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Channel-Hopping Sequence and Searching Algorithm for Rendezvous of Spectrum Sensing
Full text linkIn this paper, we propose a method for applying the p-ary m-sequence as a channel-searching pattern for rendezvous in the asymmetric channel model of cognitive radio. We mathematically analyzed and calculated the ETTR when the m-sequence is applied to the conventional scheme, and our simulation results demonstrated that the ETTR performance is significantly better than that of the JS algorithm. Furthermore, we introduced a new channel-searching scheme that maximizes the benefits of the m-sequence and proposed a method to adapt the generation of the m-sequence for use in the newly proposed scheme. We also derived the ETTR mathematically for the new scheme with the m-sequence and showed through simulations that the performance of the new scheme with the m-sequence is superior to that of the conventional scheme with the m-sequence. Notably, when there is only one common channel, the new scheme with the m-sequence achieved approximately four times the improvement in the ETTR compared to the conventional scheme. © 2024 by the authors.TRUEsciescopu
Self-attention network-based state of charge estimation for lithium-ion batteries with gapped temperature data
No full textThe accurate estimation of the state of charge (SOC), a critical indicator of the energy stored in lithium-ion batteries, is essential for ensuring reliable and safe battery management. The influence of temperature on the battery characteristics substantially affects the SOC estimation accuracy. Owing to the broad operational temperature range of batteries, it is vital to address the various temperature conditions. This study proposes a model structure for data-driven SOC estimation to enhance accuracy under diverse temperature conditions. The model leverages the analysis of the SOC characteristics derived from the measured data. The proposed structure incorporates parallel-connected self-attention and long-short-term memory modules, thus providing an innovative approach for effectively capturing intricate features in SOC estimation. This study primarily focused on evaluating the capability of the proposed model to achieve satisfactory SOC estimation for untrained temperature conditions when trained with gapped temperature data, thus emphasizing its practicality. To assess the feasibility of the proposed method, experiments were performed under a broad range of fixed and varying temperature conditions, including seasonal and daily changes. The experimental results demonstrated that the root-mean-square errors of the estimated SOC were 0.4101% and 1.5611% at fixed and time-varying temperatures, respectively, including the subzero ranges. These results highlight the robustness of the proposed model under various temperature conditions and its applicability to real-world battery operational temperatures. © 2024 Elsevier LtdFALSEsciescopu
A study on a soft tactile sensor for the incipient slip detection of a robotic hand
No full textTactile sensor, Surface slip, Strain, Soft material, DecouplingI. Introduction 1
II. Desing of the sensor 7
2.1 Slip detection method 7
2.2 Structure of the sensor 10
2.2.1 Design of the sensor 10
