2 research outputs found
๊ฐ๋ณ ์ด์จ ๋ฐ ์๋ฌผ ์์ก ์ผ์ฑ ๊ธฐ๋ฐ์ ์ ๋ฐ ์๊ฒฝ์ฌ๋ฐฐ ์์ก ๊ด๋ฆฌ ์์คํ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ) -- ์์ธ๋ํ๊ต ๋ํ์ : ๋์
์๋ช
๊ณผํ๋ํ ๋ฐ์ด์ค์์คํ
ยท์์ฌํ๋ถ(๋ฐ์ด์ค์์คํ
๊ณตํ), 2020. 8. ๊นํ์ง.In current closed hydroponics, the nutrient solution monitoring and replenishment are conducted based on the electrical conductivity (EC) and pH, and the fertigation is carried out with the constant time without considering the plant status. However, the EC-based management is unable to detect the dynamic changes in the individual nutrient ion concentrations so the ion imbalance occurs during the iterative replenishment, thereby leading to the frequent discard of the nutrient solution. The constant time-based fertigation inevitably induces over- or under-supply of the nutrient solution for the growing plants. The approaches are two of the main causes of decreasing water and nutrient use efficiencies in closed hydroponics. Regarding the issues, the precision nutrient solution management that variably controls the fertigation volume and corrects the deficient nutrient ions individually would allow both improved efficiencies of fertilizer and water use and increased lifespan of the nutrient solution. The objectives of this study were to establish the precision nutrient solution management system that can automatically and variably control the fertigation volume based on the plant-growth information and supply the individual nutrient fertilizers in appropriate amounts to reach the optimal compositions as nutrient solutions for growing plants. To achieve the goal, the sensing technologies for the varying requirements of water and nutrients were investigated and validated. Firstly, an on-the-go monitoring system was constructed to monitor the lettuces grown under the closed hydroponics based on the nutrient film technique for the entire bed. The region of the lettuces was segmented by the excess green (ExG) and Otsu method to obtain the canopy