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Optical Techniques for Tissue Engine...

Babayigit, Ceren,

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Optical Techniques for Tissue Engineering and Massive Neural Sensing /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optical Techniques for Tissue Engineering and Massive Neural Sensing // Ceren Babayigit.
Author:	Babayigit, Ceren,
Description:	1 electronic resource (149 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
Contained By:	Dissertations Abstracts International86-11B.
Subject:	Engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31845019
ISBN:	9798314858752

Optical Techniques for Tissue Engineering and Massive Neural Sensing /
Babayigit, Ceren,

Optical Techniques for Tissue Engineering and Massive Neural Sensing /Ceren Babayigit. - 1 electronic resource (149 pages)

Source: Dissertations Abstracts International, Volume: 86-11, Section: B.

The ability to precisely engineer biological tissues and decode neural activity at high resolution has the potential to transform regenerative medicine and neuro-engineering. However, current biofabrication techniques struggle to balance speed, resolution, biocompatibility, and the ability to fabricate multi-material, multi-layer structures essential for replicating complex tissue architectures. Meanwhile, conventional neural sensing methods remain limited by signal degradation, biological interference, and insufficient spatial resolution. This dissertation introduces novel photonic methodologies that overcome these challenges, enhancing 3D bioprinting precision and neural signal detection. By leveraging advanced optical systems, this work establishes scalable, high-fidelity solutions that push the boundaries of tissue engineering and neuroscience.The first part of the dissertation focuses on , an advanced high-resolution 3D bioprinting technique called Photocrosslinked Extrusion-based Optical Molding (POEM) , which integrates controlled hydrogel extrusion with spatially precise photopolymerization. Unlike conventional bioprinting approaches, POEM enables multi-material and multi-layer fabrication, ensuring higher structural complexity and biomimetic accuracy. To further enhance resolution and dynamic range, a dual-laser coherent illumination system is introduced, significantly improving feature definition, exposure control, and printing speed. Additionally, an adaptive segmentation control method is introduced to regulate photopolymerization kinetics, reducing diffusion-driven over-crosslinking while ensuring precise polymerization control, maintaining structural fidelity, and preserving cell viability. Experimental validation using GelMA and GelMA-PEGDA hydrogels demonstrates enhanced metabolic activity, long-term cell survival, and superior bioprinting accuracy, positioning this system as a next-generation biofabrication platform.In the second part of the dissertation optical methods for neural sensing have been studied. In particular, this dissertation presents a LiNbO₃-based optrode, that leverages electro-optic modulation for ultrasensitive, label-free detection of neural activity. Unlike electrode-based systems, which suffer from biofouling, electrical artifacts, and thermal effects, the proposed optrode enables real-time, multi-channel neural sensing with sub-microvolt resolution and broadband detection capabilities. The integration of coherent balanced detection and multiplexed sensing architectures enhances spatial selectivity and signal fidelity, providing a scalable, non-invasive optical neural interface for applications in brain-machine interfaces and large-scale neurophysiological research.By integrating high-resolution optical biofabrication with precise photonic neural sensing, this dissertation establishes a new photonic framework for scalable, high-fidelity biomedical applications. The proposed methodologies introduce high-speed, high-precision fabrication strategies while advancing real-time, label-free neural signal processing, paving the way for clinically viable biofabrication, enhanced neuroengineering technologies, and next-generation biophotonic systems.

English

ISBN: 9798314858752Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

3D bioprinting

Optical Techniques for Tissue Engineering and Massive Neural Sensing /
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520 $a The ability to precisely engineer biological tissues and decode neural activity at high resolution has the potential to transform regenerative medicine and neuro-engineering. However, current biofabrication techniques struggle to balance speed, resolution, biocompatibility, and the ability to fabricate multi-material, multi-layer structures essential for replicating complex tissue architectures. Meanwhile, conventional neural sensing methods remain limited by signal degradation, biological interference, and insufficient spatial resolution. This dissertation introduces novel photonic methodologies that overcome these challenges, enhancing 3D bioprinting precision and neural signal detection. By leveraging advanced optical systems, this work establishes scalable, high-fidelity solutions that push the boundaries of tissue engineering and neuroscience.The first part of the dissertation focuses on , an advanced high-resolution 3D bioprinting technique called Photocrosslinked Extrusion-based Optical Molding (POEM) , which integrates controlled hydrogel extrusion with spatially precise photopolymerization. Unlike conventional bioprinting approaches, POEM enables multi-material and multi-layer fabrication, ensuring higher structural complexity and biomimetic accuracy. To further enhance resolution and dynamic range, a dual-laser coherent illumination system is introduced, significantly improving feature definition, exposure control, and printing speed. Additionally, an adaptive segmentation control method is introduced to regulate photopolymerization kinetics, reducing diffusion-driven over-crosslinking while ensuring precise polymerization control, maintaining structural fidelity, and preserving cell viability. Experimental validation using GelMA and GelMA-PEGDA hydrogels demonstrates enhanced metabolic activity, long-term cell survival, and superior bioprinting accuracy, positioning this system as a next-generation biofabrication platform.In the second part of the dissertation optical methods for neural sensing have been studied. In particular, this dissertation presents a LiNbO₃-based optrode, that leverages electro-optic modulation for ultrasensitive, label-free detection of neural activity. Unlike electrode-based systems, which suffer from biofouling, electrical artifacts, and thermal effects, the proposed optrode enables real-time, multi-channel neural sensing with sub-microvolt resolution and broadband detection capabilities. The integration of coherent balanced detection and multiplexed sensing architectures enhances spatial selectivity and signal fidelity, providing a scalable, non-invasive optical neural interface for applications in brain-machine interfaces and large-scale neurophysiological research.By integrating high-resolution optical biofabrication with precise photonic neural sensing, this dissertation establishes a new photonic framework for scalable, high-fidelity biomedical applications. The proposed methodologies introduce high-speed, high-precision fabrication strategies while advancing real-time, label-free neural signal processing, paving the way for clinically viable biofabrication, enhanced neuroengineering technologies, and next-generation biophotonic systems.
546 $a English
590 $a School code: 0030
650 4 $a Engineering. $3 586835
650 4 $a Optics. $3 517925
650 4 $a Bioengineering. $3 657580
650 4 $a Polymer chemistry. $3 3173488
650 4 $a Biomedical engineering. $3 535387
653 $a 3D bioprinting
653 $a Digital light processing
653 $a Lithium niobate
653 $a Photopolymerization
653 $a Ring resonator modulators
653 $a Synaptic sensors
690 $a 0537
690 $a 0752
690 $a 0202
690 $a 0495
690 $a 0541
710 2 $a University of California, Irvine. $b Electrical and Computer Engineering. $e degree granting institution. $3 3824934
720 1 $a Boyraz, Ozdal $e degree supervisor.
720 1 $a Esfandyar-pour, Rahim $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-11B.
790 $a 0030
791 $a Ph.D.
792 $a 2025
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