IYTE GCRIS Database (Izmir Institute of Technology)
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Advanced Adsorptive Removal of Dimethyl Phthalate From Water Using a Tertiary Amine-Functionalized Polymeric Resin: Insights Into Experimental Design and Statistical Analysis
No full textThis study investigates the effective removal of dimethyl phthalate (DMP) from aqueous solutions using Purolite Macronet MN100, a polymer-based adsorbent containing tertiary amine functional groups. A series of batch experiments was performed to assess the influence of resin dosage and solution pH, while adsorption kinetics were analyzed to determine the optimal contact time and the underlying rate-limiting mechanism. Equilibrium data were interpreted using adsorption isotherm models, and thermodynamic parameters (Delta G degrees, Delta H degrees, and Delta S degrees) were calculated to evaluate the feasibility and spontaneity of the process. Additionally, the effect of common coexisting ions in wastewater (Na+, K+, Mn2+, Ca2+, Mg2+) on DMP removal was examined. The optimum removal efficiency (>97%) was achieved using 0.02 g of resin per 25 mL solution at pH 2-6, with equilibrium established within 300 minutes. The adsorption behavior was best described by the Langmuir isotherm, indicating monolayer adsorption with a maximum capacity of 463.37 mg g(-1). Mechanistic evaluation revealed that pi-pi interactions and hydrogen bonding were the dominant forces driving DMP adsorption. The presence of competing cations had minimal impact, demonstrating the adsorbent's strong selectivity toward DMP. Desorption studies showed complete DMP recovery using absolute ethanol (>99%), with >99% regeneration efficiency. Optimization using Central Composite Design (CCD) under Response Surface Methodology (RSM) produced a statistically robust model (R-2 = 0.98), consistent with the experimental results. Overall, Purolite MN100 proved to be a highly efficient, selective, and regenerable adsorbent suitable for DMP removal in wastewater treatment processes
A Comprehensive Life Cycle Impact Evaluation of Hydrogen Production Processes for Cleaner Applications
No full textThe worldwide energy demands have greatly increased with urbanization and population growth. Air pollution, acid rain, greenhouse gas emissions, global warming originating from CO2 emissions, depletion of energy supplies, and environmental degradation resulting from climate change are all consequences of using non-renewable fossil fuel-based energy infrastructure. To minimize emissions, renewable energy-based alternative energy sources must be investigated. In this regard, hydrogen (H2) has emerged as a promising fuel to meet energy requirements, and green H2 production with net-zero emissions has gained significant interest in recent years. Therefore, this study uses the life cycle assessment approach to evaluate the atmospheric emissions and environmental impact parameters of the gasification, electrolysis, and dark fermentation-microbial electrolysis hybrid process and assess their sustainability levels, considering the sustainable development goals. Among the studied H2 production processes, the maximum CO2 emission originates from the coal gasification process, accounting for 18.6 kg-CO2/kg-H2, while the alkaline electrolysis process provides the lowest total CO2 emission of 6.39 kg-CO2/kg-H2. Furthermore, the biological-based dark fermentation-microbial electrolysis cell process is a promising option owing to its highest negative biogenic CO2 emission of -68.69 kg-CO2/kg-H2. The environmental impact parameters of the studied processes are calculated considering the emissions, and the highest global warming potential of 21.75 kgCO2-eq./kg-H2 is obtained for the coal gasification process, considering the life cycle assessment coefficients. Overall, the lowest atmospheric emissions and environmental impacts are obtained for the electrolysis process. Consequently, these results revealed that switching from the fossil fuel resources used in the conventional H2 production methods to fully sustainable sources, such as renewables, can make energy production methods entirely sustainable from an environmental point of view
Residue-Specific Pathways in Peptide Fragmentation: the Role of Aromatic Side Chain in A3 Ion Formation From B3 Ion
