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相关概念视频

Mass Spectrometry: Isotope Effect01:13

Mass Spectrometry: Isotope Effect

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Most elements exist in nature as a mixture of isotopes. The isotopes differ in weight due to their respective number of neutrons. The molecular weight of a molecule is different depending on the specific isotope of its elements involved. As a result, the mass spectrum of the molecule exhibits peaks from the same fragment at multiple positions. The positions of these mass signals depend on the mass differences between isotopes. Furthermore, the intensity of these signals is dependent on the...
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Isotopes and Radioisotopes01:28

Isotopes and Radioisotopes

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In the early 1900s, English chemist Frederick Soddy realized that an element could have atoms with different masses that were chemically indistinguishable. These different types are called isotopes — atoms of the same element that differ in mass. Isotopes differ in mass because they have different numbers of neutrons but are chemically identical because they have the same number of protons. Soddy was awarded the Nobel Prize in Chemistry in 1921 for this discovery.
An isotope containing...
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Isotopes01:12

Isotopes

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Elements have a set number of protons that determines their atomic number (Z). For example, all atoms with eight protons are oxygen; however, the number of neutrons can vary for atoms of the same element. The sum of the number of protons and the number of neutrons is the mass number (A). Atoms with the same atomic number but different mass numbers are called isotopes. Elements can have multiple isotopes, for example, carbon-12, carbon-13, and carbon-14.
An element's atomic mass, or weight,...
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Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences01:20

Inductively Coupled Plasma-Mass Spectrometry (ICP-MS): Interferences

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Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and...
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Non-equilibrium in the Cell01:16

Non-equilibrium in the Cell

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An important concept in studying metabolism and energy is that of chemical equilibrium. Most chemical reactions are reversible. They can proceed in both directions, releasing energy into their environment in one direction, and absorbing it from the environment in the other direction. The same is true for the chemical reactions involved in cell metabolism, such as the breaking down and building up of proteins into and from individual amino acids, respectively. Reactants within a closed system...
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Atomic Emission Spectroscopy: Lab01:29

Atomic Emission Spectroscopy: Lab

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AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
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相关实验视频

Updated: Feb 23, 2026

Author Spotlight: Development of an Automated Camera-Based System for Real-Time Blast Overpressure Monitoring and TBI Risk Assessment in Military Training
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从人工智能生成的内容中审计未经授权的培训数据,使用信息同位素.

Tao Qi1,2, Jinhua Yin2, Dongqi Cai3

  • 1State Key Laboratory of Networking and Switching Technology, School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China.

Nature communications
|February 21, 2026
PubMed
概括
此摘要是机器生成的。

研究人员开发了"信息同位素",以在黑盒模型中追踪AI训练数据. 该方法精确检测数据使用情况,保护人工智能系统中的知识产权和隐私.

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Sampling and Pretreatment of Tooth Enamel Carbonate for Stable Carbon and Oxygen Isotope Analysis
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科学领域:

  • 人工智能的人工智能
  • 数据科学数据科学数据科学
  • 计算机科学 计算机科学

背景情况:

  • 人工智能的快速增长依赖于大量的人类数据,引发了对知识产权和隐私侵犯的担忧.
  • 由于隐藏培训和推断过程的黑子系统,审计AI滥用是很困难的.

研究的目的:

  • 在不透明的AI系统中引入一种用于追踪训练数据的新方法.
  • 为在人工智能模型中检测和证明数据利用提供一个框架.

主要方法:

  • 在化学同位素追踪的启发下,该研究提出了"信息同位素"来标记和跟踪数据.
  • 一个框架选择性地标记目标数据元素,并检测它们在AI模型输出中的传播.
  • 该方法允许对数据使用进行黑子审计.

主要成果:

  • 在6个数据集中对13个AI模型进行的实验显示,训练与非训练数据的区分精度高 (高达99%).
  • 结果显示强大的统计学意义 (p < 0.01),大约有4000个字的证据.
  • 该方法提供了数据利用的具体证据.

结论:

  • 信息同位素追踪框架为保护人工智能数据权利提供了切实可行的解决方案.
  • 有一个开源工具可用于支持这种数据追踪方法的实施.
  • 这项工作解决了人工智能透明度和数据治理方面的关键挑战.