Researcher:
Doğan, Nihal Olcay

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Master Student

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Nihal Olcay

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Doğan

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Doğan, Nihal Olcay

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2018 - 201932020 - 20222

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Researcher Of Publications: search.filters.isAuthorOfPublication.7ece052b-5866-468b-b8c0-61b4e053028f

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    Optimization of a gelatin–potassium phosphate aqueous two-phase system for the preparation of hydrogel microspheres
    (Springer, 2019) Erkoç, Pelin; Department of Chemical and Biological Engineering; Department of Chemical and Biological Engineering; Doğan, Nihal Olcay; Kızılel, Seda; Master Student; Faculty Member; Graduate School of Sciences and Engineering; College of Engineering; N/A; 28376
    An aqueous two-phase system provides a simple route toward the preparation of gelatin emulsions. Here, we present a simple method to generate water-in-water (w/w) emulsions from an aqueous two-phase system: gelatin and potassium phosphate (K2HPO4) salt. Liquid gelatin forms as the dispersed phase of the two-phase emulsion system, and gelatin microspheres can be retrieved after a visible light-induced crosslinking reaction. We investigated the effect of the continuous phase volume ratio on the formation of the phase-separation and emulsification process. We also studied the influence of the polymerization method on the size and morphology of gelatin hydrogel particles. The results demonstrated that K2HPO4 is an appropriate phase-forming salt, where biodegradable gelatin particles obtained through this w/w emulsion system have potential for biomedical applications. In addition, sustained release of a model molecule, methylene blue, was observed for up to 5days from gelatin particles. This system is advantageous because if provides an inexpensive emulsion platform that avoids the use of organic solvents or auxiliary polymers to form a continuous phase.
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    Deep insight into PEGylation of bioadhesive chitosan nanoparticles: sensitivity study for the key parameters through artificial neural network model
    (Amer Chemical Soc, 2018) N/A; N/A; Department of Chemical and Biological Engineering; Department of Chemical and Biological Engineering; Bozüyük, Uğur; Doğan, Nihal Olcay; Kızılel, Seda; PhD Student; Master Student; Faculty Member; Graduate School of Sciences and Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; N/A; 28376
    lonically cross-linked chitosan nanoparticles have great potential in nanomedicine due to their tunable properties and cationic nature. However, low solubility of chitosan severely limits their potential clinical translation. PEGylation is a well-known method to increase solubility of chitosan and chitosan nanoparticles in neutral media; however, effect of PEG chain length and chitosan/PEG ratio on particle size and zeta potential of nanoparticles are not known. This study presents a systematic analysis of the effect of PEG chain length and chitosan/PEG ratio on size and zeta potential of nanoparticles. We prepared PEGylated chitosan chains prior to the nanoparticle synthesis with different PEG chain lengths and chitosan/PEG ratios. To precisely estimate the influence of critical parameters on size and zeta potential of nanoparticles, we both developed an artificial neural network (ANN) model and performed experimental characterization using the three independent input variables: (i) PEG chain length, (ii) chitosan/PEG ratio, and (iii) pH of solution. We studied the influence of PEG chain lengths of 2, 5, and 10 kDa and three different chitosan/PEG ratios (25 mg chitosan to 4, 12, and 20 mu moles of PEG) for the synthesis of chitosan nanoparticles within the pH range of 6.0-7.4. Artificial neural networks is a modeling tool used in nanomedicine to optimize and estimate inherent properties of the system. Inherent properties of a nanoparticle system such as size and zeta potential can be estimated based on previous experiment results, thus, nanoparticles with desired properties can be obtained using an ANN. With the ANN model, we were able to predict the size and zeta potential of nanoparticles under different experimental conditions and further confirmed the cell-nanoparticle adhesion behavior through experiments. Nanoparticle groups that had higher zeta potentials promoted adhesion of HEK293-T cells to nanoparticle-coated surfaces in cell culture medium, which was predicted through ANN model prior to experiments. Overall, this study comprehensively presents the PEGylation of chitosan, synthesis of PEGylated chitosan nanoparticles, utilizes ANN model as a tool to predict important properties such as size and zeta potential, and further captures the adhesion behavior of cells on surfaces prepared with these engineered nanoparticles.
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    PublicationOpen Access