2.2.2 Optimal gap size of the sensor 14
2.2.3 Optimal internal structure of the sensor 19
2.2.4 Optimal shape of the sensor 22
2.3 Materials and methods 25
2.4 Verification of slip detecting capability 27
III. Performance test of the tactile sensor 31
3.1 Cross-coupling error of the sensor 31
3.2 Random angle sensor performance 40
3.3 Sensitivity and measurement range of the sensor 44
3.4 Slip detection verification experiment 46
3.5 Resistance drift of the sensor 54
IV. Conclusion 56
References 59
요 약 문 63MasterdCollectio
마이크로 오가노이드 간의 상호작용을 통한 약물 테스트를 위한 표준 96 -웰 기반의 다중 미세 장기 칩 플랫폼 개발
No full textMicrofluidic chip platform, Micro-organ, 3D culture, High-Throughput Screening, TranswellCurrent preclinical experiments for drug evaluation mainly rely on conventional two- dimensional cell cultures conducted in static environments that lack interaction with the external materials. This approach is inefficient and too simplistic, and often involves the use of animal models, which are costly and exhibit discrepancies in accuracy and similarity to humans. To overcome these limitations, the Body-on-a-Chip (BoC) technology using robust and flexible three-dimensional microtissues is emerging. BoC represents a microphysiological system that mimics the physiology and functionality of human organs in vitro, applied in drug development for toxicity screening and personalized medicine. Utilizing a 96-well based microfluidic chip, drug effects on multi-organs can be efficiently predicted and assessed. Applying the characteristic of drug metabolism by the liver upon administration in the body, it can demonstrate the conversion of the cardiac toxic compound Terfenadine into the non-cardiotoxic metabolite Fexofenadine within liver microtissues, proving physiological-pathological responses of cardiac microtissues. The development of an optimized liver-heart on a chip platform demonstrates multi-tissue functionality, emphasizing the significance of continuous media circulation and tissue interactions. Furthermore, a second platform was developed by integrating the Transwell system into the device. The membrane-separated interface allowed for the cultivation and study of various cell types, such as muscle and vascular cells. This resulted in a multifunctional, high- throughput drug screening platform capable of supporting 2D, 3D and 3D Transwell cell cultures. The functionality and efficacy of multi-micro-organ Transwell device were demonstrated through drug evaluation using 5-FU.|현재 약물 평가를 위한 전임상 실험은 정적인 환경에서 외부 물질과의 상호작용이 결여된 기존의 2차원 세포 배양에 의존하고 있다. 이러한 방법은 지나치게 단순하고 비효율적이며, 정확도와 구현성이 떨어진다. 또한 세포 배양 외에도 동물 실험이 진행되는데, 이러한 방법은 상당히 많은 비용이 들뿐만 아니라 인간과의 유사성에 차이가 나고, 윤리적인 문제도 가지고 있다. 이러한 한계를 극복하기 위해, 견고하고 유연한 3차원 미세조직을 사용하는 바디-온-어-칩 기술이 부상하고 있다. 바디-온-어-칩은 인간 장기의 생리학과 기능을 시험관 내에서 모사하는 미세 생리학적 시스템으로, 약물 독성 스크리닝 및 맞춤형 의료를 위한 약물 개발에 적용된다. 96-웰 기반의 미세유체 칩을 활용하여 다중 장기에 대한 약물의 효과를 효율적으로 예측하고 평가할 수 있다. 그에 대한 첫 번째 연구에서는, 체내 투여 시 간에 의한 약물 대사 특성을 활용하여 간-심장 칩 플랫폼에 대한 실험을 진행하였다. 심장 독성을 유발하는 화합물인 테르페나딘이 간 미세조직 내에서 비심장독성 대사체인 펙소페나딘으로 전환되는 과정을 시연함으로써 약물에 대한 심장 미세조직의 생리병리학적 반응을 입증하였다. 간-심장 칩 플랫폼의 최적화 과정을 통해 다중 조직 기능을 보여주며, 지속적인 배지 순환과 조직 간의 상호작용의 중요성을 강조한다.