cover (CC). The feasibility of the image processing for assessing the canopy (CC) was validated by comparing the computed CC values with the manually analyzed CC values. From the validation, it was confirmed the image monitoring and processing for the CC measurements were feasible for the lettuces before harvest. Then, a transpiration rate model using the modified Penman-Monteith equation was fitted based on the obtained CC, radiation, air temperature, and relative humidity to estimate the water need of the growing lettuces. Regarding the individual ion concentration measurements, two-point normalization, artificial neural network, and a hybrid signal processing consisting of the two-point normalization and artificial neural network were compared to select an effective method for the ion-selective electrodes (ISEs) application in continuous and autonomous monitoring of ions in hydroponic solutions. The hybrid signal processing showed the most accuracy in sample measurements, but the vulnerability to the sensor malfunction made the two-point normalization method with the most precision would be appropriate for the long-term monitoring of the nutrient solution. In order to determine the optimal injection amounts of the fertilizer salts and water for the given target individual ion concentrations, a decision tree-based dosing algorithm was designed. The feasibility of the dosing algorithm was validated with the stepwise and varying target focusing replenishments. From the results, the ion-specific replenishments formulated the compositions of the nutrient solution successfully according to the given target values. Finally, the proposed sensing and control techniques were integrated to implement the precision nutrient solution management, and the performance was verified by a closed lettuce cultivation test. From the application test, the fertigation volume was reduced by 57.4% and the growth of the lettuces was promoted in comparison with the constant timer-based