No full textPeptide fragmentation chemistry is essential for the sequence elucidation of proteins through tandem mass spectrometry (MS/MS). In this study, we examine the gas-phase fragmentation of b3 ions from model tripeptides under low-energy CID conditions, focusing on the pathway leading to the stable formation of a3 ions from b3 ions. The study utilized C-terminal amidated model tripeptides, including YGG-NH2, GYG-NH2, and GGX-NH2, where X represents D, E, H, Q, C, S, F, and Y. Our results reveal that only tripeptides with phenylalanine (F) and tyrosine (Y) as the third residue yield a3 ions upon b3 ion fragmentation under the applied experimental conditions, suggesting a unique stabilizing role of aromatic side chains in facilitating this pathway. Our theoretical studies indicate that the a3 ions from GGF-NH2 and GGY-NH2 preferentially adopt an energetically favored linear imineprotonated isomer, which is lower in energy by 3.29 kcal/mol and 4.17 kcal/mol, respectively, compared to their 7-membered ring isomers protonated at the ring imine. The latter structure has been previously assigned for the GGG sequence as a predominant structure, supported by IR spectroscopy and DFT calculations (JACS, 2010, 132, 14,766-14779). We proposed a plausible fragmentation mechanism for the a3 ions based on the linear imineprotonated structure. These findings provide insights into residue-specific fragmentation mechanisms and enhance our understanding of peptide ion dissociation, particularly in small peptides
Navigating Bureaucratic Transition From Below: Petitioning Practices of Late Ottoman Purge Victims (1909-12)
No full textThe restoration of the constitution in 1908 heralded a new era in Ottoman state administration. This included the dismissal of thousands of bureaucrats of various ranks. The victims of the purges grappled with the challenges of constitutional rule as they explored potential means to regain their roles within the state bureaucracy. This article examines the petitions authored by dismissed officials after the tensikdtlawwas enacted in early July 1909, revealing the complex interplay between traditional and modem elements in their structure and content. Our analysis of the petitions, treated as a compulsory form of liminal petitioning, offers insights into the adaptive strategies of the dismissed officials during a period of political transition, sheddinglight on the dynamic nature of petitioning practices amidst significant bureaucratic upheaval in the late Ottoman Empire
Predicting the Area Moment of Inertia of Beam and Column Using Machine Learning and Hypernetexplorer
No full textBeams and columns are the most important elements of steel frame structures. Damage to the beam or column can lead the structure to serious hazards and cause collapse. In the structural engineering literature, it has been observed that there is not much work for area moment of inertia estimation of beam and column. The aim of this study was to predict the area moment of inertia of beam and column using HyperNetExplorer developed by the authors. This method aims to bring innovation by optimizing artificial neural networks (ANNs). In this study, a prediction study is performed using 306 collected data on beam and column area moment of inertia. Classical ML models (linear regression (LR), decision tree regression (DTR), K neighbors regression (KNN), polynomial regression (PR), random forest regression (RFR), gradient boosting regression (GBR), histogram gradient boosting regression (HGBR)) and NAS and HyperNetExplorer were applied to predict beam and column area moment of inertia. The prediction performances were compared using different performance metrics (coefficient of determination (R2) and mean squared error (MSE)) and HyperNetExplorer developed by the authors showed the highest performance (R2 = 0.98, MSE = 246.88). Furthermore, SHapley additive explanations (SHAP) were used to explain the effects of features in the prediction models and it was observed that the most effective features for model predictions were loading on beam and length. The results show that the proposed NAS base approach and the developed tool, HyperNetExplorer, provides better performance when compared with classical ML methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025
Injectable Nanocomposite Hydrogels With Co-Delivery of Oxygen and Anticancer Drugs for Higher Cell Viability of Healthy Cells Than Cancer Cells Under Normoxic and Hypoxic Conditions
