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相关概念视频

Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

2.0K
Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
2.0K
Electrodeposition01:08

Electrodeposition

709
Electrodeposition is a technique used to separate an analyte from interferents by electrochemical processes. Here, the analyte is a metal ion that can be deposited on an electrode immersed in the sample solution. The electrochemical setup consists of an anode and a cathode. When an electric current is applied to the setup, oxidation occurs at the anode. At the cathode, which consists of a large metal surface, metal ions undergo reduction and deposit onto the surface.
Electrodeposition can...
709
Extraction: Advanced Methods00:56

Extraction: Advanced Methods

526
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
526
Qualitative Analysis03:46

Qualitative Analysis

22.6K
For solutions containing mixtures of different cations, the identity of each cation can be determined by qualitative analysis. This technique involves a series of selective precipitations with different chemical reagents, each reaction producing a characteristic precipitate for a specific group of cations. Metal ions within a group are further separated by varying the pH, heating the mixture to redissolve a precipitate, or adding other reagents to form complex ions.
For instance, group IV...
22.6K
Biasing of Metal-Semiconductor Junctions01:27

Biasing of Metal-Semiconductor Junctions

331
Biasing metal-semiconductor junctions involves applying a voltage across the junction. Specifically, the metal is connected to a voltage source, while the semiconductor is grounded. This technique is essential for controlling the direction and magnitude of current flow in electronic devices, including diodes, transistors, and photovoltaic cells.
In Schottky junctions, where the semiconductor is n-type, applying a positive voltage to the metal relative to the semiconductor reduces its Fermi...
331
Precipitation of Ions03:11

Precipitation of Ions

28.1K
Predicting Precipitation
The equation that describes the equilibrium between solid calcium carbonate and its solvated ions is:
28.1K

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3D Depth Profile Reconstruction of Segregated Impurities Using Secondary Ion Mass Spectrometry
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3D Depth Profile Reconstruction of Segregated Impurities Using Secondary Ion Mass Spectrometry

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使用机器学习方法预测金属/氧化物接口的溶液分离

Yizhou Lu1, Blas Pedro Uberuaga2, Samrat Choudhury1

  • 1Department of Mechanical Engineering, University of Mississippi, University, MS 38677, USA.

Molecules (Basel, Switzerland)
|August 28, 2025
PubMed
概括

这项研究将密度函数理论 (DFT) 与机器学习 (ML) 结合起来,预测金属/氧化物界面上的溶液分离. 机器学习方法显著降低了计算成本,同时保持了预测分离行为的高准确性.

科学领域:

  • 材料科学
  • 计算材料科学
  • 表面科学

背景情况:

  • 金属/氧化物接口的原子结构和化学结构决定了材料的特性.
  • 由于原子数量很大,使用 DFT 来研究具有不合适位移的半连贯接口是计算密集的.

研究的目的:

  • 为了探索Fe/Y2O3接口的溶液分离行为,核反应堆覆盖的模型.
  • 开发和验证机器学习 (ML) 方法与DFT计算相结合,用于预测溶液分离.
  • 确定影响溶液分离的关键因素并降低计算成本.

主要方法:

  • 使用密度函数理论 (DFT) 来计算分离能量 (例如.
  • 使用DFT衍生ESeg数据开发和训练机器学习 (ML) 模型.
  • 在金属/氧化物界面影响溶液分离的化学和几何因素.

主要成果:

  • 确定了影响溶液分离的关键化学和几何因素及其对ESeg的竞争效应.
  • 在使用ML模型预测特定Fe/Y2O3接口的溶液分离时取得了高准确性.
  • 证明了ML模型在不同的Fe/Y2O3接口定向下预测分离的能力,与DFT相比,计算成本降低了45倍以上.

结论:

关键词:
密度函数理论机器学习金属/氧化物接口溶解物分离行为

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  • 结合DFT-ML方法可以准确地预测金属/氧化物界面上的溶液分离.
  • 机器学习模型显著降低了研究复杂接口的计算成本.
  • 这种方法为了解和设计核等应用材料提供了强大的工具.