    High-yield production of biohybrid microalgae for on-demand cargo delivery
    (Wiley, 2020) Akolpoğlu, Mukrime Birgul; Bozüyük, Uğur; Ceylan, Hakan; Department of Chemical and Biological Engineering; Department of Mechanical Engineering; Department of Chemical and Biological Engineering; Department of Mechanical Engineering; Kızılel, Seda; Doğan, Nihal Olcay; Sitti, Metin; Faculty Member; Faculty Member; College of Engineering; Graduate School of Sciences and Engineering; School of Medicine; 28376; N/A; 297104
    Biohybrid microswimmers exploit the swimming and navigation of a motile microorganism to target and deliver cargo molecules in a wide range of biomedical applications. Medical biohybrid microswimmers suffer from low manufacturing yields, which would significantly limit their potential applications. In the present study, a biohybrid design strategy is reported, where a thin and soft uniform coating layer is noncovalently assembled around a motile microorganism.Chlamydomonas reinhardtii(a single-cell green alga) is used in the design as a biological model microorganism along with polymer-nanoparticle matrix as the synthetic component, reaching a manufacturing efficiency of approximate to 90%. Natural biopolymer chitosan is used as a binder to efficiently coat the cell wall of the microalgae with nanoparticles. The soft surface coating does not impair the viability and phototactic ability of the microalgae, and allows further engineering to accommodate biomedical cargo molecules. Furthermore, by conjugating the nanoparticles embedded in the thin coating with chemotherapeutic doxorubicin by a photocleavable linker, on-demand delivery of drugs to tumor cells is reported as a proof-of-concept biomedical demonstration. The high-throughput strategy can pave the way for the next-generation generation microrobotic swarms for future medical active cargo delivery tasks.
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    PublicationOpen Access
    Parameters influencing gene delivery efficiency of PEGylated chitosan nanoparticles: experimental and modeling approach
    (Wiley, 2022) Bozüyük, Uğur; Erkoç, Pelin; Karacakol, Alp Can; Department of Chemical and Biological Engineering; Department of Mechanical Engineering; Department of Chemical and Biological Engineering; Department of Mechanical Engineering; Kızılel, Seda; Sitti, Metin; Önder, Tuğba Bağcı; Doğan, Nihal Olcay; Cingöz, Ahmet; Şeker-Polat, Fidan; Nazeer, Muhammad Anwaar; Faculty Member; Faculty Member; PhD Student; PhD Student; College of Engineering; School of Medicine; 28376; 297104; 184359; N/A; N/A; N/A; N/A
    Experimentation of nanomedicine is labor-intensive, time-consuming, and requires costly laboratory consumables. Constructing a reliable mathematical model for such systems is also challenging due to the difficulties in gathering a sufficient number of data points. Artificial neural networks (ANNs) are indicated as an efficient approach in nanomedicine to investigate the cause-effect relationships and predict output variables. Herein, an ANN is adapted into plasmid DNA (pDNA) encapsulated and PEGylated chitosan nanoparticles cross-linked with sodium tripolyphosphate (TPP) to investigate the effects of critical parameters on the transfection efficiencies of nanoparticles. The ANN model is developed based on experimental results with three independent input variables: 1) polyethylene glycol (PEG) molecular weight, 2) PEG concentration, and 3) nanoparticle concentration, along with one output variable as a percentage of green fluorescent protein (GFP) expression, which refers to transfection efficiency. The constructed model is further validated with the leave-p-out cross-validation method. The results indicate that the developed model has good prediction capability and is influential in capturing the transfection efficiencies of different nanoparticle groups. Overall, this study reveals that the ANN could be an efficient tool for nanoparticle-mediated gene delivery systems to investigate the impacts of critical parameters in detail with reduced experimental effort and cost.
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    PublicationOpen Access
    A novel method for PEGylation of chitosan nanoparticles through photopolymerization
    (Royal Society of Chemistry (RSC), 2019) Department of Chemical and Biological Engineering; Department of Chemical and Biological Engineering; Bozüyük, Uğur; Gökulu, İpek Simay; Doğan, Nihal Olcay; Kızılel, Seda; PhD Student; Faculty Member; College of Engineering; N/A; N/A; N/A; 28376
    An ultrafast and convenient method for PEGylation of chitosan nanoparticles has been established through a photopolymerization reaction between the acrylate groups of PEG and methacrylated-chitosan nanoparticles. The nanoparticle characteristics under physiological pH conditions were optimized through altered PEG chain length, concentration and duration of UV exposure. The method developed here has potential for clinical translation of chitosan nanoparticles. It also allows for the scalable and fast synthesis of nanoparticles with colloidal stability.