나아가, 트렌스웰 시스템을 디바이스에 통합하여 두 번째 플랫폼이 연구되었다. 막으로 분리된 인터페이스를 통해 근육 및 혈관 세포와 같은 다양한 세포 유형의 배양과 실험이 가능한 도구를 개발하였다. 이로 인해 2차원, 3차원 및 3차원 트렌스웰 세포 배양을 지원할 수 있는 다기능 고처리량 약물 스크리닝 플랫폼이 완성되었다. 다중 미세 장기 트렌스웰 장치의 기능성과 효과는 항암제를 이용한 약물 평가를 통해 확인하였다.Ⅰ. Introduction 1
1.1 Background 1
1.1.1 Drug evaluation 1
1.1.2 Microphysiological systems, Organs-on-a chip 3
1.2 Research trends 5
1.2.1 Microfluidic device for drug screening 5
1.2.2 Structure and application of BoC 8
1.2.2.1 Single-organ-on-a-chip 9
1.2.2.2 Multi-organ-on-a-chip 11
1.3 Aims of this research 13
ⅠI. 96-well format-based liver-heart on a chip platform to investigate cardiotoxicity of drugs metabolized by a liver 17
2.1 Introduction 17
2.1.1 Background 17
2.1.2 Goal 19
2.2 Materials & Methods 20
2.2.1 Microfluidic chip design and fabrication 20
2.2.2 Tilting tower system for facile handling of the device and gravity-driven perfusion 23
2.2.3 Human hepatic and cardiac MTs fabrication 24
2.2.4 MT culture under static and perfusion conditions 26
2.2.5 Drug dose-response curve of individual MT 27
2.2.6 Pro-drug, TFND, bioactivation in the Liver-Heart on a chip 28
2.2.7 Morphological size measurement 28
2.2.8 Cell-based assay for viability 29
2.2.9 Biochemical assay for liver functionality 30
2.2.10 Beating measurement for cardiac functionality 31
2.2.11 Immune-fluorescence confocal imaging 31
2.3 Experimental results 33
2.3.1 Microfluidic device and setup 33
2.3.2 Drug dose-response tests for pro-drug bioactivation in the Liver-Heart on a chip 34
2.3.3 Toxicity characterization of TFND on LiMTs and CdMTs with liver bioactivation 37
2.4 Conclusion & Discussion 44
IⅠI. 96-well format-based multi-micro-organ chip platform with Transwell for parallel drug testing among multiple micro-organoids 46
3.1 Introduction 46
3.1.1 Background 46
3.1.2 Goal 48
3.2 Materials & Methods 49
3.2.1 Transwell-based microfluidic chip design and fabrication 49
3.2.2 Tilting tower or gravity driven perfusion system 50
3.2.3 Culturing of microtissues 50
3.2.4 Loading tissues on the insert and microfluidic device 52
3.2.5 Drug dose-response curve of tumor MT 53
3.2.6 5-FU drug activation in the Muscle/Vascular-Liver Cancer-Colon Cancer on a chip 54
3.3 Experimental results 54
3.3.1 Cell cultivation on the membrane filter 54
3.3.2 Transwell insert overflowing testing 55
3.3.3 Drug dose-response tests of tumor MT 56
3.3.4 Toxicity characterization of 5-FU on micro-organoids 56
3.4 Conclusion & Discussion 59
IV. Conclusion 61
4.1 Overall conclusion 61
4.2 Future work 63
4.2.1 Implementing a multi-micro-organ chip platform with BBB 63
4.2.2 Injection molding process for 96-well format-based microfluidic chip platform with mass production capability 64
4.2.3 Developing inserts with sensor that can implement cell contraction using Transwell-based platform 64
Reference 66
요약문 70MasterdCollectio
해수 내 방사성 세슘 제거를 위한 다양한 전이금속-프러시안 블루 유사체 개발 및 실용성 평가
No full textRadioactive cesium, Prussian blue analog, Cesium ion, Adsorption mechanism, Cesium desorption, Practical applicationList of Contents
Abstract i
List of contents iii
List of tables vii
List of figures ix
1. Research Background
1.1 Sources and risks of 137Cs 1
1.2 Researches of 137Cs removal 4
1.2.1 Methods of 137Cs removal 4
1.2.2 Materials of 137Cs removal 6
1.3 Limitations of current 137Cs removal research on PBAs 10
1.4 Research topics 12
