37 research outputs found

    SI-STM Study on Novel Fe Based Superconductor Ca0.9La0.1FeAs2

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    ν•™μœ„λ…Όλ¬Έ (박사) -- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : μžμ—°κ³Όν•™λŒ€ν•™ λ¬Όλ¦¬Β·μ²œλ¬Έν•™λΆ€(물리학전곡), 2020. 8. μ΄μ§„ν˜Έ.After the discovery of the first Fe-based superconductor in 2006, several different types of Fe-based superconductors are synthesized and theoretical and experimental efforts were devoted to understand the mechanism of superconductivity. Ca0.9La0.1FeAs2 was first synthesized in 2013 and attracted attentions of many theoretical researchers due to its unique As zigzag chain layer which can induce a topological superconductor. To elucidate the existence of topologically non-trivial property in this material, spectroscopic study is urgent. While Angle-Resolved PhotoEmission Spectroscopy (ARPES) results on Ca0.9La0.1FeAs2 were reported, there is no Scanning Tunneling Microscope (STM) result so far. In this thesis, the first STM result on Ca0.9La0.1FeAs2 will be discussed in detail. All four types of different terminating layer were identified by analyzing topographic images, differential conductance maps, as well as spectral features. Each terminating layer exhibits a distinctive spectroscopic character. A nematic feature was observed on FeAs layer which has dxz and dyz orbital character. Ca layer above As zigzag chain layer shows an inhomogeneity in gap distribution and conductance map revealed an influence from La dopants on the underlying Ca layer. Cross-correlation analysis seems to suggest that the local superconductivity is disturbed by La dopants. Ca terminating layer above FeAs layer contained many crevices