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Identification of active sites and their redox strength to select products from photocatalysis in aqueous solutions
(2026-04-01)
Nguyen, Van Can
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Ardchon, Chatiya
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Wongpromrat, Patthranit
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Lee, Yuh Lang
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Teng, Hsisheng
Solar-driven photocatalytic H<inf>2</inf> evolution from aqueous solutions is promising for sustainable energy production, but it typically requires sacrificial electron donors to expedite the evolution. The fate and reaction pathways of these donors, especially their selective oxidation driven by active sites with distinct redox strengths, are rarely studied. The present study demonstrates that the reforming of sacrificial methanol in photocatalytic H<inf>2</inf> evolution can be directed toward selective production of value-added C<inf>2</inf> chemicals by tuning the redox properties of co-catalyst-derived active sites. Model catalysts, potassium poly(heptazine imide) (KPHI) decorated with Pt and CoP co-catalysts, i.e., Pt@KPHI and CoP@KPHI, are used to elucidate the reaction pathways, where acetate and ethylene glycol are the predominant liquid-phase products, respectively, alongside the gaseous H<inf>2</inf> evolution. In the Pt@KPHI system, Pt serves as a strong electron trap to effectively reduce water into H<inf>2</inf> and methanol into •CH<inf>3</inf> and induces hole-driven deep oxidation of methanol into •CO<inf>2</inf>− on KPHI. The C−C coupling of •CH<inf>3</inf> and •CO<inf>2</inf>− radicals selectively forms acetate. In the CoP@KPHI system, CoP served as a mild hole trap to proceed with methanol dehydrogenation into •CH<inf>2</inf>OH radicals and then ethylene glycol, while mild reduction takes place over KPHI to produce H<inf>2</inf>, presenting parallel redox reactions without any interaction. Our work illustrates how co-catalyst-induced active sites govern charge transfer and interaction between the redox reactions, thus selectively producing valuable chemicals from H<inf>2</inf>-evolution photocatalysis.
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Novel method for predicting the cracks of oxide scales during high temperature oxidation of metals and alloys by using machine learning
(2025-12-01)
Chawuthai, Rathachai
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Promchan, Teeratat
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Rojsanga, Jularak
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Chandra-ambhorn, Somrerk
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Nilsonthi, Thanasak
Material degradation is one of the main problems in various high-temperature processes, directly resulting in the failure of the material. Crack and protective oxide film spallation caused either by mechanical stress development in the oxidation process or thermal stress due to a mismatch of the thermal expansions of the formed oxide and alloy are common forms of failure in high-temperature processes. Typically, the Pilling-Bedworth ratio (PBR) is employed to predict crack and spallation of the oxide by determining the volume changes of oxide and alloy because of its simplicity. However, this approach provides poor crack and spallation predictions. Hence, machine learning was adopted in the present work to predict oxide formation and spallation in the temperature range of 600-1,200 °C. The inputs for the present developed model were alloy compositions, oxide formed during oxidation, and oxidation conditions and periods. Furthermore, the predicted results of the present developed machine learning model were compared to those obtained by the PBR method. The present results revealed that the accuracy of the oxide spallation prediction of the present model was better than that of the PBR method. The random forest with 15 estimators was the best machine learning model. Finally, it can be concluded that the machine learning model is essential for accurate material failure prediction.
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Simultaneous extraction of crude polysaccharides, soluble proteins, gingerols, and shogaols from dried ginger by subcritical water extraction
(2025-06-01)
Sakdasri, Winatta
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Kaomaneechot, Sunanta
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Lasim, Sarida
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Somboon, Pichayada
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Chotigavin, Natthaporn
Subcritical water extraction (SWE) is a remarkable method for the simultaneous extraction of high- and low-polarity bioactive compounds from ginger. This study investigated the effects of temperature, pressure, and extraction time on the crude polysaccharide yield, protein content, total phenolic content (TPC), and amounts of 6-gingerol and 6-shogaol. The pressure was maintained at 30 bars, and the temperature and extraction time were optimized to maximize the polysaccharide yield, protein, TPC, gingerols, and shogaols contents. The rising temperatures (100–160 °C) and extended extraction times (10–50 min) enhanced the yield and TPC but negatively affected protein stability, leading to thermal degradation when the temperature surpassed 180 °C. Specifically, the content of 6-gingerol decreased at high temperatures because of its conversion into 6-shogaol. The optimal extraction conditions were 160 °C for 40 min at 30 bars, resulting in a polysaccharide yield of 8.54 %, protein content of 100.35 mg body surface area/g dry mass, TPC of 18.10 mg Galic acid (GA)/g dry mass, 6-gingerol content of 2.14 mg/g dry mass, and 6-shogaol content of 0.28 mg/g dry mass. Therefore, SWE effectively extracts various polar bioactive compounds from plant materials in a single step.