fertigation strategy. Furthermore, the system successfully maintained the nutrient balance in the recycled solution during the cultivation with the coefficients of variance of 4.9%, 1.4%, 3.2%, 5.2%, and 14.9%, which were generally less than the EC-based replenishment with the CVs of 6.9%, 4.9%, 23.7%, 8.6%, and 8.3% for the NO3, K, Ca, Mg, and P concentrations, respectively. These results implied the developed precision nutrient solution management system could provide more efficient supply and management of water and nutrients than the conventional methods, thereby allowing more improved water and nutrient use efficiencies and crop productivity.ํ์ฌ์ ์ํ์ ์๊ฒฝ์ฌ๋ฐฐ ์์คํ
์์ ์์ก์ ๋ถ์๊ณผ ๋ณด์ถฉ์ ์ ๊ธฐ์ ๋๋ (EC, electrical conductivity) ๋ฐ pH๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ์ํ๋๊ณ ์์ผ๋ฉฐ, ์์ก์ ๊ณต๊ธ์ ์๋ฌผ์ ์์ก ์ํ์ ๋ํ ๊ณ ๋ ค ์์ด ํญ์ ์ผ์ ํ ์๊ฐ ๋์ ํํ๊ฐ ๋์ํ์ฌ ๊ณต๊ธ๋๋ ํํ์ด๋ค. ๊ทธ๋ฌ๋ EC ๊ธฐ๋ฐ์ ์์ก ๊ด๋ฆฌ๋ ๊ฐ๋ณ ์ด์จ ๋๋์ ๋์ ์ธ ๋ณํ๋ฅผ ๊ฐ์งํ ์ ์์ด ๋ฐ๋ณต๋๋ ๋ณด์ถฉ ์ค ๋ถ๊ท ํ์ด ๋ฐ์ํ๊ฒ ๋์ด ์์ก์ ํ๊ธฐ๋ฅผ ์ผ๊ธฐํ๋ฉฐ, ๊ณ ์ ๋ ์๊ฐ ๋์์ ์์ก ๊ณต๊ธ์ ์๋ฌผ์ ๋ํด ๊ณผ์ ๋๋ ๋ถ์ถฉ๋ถํ ๋ฌผ ๊ณต๊ธ์ผ๋ก ์ด์ด์ ธ ๋ฌผ ์ฌ์ฉ ํจ์จ์ ์ ํ๋ฅผ ์ผ์ผํจ๋ค. ์ด๋ฌํ ๋ฌธ์ ๋ค์ ๋ํด, ๊ฐ๋ณ ์ด์จ ๋๋์ ๋ํด ๋ถ์กฑํ ์ฑ๋ถ๋ง์ ์ ํ์ ์ผ๋ก ๋ณด์ถฉํ๊ณ , ์๋ฌผ์ ์์ก ์ ๋์ ๊ธฐ๋ฐํ์ฌ ํ์ํ ์์ค์ ๋ง๊ฒ ์์ก์ ๊ณต๊ธํ๋ ์ ๋ฐ ๋์
์ ๊ธฐ๋ฐํ ์์ก ๊ด๋ฆฌ๋ฅผ ์ํํ๋ฉด ๋ฌผ๊ณผ ๋น๋ฃ ์ฌ์ฉ ํจ์จ์ ํฅ์๊ณผ ์์ก์ ์ฌ์ฌ์ฉ ๊ธฐ๊ฐ ์ฆ์ง์ ๊ธฐ๋ํ ์ ์๋ค. ๋ณธ ์ฐ๊ตฌ์ ๋ชฉ์ ์ ์๋์ผ๋ก, ๊ทธ๋ฆฌ๊ณ ๊ฐ๋ณ์ ์ผ๋ก ์๋ฌผ ์์ก ์ ๋ณด์ ๊ธฐ๋ฐํ์ฌ ์์ก ๊ณต๊ธ๋์ ์ ์ดํ๊ณ , ์๋ฌผ ์์ฅ์ ์ ํฉํ ์กฐ์ฑ์ ๋ง๊ฒ ํ์ฌ ์์ก์ ์ด์จ ๋๋ ์ผ์ฑ์ ๊ธฐ๋ฐํ์ฌ ์ ์ ํ ์์ค๋งํผ์ ๋ฌผ๊ณผ ๊ฐ๋ณ ์๋ถ ๋น๋ฃ๋ฅผ ๋ณด์ถฉํ ์ ์๋ ์ ๋ฐ ์๊ฒฝ์ฌ๋ฐฐ ์์ก ๊ด๋ฆฌ ์์คํ
์ ๊ฐ๋ฐํ๋ ๊ฒ์ด๋ค. ํด๋น ๋ชฉํ๋ฅผ ๋ฌ์ฑํ๊ธฐ ์ํด, ๋ณ์ดํ๋ ๋ฌผ๊ณผ ์๋ถ ์๊ตฌ๋์ ์ธก์ ํ ์ ์๋ ๋ชจ๋ํฐ๋ง ๊ธฐ์ ๋ค์ ๋ถ์ํ๊ณ ๊ฐ ๋ชจ๋ํฐ๋ง ๊ธฐ์ ๋ค์ ๋ํ ๊ฒ์ฆ์ ์ํํ์๋ค. ๋จผ์ , ์๋ฌผ์ ๋ฌผ ์๊ตฌ๋์ ์ค์๊ฐ์ผ๋ก ๊ด์ธกํ ์ ์๋ ์์ ๊ธฐ๋ฐ ์ธก์ ๊ธฐ์ ์ ์กฐ์ฌํ์๋ค. ์์ ๊ธฐ๋ฐ ๋ถ์ ํ์ฉ์ ์ํด ๋ฐ๋ง๊ฒฝ ๊ธฐ๋ฐ์ ์ํ์ ์๊ฒฝ์ฌ๋ฐฐ ํ๊ฒฝ์์ ์๋ผ๋ ์์ถ์ ์ด๋ฏธ์ง๋ค์ ์ ์ฒด ๋ฒ ๋์ ๋ํด ์์งํ ์ ์๋ ์์ ๋ชจ๋ํฐ๋ง ์์คํ
