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相关概念视频

IR Spectroscopy: Molecular Vibration Overview01:24

IR Spectroscopy: Molecular Vibration Overview

2.5K
When Infrared (IR) radiation passes through a covalently bonded molecule, the bonds transition from lower to higher vibrational levels. The fundamental vibrational motions that result in infrared absorption can be classified as stretching or bending vibrations.
Stretching vibrations are vibrational motions that occur along the bond line, changing the bond length or distance between two bonded atoms. They are further distinguished as symmetric or asymmetric. In symmetric stretching, the...
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Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

468
A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
468
Mass Spectrometry: Complex Analysis01:21

Mass Spectrometry: Complex Analysis

832
Mass spectrometry is an important technique for the identification of pure compounds. However, it has some limitations for the analysis of complex mixtures, often due to excessive fragmentation making the spectrum too complicated to decipher. Mass spectrometry can be combined with suitable separation methods in sequence, forming hyphenated methods, which are useful in the analysis of complex mixtures.
GC–MS is a powerful hyphenated method commonly used in forensics and environmental...
832
Tandem Mass Spectrometry01:21

Tandem Mass Spectrometry

1.1K
Tandem mass spectrometry is a technique that uses multiple mass analyzers in series to obtain a higher selectivity and signal-to-noise ratio for the analyte. Instruments with multiple analyzers separated by an interaction cell enable secondary fragmentation and selected study of the fragment ions.
Secondary fragmentations occur in the interaction cell and can be induced by various factors. Fragmentation induced by collision with inert gases, such as N2, Ar, He, etc., is called collision-induced...
1.1K
Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

474
The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
474
Inductively Coupled Plasma Atomic Emission Spectroscopy: Principle01:19

Inductively Coupled Plasma Atomic Emission Spectroscopy: Principle

712
Inductively coupled plasma (ICP) is the most widely used plasma source in atomic emission spectroscopy (AES), also known as Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES). The ICP source, or torch, consists of three concentric quartz tubes with argon gas flowing through them. A spark from a Tesla coil initiates the ionization of argon, generating a high-temperature plasma.
The ions and electrons produced interact with the fluctuating magnetic field created by a water-cooled...
712

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相关实验视频

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Author Spotlight: Unveiling the Potential of VSFG Microscopy in Studying Mesoscopically Heterogeneous Self-Assembled Structures
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机器学习光谱学使用二阶段的,一般化组成部分贡献协议.

Jinming Fan1,2, Chao Qian1,2, Shaodong Zhou1,2

  • 1College of Chemical and Biological Engineering, Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, Zhejiang University, 310027 Hangzhou, P. R. China.

Research (Washington, D.C.)
|June 8, 2023
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概括
此摘要是机器生成的。

一个结合贝叶斯神经网络和群体贡献方法的新协议使用最小的数据准确地预测分子吸收光谱. 这种方法提高了单个分子和混合物的效率和准确性.

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科学领域:

  • 计算化学是一种计算化学.
  • 在光谱学中的机器学习应用.

背景情况:

  • 准确预测分子吸收光谱对于各种化学应用至关重要.
  • 现有的机器学习方法通常需要大型数据集来进行可靠的光谱预测.

研究的目的:

  • 开发一种高精度和高效的协议,用于预测分子吸收光谱.
  • 为了减少对光谱预测模型的数据要求.

主要方法:

  • 贝叶斯神经网络 (BNN) 与纠正组贡献 (CGC) 方法的整合.
  • 开发一种分子贡献 (MC) 方法来解释CGC的混合规则.
  • 使用小型训练数据集 (<100个样本) 进行初始模型训练.

主要成果:

  • 准确预测单个分子的最大波长,培训样本少于100个.
  • 在<500个样本的全紫外光谱预测中,平均平方误差达到了<2%.
  • 使用MC-CGC方法预测混合物的光谱的高精度.

结论:

  • 结合的CGC-MC-BNN协议在光谱预测准确性和效率方面取得了重大进展.
  • 这种以数据为导向的方法,与化学原理相结合,对更广泛的分子性质预测任务具有前景.