through which we could directly access the FeAs layer revealing a clear gap with coherence peaks. On As zigzag chain layer, our Fourier analysis revealed a Dirac-cone dispersion which is compatible with the previous ARPES results. Remarkably, many zero bias conductance peaks were observed on As zigzag chain layer especially only on top of the crevices of underlying Ca layer. The existence of zero bias conductance peak on As chain layer might provide a clue to verifying if Ca0.9La0.1FeAs2 is topologically non-trivial superconductor. We hope our study opens a gate to a new direction in the study of this fascinating material.졜초의 μ²  기반 μ΄ˆμ „λ„μ²΄κ°€ 2006년에 발견된 μ΄ν›„λ‘œ λ‹€λ₯Έ νƒ€μž…μ˜ μ²  기반 μ΄ˆμ „λ„μ²΄κ°€ μ—¬λŸΏ λ°œκ²¬λ˜μ—ˆμœΌλ©°, μ΄ˆμ „λ„ λ©”μ»€λ‹ˆμ¦˜μ„ μ΄ν•΄ν•˜κΈ° μœ„ν•΄ 그것듀에 λŒ€ν•œ λ§Žμ€ 이둠적 계산 및 μ‹€ν—˜μ΄ μ§„ν–‰λ˜μ—ˆλ‹€. Ca0.9La0.1FeAs2 λŠ” 2013년에 발견된 물질둜 κ³ μœ ν•œ As μ§€κ·Έμž¬κ·Έ μΈ΅κ³Ό μœ„μƒ μ΄ˆμ „λ„μ²΄μ˜ κ°€λŠ₯μ„± λ•Œλ¬Έμ— μ‹€ν—˜μ  이둠적인 관심을 λŒμ—ˆλ‹€. κ·ΈλŸΌμ—λ„ λΆˆκ΅¬ν•˜κ³  아직 μ£Όμ‚¬ν˜• 터널링 ν˜„λ―Έκ²½ (STM)을 μ΄μš©ν•œ κ²°κ³ΌλŠ” λ³΄κ³ λ˜μ§€ μ•Šμ•˜λ‹€. 이 논문에선 Ca0.9La0.1FeAs2 μ‹œλ£Œμ— λŒ€ν•œ 졜초의 STM κ²°κ³Όλ₯Ό λ³΄μ˜€λ‹€. κ°€λŠ₯ν•œ 4개의 ν‘œλ©΄λ“€μ΄ λͺ¨λ‘ κ΄€μΈ‘λ˜μ—ˆμœΌλ©° 각각은 μ§€ν˜• 이미지, μ „λ„μœ¨ 맡, μ „λ„μœ¨ μŠ€νŽ™νŠΈλŸΌμ„ μ΄μš©ν•˜μ—¬ ν™•μΈν•˜κ³  각 ν‘œλ©΄μ˜ κ³ μœ ν•œ μ„±μ§ˆλ“€μ— λŒ€ν•΄ μ—°κ΅¬ν•˜μ˜€λ‹€. FeAs 측에선 λ„€λ§ˆν‹±ν•œ λͺ¨μŠ΅μ΄ κ΄€μΈ‘λ˜μ—ˆμœΌλ©° 그것듀이 dxz, dyz ꢀ도 μƒνƒœ μ„±μ§ˆμ„ κ°–κ³  μžˆμŒμ„ λ³΄μ˜€λ‹€. As μ§€κ·Έμž¬κ·Έ 체인 μΈ΅ μœ„μ— μžˆλŠ” 칼슘 λ ˆμ΄μ–΄λŠ” λΉ„κ· μ§ˆν•œ 갭의 뢄포λ₯Ό λ³΄μ˜€μœΌλ©°, μ „λ„μœ¨ 맡은 μ•„λž˜μ˜ Ca 측에 μžˆλŠ” La λ„νŽ€νŠΈλ“€μ„ λ³΄μ—¬μ£Όμ—ˆλ‹€. λ˜ν•œ 상관관계 뢄석을 톡해 La λ„νŽ€νŠΈκ°€ κ΅­μ†Œμ μœΌλ‘œ μ΄ˆμ „λ„ ν˜„μƒμ„ μ•½ν™”μ‹œν‚€λŠ” 것을 ν™•μΈν•˜μ˜€λ‹€. FeAs μΈ΅ μœ„μ— μžˆλŠ” Ca 측은 ν‘œλ©΄μ— μžˆλŠ” ν‹ˆμ„ ν†΅ν•˜μ—¬ ν‘œλ©΄ μž¬κ΅¬μ„±μ΄ μΌμ–΄λ‚˜μ§€ μ•Šμ€ FeAs 측을 직접 κ΄€μΈ‘ν•  수 μžˆλŠ” κ°€λŠ₯성을 λ³΄μ—¬μ£Όμ—ˆλ‹€. As μ§€κ·Έμž¬κ·Έ 체인 μΈ΅μ—μ„œλŠ” 푸리에 λ³€ν™˜μ„ μ΄μš©ν•˜μ—¬ Dirac 콘 뢄산을 λ³΄μ˜€λŠ”λ° 그것은 이 측의 κ³ μœ ν•œ μ„±μ§ˆμ΄λ‹€. μ œλ‘œλ°”μ΄μ–΄μŠ€ μ „λ„μœ¨ λ΄‰μš°λ¦¬κ°€ FeAs μΈ΅ μœ„μ— μžˆλŠ” Ca 측의 ν‹ˆ μœ„μ— μžˆλŠ” As μ§€κ·Έμž¬κ·Έ 체인 μΈ΅ μ—μ„œ 관츑이 λ˜μ—ˆλ‹€. μœ„μƒ μ ˆμ—°μ²΄μ— μ΄ˆμ „λ„ κ·Όμ ‘ νš¨κ³Όκ°€ μœ„μƒμ ˆμ—°μ²΄μ— 적용 된 κ°€λŠ₯μ„± 외에 λ‹€λ₯Έ μ•„λŠ” κ°€λŠ₯성듀은 제거 λ˜μ—ˆλ‹€. 제둜 λ°”μ΄μ–΄μŠ€ μ „λ„μœ¨ λ΄‰μš°λ¦¬λŠ” 이 μ‹œλ£Œμ—μ„œ μ–΄λ– ν•œ μ‹€ν—˜μ„ 더 ν•΄μ•Όν•˜λŠ”μ§€ λ°©ν–₯을 μ œμ‹œν•œλ‹€.Chapter 1. Introduction on Scanning Tunneling Microscope 1 1.1 Tunneling current and differential conductance 1 1.2 Topographic image 4 1.3 I-V spectrum, differential conductance spectrum and Spectroscopic Imaging Scanning Tunneling Microscopy 5 1.4 Work function measurement and work function map 6 1.5 Systems and equipment in Seoul National University. 6 1.5.1 Electronics 6 1.5.2 The cryostat and the Dewar 8 1.5.3 Sample cleaving stage 8 1.5.4 STM haed with tip treatment stage 10 1.5.5 Ultra low vibration system 12 Chapter 2. Introduction of Superconductivity and Ca0.9La0.1FeAs2 15 2.1 Brief history of superconductor 15 2.2 Fe-Based superconductors 17 2.3 Ca0.9La0.1FeAs2 21 Chapter 3. Nematic feautres on FeAs layer 25 3.1 Identification of FeAs layer 25 3.2. Nematic features observed on FeAs layer. 29 3.3 Summary 34 Chapter 4. Inhomogenous gap distribution and underlying La dopants on Ca/La layer abofe As chain layer 35 4.1 Identification of Ca/La layer above As chain layer 36 4.2 Inhomogeneous gap distribution of Ca-1 layer. 