์ ๊ตฌ์ฑํ์๊ณ , ์์งํ ์์ ์ค ์์ถ ๋ถ๋ถ๋ง์ excess green (ExG)๊ณผ Otsu ๋ฐฉ๋ฒ์ ํตํด ๋ถ๋ฆฌํ์ฌ ํฌ์์๋ฌผ๋ฉด์ (CC, canopy cover)์ ํ๋ํ์๋ค. ์์ ์ฒ๋ฆฌ ๊ธฐ์ ์ ์ ์ฉ์ฑ ํ๊ฐ๋ฅผ ์ํด ์ง์ ๋ถ์ํ ํฌ์์๋ฌผ๋ฉด์ ๊ฐ๊ณผ ์ด๋ฅผ ๋น๊ตํ์๋ค. ๋น๊ต ๊ฒ์ฆ ๊ฒฐ๊ณผ์์ ํฌ์์๋ฌผ๋ฉด์ ์ธก์ ์ ์ํ ์์ ์์ง ๋ฐ ๋ถ์์ด ์ํ ์ ๊น์ง์ ์์ถ์ ๋ํด ์ ์ฉ ๊ฐ๋ฅํจ์ ํ์ธํ์๋ค. ์ดํ ์์งํ ํฌ์์๋ฌผ๋ฉด์ ๊ณผ ๊ธฐ์จ, ์๋์ต๋, ์ผ์ฌ๋์ ๊ธฐ๋ฐ์ผ๋ก ์์ก ์ค์ธ ์์ถ๋ค์ด ์๊ตฌํ๋ ๋ฌผ์ ์์ ์์ธกํ๊ธฐ ์ํด Penman-Monteith ๋ฐฉ์ ์ ๊ธฐ๋ฐ์ ์ฆ์ฐ๋ ์์ธก ๋ชจ๋ธ์ ๊ตฌ์ฑํ์์ผ๋ฉฐ ์ค์ ์ฆ์ฐ๋๊ณผ ๋น๊ตํ์์ ๋ ๋์ ์ผ์น๋๋ฅผ ํ์ธํ์๋ค. ๊ฐ๋ณ ์ด์จ ๋๋ ์ธก์ ๊ณผ ๊ด๋ จํ์ฌ์๋, ์ด์จ์ ํ์ฑ์ ๊ทน (ISE, ion-selective electrode)๋ฅผ ์ด์ฉํ ์๊ฒฝ์ฌ๋ฐฐ ์์ก ๋ด ์ด์จ์ ์ฐ์์ ์ด๊ณ ์์จ์ ์ธ ๋ชจ๋ํฐ๋ง ์ํ์ ์ํด 2์ ์ ๊ทํ, ์ธ๊ณต์ ๊ฒฝ๋ง, ๊ทธ๋ฆฌ๊ณ ์ด ๋์ ๋ณตํฉ์ ์ผ๋ก ๊ตฌ์ฑํ ํ์ด๋ธ๋ฆฌ๋ ์ ํธ ์ฒ๋ฆฌ ๊ธฐ๋ฒ์ ์ฑ๋ฅ์ ๋น๊ตํ์ฌ ๋ถ์ํ์๋ค. ๋ถ์ ๊ฒฐ๊ณผ, ํ์ด๋ธ๋ฆฌ๋ ์ ํธ ์ฒ๋ฆฌ ๋ฐฉ์์ด ๊ฐ์ฅ ๋์ ์ ํ์ฑ์ ๋ณด์์ผ๋, ์ผ์ ๊ณ ์ฅ์ ์ทจ์ฝํ ์ ๊ฒฝ๋ง ๊ตฌ์กฐ๋ก ์ธํด ์ฅ๊ธฐ๊ฐ ๋ชจ๋ํฐ๋ง ์์ ์ฑ์ ์์ด์๋ ๊ฐ์ฅ ๋์ ์ ๋ฐ๋๋ฅผ ๊ฐ์ง 2์ ์ ๊ทํ ๋ฐฉ์์ ์ผ์ ์ด๋ ์ด์ ์ ์ฉํ๋ ๊ฒ์ด ์ ํฉํ ๊ฒ์ผ๋ก ํ๋จํ์๋ค. ๋ํ, ์ฃผ์ด์ง ๊ฐ๋ณ ์ด์จ ๋๋ ๋ชฉํ๊ฐ์ ๋ง๋ ๋น๋ฃ ์ผ ๋ฐ ๋ฌผ์ ์ต์ ์ฃผ์
๋์ ๊ฒฐ์ ํ๊ธฐ ์ํด ์์ฌ๊ฒฐ์ ํธ๋ฆฌ ๊ตฌ์กฐ์ ๋น๋ฃ ํฌ์
์๊ณ ๋ฆฌ์ฆ์ ์ ์ํ์๋ค. ์ ์ํ ๋น๋ฃ ํฌ์
์๊ณ ๋ฆฌ์ฆ์ ํจ๊ณผ์ ๋ํด์๋ ์์ฐจ์ ์ธ ๋ชฉํ์ ๋ํ ๋ณด์ถฉ ๋ฐ ํน์ ์ฑ๋ถ์ ๋ํด ์ง์ค์ ์ธ ๋ณํ๋ฅผ ๋ถ์ฌํ ๋ณด์ถฉ ์ํ ์คํ์ ํตํด ๊ฒ์ฆํ์์ผ๋ฉฐ, ๊ทธ ๊ฒฐ๊ณผ ์ ์ํ ์๊ณ ๋ฆฌ์ฆ์ ์ฃผ์ด์ง ๋ชฉํ๊ฐ๋ค์ ๋ฐ๋ผ ์ฑ๊ณต์ ์ผ๋ก ์์ก์ ์กฐ์ฑํ์์์ ํ์ธํ์๋ค. ๋ง์ง๋ง์ผ๋ก, ์ ์๋์๋ ์ผ์ฑ ๋ฐ ์ ์ด ๊ธฐ์ ๋ค์ ํตํฉํ์ฌ NFT ๊ธฐ๋ฐ์ ์ํ์ ์๊ฒฝ์ฌ๋ฐฐ ๋ฐฐ๋์ ์์ถ ์ฌ๋ฐฐ๋ฅผ ์ํํ์ฌ ์ค์ฆํ์๋ค. ์ค์ฆ ์คํ์์, ์ข
๋์ ๊ณ ์ ์๊ฐ ์์ก ๊ณต๊ธ ๋๋น 57.4%์ ์์ก ๊ณต๊ธ๋ ๊ฐ์์ ์์ถ ์์ก์ ์ด์ง์ ํ์ธํ์๋ค. ๋์์, ๊ฐ๋ฐ ์์คํ
์ NO3, K, Ca, Mg, ๊ทธ๋ฆฌ๊ณ P์ ๋ํด ๊ฐ๊ฐ 4.9%, 1.4%, 3.2%, 5.2%, ๊ทธ๋ฆฌ๊ณ 14.9% ์์ค์ ๋ณ๋๊ณ์ ์์ค์ ๋ณด์ฌ EC๊ธฐ๋ฐ ๋ณด์ถฉ ๋ฐฉ์์์ ๋ํ๋ ๋ณ๋๊ณ์ 6.9%, 4.9%, 23.7%, 8.6%, ๊ทธ๋ฆฌ๊ณ 8.3%๋ณด๋ค ๋์ฒด์ ์ผ๋ก ์ฐ์ํ ์ด์จ ๊ท ํ ์ ์ง ์ฑ๋ฅ์ ๋ณด์๋ค. ์ด๋ฌํ ๊ฒฐ๊ณผ๋ค์ ํตํด ๊ฐ๋ฐ ์ ๋ฐ ๊ด๋น ์์คํ
์ด ๊ธฐ์กด๋ณด๋ค ํจ์จ์ ์ธ ์์ก์ ๊ณต๊ธ๊ณผ ๊ด๋ฆฌ๋ฅผ ํตํด ์์ก ์ด์ฉ ํจ์จ์ฑ๊ณผ ์์ฐ์ฑ์ ์ฆ์ง์ ๊ธฐ์ฌํ ์ ์์ ๊ฒ์ผ๋ก ํ๋จ๋์๋ค.CHAPTER 1. INTRODUCTION 1
BACKGROUND 1
Nutrient Imbalance 2
Fertigation Scheduling 3
OBJECTIVES 7
ORGANIZATION OF THE DISSERTATION 8