2. Impact of Transition Metal in Corporation on the Adsorption Mechanism of Radioactive Cesium in Prussian Blue Analogs
2.1 Introduction 15
2.2 Experimental section 18
2.2.1 Preparation of PBAs 18
2.2.2 Characterization of PBAs 18
2.2.3 Cs+ adsorption experiment 20
2.3 Results and discussion 22
2.3.1 Physicochemical characteristics of PBAs 22
2.3.2 Cs+ adsorption on PBAs 35
2.3.3 Photoinduced enhancement of Cs+ adsorption on PBAs 43
2.4 Summary 49
3. A Novel Insight of ZnFe Prussian Blue Analogs for Enhanced Cesium Adsorption and Desorption Mechanism: Structural Transformation and Reusability
3.1 Introduction 51
3.2 Experimental section 55
3.2.1 Materials 55
3.2.2 Preparation of ZnFe samples 55
3.2.3 Characterization of ZnFe samples 58
3.2.4 Cs+ adsorption performance test 58
3.2.5 Cs+ desorption performance test 59
3.2.6 Reusability test of ZnFe samples 60
3.3 Results and discussion 61
3.3.1 Characterization of ZnFe samples 61
3.3.2 Cs+ adsorption behavior of ZnFe samples 67
3.3.3 Cs+ desorption behavior of ZnFe samples 77
3.3.4 Cs+ adsorption-desorption performance of ZnFe samples 82
3.4 Summary 84
4. Innovative Photoinduced Enhancement of 137Cs Removal Using NiFe Prussian Blue Analog-Alginate Hydrogel
4.1 Introduction 86
4.2 Experimental section 89
4.2.1 Materials 89
4.2.2 Preparation of NiFe-AH 89
4.2.3 Characterization of NiFe-AH 90
4.2.4 Cs+ adsorption experiments 92
4.2.5 Removal of 137Cs experiment 93
4.3 Results and discussion 94
4.3.1 Optimization of NiFe-AH synthesis 94
4.3.2 Physicochemical characteristics of NiFe-AH 97
4.3.3 Cs+ adsorption on NiFe-AH 100
4.3.4 Photoinduced enhancement of Cs+ adsorption experiment 105
4.3.5 137Cs removal performance evaluation in seawater 112
4.4 Summary 116
5. Instantaneous Cesium Removal from Seawater Using ZnFe Prussian blue embedded Electrospun Nanofiber Membrane
5.1 Introduction 118
5.2 Experimental section 121
5.2.1 Materials 121
5.2.2 Preparation of ZnFe-EFs 121
5.2.3 Characterization of ZnFe-EFs 123
5.2.4 Cs+ adsorption batch test 123
5.2.5 ZnFe-EF Cs+ filtration test 124
5.3 Results and discussion 125
5.3.1 Physicochemical properties of ZnFe-EFs 125
5.3.2 Cs+ adsorption batch test 128
5.3.3 ZnFe-EFs Cs+ filtration test 132
5.3.4 ZnFe-EF Cs+ filtration test from seawater 139
5.4 Summary 142
6. Reference 143DoctordCollectio
Method For Deep Transfer Learning-based Encrypted Data Classification and System Thereof
No full text본 발명은 심층 전이학습 기반 암호화된 데이터 분류 방법 및 그 시스템에 관한 것이다. 본 발명에 따르면, 제 1 데이터 세트를 암호화하는 단계; 암호화된 제 1 데이터 세트를 이용하여 딥러닝을 수행하여 제 1 학습 모델을 생성하는 단계; 제 2 데이터 세트를 암호화하는 단계; 및 암호화된 제 2 데이터 세트를 이용하여 상기 제 1 학습 모델을 튜닝하여 제 2 학습 모델을 생성하는 단계를 포함하고, 필요에 따라, 제 2 데이터 세트의 데이터 규모를 증강하는 단계를 더 포함할 수 있다. 이와 같이 본 발명에 따르면, 광학 기반의 암호화 방법인 이중 랜덤 위상 암호화 방법을 사용하여 동형암호와 같은 다른 암호화 방법에 비해 처리 속도가 향상되고, 클라우드에 전송 및 저장되는 데이터에 포함된 개인정보를 보호하면서 데이터를 분류할 수 있다. 또한, 실생활에서 획득한 데이터를 사용함으로써 복잡한 데이터에 대해서도 높은 분류 성능을 보일 수 있고, 타겟 데이터의 데이터 규모가 작은 경우, 데이터 증강을 적용하여 전이학습을 수행함으로써 분류가 가능할 수 있다
FUST: 집속 초음파 변환기를 이용한 푸리에-웨이블렛 정규화 역컨볼루션 기반 실시간 영상화 기법
No full textFocused Ultrasound(FUS), Deconvolution, Fourier-wavlet regularization deconvolution (ForWaRD)Ⅰ. INTRODUCTION 1
1.1 Focused Ultrasound 1
1.2 Image Guidance and Monitoring 2
1.3 System Integration for USgFUS 3
1.4 Objectives of Research 5
ⅠI. METHODS 6
2.1 Principles for Ultrasound Imaging with a FUS Transducer 6