39 4.3. La dopants on underlying layer 43 4.4 Summary 47 Chapter 5. Superconductivity through crevices of Ca/La layer above FeAs layer 49 Chapter 6. Dirac cone dispersion and zero bias conductance peak on As chain layer 55 6.1 Identification of As zigzag chain layer. 56 6.2. Observation of Dirac cone like dispersion 57 6.3. Observation of zero bias conductance peaks 61 6.4. Summary 69 Chapter 7. Conclusion 71 Bibliography 73 Abstract in Korean 77Docto

    거리기반 후보ꡰ μ„ μ •κ³Ό 심측신경망을 κ²°ν•©ν•œ λ‹€λͺ©μ  νžˆμŠ€ν† λ¦¬λ§€μΉ­ 연ꡬ

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    ν•™μœ„λ…Όλ¬Έ (박사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› κ³΅κ³ΌλŒ€ν•™ μ—λ„ˆμ§€μ‹œμŠ€ν…œκ³΅ν•™λΆ€, 2017. 8. κ°•μ£Όλͺ….이 μ—°κ΅¬μ—μ„œλŠ” 심측신경망과 거리기반 후보ꡰ 선정법을 κ²°ν•©ν•˜μ—¬ λ‹€λͺ©μ  νžˆμŠ€ν† λ¦¬λ§€μΉ­μ„ μˆ˜ν–‰ν•˜κ³  신뒰도 λ†’κ²Œ μ €λ₯˜μΈ΅μ˜ 생산좔이λ₯Ό μ˜ˆμΈ‘ν•˜λŠ” 기법을 κ°œλ°œν•˜μ˜€λ‹€. 기쑴의 μ•™μƒλΈ”κΈ°λ°˜μ˜ νžˆμŠ€ν† λ¦¬λ§€μΉ­ 기법은 μž…λ ₯μžλ£Œλ“€μ΄ μ •κ·œλΆ„ν¬λ₯Ό λ”°λ₯΄μ§€ μ•ŠλŠ” κ²½μš°μ—λŠ” μ΅œμ ν•΄μ˜ 신뒰성이 λ–¨μ–΄μ§„λ‹€λŠ” ν•œκ³„μ μ΄ μžˆλ‹€. λ˜ν•œ, λ‹€λͺ©μ  μ΅œμ ν™” μ•Œκ³ λ¦¬μ¦˜μ€ λͺ©μ ν•¨μˆ˜κ°€ λ§Žμ•„μ§€λ©΄, λͺ¨λ“  λͺ©μ ν•¨μˆ˜λ₯Ό λ§Œμ‘±μ‹œν‚€λŠ” μ΅œμ ν•΄κ°€ μ œλŒ€λ‘œ μˆ˜λ ΄ν•˜μ§€ λͺ»ν•˜λŠ” 문제점이 μžˆλ‹€. 이 μ—°κ΅¬μ—μ„œ μ œμ•ˆν•œ 기법은 λ‹€λͺ©μ  μ΅œμ ν™” μ•Œκ³ λ¦¬μ¦˜μ˜ λΉ„μˆ˜λ ΄μ„±κ³Ό μ•™μƒλΈ”κΈ°λ°˜κΈ°λ²•μ˜ ν•œκ³„μ μ„ κ·Ήλ³΅ν•˜μ˜€λ‹€. μ €λ₯˜μΈ΅μ˜ 생산좔이λ₯Ό μž…λ ₯κ°’, μ €λ₯˜μΈ΅μ˜ 물성을 좜λ ₯κ°’μœΌλ‘œ ν•œ μ—­μ‚°λͺ¨λΈ 신경망을 κ΅¬μ„±ν•˜κ³ , 거리기반 ꡰ집화λ₯Ό 톡해 μ‹€μ œ μƒμ‚°μžλ£Œμ™€ μœ μ‚¬ν•œ 좔이λ₯Ό λ³΄μ΄λŠ” 후보 μ €λ₯˜μΈ΅μ„ μƒˆλ‘œμš΄ ν•™μŠ΅μœΌλ‘œ μΈκ³΅μ‹ κ²½λ§μ˜ μ§€λ„νš¨κ³Όλ₯Ό κ°œμ„ ν•˜μ˜€λ‹€. λ˜ν•œ, 심측신경망 기법인 μ˜€ν† μΈμ½”λ”λ₯Ό μ΄μš©ν•˜μ—¬ ν•™μŠ΅μžλ£Œλ₯Ό λ‚΄μž¬ν™”ν•¨μœΌλ‘œμ¨ μ‹ κ²½λ§μ˜ ν•™μŠ΅μ†λ„μ™€ ν•™μŠ΅μ„±λŠ₯을 ν–₯μƒμ‹œμΌ°λ‹€. μ œμ•ˆν•œ κΈ°λ²•μ˜ 방법둠을 κ²€μ¦ν•˜κΈ° μœ„ν•˜μ—¬ λͺ©μ ν•¨μˆ˜κ°€ κ°€μŠ€ μƒμ‚°λŸ‰ ν•˜λ‚˜μ΄λ©°, μ§€μ§ˆν•™μ  λ³΅μž‘μ„±μ΄ 거의 