CHAPTER 2. LITERATURE REVIEW 10
VARIABILITY OF NUTRIENT SOLUTIONS IN HYDROPONICS 10
LIMITATIONS OF CURRENT NUTRIENT SOLUTION MANAGEMENT IN CLOSED HYDROPONIC SYSTEM 11
ION-SPECIFIC NUTRIENT MONITORING AND MANAGEMENT IN CLOSED HYDROPONICS 13
REMOTE SENSING TECHNIQUES FOR PLANT MONITORING 17
FERTIGATION CONTROL METHODS BASED ON REMOTE SENSING 19
CHAPTER 3. ON-THE-GO CROP MONITORING SYSTEM FOR ESTIMATION OF THE CROP WATER NEED 21
ABSTRACT 21
INTRODUCTION 21
MATERIALS AND METHODS 23
Hydroponic Growth Chamber 23
Construction of an On-the-go Crop Monitoring System 25
Image Processing for Canopy Cover Estimation 29
Evaluation of the CC Calculation Performance 32
Estimation Model for Transpiration Rate 32
Determination of the Parameters of the Transpiration Rate Model 33
RESULTS AND DISCUSSION 35
Performance of the CC Measurement by the Image Monitoring System 35
Plant Growth Monitoring in Closed Hydroponics 39
Evaluation of the Crop Water Need Estimation 42
CONCLUSIONS 46
CHAPTER 4. HYBRID SIGNAL-PROCESSING METHOD BASED ON NEURAL NETWORK FOR PREDICTION OF NO3, K, CA, AND MG IONS IN HYDROPONIC SOLUTIONS USING AN ARRAY OF ION-SELECTIVE ELECTRODES 48
ABSTRACT 48
INTRODUCTION 49
MATERIALS AND METHODS 52
Preparation of the Sensor Array 52
Construction and Evaluation of Data-Processing Methods 53
Preparation of Samples 57
Procedure of Sample Measurements 59
RESULTS AND DISCUSSION 63
Determination of the Artificial Neural Network (ANN) Structure 63
Evaluation of the Processing Methods in Training Samples 64
Application of the Processing Methods in Real Hydroponic Samples 67
CONCLUSIONS 72
CHAPTER 5. DECISION TREE-BASED ION-SPECIFIC NUTRIENT MANAGEMENT ALGORITHM FOR CLOSED HYDROPONICS 74
ABSTRACT 74
INTRODUCTION 75
MATERIALS AND METHODS 77
Decision Tree-based Dosing Algorithm 77
Development of an Ion-Specific Nutrient Management System 82
Implementation of Ion-Specific Nutrient Management with Closed-Loop Control 87
System Validation Tests 89
RESULTS AND DISCUSSION 91
