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Related Concept Videos

Precipitation Processes01:12

Precipitation Processes

The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Noncompartmental Analysis: Mean Residence Time01:05

Noncompartmental Analysis: Mean Residence Time

According to statistical moment theory, mean residence time (MRT) is an important measure in pharmacokinetics. MRT can be defined as the expected mean of a probability density function distribution. It provides valuable insights into drug disposition in the body.
After the administration of a drug through intravenous bolus injection, the drug molecules are distributed throughout the body and remain there for varying periods. The MRT represents the average time these drug molecules stay in the...
Precipitation Gravimetry01:03

Precipitation Gravimetry

Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
Isochoric and Isobaric Processes01:21

Isochoric and Isobaric Processes

A thermodynamic process that occurs at constant volume is called an isochoric process. According to the first law of thermodynamics, heat supplied or removed from the system is partially utilized to perform work and change the internal energy of the system. However, in an isochoric process, the volume remains constant. Hence, the work done by the system is zero. Therefore, the exchange of heat changes the internal energy of the system only. 
Suppose 1000 g of water is heated from 40 degrees...
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...

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Related Experiment Video

Updated: Jun 25, 2026

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

A Spatiotemporal Decomposition Framework for Temporal Persistence and Regional Transport in PM2.5 Variability.

Zhengyi Cui1, Aodong Mei1, Yingjie Liu2

  • 1Department of Environmental and Occupational Health Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, Texas 77030, United States.

Environmental Science & Technology
|June 24, 2026
PubMed
Summary

This study introduces a novel spatiotemporal model to better predict fine particulate matter (PM2.5) air quality. The new method improves accuracy by separating temporal and spatial factors, crucial for public health.

Keywords:
PM2.5 forecastingPM2.5 variabilitymonitoring heterogeneityregional transportspatiotemporal modeling

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Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy
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Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

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Related Experiment Videos

Last Updated: Jun 25, 2026

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy
08:17

Probing Structural and Dynamic Properties of Trafficking Subcellular Nanostructures by Spatiotemporal Fluctuation Spectroscopy

Published on: August 16, 2021

Area of Science:

  • Environmental Science
  • Data Science
  • Air Quality Modeling

Background:

  • Fine particulate matter (PM2.5) significantly impacts environmental health in the U.S.
  • Existing models struggle to differentiate local PM2.5 persistence from regional transport effects.
  • Accurate spatiotemporal prediction of PM2.5 is essential for public health and environmental policy.

Purpose of the Study:

  • To develop an advanced spatiotemporal model for PM2.5 prediction.
  • To explicitly separate temporal dynamics from spatial predictors in PM2.5 modeling.
  • To improve the characterization of short-term PM2.5 variability.

Main Methods:

  • Integration of a bidirectional long short-term memory (BiLSTM) network for temporal dependencies.
  • Utilization of Kolmogorov-Arnold Networks (KAN) with splines for nonlinear spatial gradients.
  • Application of a case study in Texas to evaluate model performance.

Main Results:

  • Significant reductions in RMSE (39.6%) and MAE (41.9%) compared to conventional LSTM.
  • A notable increase in R-squared (16.7%) indicating improved predictive power.
  • High accuracy (precision 0.93, recall 0.96) in county-scale next-day exceedance assessment.

Conclusions:

  • The proposed model effectively separates temporal persistence from spatial transport influences for PM2.5.
  • This approach offers superior characterization of short-term PM2.5 variations.
  • The framework is adaptable for regions with varying air quality monitoring coverage.