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相关概念视频

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

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Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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Elastic Collisions: Introduction01:00

Elastic Collisions: Introduction

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An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
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Types of Collisions - II01:19

Types of Collisions - II

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When two or more objects collide with each other, they can stick together to form one single composite object (after collision). The total mass of the object after the collision is the sum of the masses of the original objects, and it moves with a velocity dictated by the conservation of momentum. Although the system's total momentum remains constant, the kinetic energy decreases, and thus such a collision is an inelastic collision. Most of the collisions between objects in daily life are...
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Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Updated: Sep 9, 2025

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基于大型语言模型的复合碰撞截面预测

Zeyu Zhu1, Chengyi Xie2, Shaojie Lin1

  • 1Department of Electronic Science, National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China.

Analytical chemistry
|September 1, 2025
PubMed
概括
此摘要是机器生成的。

通过使用化学大语言模型 (CLLM),HyperCCS提高了离子移动性质谱的复合注释精度. 这种新的框架改善了碰撞截面 (CCS) 预测,优于现有的多种分子方法.

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科学领域:

  • 计算化学
  • 分析化学
  • 生物信息学

背景情况:

  • 碰撞截面 (CCS) 对于离子移动性质谱 (IM-MS) 中精确的化合物识别至关重要.
  • 目前的计算CCS预测方法在有限的数据和不充分的多式联网功能处理方面扎,导致性能不足.
  • 准确的CCS预测对于构建IM-MS应用的大规模化合物数据库至关重要.

研究的目的:

  • 开发一个新的计算框架,HyperCCS,用于准确的碰撞截面 (CCS) 预测.
  • 利用化学大型语言模型 (CLLM) 来捕获复杂的分子信息.
  • 有效地整合多式联网功能,以提高IM-MS的预测性能.

主要方法:

  • 在广泛的SMILES序列上预先训练的化学大型语言模型 (CLLM) 进行了微调.
  • 开发了一种跨模态特征融合模块,将CLLM衍生特征与其他异质数据集成.
  • 在基准数据集 (METLIN-CCS,AllCCS2) 和内部实验数据上评估HyperCCS.

主要成果:

  • 超CCS在各种分子质量,附加型和离子模式中展示了强大的CCS预测,超过了现有的方法.
  • 该框架准确地分辨出异构体,并将预测推断到实验数据上的高质量分析物.
  • SHAP分析和废弃研究证实了CLLM特征和聚变机制的显著贡献.

结论:

  • 在IM-MS的计算CCS预测方面,HyperCCS提供了显著的进步.
  • 集成CLLM和跨模式融合有效地解决了先前预测模型的局限性.
  • HyperCCS为代谢学和结构生物学研究提供了高吞吐量,可适应的计算工具.