Five-stepwise Replenishment Test 91
Replenishment Test Focused on The Ca 97
CONCLUSIONS 99
CHAPTER 6. ION-SPECIFIC AND CROP GROWTH SENSING BASED NUTRIENT SOLUTION MANAGEMENT SYSTEM FOR CLOSED HYDROPONICS 101
ABSTRACT 101
INTRODUCTION 102
MATERIALS AND METHODS 103
System Integration 103
Implementation of the Precision Nutrient Solution Management System 106
Application of the Precision Nutrient Solution Management System to Closed Lettuce Soilless Cultivation 112
RESULTS AND DISCUSSION 113
Evaluation of the Plant Growth-based Fertigation in the Closed Lettuce Cultivation 113
Evaluation of the Ion-Specific Management in the Closed Lettuce Cultivation 118
CONCLUSIONS 128
CHAPTER 7. CONCLUSIONS 130
CONCLUSIONS OF THE STUDY 130
SUGGESTIONS FOR FUTURE STUDY 134
LIST OF REFERENCES 136
APPENDIX 146
A1. Python Code for Controlling the Image Monitoring and CC Calculation 146
A2. Ion Concentrations of the Solutions used in Chapter 4 (Unit: mgโLโ1) 149
A3. Block Diagrams of the LabVIEW Program used in Chapter 4 150
A4. Ion Concentrations of the Solutions used in Chapters 5 and 6 (Unit: mgโLโ1) 154
A5. Block Diagrams of the LabVIEW Program used in the Chapters 5 and 6 155
ABSTRACT IN KOREAN 160Docto
๋ง ๋ผ๋ฅผ ์ด์ฉํ ์ฒ์ฐ ์ธ์ฐ์นผ์๊ณ ์์ฒด์์ฌ ๊ฐ๋ฐ
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๋ฐ์ด์ค์์คํ
ยท์์ฌํ๋ถ(๋ฐ์ด์ค์์คํ
๊ณตํ), 2014. 2. ์ ์ข
ํ.์ธ์ฐ ์นผ์์ ๋ผ์ ์น์ ๋ฑ์ ์ธ์ฒด ๊ฒฝ์กฐ์ง์ ์ฃผ์ ์ฑ๋ถ์ด๋ค. ์ด ๋๋ฌธ์, ์ธ์ฐ ์นผ์์ ์๋ฃ๋ถ์ผ (์๋ฅผ ๋ค๋ฉด ์น๊ณผ์ฉ ์ถฉ์ ์ , ๊ณจ ์ง์ง์ฒด, ๊ทธ๋ฆฌ๊ณ ๊ณจ ์๋ฉํธ ๋ฑ)์ ์ ์ฉํ๊ธฐ ์ํด ์ฐ๊ตฌ๋์ด ์๋ค. ํนํ, ์ธ์ฐ ์นผ์์ ํ ์ข
๋ฅ์ธ ์์ฐํ์ธํ์ (HA, Ca10(PO4)6(OH)2)์ ๋์ ์์ฒด ์ ํฉ์ฑ, ์์ฒด ํ์ฑ, ๊ณจ ์ ๋์ฑ, ๊ณจ ์ ๋์ฑ, ๋ฌด๋
์ฑ, ๊ทธ๋ฆฌ๊ณ ๋ฎ์ ๋ฉด์ญ์ฑ ๋ฑ๊ณผ ๊ฐ์ ์ ์ฉํ ํน์ฑ์ ๊ฐ์ง๊ณ ์๋ ๊ฒ์ผ๋ก ๋ณด๊ณ ๋์๋ค. ๋ํ, ๋ผ์ง๋ผ, ์๋ผ, ์์ ๋น๋, ์์ ๋ผ ๋ฑ์ ์์ฒด ํ๊ธฐ๋ฌผ์ด ๋ฎ์ ๊ฐ๊ฒฉ์ผ๋ก ๋๋์ HA ์์ฐ์ ์ํ ์๋ฃ๋ก ์ฌ์ฉ๋ ์ ์๋ค๋ ๊ฒ์ด ๋๋ฆฌ ์๋ ค์ ธ ์๋ค. ๊ทธ๋ฌ๋ ๋ง๋ผ๋ ๊ทธ ์์ฐ์ ์ฆ๊ฐ์ ๋ฎ์ ๊ฐ๊ฒฉ, ๊ทธ๋ฆฌ๊ณ ๊ตฌ์ ์ญ์ผ๋ก๋ถํฐ ์์ ๋ก์์๋ ๋ถ๊ตฌํ๊ณ , ๊ฑฐ์ ์ฒ์ฐ ์์ฐํ์ธํ์์ ์์ฐ์ ์ํ ์๋ฌผ์์์ผ๋ก ์ฌ์ฉ๋์ง ์์์ผ๋ฉฐ ์ ์กฐ ๋ฐฉ๋ฒ ๋ํ ๊ตฌ์ฑ๋์ง ์์๋ค.