2.2 Implementation Process 14
2.3 Simulation Study 16
2.4 Phantom Study 19
2.5 U-net based deep learning approach 21
ⅠII. RESULTS 25
3.1 Single wire Experiment in Water 25
3.2 Evaluation on Resolution 26
3.3 Evaluation on CNR 28
3.4 Evaluation Inference time 28
IV. CONCLUSION 30
V. REFERENCES 31MasterdCollectio
스파이크 신경망의 고효율 작동을 위한 하드웨어-소프트웨어 기술
No full textSpiking Neural Network, Neuromorphic, Deep Learning본 논문은 에너지 제약이 있는 환경에서 스파이크 신경망(SNN)을 위한 계산적으로 효율적인 접근 방식을 제안한다. 스파이킹 신경망(SNN)은 에너지 효율적인 뉴로모픽 컴퓨팅에 상당한 잠재력을 가지고 있지만, 소프트웨어 구현과 하드웨어 통합의 복잡성으로 인해 실제 애플리케이션에서 사용이 제한되고 있다. 본 논문은 스파이킹 신경망의 정확도 손실을 최소화하면서 모델 크기를 줄이는 방법과 각 기법에 최적화된 하드웨어 설계를 제안한다. 가장 먼저, 낭비되는 비활성 뉴런을 제거하여 분류 정확도 저하 없이 모델 크기를 4배로 줄이는 LSM (Liquid State Machine)의 pruning을 제안하고 FPGA에 구현하여 에너지 효율성을 증명하였다. 다음으로, LSM에서 뉴런 간의 시냅스 중 가장 관련성이 높은 시냅스를 동적으로 선택하는 SA-STL 학습 알고리즘을 제시하였다. FPGA에서 구현된 이 접근 방식은 더 적은 하드웨어 리소스로 실시간 스파이크 재생산을 가능하게 했다. 또한 단일 스파이크 위상 코딩을 활용하여 컨볼루션 신경망(CNN)을 스파이크 신경망으로 효율적으로 변환하여 에너지 효율을 최대 17.26 배까지 증가시켰다. 이에 더불어 구조적 수정 없이 트랜스포머를 원 스파이크 SNN 으로 변환하는 방법도 제안하여 에너지 효율은 2 배이상 증가시켰다.마지막으로, 극히 희박한 SNN의 성능을 개선하기 위해 최적화된 SpMM(Sparse Matrix-Matrix) 가속기의 설계와 에너지 소모를 줄이는 방법을 제시한다. 저희가 제시한 에너지 효율을 높이는 방법은 탄소 배출을 줄이고 에너지 제약이 있는 디바이스에서 SNN을 더욱 실용적으로 사용할 수 있게 한다.
핵심어: 뉴로모픽, 스파이크 신경망, 인공지능 경량화, 희소행렬 가속기, FPGA|Spiking neural networks (SNNs) have significant potential for energy-efficient neuromorphic computing, but the complexity of software implementation and hardware integration limits their adoption in real-world applications. Our thesis introduces a computationally efficient approach for Spiking Neural Networks (SNNs) in energy-constrained environments. We developed methods to reduce the model size with minimal accuracy loss and optimized hardware designs for each technique. First, we proposed a pruning strategy for Liquid State Machines (LSMs) to remove inactive neurons, achieving a 4× model size reduction without compromising classification accuracy. We also implemented a look-up table (LUT) based LSM on FPGA, significantly enhancing performance compared to CPU/GPU-based methods. Next, we presented the SA-STL learning algorithm to prune recurrent connections in liquid neurons, dynamically selecting the most relevant synapses. This approach, implemented on FPGA, allowed real-time spike reproduction with fewer hardware resources. Furthermore, we proposed an efficient conversion method for Convolutional Neural Networks (CNNs) into SNNs, leveraging single-spike phase coding to increase energy efficiency by up to 17.26× while maintaining accuracy on benchmarks such as CIFAR-10, CIFAR-100, and ImageNet. We also proposed converting transformers into one-spike SNNs, using spike-based computations to enhance energy efficiency without structural modifications. Lastly, we designed an optimized Sparse Matrix-Matrix (SpMM) accelerator based on Gustavson product to improve the performance of highly sparse SNNs, making it ideal for edge devices. Future work will focus on converting complex models like Large Language Models (LLMs) into SNNs and optimizing training and hardware modules for further improvements. In summary, our pruning and energy-efficient techniques make SNNs more viable for energy-constrained devices, without increasing the carbon footprint.