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Monitoring of salinity of water on the THA CHIN River basin using portable Vis-NIR spectrometer combined with machine learning algorithms
(2023-09-05)
Wongpromrat, Patthranit
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Phuphanutada, Jirawat
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Lapcharoensuk, Ravipat
The goal of this work is to study the alternative practices for monitoring the salinity of water using a combination of portable vis-NIR spectrometer and machine learning approachs. Along 80 km of the Tha Chin River basin from the Gulf of Thailand, the data of salinity and NIR spectrum were collected during winter and summer seasons of Thailand. Salinity of water samples was measured by using a handheld electrical conductivity meter and NIR spectra was recorded with portable FQA-NIR GUN in the wavelength range of 600 to 1100 nm. The 10 machine learning models including partial least square regression (PLS), support vector machine (SVR), decision tree (DT), random forest (RF), adaptive boosting (AB), gradient boosting (GB), bagging meta-estimator (BME), extremely randomized trees (ERT), backpropagation neural networks (BPNN) and hybrid principal component analysis-neural network (PC[sbnd]NN) were applied to train the NIRs models for predicting salinity. All machine learning algorithms showed good prediction results which R<inf>p</inf>2 values were higher than 0.84. The models built by tree-based algorithms (DT, RF, AB, GB, BME and ERT) displayed higher performances of calibration set and prediction set than those of PLS, SVM, BPNN and PC[sbnd]NN. Among these, the ERT algorithm showed the best performance R<inf>p</inf>2 of 0.97, RMSEP of 0.41 g/L and RPD of 6.00. It was shown that NIR spectroscopy coupled with machine learning could be an alternative simpler way for predicting salinity of water.
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Simple Empirical Relation for an Evacuated-Tube Solar Collector Performance Prediction from Solar Intensity
(2023-09-01)
Pongboriboon, Nattapat
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Wu, Wei
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Chandra-ambhorn, Walairat
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Wongpromrat, Patthranit
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Bumrungthaichaichan, Eakarach
In this paper, the effect of solar intensity on the heat pipe tip temperature in a heat pipe type—evacuated-tube solar collector (HP-ETSC) was investigated. A simple relation was proposed, relating the solar intensity to the heat pipe tip temperature generated from the experimental data. This simple empirical relation was applied in a set of heat transfer equations derived to predict the heating medium temperature at the manifold outlet of the evacuated-tube solar collector. The calculated results corresponding to two types of heating medium, i.e., palm oil and water, were compared with experimental results from the literature. The results show that the average error was 6.41% for the case of palm oil and 4.66% for the case of water. Based on the case of water as a heating medium fluid, it was found that the flow rate of the heating medium fluid affected the accuracy of prediction, as the percentage error increased with the heating medium flow rate. The maximum percentage error increased from only 1.83% for a water inlet flowing at a Reynolds number of about 2.4 × 103 to 15.23% for a water flow rate at a Reynolds number of about 2.6 × 104. The correction factor was added into the correlation to predict the heat transfer coefficients of heating medium fluids. With this correction factor, the maximum error could be reduced from 11.78% to 7.29% for the palm oil case and from 15.23% to 5.57% for the water case. The average errors corresponding to palm oil and water cases could be reduced to 0.74% and 1.26%, respectively.
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Possibility of metallic cobalt formation in the oxide scale during high-temperature oxidation of Co-27Cr-6Mo alloy in air
(2023-01-01)
Wongpromrat, Patthranit
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Tunthawiroon, Phacharaphon
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Bumrungthaichaichan, Eakarach
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Ponpo, Phisan
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Nilsonthi, Thanasak
Co-based alloys are known to be high oxidation-resistant material and used in several high temperature applications. During high temperature oxidation, duplex oxides containing Co and Cr were formed. It was thermodynamically elucidated that when the growing scale was thick enough, the partial pressure of O<inf>2</inf> in the scale dropped. Then, the reduction of CoO occurred for promoting O<inf>2</inf> which was responsible for Cr<inf>2</inf>O<inf>3</inf> production. This work experimentally proved this point by in situ char-acterising Co-27Cr-6Mo at high temperatures in air by X-ray diffractometer in a grazing incident mode and metallic Co was confirmed to be formed by the reduction of CoO consistent with the image taken and analysed by field emission scanning electron microscope, energy-dispersive X-ray, and electron backscatter diffraction. Furthermore, the change in lattice parameter and the phase transition were observed when the temperature was altered.