์ด ์ฐ๊ตฌ๋ ๋ง๋ผ๋ฅผ ์ฌ์ฉํ ์๋ก์ด ์ฒ์ฐ ์ธ์ฐ ์นผ์ ์์ฒด๋ฌผ์ง์ ์์ฐ์ ์ํ ์ต์ ํ๋ ๋ฐฉ๋ฒ์ ์ ์ํ๊ธฐ ์ํด ์ํ๋์๋ค. ์ด๊ธฐ ์์ฐ๊ณต์ ์ ์ด์ ์ ๋ผ์ง๋ผ๋ฅผ ๋์์ผ๋ก ํ ๊ณต์ (48์๊ฐ ๋์ ๊ณผ์ฐํ์์์์์ ์ ์ฒ๋ฆฌ ํ 16์๊ฐ ๋์ 1200 โ์์ ์๊ฒฐ)์ ์ฐธ๊ณ ๋ก ํ์๋ค. ๋ง๋ผ์์ ์ ๋ํ ์ธ์ฐ ์นผ์ ์์ฒด๋ฌผ์ง์ ํน์ฑ์ ์๋ก ๋ค๋ฅธ ๋ผ์ ์กฐ์ง ์ ํ (์ฆ, ๊ฒฝ๊ณจ๊ณผ ํด๋ฉด๊ณจ), ๋ผ ์ ํ (์ฆ, ์ฒ์ถ์ ์ฌ๊ณจ), ์ ์ฒ๋ฆฌ ๋ฐฉ๋ฒ (๋์, ๊ณผ์ฐํ์์์, ์จ์), ์๊ฒฐ ์๊ฐ, ์๊ฒฐ ์จ๋์ ๋ฐ๋ผ ์กฐ์ฌํ์๋ค. ๋ํ, ๋ ๋์ ์๋ฃ ๋ถ์ผ๋ก์ ์ ์ฉ์ ์ํด ์ ์๋ ์ต์ข
์ฐ๋ฌผ์ ๋ถ๋งํ์ผ๋ก ์ ์๋์ด์ผ ํ์ผ๋ฉฐ, ์ด๋ฅผ ์ํด ์ต์ ๋ถ์ (๋ณผ ๋ฐ) ๋ฐ ๊ฑด์ ๋ถ์ (์นผ๋ ๋ถ์๊ธฐ)์ ๊ธฐ๋ฐํ์ฌ ๋ถ์ ๋ฐฉ๋ฒ์ ๋น๊ตํ์๋ค. ์์ฒด์ฌ๋ฃ๋ก์จ ๊ฐ๊ณต๋ ๋ง๋ผ ๊ฐ๋ฃจ์ ์ ์ฌ๋ ฅ์ ํ๊ฐํ๊ธฐ ์ํด, ์์ฒด์ธ ์ธํฌ ์ํ์ด ํฉ์ฑ๋ HA ๋ฐ ์์ฉํ๋ ์๋ผ ์ฌ๋ฃ (Bio-Oss)์ ํจ๊ป MC3T3-E1 ์ธํฌ๋ฅผ ์ฌ์ฉํ์ฌ ์ํ ๋์๋ค. ์๋ํ ๋ง๋ผ ๋ถ๋ง์ ํน์ฑ์ ์ด์ค๋ ๋ถ์๋ฒ (TGA, X- ์ ํ์ ๋ถ์๋ฒ (XRD), ํธ๋ฆฌ์ ๋ณํ ์ ์ธ์ ๋ถ๊ด๋ฒ (FT -IR), X ์ ํ๊ด ๋ถ์๋ฒ (XRF), ๊ทธ๋ฆฌ๊ณ ์ ๊ณ ๋ฐฉ์ถ ์ฃผ์ฌ ์ ์ ํ๋ฏธ๊ฒฝ ๊ฒ์ฌ๋ฒ (FE-SEM)์ผ๋ก ์กฐ์ฌ๋์๋ค. ๊ทธ ๊ฒฐ๊ณผ ๊ณจ์กฐ์ง๊ณผ ๊ณจ์ ์ ํ์ ๋ฐ๋ฅธ ์ฐจ์ด๋ ๋ํ๋์ง ์์๋ค. ๋ํ, ์ ์ฒ๋ฆฌ ๋ฐฉ๋ฒ์ ๋ฐ๋ฅธ ์ํฅ๋ ์์์ง๋ง ์ฒ๋ฆฌ ์๊ฐ์ ๊ณ ๋ คํ๋ฉด, ์จ์ ๋ฐฉ๋ฒ์ด ์ข์๋ค. ์๊ฒฐ ์๊ฐ์ 12 ์๊ฐ์ผ๋ก ์ถฉ๋ถํ์ผ๋ฉฐ, 600โ์ ์๊ฒฐ๋ ๋ง๋ผ๊ฐ HA์ ๊ฐ์ฅ ๊ฐ๊น์ด XRD ํผํฌ๋ฅผ ๋ณด์ฌ์ฃผ์์ง๋ง, 1100โ ์์ ์๊ฒฐํ ๋ง๋ผ์ 1300โ ์์ ์๊ฒฐํ ๋ง๋ผ๊ฐ ๊ฐ๊ฐ ์ธํฌ ๋
์ฑ๊ณผ ์ฆ์์์ ๊ฐ์ฅ ์ข์ ๊ฒฐ๊ณผ๋ฅผ ๋ณด์ฌ์ฃผ์๋ค.
