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Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task11:18

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This paper aims to describe the techniques involved in the collection and synchronization of the multiple dimensions (behavioral, affective and cognitive) of learners’ engagement during a task.
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Here, the synthesis of gold (Au) seeds is described using the Turkevich method. These seeds are then used to synthesize gold-tin alloy (Au-Sn) nanoparticles with tunable plasmonic...
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A Live-cell Image-Based Machine Learning Strategy to Monitor Pluripotent Stem Cell Differentiation11:38

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Available pluripotent stem cell (PSC)-to-functional cell differentiation systems are currently impeded by problems of severe line-to-line and batch-to-batch variability. Here, using cardiac differentiation as the main example, we present a protocol to intelligently monitor and modulate the process of PSC differentiation based on image-based machine learning.
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相关实验视频

Updated: Jan 20, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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在更高的维度中学习:合金电催化剂发现的战略

Vladislav A Mints1,2, Jack K Pedersen3, Gustav K H Wiberg1

  • 1Department for Chemistry, Biochemistry and Pharmaceutical Sciences, University of Bern, Freiestrasse 3 3012 Bern Switzerland matthias.arenz@unibe.ch.

EES catalysis
|January 19, 2026
PubMed
概括

本研究引入了一种自上而下的方法,用于发现更好的能量转换电催化剂. 通过从复杂的合金开始并去除元素,研究人员有效地确定了最佳材料,减少了实验力度.

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科学领域:

  • 材料科学 材料科学 材料科学
  • 电化学 电化学 电化学
  • 催化剂是一种催化剂.

背景情况:

  • 传统的催化剂发现采用了自下而上的方法,这是耗时的.
  • 复杂的合金提供了改善催化性能的潜力,但对全面研究具有挑战性.

研究的目的:

  • 展示一种新的自上而下的战略,用于发现能量转换电催化剂.
  • 开发一种数据驱动的方法,减少催化剂优化所需的实验数量.

主要方法:

  • 采用了自上而下的方法,从含有多个元素的复杂高合金 (HEA) 开始.
  • 通过将低性能成分从合金中去除,进行了元素下降选择.
  • 基于200种合金组合物的实验数据创建了一个机器学习的活动模型.

主要成果:

  • 该研究成功地将自上而下的方法应用于Au-Ir-Os-Pd-Pt-Re-Rh-Ru HEA系统的氧降解反应 (ORR).
  • 机器学习模型证明了HEA空间内较不复杂的合金活性的预测能力.
  • 与单独研究所有组成合金相比,这种方法显著降低了实验负担.

结论:

  • "自上而下的方法"是发现改进的电催化剂的有效策略.
  • 这种方法有助于将实验数据与理论模拟进行比较,用于催化剂活动建模.
  • 开发的机器学习模型可以预测由复杂的HEAs衍生出的简单合金的性能.