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相关概念视频

Types of Fluids01:27

Types of Fluids

108
Fluids can be classified into Newtonian and non-Newtonian fluids based on their response to shear stress. Newtonian fluids have a linear relationship between shear stress and the shear strain rate, following Newton's law of viscosity. Their viscosity remains constant regardless of the shear rate, making their behavior predictable and easier to analyze. Common examples include water, air, oil, and gasoline.
In contrast, non-Newtonian fluids do not follow Newton's law of viscosity, and...
108
Factors Influencing Drug Absorption: Pharmaceutical Parameters01:28

Factors Influencing Drug Absorption: Pharmaceutical Parameters

101
Solid dosage forms such as tablets and capsules undergo rigorous manufacturing processes to ensure stability and effectiveness. Their dissolution and absorption properties are influenced significantly by the choice of excipients (inactive ingredients that serve various roles in the formulation), and the methodology applied during production. The manufacturing parameters, such as compression force and granulation techniques, significantly affect dissolution rates. Elevated compression forces...
101
Flow Table Test01:12

Flow Table Test

98
The flow table test is an established method used to assess the workability of concrete, particularly useful for evaluating highly flowable concrete mixes. This test employs an apparatus that consists of a wooden board topped with a steel plate, collectively weighing 35 pounds. The board is connected to a base via a hinge and measures 27.6 inches on each side.
Concrete is placed within a truncated cone mold that is 8 inches high with an 8-inch base diameter and a 5-inch top diameter. The...
98
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

7.8K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Accelerating Fluids01:17

Accelerating Fluids

972
When a fluid is in constant acceleration, the pressure and buoyant force equations are modified. Suppose a beaker is placed in an elevator accelerating upward with a constant acceleration, a. In the beaker, assume there is a thin cylinder of height h with an infinitesimal cross-sectional area, ΔS.
The motion of the liquid within this infinitesimal cylinder is considered to obtain the pressure difference. Three vertical forces act on this liquid:
972
Fluid Mosaic Model01:19

Fluid Mosaic Model

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Scientists identified the plasma membrane in the 1890s and its principal chemical components (lipids and proteins) by 1915. The model for plasma membrane structure, proposed in 1935 by Hugh Davson and James Danielli, was the first model to be widely accepted in the scientific community. The model was based on the plasma membrane's "railroad track" appearance in early electron micrographs. Davson and Danielli theorized that the plasma membrane's structure resembled a sandwich...
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Frugal Imaging Technique of Capillary Flow Through Three-Dimensional Polymeric Printing Powders
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使用机器学习预测粉末混合物从单个成分属性的流动性.

Anna Owasit1, Siddharth Tripathi1, Rajesh Davé1

  • 1Otto H. York Department of Chemical and Materials Engineering, New Jersey Institute of Technology, 138 Warren St, Newark, NJ, 07103, USA.

Pharmaceutical research
|April 17, 2025
PubMed
概括
此摘要是机器生成的。

机器学习模型准确地预测粉末混合物的流动性,识别干涂层参数作为改善制药配方和减少实验力度的关键因素.

关键词:
干燥涂层是一种干燥涂层.流动性 流动性 流动性机器学习 (ML) 是指机器学习.制药粉末混合物 制药粉末混合物粉末流量预测的预测

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科学领域:

  • 制药科学 制药科学
  • 材料科学 材料科学 材料科学
  • 计算化学计算化学

背景情况:

  • 预测粉末混合物的流动性对于高效的制药制造至关重要.
  • 当前的方法往往具有挑战性,资源密集,缺乏预测能力.
  • 需要先进的计算方法来优化混合特性.

研究的目的:

  • 开发机器学习 (ML) 模型,以预测粉末混合物在多个类别中的流动性.
  • 确定影响混合物流动性的关键特征.
  • 指导设计具有增强流动性质的制药配方.

主要方法:

  • 分析了410种混合物的数据集,使用各种活性药物成分和辅助剂.
  • 雇员监督的ML模型 (随机森林,XGBoost) 用于预测流动性类别.
  • 利用颗粒大小,形态,表面特性和干涂层参数作为预测特征.

主要成果:

  • 实现了流动性制度的高预测准确度 (混合物高达87%).
  • 干涂层参数成为最有影响力的特征,其次是颗粒大小和形态.
  • 机器学习模型成功地识别了流量模式之间的过渡,有助于混合优化.

结论:

  • 集成的ML模型有效地预测粉末混合物的流动性,并阐明特征-属性关系.
  • 这种方法促进了混合物的合理设计,并改善了流动特性.
  • 减少了制药过程和产品开发中的实验力度.