์ด ์ฐ๊ตฌ๋ ๋ง๋ผ๊ฐ ์ฒ์ฐ ์ธ์ฐ ์นผ์ ์์ฒด์์ฌ๋ก์ ์ ๋งํ ์์์ด ๋ ์ ์์ผ๋ฉฐ ๋ง ๋ผ ๋ถ๋ง์ ๊ณจ ์กฐ์ง๊ณตํ์ ์ํ ์์ฒด์์ฌ๋ก์ ํฐ ์ ์ฌ๋ ฅ์ ๊ฐ์ง๊ณ ์์์ ๋ณด์ฌ์ฃผ์๋ค.Abstract ----------------------------------------------------------------------------------------------- i
Contents -------------------------------------------------------------------------------------------- โ
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List of Figures ------------------------------------------------------------------------------------- โ
ถ
List of Tables -------------------------------------------------------------------------------------- โ
ธ
List of Terms and Abbreviations ---------------------------------------------------------------- โ
น
1. Introduction --------------------------------------------------------------------------------------- 1
2. Objectives ----------------------------------------------------------------------------------------- 4
3. Literature Review -------------------------------------------------------------------------------- 5
3.1. Calcium phosphate as bone replacement materials ---------------------------------------- 5
3.2. Hydroxyapatite --------------------------------------------------------------------------------- 5
3.3. Advantages of natural HA -------------------------------------------------------------------- 6
3.4. Sintered bone as a source of natural HA ---------------------------------------------------- 8
3.5. Characterization and production process of natural HA --------------------------------- 11
4. Materials and Methods ------------------------------------------------------------------------- 13
4.1. Preparation of horse bone powders --------------------------------------------------------- 13
4.2. Characteristics according to the types of osseous tissue and bones -------------------- 13
4.3. Characteristics according to the pretreatment methods ---------------------------------- 14
4.4. Characteristics according to the sintering conditions ------------------------------------ 14
4.5. Characterization and physiochemical analysis -------------------------------------------- 15
iv
4.5.1. Thermal gravimetric analysis (TGA) ---------------------------------------------------- 15
4.5.2. Fourier transform infrared spectroscopy (FT-IR) and X-ray diffraction analysis (XRD) ----------------------------------------------------------------------------------------------- 15
4.5.3. X-ray fluorescence spectrometry (XRF) ------------------------------------------------ 16
4.5.4. Morphologic observation ------------------------------------------------------------------ 16
4.6. Fine powder production ---------------------------------------------------------------------- 16
4.7. In vitro test ------------------------------------------------------------------------------------- 17
4.7.1. Cell culture ---------------------------------------------------------------------------------- 17
4.7.2. Cell toxicity test ---------------------------------------------------------------------------- 17
4.7.3. Cell proliferation test ----------------------------------------------------------------------- 18
4.8. Statistical data analysis ----------------------------------------------------------------------- 19
5. Results and Discussion ------------------------------------------------------------------------- 20
5.1. Characteristics according to the types of osseous tissue and bone --------------------- 20
5.1.1. FT-IR ----------------------------------------------------------------------------------------- 20
5.1.2. XRD ------------------------------------------------------------------------------------------ 21
5.1.3. XRF ------------------------------------------------------------------------------------------ 23
5.2. Characteristics according to the pretreatment methods ---------------------------------- 27
5.2.1. FT-IR ----------------------------------------------------------------------------------------- 27
5.2.2. XRD ------------------------------------------------------------------------------------------ 27
5.2.3. XRF ------------------------------------------------------------------------------------------ 28
5.3. Sintering conditions -------------------------------------------------------------------------- 31
5.3.1. Sintering time selection -------------------------------------------------------------------- 31
v
5.3.2. Characteristics according to the sintering temperature -------------------------------- 32
5.4. Grinding methods ----------------------------------------------------------------------------- 41
5.5. In vitro test ------------------------------------------------------------------------------------- 44
5.5.1. Cell toxicity test ---------------------------------------------------------------------------- 44
5.5.2. Cell proliferation test ----------------------------------------------------------------------- 46
6. Conclusions -------------------------------------------------------------------------------------- 50
7. References --------------------------------------------------------------------------------------- 54
Abstract (Korean) ---------------------------------------------------------------------------------- 57Maste