相关实验视频

Updated: Jan 20, 2026

Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
11:18

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

Published on: June 1, 2015

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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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Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

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Author Spotlight: Designing Sustainable Nanomaterials for Advancing Synthesis and Element Mixing03:54

Author Spotlight: Designing Sustainable Nanomaterials for Advancing Synthesis and Element Mixing

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相关概念视频

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

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

A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning...
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Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

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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Aqueous Synthesis of Plasmonic Gold-Tin Alloy Nanoparticles03:54

Aqueous Synthesis of Plasmonic Gold-Tin Alloy Nanoparticles

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 Differentiation

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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Nanocrystalline Alloys and Nano-grain Size Stability06:52

Nanocrystalline Alloys and Nano-grain Size Stability

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Alloys with grain size less than 100 nm are known as nanocrystaline alloys. Due to their enhanced physical and mechanical properties, there is an ever-increasing demand to employ them in various industries such as semiconductor, biosensors and aerospace. 
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Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory07:59

Using Practice Testing, Public Speaking, and Source Monitoring to Examine the Influences of Learning Strategies and Stress on Episodic Memory

The present experiment combined three experimental procedures — a retrieval-practice learning manipulation, a list-discrimination task, and a stress-induction technique — to examine the influences of different learning strategies and acute stress on multiple measures of episodic...
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