Keywords: Neuromorphic, Spiking Neural Network, Lightweighting AI, Sparse Matrix Multiplication Accelerator, FPGAⅠ. Introduction 1
1.1 Spiking Neural Network 1
1.2 Neuron Model 4
1.3 Spike Coding 5
1.4 SNN Architecture 6
1.5 Training rule of SNN 9
1.6 ANN-to-SNN Conversion 12
1.7 Overview of Thesis 14
ⅠI. Optimization of SNN Connectivity 16
2.1 Background 16
2.2 Adaptive Re-connection of Synapse 17
2.3 STDP assisted Spike Timing Learning (SA-STL) 22
ⅠII. ANN-to-SNN Conversion with Single Spike 39
3.1 Background 39
3.2 One-Spike SNN for Convolution Neural Network 40
3.3 SpikedAttention for Transformer 56
3.4 SpikedGustavson: SpMM Accelerator for SNN 73
ⅠV Conclusion 85DoctordCollectio
압전 재료 기반 음향 디바이스 제작 및 특성평가
No full textPiezoelectric acoustic device, Transparent piezoelectric ceramic, PLZT, MEMS, Ultrasound imaging, PMUTⅠ. INTRODUCTION 1
1.1 Research Background 1
1.2 Piezoelectricity 2
Ⅱ. TRANSPARENT PIEZOELECTRIC LOUDSPEAKER 5
2.1 Introduction 5
2.1.1 Transparent electronics 5
2.1.2 Transparent piezoelectric materials 6
2.1.3 Piezoelectric loudspeaker 8
2.1.4 Research objective 9
2.2 Materials and Methods 11
2.2.1 Fabrication process of PLZT ceramics 11
2.3. Result and discussion 13
2.3.1 Material properties of PLZT ceramics 13
2.3.2 A transparent piezoelectric PLZT transducer with a glass diaphragm 17
2.3.3 A piezoelectric transparent PLZT loudspeaker with a PET diaphragm 20
Ⅲ. MULTI FREQUENCY PIEZOELECTRIC MICROMACHINED ULTRASOUND TRANSDUCER (PMUT) 25
3.1 Introduction 25
3.1.1 Background 25
3.1.2 Ultrasound transducer 26
3.1.3 Advantage of Broadband ultrasound transducer for ultrasound imaging 27
3.1.4 State of art for improving bandwidth of PMUT 28
3.1.5 Research objective 30
3.2 Design and fabrication 31
3.2.1 Design of a single cell PMUT 31
3.2.2 Design of Multi Frequency PMUT array 34
3.2.3 Fabrication process of Multifrequency PMUT 35
3.3 Characterization of Multifrequency PMUT 37
3.3.1 Image and Material properties of PMUT 37
3.3.2 Mechanical characterization of PMUT in the air 40
3.3.3 Acoustic characterization of PMUT in the water 42
Ⅳ. CONCLUSION 46
REFERENCES 48
요약문 54MasterdCollectio
에너지 하베스팅과 연속 혈당 모니터링을 위한 장기간 사용 가능 이식형 전자기기
No full textimplantable electronics, electrochemical biosensor, continuous glucose monitoring, energy harvesting, piezoelectric nanogeneratorAlong with advancements in cutting-edge technologies, research on implantable electronics has been actively progressing, covering a wide range of applications from physiological signal monitoring and disease treatment to human augmentation. These technologies play a significant role not only in medical and healthcare fields but also in future-oriented applications such as humanoid robots and cyborg systems. Despite recent advances, conventional implantable electronics face limitations in long-term use, particularly due to their dependence on external power sources and the frequent need for component replacement. Furthermore, biological environments inside the body, such as continuous exposure to fluids and mechanical stress, accelerate the degradation of components, complicating the long-term stability of these devices. These combined factors not only limit device performance but also increase maintenance frequency, presenting significant challenges for critical applications. Therefore, achieving a self-sustained power supply and ensuring the long-term stability of components are essential for advancing these devices.
This dissertation explores two key technological strategies aimed at overcoming these challenges, focusing on the development of implantable nanogenerators for sustainable power generation and the creation of systems for long-term continuous glucose monitoring. The first research enhances the energy efficiency of an implantable nanogenerator to ensure a sustainable power supply, while the second research develops biosensors that enable long-term monitoring. To improve the energy efficiency of a stretchable nanogenerator, an electrode structure was designed to be compatible with three-dimensional architectures and inorganic piezoelectric materials, maximizing energy conversion efficiency and enhancing applicability in diverse biological environments. This development mitigates inefficiencies in existing electrodes, enabling continuous energy harvesting. A biosensor system for long-term continuous glucose monitoring was developed by integrating a bioresorbable polymer layer into the working electrodes. This polymer layer, designed for controlled degradation, sequentially exposes multiple electrodes, thereby maintaining enzyme stability initially and activating it when needed. The system’s long-term stability and functionality were validated through in-vitro studies and in-vivo experiments, demonstrating the potential for practical long-term use.
