• Title/Summary/Keyword: Energy Materials

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Water Oxidation Mechanism for 3d Transition Metal Oxide Catalysts under Neutral Condition

  • Seo, Hongmin;Cho, Kang Hee;Ha, Heonjin;Park, Sunghak;Hong, Jung Sug;Jin, Kyoungsuk;Nam, Ki Tae
    • Journal of the Korean Ceramic Society
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    • v.54 no.1
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    • pp.1-8
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    • 2017
  • Electrochemical water splitting to produce hydrogen energy is regarded as a promising energy conversion process for its environmentally friendly nature. To improve cell efficiency, the development of efficient water oxidation catalysts is essentially demanded. For several decades, 3d transition metal oxides have been intensively investigated for their high activity, good durability and low-cost. This review covers i) recent progress on 3d transition metal oxide electrocatalysts and ii) the reaction mechanism of oxygen evolving catalysis, specifically focused on the proposed pathways for the O-O bond formation step.

Development of Passive Solar Water Heater 1. Selective absorbers (자연형 온수 급탕시스템 개발)

  • Lee, K.D.;Auh, P.C.M.;You, C.K.
    • Solar Energy
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    • v.4 no.2
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    • pp.29-36
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    • 1984
  • This paper reviews the current status of R&D work on selective absorber materials. For the efficient utilization of solar energy, various types of selective absorber materials are being used for solar hot water heaters. Many selective absorbers which have been proposed and designed up to data are classified according to the absorption mechanisms. Temperature-time cycle method is often recommended for the measurement of solar absorptance. In addition, conversion efficiency of the solar collector with selective surface is compared with one with black paint surface.

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Band Gap Energy Engineering of Electron Emission Layer of ac-PDPs

  • Yoon, Sang-Hoon;Kim, Yong-Seog
    • 한국정보디스플레이학회:학술대회논문집
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    • 2009.10a
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    • pp.262-264
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    • 2009
  • Ternary oxides with controlled band gap energy and reduced reactivity against moisture and carbon dioxide gas were designed and studied as a potential material for protective layer of ac-PDPs. The results showed a significant reduction in firing voltage and improved environmental stability.

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Radiation effect on the corrosion of disposal canister materials

  • Minsoo Lee;Junhyuk Jang;Jin Seop Kim
    • Nuclear Engineering and Technology
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    • v.56 no.3
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    • pp.941-948
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    • 2024
  • The effects of radiation on the corrosion of canister materials were investigated for the reliable disposal of high-level radioactive waste. The test specimens were gamma-irradiated at a very low dose rate of approximately 0.1 Gy/h for six and twelve months. The copper and cast iron species were less corroded when irradiated. It is hypothesized that gamma rays suppress the formation of lower-enthalpy species like metal oxides and activate reductive reactions. In contrast, it was difficult to evaluate the effect of radiation on the corrosion of titanium and stainless steel.

Recent progress on Performance Improvements of Thermoelectric Materials using Atomic Layer Deposition (원자층 증착법을 이용한 열전 소재 연구 동향)

  • Lee, Seunghyeok;Park, Tae Joo;Kim, Seong Keun
    • Journal of Powder Materials
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    • v.29 no.1
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    • pp.56-62
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    • 2022
  • Atomic layer deposition (ALD) is a promising technology for the uniform deposition of thin films. ALD is based on a self-limiting mechanism, which can effectively deposit thin films on the surfaces of powders of various sizes. Numerous studies are underway to improve the performance of thermoelectric materials by forming core-shell structures in which various materials are deposited on the powder surface using ALD. Thermoelectric materials are especially relevant as clean energy storage materials due to their ability to interconvert between thermal and electrical energy by the Seebeck and Peltier effects. Herein, we introduce a surface and interface modification strategy based on ALD to control the performance of thermoelectric materials. We also discuss the properties of the interface between various deposition materials and thermoelectric materials.

Structural Study of Oxygen Vacancy in CaO Stabilized Cubic-HfO2 Using Density Functional Theory (Density Functional Theory를 이용한 CaO 안정화 Cubic-HfO2의 산소 공공 구조 연구)

  • Kim, Jong-Hoon;Kim, Dae-Hee;Lee, Byeong-Eon;Hwang, Jin-Ha;Kim, Yeong-Cheol
    • Korean Journal of Materials Research
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    • v.18 no.12
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    • pp.673-677
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    • 2008
  • Calcia (CaO) stabilized cubic-$HfO_2$ is studied by density functional theory (DFT) with generalized gradient approximation (GGA). When a Ca atom is substituted for a Hf atom, an oxygen vacancy is produced to satisfy the charge neutrality. The lattice parameter of a $2{\times}2{\times}2$ cubic $HfO_2$ supercell then increases by $0.02\;{\AA}$. The oxygen atoms closest to the oxygen vacancy are attracted to the vacancy as the vacancy is positive compared to the oxygen ion. When the oxygen vacancy is located at the site closest to the Ca atom, the total energy of $HfO_2$ reaches its minimum. The energy barriers for the migration of the oxygen vacancy were calculated. The energy barriers between the first and the second nearest sites, the second and the third nearest sites, and the third and fourth nearest sites are 0.2, 0.5, and 0.24 eV, respectively. The oxygen vacancies at the third and fourth nearest sites relative to the Ca atom represent the oxygen vacancies in undoped $HfO_2$. Therefore, the energy barrier for oxygen migration in the $HfO_2$ gate dielectric is 0.24 eV, which can explain the origin of gate dielectric leakage.

Performance Prediction Model of Solid Oxide Fuel Cell Stack Using Deep Neural Network Technique (심층 신경망 기법을 이용한 고체 산화물 연료전지 스택의 성능 예측 모델)

  • LEE, JAEYOON;PINEDA, ISRAEL TORRES;GIAP, VAN-TIEN;LEE, DONGKEUN;KIM, YOUNG SANG;AHN, KOOK YOUNG;LEE, YOUNG DUK
    • Transactions of the Korean hydrogen and new energy society
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    • v.31 no.5
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    • pp.436-443
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    • 2020
  • The performance prediction model of a solid oxide fuel cell stack has been developed using deep neural network technique, one of the machine learning methods. The machine learning has been received much interest in various fields, including energy system mo- deling. Using machine learning technique can save time and cost requried in developing an energy system model being compared to the conventional method, that is a combination of a mathematical modeling and an experimental validation. Results reveal that the mean average percent error, root mean square error, and coefficient of determination (R2) range 1.7515, 0.1342, 0.8597, repectively, in maximum. To improve the predictability of the model, the pre-processing is effective and interpolative machine learning and application is more accurate than the extrapolative cases.