No full textInjectable nanocomposite hydrogels (NC hydrogels) have the potential to be used for minimally invasive local drug delivery. In particular, pH-sensitive injectable NC hydrogels can be used in cancer treatment to deliver high doses of anticancer drugs to the target site in cancer tissue without damaging healthy tissue. Recent studies have shown that in addition to stimuli-responsive delivery of anticancer drugs to cancer cells, oxygen delivery to the hypoxic environment of cancer tissue can lead to advanced effects, as hypoxia and an acidic pH are common characteristics of cancer tissue. However, few studies have investigated the effects of simultaneous administration of oxygen (O2) and pH-dependent anticancer drugs via injectable NC hydrogels on the viability of healthy and cancer cells under normoxic and hypoxic conditions. In this context, we describe the synthesis of injectable NC hydrogels composed of pH-responsive nanomaterials carrying oxygen and anticancer drugs. Our system provides sustained O2 release and pH-responsive sustained release of anticancer drugs for 15 and 30 d, respectively. Moreover, O2 delivery and/or simultaneous delivery of O2 and anticancer drug resulted in higher cell survival of healthy fibroblast cells than malignant Colo-818 cells under hypoxic conditions (1% O2) after 7 d of incubation
Poly(Ethylene Glycol)-Keratin Hydrogels Prepared Via Thiol-Maleimide Reaction
No full textThe mechanical properties of hydrogels have a profound effect on cellular responses in tissue engineering applications. In this study, poly(ethylene glycol)-keratin (PEG-KRTN) hydrogels with tunable mechanical properties were prepared by varying molar mass of the maleimide functionalized PEG in the thiol-maleimide chemistry. Reduced keratins were reacted with PEG-maleimides having 2000 Da and 6000 Da molar masses. Viscoelastic and physiochemical properties and cytocompatibility of these hydrogels were tested. Storage modulus values were obtained as 2613 +/- 254 Pa and 1313 +/- 345 Pa for PEG2000-KRTN and PEG6000-KRTN hydrogels, respectively. Strain sweep data indicate that the linear viscoelastic region (LVER) of the PEG6000-KRTN hydrogel spans up to 40% strain value, whereas it is limited to 10% critical strain for the PEG2000-KRTN hydrogel. PEG6000-KRTN hydrogel presented higher swelling ratios and porosity. CCK-8 test showed that both hydrogels promoted the proliferation of L929 mouse fibroblast cells and, hence, can be applied in soft tissue engineering
Comparison of Conventional and Machine Learning Models for Kinetic Modelling of Biomethane Production From Pretreated Tomato Plant Residues
No full textTomato plant residues (Solanum lycopersicum L.) lack sustainable applications as abundant lignocellulosic biomass after harvest. These residues can be utilized as substrates in anaerobic digestion for biomethane production, generating energy and reducing waste. The purpose of this study was to investigate the sustainable utilization of tomato plant residues for biomethane production at varying conditions and to model biological kinetics. The study aimed to evaluate the effects of varying substrate/inoculum ratios, sulfuric acid pretreatment concentrations, and yeast (Saccharomyces cerevisiae) addition on biogas and biomethane yields under mesophilic conditions (37 degrees C). Maximum biogas and biomethane yields in the studied range were obtained when the substrate/inoculum ratio was 3 (g substrate/g inoculum), the sulfuric acid concentration used for residue pretreatment was 2 %v/v, and the substrate/yeast ratio was 10 (g substrate/g yeast). The yeast ratio of 10 increased the cumulative biogas and biomethane production by 96.5 and 128.9%, respectively. Conventional models (Modified Gompertz, Cone, First-order, Logistic) and Machine Learning models (Support Vector Machine and Neural Network) were compared for biological kinetics. Machine Learning models were also observed to give good fitting results similar to conventional models. Results suggest that Machine Learning models (RMSE: 2.5833-12.0500) are reliable methods like conventional kinetic models (RMSE: 2.1796-13.4880) for forecasting biomethane production in anaerobic digestion processes and Machine Learning models can be applied without needing prior understanding of biomethane production kinetics
Sign of Slant Receiver for Skewed Alpha-Stable Noise Shift Keying-Based Random Communication System