μ—†λŠ” κ· μ§ˆν•œ 셰일저λ₯˜μΈ΅μ—μ„œ νžˆμŠ€ν† λ¦¬λ§€μΉ­μ„ μˆ˜ν–‰ν•˜μ˜€λ‹€. μ œμ•ˆν•œ 거리기반 후보ꡰ μ„ μ • 기법을 ν†΅ν•˜μ—¬ μƒˆλ‘œμš΄ ν•™μŠ΅μ§‘λ‹¨μ„ μ„ μ •ν•¨μœΌλ‘œμ¨ 신경망 κΈ°λ²•μ˜ ν•™μŠ΅μ„±λŠ₯이 κ°œμ„ λ¨μ„ ν™•μΈν•˜μ˜€λ‹€. μ €λ₯˜μΈ΅μ˜ 암상뢄포가 μƒμ‚°λŸ‰μ— 큰 영ν–₯을 μ£Όλ©°, μ •κ·œλΆ„ν¬λ₯Ό λ”°λ₯΄μ§€ μ•ŠλŠ” 채널저λ₯˜μΈ΅μ—μ„œ 암상뢄포λ₯Ό μΆ”μ •ν•˜μ˜€λ‹€. μ•™μƒλΈ”μΉΌλ§Œν•„ν„°λŠ” 기본가정인 μ •κ·œλΆ„ν¬λ₯Ό λ§Œμ‘±μ‹œν‚€μ§€ μ•ŠλŠ” 채널저λ₯˜μΈ΅μ—μ„œ νžˆμŠ€ν† λ¦¬λ§€μΉ­μ— μ‹€νŒ¨ν•œ λ°˜λ©΄μ—, μ œμ•ˆν•œ 기법은 암상뢄포λ₯Ό μ‹ λ’°μ„± 있게 μΆ”μ •ν•˜μ˜€λ‹€. ν•„λ“œ μ μš©μ„±μ„ ν‰κ°€ν•˜κΈ° μœ„ν•˜μ—¬ 뢈균질 ν•„λ“œμ—μ„œ 였일 μƒμ‚°λŸ‰, μ •μ €μ••λ ₯, κ°€μŠ€-μ˜€μΌλΉ„ λ“± 총 18개의 λͺ©μ ν•¨μˆ˜μ— λŒ€ν•œ λ‹€λͺ©μ  νžˆμŠ€ν† λ¦¬λ§€μΉ­μ„ μˆ˜ν–‰ν•˜μ˜€λ‹€. λ‹€λͺ©μ  μ΅œμ ν™” μ•Œκ³ λ¦¬μ¦˜μ€ λͺ©μ ν•¨μˆ˜κ°€ λ§Žμ•„ μ΅œμ ν•΄κ°€ μˆ˜λ ΄ν•˜μ§€ λͺ»ν•˜λŠ” λ¬Έμ œκ°€ λ°œμƒν•œ λ°˜λ©΄μ—, μ œμ•ˆν•œ 기법은 μ—­μ‚°λͺ¨λΈ 신경망을 ν†΅ν•˜μ—¬ λͺ©μ ν•¨μˆ˜ 의쑴적 문제λ₯Ό κ·Ήλ³΅ν•˜μ—¬ μ •ν™•ν•œ 예츑치λ₯Ό μ‚°μΆœν•˜μ˜€λ‹€. κΈ°μ‘΄ κΈ°λ²•κ³Όμ˜ 비ꡐλ₯Ό ν†΅ν•˜μ—¬ μ œμ•ˆν•œ 기법이 거리기반 후보ꡰ 선정을 톡해 μ‹¬μΈ΅μ‹ κ²½λ§μ˜ ν•™μŠ΅μ„±λŠ₯을 κ°œμ„ ν•¨μœΌλ‘œμ¨ μ‹ λ’°μ„± μžˆλŠ” μ΅œμ ν•΄λ₯Ό λ„μΆœν•¨μ„ ν™•μΈν•˜μ˜€λ‹€. μ œμ•ˆν•œ 기법은 λ‹€μˆ˜μ˜ 생산정이 μ‘΄μž¬ν•˜λŠ” μ €λ₯˜μΈ΅μ—μ„œλ„ 신뒰도 높은 μƒμ‚°λŸ‰ 예츑이 κ°€λŠ₯ν•˜λ―€λ‘œ μ €λ₯˜μΈ΅ κ°œλ°œμ„ μœ„ν•œ μ˜μ‚¬κ²°μ •μ˜ λ„κ΅¬λ‘œμ„œ ν™œμš©λ  수 μžˆλ‹€.1. μ„œλ‘  1 2. 이둠적 λ°°κ²½ 7 2.1 거리기반 ꡰ집화 7 2.2 심측신경망 12 3. 연ꡬ 방법 17 3.1 심측신경망 ꡬ성 20 3.2 거리기반 후보ꡰ을 ν†΅ν•œ ν•™μŠ΅μ„±λŠ₯ κ°œμ„  26 4. κ°œλ°œκΈ°λ²•μ˜ 검증 및 비ꡐ뢄석 방법 30 4.1 λ‹€λͺ©μ  νžˆμŠ€ν† λ¦¬λ§€μΉ­ μ„±λŠ₯뢄석 31 4.2 ν˜„μž₯ μ μš©μ„± 평가 77 5. κ²°λ‘  98 μ°Έκ³ λ¬Έν—Œ 101 Appendices 108 Abstract 118Docto

    Analysis of drought transcriptome and functional study of CaLEA2, a water stress responsive gene, in hot pepper (capsicum annuum L.)

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