Comparison of photocatalytic performances of solar-driven hybrid catalysts for hydrogen energy evolution from 1,8–Diazabicyclo[5.4.0]undec-7-ene (DBU) solution

Orak, Ceren; Yuksel, Asli

doi:10.1016/j.ijhydene.2021.12.254

Cited by 10 publications

(6 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Additionally, the energy levels of calculated conduction band edge ( E CB ) for BFO, LRO, and graphene were reported to be 0.50, 0.45, and −1.79, respectively. Considering the results, it could be concluded that the CB position of BFO and LRO is lower than that of the graphene, which reduces the recombination of electron–hole pairs and enhances the photoactivity of GBFO and GLRO . A schematic diagram of the separation process of electron–hole pairs of GBFO and GLRO is shown in Figure .…”

Section: Resultsmentioning

confidence: 97%

“…Considering the results, it could be concluded that the CB position of BFO and LRO is lower than that of the graphene, which reduces the recombination of electron–hole pairs and enhances the photoactivity of GBFO and GLRO. 25 A schematic diagram of the separation process of electron–hole pairs of GBFO and GLRO is shown in Figure 4 . In this study, the introduction of graphene into the perovskite structure led to fast charge transfer, thereby enhancing the hydrogen evolution from sucrose model solution and sugar factory wastewater.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Box–Behnken Design for Hydrogen Evolution from Sugar Industry Wastewater Using Solar-Driven Hybrid Catalysts

Orak

Yuksel

2022

ACS Omega

Self Cite

View full text Add to dashboard Cite

Hydrogen is a clean and green fuel and can be produced from renewable sources via photocatalysis. Solar-driven hybrid catalysts were synthesized and characterized (scanning electron microscopy (SEM), transmission electron microscopy (TEM), Brunauer−Emmett−Teller (BET), X-ray diffraction (XRD), photoluminescence (PL) spectroscopy, and UV−vis diffuse reflectance spectroscopy (DSR)), and the results implied that graphene-supported LaRuO 3 is a more promising photocatalyst to produce hydrogen and was used to produce hydrogen from sugar industry wastewater. To investigate the main and interaction effects of reaction parameters (pH, catalyst amount, and [H 2 O 2 ] 0 ) on the evolved hydrogen amount, the Box−Behnken experimental design model was used. The highest hydrogen evolution obtained was 6773 μmol/g cat from sugar industry wastewater at pH 3, 0.15 g/L GLRO, and 15 mM H 2 O 2 . Based on the Pareto chart for the evolved hydrogen amount using GLRO, among the main effects, the only effective parameter was the catalyst amount for the photocatalytic hydrogen evolution from sugar industry wastewater. In addition, the squares of pH and two-way interaction of pH and [H 2 O 2 ] 0 were also statistically efficient over the evolved hydrogen amount.

show abstract

Section: Resultsmentioning

confidence: 97%

Section: Resultsmentioning

confidence: 99%

Box–Behnken Design for Hydrogen Evolution from Sugar Industry Wastewater Using Solar-Driven Hybrid Catalysts

Orak

Yuksel

2022

ACS Omega

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, although this method can be used to produce hydrogen, the yield remains relatively low through this route. Therefore, it requires some improvements, such as the development of photocatalysts that are sensitive to solar light to obtain high solar-to-hydrogen ratios (Iervolino et al 2017, Hosseini and Wahid 2020, Muniyappa et al 2022, Orak and Yüksel 2022a, 2022b. In this context, graphene serves as an exceptional catalyst support material due to its remarkable properties, including high chemical stability and a vast surface area.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, rGO facilitated charge transfer, expanding the active sites on the composite material's surface, thereby enhancing the overall photocatalytic activity of LaFeO 3 /rGO (Acharya et al 2020). Therefore, hybrid photocatalysts (graphene-supported LaFeO 3 (GLFO), graphene-supported BiFeO 3 (GBFO), and graphenesupported LaRuO 3 (GLRO)) that are sensitive to solar light were developed to use hydrogen evolution from 1,8-Diazabicyclo [5.4.0] undec-7-ene (DBU) solution (Orak and Yüksel 2022b), sucrose solution and sugar industry wastewater (Orak and Yüksel 2022a). DBU is an amidine base, a nitrogen-containing heterocyclic organic compound typically found in the form of a pale yellow or colorless liquid at room temperature.…”