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Corrosion Resistance Enhancement of Reinforced Concrete in Marine Environment by Partial Replacement of Black Rice Husk Ash
(2023-01-01)
Chandra-Ambhorn, Walairat
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Bumrungthaichaichan, Eakarach
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Rotchan, Chayanit
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Haema, Narunat
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Wongpromrat, Patthranit
In this work, the black rice husk ash (BRHA), a pozzolanic material, was used as a partial replacement in concrete with the weight percentages of 0%, 10%, 20%, 30%, 40% and 50% for enhancing the corrosion resistivity in the marine environment. The compressive strength, corrosion by accelerated corrosion test by impressed voltage (ACTIV), and chloride (Cl− ) penetration of concrete specimens were investigated after 28 days of curing. For corrosion and chloride penetration analyses, the 20% of BRHA replacement specimen was the most effective concrete specimen because the deformation was not observed within 19 days of the test. The cement specimens with lower BRHA percentages were cracked due to the development of stress by the rust formation. For higher BRHA percentages, the protective Fe2O3 was dissolved due to the acidic environment caused by higher chloride accumulation in the cement specimens. The steel rebar was then aggressively attacked by the chloride and it was finally broken. Therefore, the optimization of the BRHA percentage is needed to minimize corrosion. However, the longer curing time of 20% BRHA replacement specimen is required for increasing the compressive strength because its compressive strength is slightly lower than the standard.
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Evidence for chromium, cobalt and molybdenum volatilisations during high temperature oxidation of Co-27Cr-6Mo Alloy
(2022-07-01)
Wongpromrat, Patthranit
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Galerie, Alain
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Thublaor, Thammaporn
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Chandra-ambhorn, Walairat
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Ponpo, Phisan
A Co-27Cr-6Mo alloy was oxidised in pure O<inf>2</inf> between 800 and 1000 °C for durations up to 96 h. The flow rate was varied between 2 and 5 cm.s–1. In these conditions, volatilisations of chromium, cobalt and molybdenum were observed. The chromium volatilisation values were in good agreement with calculations assuming (CrO<inf>3</inf>)g volatilisation limited by diffusion in the gas boundary layer. On the contrary, the measured flux of evaporated Co was higher than the calculated Co volatilisation flux from the metallic Co. The theoretical relation between the solid Co particle size and its vapour pressure was suggested to help explaining such difference.
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Characterisation of thermal oxide scales on stainless steels
(2020-01-01)
Srisrual, Anusara
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Thublaor, Thammaporn
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Wongpromrat, Patthranit
This chapter aims at reviewing the characterisation techniques that are commonly used for high temperature oxidation study, especially on stainless steels. In addition, the experimental studies about the high temperature oxidation i.e. thermogravimetric method and chromium volatilisation measurement are explained. The various kinds of characterisation techniques for physico-chemical and electronic properties of thermal oxide scales are reviewed, starting from optical microscopy (OM), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), focused ion beam coupled with scanning electron microscope (FIB/SEM), X-ray diffractometer (XRD), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy (RS), and photoelectrochemical characterisation (PEC). The review focuses on the basic concepts and shows how the characterising tools can be applied to thermal oxide characterisation.
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Effect of water vapour on the high temperature oxidation of stainless steels
(2020-01-01)
Chandra-Ambhorn, Somrerk
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Wongpromrat, Patthranit
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Thublaor, Thammaporn
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Chandra-Ambhorn, Walairat
This chapter primarily reviews the nature of water vapour when it presents in bulk gas. The change in a ratio between water vapour and corresponding dissociated hydrogen, which determine the thermodynamic stability of the oxide formation, is analysed when the oxidation kinetics are linear and parabolic. When water vapour reaches the solid/gas interface, chromium species volatilisation and oxidation controlled by surface reaction can occur. The adsorbed water vapour can be further incorporated into the oxide possibly in the form of hydrogen defects. The role of these defects on altering the defect structure of the oxide is discussed. Finally, characteristics of the oxide scale on stainless steels formed in the atmosphere containing water vapour are reviewed.

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