Chemical Engineering / Kimya Mühendisliği
Permanent URI for this collectionhttps://hdl.handle.net/11147/14
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Article Citation - WoS: 15Citation - Scopus: 16A Machine Learning Ensemble Approach for Predicting Solar-Sensitive Hybrid Photocatalysts on Hydrogen Evolution(IOP Publishing, 2024) Bakır, Rezan; Orak, Ceren; Yuksel, AsliHydrogen, 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.Correction Citation - WoS: 1Erratum: Bioactive Snail Mucus-Slime Extract Loaded Chitosan Scaffolds for Hard Tissue Regeneration: the Effect of Mucoadhesive and Antibacterial Extracts on Physical Characteristics and Bioactivity of Chitosan Matrix (Biomedical Materials (Bristol) (2021) 16 (065008) Doi: 10.1088/1748-605x(IOP Publishing, 2023) Perpelek, M.; Tamburaci, S.; Aydemi̇r, S.; Tıhmınlıoğlu, F.; Baykara, B.; Karakaşli, A.; Havitçioǧlu, H.The authors regret that some errors were identified in 'figures 12 and 13' on pages 14 and 15, in the published manuscript concerning fluorescence microscopy images of Saos-2 and SW1353 cells on scaffolds for 1 and 3 d of incubation. The fluorescence images in figures 12 and 13 were mistakenly used as duplicated due to the inadvertently mislabeling during the processing of files and integrating them into the final figures. Intensity data regarding corrected fluorescence images were also measured and corrected. The revised figures (figures 12 and 13) and their captions appear below. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. (Figure Presented). © 2023 IOP Publishing Ltd.Article Citation - WoS: 22Citation - Scopus: 23Bioactive Snail Mucus-Slime Extract Loaded Chitosan Scaffolds for Hard Tissue Regeneration: the Effect of Mucoadhesive and Antibacterial Extracts on Physical Characteristics and Bioactivity of Chitosan Matrix(IOP Publishing, 2021) Perpelek, Merve; Tamburacı, Sedef; Aydemir, Selma; Tıhmınlıoğlu, Funda; Baykara, Başak; Karakaşlı, Ahmet; Havıtçıoğlu, HasanBiobased extracts comprise various bioactive components and they are widely used in tissue engineering applications to increase bioactivity as well as physical characteristics of biomaterials. Among animal sources, garden snail Helix aspersa has come into prominence with its antibacterial and regenerative extracts and show potential in tissue regeneration. Thus, in this study, bioactive H. aspersa extracts (slime, mucus) were loaded in chitosan (CHI) matrix to fabricate porous scaffolds for hard tissue regeneration. Physical, chemical properties, antimicrobial activity was determined as well as in vitro bioactivity for bone and cartilage regeneration. Mucus and slime incorporation enhanced mechanical properties and biodegradation rate of CHI matrix. Scanning electron microscopy images showed that the average pore size of the scaffolds decreased with higher extract content. Mucus and slime extracts showed antimicrobial effect on two bacterial strains. In vitro cytotoxicity, osteogenic and chondrogenic activity of the scaffolds were evaluated with Saos-2 and SW1353 cell lines in terms of Alkaline phosphatase activity, biomineralization, GAG, COMP and hydroxyproline content. Cell viability results showed that extracts had a proliferative effect on Saos-2 and SW1353 cells when compared to the control group. Mucus and slime extract loading increased osteogenic and chondrogenic activity. Thus, the bioactive extract loaded CHI scaffolds showed potential for bone and cartilage regeneration with enhanced physical properties and in vitro bioactivity.