No full textPurpose: In contrast to traditional communication systems, slower data rate has always remained a weak link for non-traditional random communication systems (RCSs), which use alpha-stable (a-stable) noise as a carrier. This paper aims to introduce a fast receiver for skewed a-stable noise shift keying (SkaSNSK)-based RCSs. Design/methodology/approach: The introduced receiver is based on the sign of slant estimator (SoSE), which provides rapid estimation of the skewed a-stable random noise signals (RNSs) received from the additive white Gaussian noise channel. The SoSE-based receiver minimizes the number of samples required to extract the encoded information from the received RNSs. This is achieved by manipulating the antipodal properties of the slant/skewness parameter of the a-stable carrier. Hence, a high data rate with relatively low complexity is guaranteed. Findings: In comparison with the previously introduced sinc, logarithmic and modified extreme value method-based receivers, the proposed SoSE-based receiver also achieves improved bit error rate (BER) along with the better covertness values so that the essence of security provided by SkaSNSK-based RCSs remains intact. Research limitations/implications: Because of the selected range of the associated parameters of the a-stable noise as a carrier, the BER vs MSNR results are may lack applicability for the complete range of values. Therefore, further research is required to produce results in different ranges. Practical implications: The study includes implications for the hardware development based on the proposed communication scheme. Originality/value: It can be seen that the paper fulfils the desired need of a fast receiver design for RCS. © 2024, Emerald Publishing Limited
Markov Olmayan Ortamlar ile Etkileşimdeki Süperiletken Transmon Kübitlerin Kesirli Dinamiği
No full textIn this thesis, we have introduced the Mittag-Leffler (ML) type correlations to the field of both decoherence suppression in a system consisting of a single transmon qubit interacting with a Markovian bath and the characterization of the dynamics of a transmon qubit interacting with a defect in an amorphous medium, respectively. The effect of the characteristic exponent (CE) of the ML correlated noise, being spatially orthogonal to the Markovian noise, on the decoherence time and population have been analyzed. Two types of ML correlated noise, one of which is diffusive, and the other being constructed by fractional generalization of the random telegraph noise, have been considered. It has been observed that the coherence time increases depending on the memory represented by the CE. Lévy type energy fluctuations with heavy tailed correlations, which might give rise to an anomalous diffusion, between the transmon qubit and the neighboring single defect in the non-Markovian bath of an amorphous coating have been analyzed. The heavy tailed correlations might cause a long-term memory in the dynamics of the interacting system. We have characterized the fluctuations by a time dependent CE for the case in which the bath has a time varying power spectral density. Therefore, we have introduced the variable order fractional master equation to model the dynamics of the transmon qubit, the simulation codes which do not exist in the literature of open quantum systems and analyzed the effects of the bath with a time dependent CE on the decoherence times.Bu tezde, literatürde ilk defa olmak üzere, Mittag-Leffler (ML) tipi ilintilerin Markov bir çevre ile etkileşimde olan transmon kübitlerin eşfazlılık sürelerinin iyileştirilmesine olan etkisini irdeledik. Tez kapsamında gerçekleştirdiğimiz ikinci çalışmada, literatürde ilk defa, amorf bir çevre içindeki kusur ile etkileşimde olan transmon kübitin zaman dinamiğini analiz ettik. Markov karakteristiğine sahip gürültü eksenine dik uygulanan ML ilintili gürültüye ait karakteristik üstelin (KÜ), eşfazlılık süresine ve kubit durum dağılımına etkisini analiz ettik. Bu kapsamda yayıngan ve rastsal telegraf gürültüsünün kesirli genelleştirilmesinden inşaa ettiğimiz iki tip ML ilintili gürültü kullandık. Eşfazlılık süresinin KÜ ile ifade edilen hafızaya bağlı olarak arttığını gözlemledik. Transmon kübit ve üzerinde üretim aşamasında oluşan amorf kaplamadaki tek bir kusur arasında Gaussian olmayan karakteristiğe sahip enerji yayılımına sebep olabilecek, aynı zamanda etkileşimdeki sistem dinamiğinde uzun süreli hafızaya sebep olabilecek ağır kuyruklu Lévy tipi enerji salınımlarını irdeledik. Çevrenin güç tayfı yoğunluğunun zaman bağımlı olma durumunu zamana bağlı kesirli üstel ile betimledik. Bu ifadeye bağlı olarak, transmon kübitin zaman dinamiğini değişken mertebeli kesirli ana denklem ile ifade ettik; zamana bağlı KÜ ile betimlenen ortamın, kübit eşfazlılık süresine etkisinin, açık kuantum sistemlerinde ilk olmak üzere, bilgisayar benzetimlerini gerçekleştirdik