Coastal wastes such as crab shells, shrimp shells, and seaweed are rich in proteins, lipids, and bioactive compounds, making them valuable raw materials for aquafeed production. In this work, three aquatic feed pellets were developed and tested under different drying temperatures from 70 to 110 °C to evaluate the pellet durability index (PDI), specific energy consumption in water removal (SEW), and nutrient quality. The formulation containing high crab shell content showed the most balanced nutritional profile but required further improvement in mechanical strength. To address this, chitosan coating was applied using a top-spray fluidized bed system, with process conditions optimized through response surface methodology (RSM). The RSM demonstrated the optimal coating condition at a concentration of about 1.25% (w/v), a spray rate of about 32.5 mL/min, and a temperature of about 110 °C, with the lowest of drying time (DT) and specific energy consumption (SEC). The optimized coating significantly improved PDI and water solubility index while preserving nutritional balance. It also enhanced antimicrobial properties, which are desirable for feed storage. Microscopic and structural analyses confirmed good adhesion of the coating. Overall, this study demonstrates a sustainable pathway to convert coastal waste into high-quality aquafeed, offering both environmental benefits and practical value for aquaculture industries.

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