Section: Introductionmentioning

confidence: 99%

“…Industries such as pharmaceuticals, dyes, and cosmetics heavily rely on its utility. However, the petroleum and chemical sectors generate significant volumes of wastewater, which often contains nonbiodegradable and chemically stable heterocyclic compounds like DBU Yüksel Özşen 2020, Orak andYüksel 2022b). Depending on its hazardous impact it should be treated and thus, it was used to evolve hydrogen using the hybrid photocatalysts (GLFO, GBFO, and GLRO).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A machine learning ensemble approach for predicting solar-sensitive hybrid photocatalysts on hydrogen evolution

Bakır,

Orak,

Yüksel

2024

Phys. Scr.

Self Cite

View full text Add to dashboard Cite

Hydrogen, as the lightest and most abundant element in the universe, has emerged as a pivotal player in the quest for sustainable energy solutions. Its remarkable properties, such as high energy density and zero emissions upon combustion, make it a promising candidate for addressing the pressing challenges of climate change and transitioning towards a clean and renewable energy future. In an effort to improve efficiency and reduce experimental costs, we adopted machine learning techniques in this study. Our focus turned to predictive analyses of hydrogen evolution values using three photocatalysts, namely, graphene-supported LaFeO3 (GLFO), graphene-supported LaRuO3 (GLRO), and graphene-supported BiFeO3 (GBFO), examining their correlation with varying levels of pH, catalyst amount, and H2O2 concentration. To achieve this, a diverse range of machine learning models are used, including Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), XGBoost, Gradient Boosting, and AdaBoost—each bringing its strengths to the predictive modeling arena. An important step involved combining the most effective models—Random Forests, Gradient Boosting, and XGBoost—into an ensemble model. This collaborative approach aimed to leverage their collective strengths and improve overall predictability. The ensemble model emerged as a powerful tool for understanding photocatalytic hydrogen evolution. Standard metrics were employed to assess the performance of our ensemble prediction model, encompassing R squared, Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Error (MAE). The yielded results showcase exceptional accuracy, with R squared values of 96.9%, 99.3%, and 98% for GLFO, GBFO, and GLRO, respectively. Moreover, our model demonstrates minimal error rates across all metrics, underscoring its robust predictive capabilities and highlighting its efficacy in accurately forecasting the intricate relationships between GLFO, GBFO, and GLRO values and their influencing factors.

show abstract

Biochar-Based Catalyst Derived from Corn Husk Waste for Efficient Hydrogen Generation via NaBH4 Hydrolysis

López,

Fajardo,

Ferreira

et al. 2024

Waste Biomass Valor

View full text Add to dashboard Cite

Comparison of photocatalytic performances of solar-driven hybrid catalysts for hydrogen energy evolution from 1,8–Diazabicyclo[5.4.0]undec-7-ene (DBU) solution

Cited by 10 publications

References 74 publications

Box–Behnken Design for Hydrogen Evolution from Sugar Industry Wastewater Using Solar-Driven Hybrid Catalysts

Box–Behnken Design for Hydrogen Evolution from Sugar Industry Wastewater Using Solar-Driven Hybrid Catalysts

A machine learning ensemble approach for predicting solar-sensitive hybrid photocatalysts on hydrogen evolution

Biochar-Based Catalyst Derived from Corn Husk Waste for Efficient Hydrogen Generation via NaBH4 Hydrolysis

Contact Info

Product

Resources

About