|최첨단 기술의 발전과 함께, 이식형 전자기기에 대한 연구는 생리 신호 모니터링, 질병 치료, 인간 기능 증강 등 다양한 응용 분야에서 활발히 진행되고 있습니다. 이러한 기술들은 의료 및 헬스케어 분야뿐만 아니라 휴머노이드 로봇, 사이보그 시스템과 같은 미래 지향적 응용에도 중요한 역할을 합니다. 그러나 기존의 이식형 전자기기는 외부 전력 공급에 의존하고, 부품 교체가 빈번하게 이루어져 장기적인 사용에 한계를 겪고 있습니다. 또한, 체내의 생체 환경에서 지속적인 체액 노출과 기계적 스트레스는 부품 열화를 가속화하여 장기적 안정성을 저해합니다. 이러한 요인들은 장치의 성능을 제한할 뿐만 아니라 유지 보수 빈도를 증가시켜 중요한 응용 분야에서 상당한 도전을 야기합니다. 따라서 자가 전력 공급을 달성하고, 부품의 장기적인 안정성을 보장하는 것이 이러한 장치를 발전시키는 데 필수적입니다.
이 논문은 이러한 문제를 해결하기 위한 두 가지 주요 기술 전략을 탐구하며, 지속 가능한 전력 생성을 위한 이식형 나노발전기 개발과 장기 연속 혈당 모니터링 시스템 구축에 중점을 둡니다. 첫 번째 연구는 이식형 나노발전기의 에너지 효율을 향상시켜 지속 가능한 전력 공급을 보장하는 것이며, 두 번째 연구는 장기 모니터링을 가능하게 하는 바이오센서를 개발하는 것입니다. 신축성이 있는 나노발전기의 에너지 효율을 개선하기 위해, 전극 구조는 3차원 구조와 무기 압전 물질에 적합하게 설계되어 에너지 변환 효율을 극대화하고 다양한 생물학적 환경에서의 적용 가능성을 높였습니다. 이 개발은 기존 전극의 비효율성을 완화하여 지속적인 에너지 수확을 가능하게 합니다. 장기 연속 혈당 모니터링을 위한 바이오센서 시스템은 생분해성 고분자층을 작업 전극에 통합하여 개발되었습니다. 이 고분자층은 제어된 분해를 통해 다수의 전극을 순차적으로 노출하여, 초기에 효소의 안정성을 유지하고 필요한 경우 효소를 활성화합니다. 시스템의 장기 안정성과 기능성은 철저한 검증 실험과 돼지를 활용한 생체 내 실험을 통해 검증되었으며, 실용적인 장기 사용 가능성을 입증했습니다.List of Contents
Abstract i
List of contents ii
List of figures and tables ⅳ
Ⅰ. Introduction
1.1 Background of implantable electronics 1
1.2 Importance and applications in healthcare 1
1.3 Challenges in long-term operation 2
1.3.1 Power supply challenges 2
1.3.2 Durability and stability challenges 3
1.4 Sustainable power supply 3
1.4.1 Self-sustaining power necessity 3
1.4.2 Nanogenerators for energy harvesting 4
1.4.3 Comparison with wireless power transfer (WPT) 4
1.5 Challenges and approaches for stability 5
1.5.1 Role of biosensors in implantable devices 5
1.5.2 Stability in biological environments 5
1.5.3 Strategies for enhancing durability and reliability 6
1.6 Contributions of the dissertation 6
1.6.1 Addressing the power supply challenge 6
1.6.2 Addressing the stability challenge for long-term operation 7
II. Results
2.1 Curvature-specific coupling electrodes design for stretchable three-dimensional
inorganic piezoelectric nanogenerator 8
2.1.1 Principle and design of S-PENG 13
2.1.2 Fabrication process of S-PENG 21
2.1.3 Experimental demonstration of the curvature-specific electrode design 30
2.1.4 On-body applications of the S-PENG 46
2.1.5 Energy harvesting in animal model 47
2.1.6 Conclusion 53
2.2 Long-term glucose monitoring with bioresorbable polymer-coated electrodes 55
2.2.1 Principle and working principle of long-term CGM 60
2.2.2 Fabrication and characteristics of the glucose sensor 67
2.2.3 Preparation and characteristics of the bioresorbable polyurethane (b-PU) 94
2.2.4 In-vitro demonstration of long-term glucose monitoring with b-PU· 107
2.2.5 In-vivo experiments of the Long-term CGM 109
2.2.6 Conclusion 113
III. Conclusion
References
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