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Precipitation Processes

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...

Precipitation and Co-precipitation

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...

Precipitation Gravimetry

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...

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...

Types of Coprecipitation

Types of Coprecipitation

Coprecipitation is the contamination of a precipitate by otherwise soluble species and occurs via different processes. In colloidal precipitates, coprecipitation occurs via surface adsorption. For instance, barium sulfate has a primary layer of adsorbed barium ions and a secondary layer of nitrate counterions. This results in contamination of the precipitate by barium nitrate.
Sometimes, ions in a crystal lattice can undergo isomorphous replacement by inclusions of similar charge and size. For...

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Related Experiment Video

Updated: May 23, 2025

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Published on: December 12, 2013

End-to-end data-driven weather prediction.

Anna Allen¹, Stratis Markou², Will Tebbutt^3,4

¹Department of Computer Science and Technology, University of Cambridge, Cambridge, UK. av555@cam.ac.uk.

|March 20, 2025

Summary

This summary is machine-generated.

Aardvark Weather is a novel, end-to-end machine learning system that replaces traditional numerical weather prediction (NWP) systems. This data-driven approach achieves accurate global and local weather forecasts, outperforming existing methods.

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Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Surface Renewal: An Advanced Micrometeorological Method for Measuring and Processing Field-Scale Energy Flux Density Data

Published on: December 12, 2013

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Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface

Published on: June 8, 2015

Area of Science:

Artificial Intelligence
Atmospheric Sciences
Computational Science

Background:

Accurate weather prediction is vital for numerous sectors and public safety.
Machine learning (ML) has enhanced numerical weather prediction (NWP) but often still relies on NWP components.
Existing ML models face limitations in speed and accuracy due to dependence on NWP.

Purpose of the Study:

To develop and evaluate an end-to-end, data-driven machine learning system for weather prediction.
To demonstrate that a single ML model can replace the entire NWP pipeline.
To assess the performance of this system against established NWP baselines.

Main Methods:

Developed Aardvark Weather, an end-to-end data-driven system ingesting observational data.
Generated global gridded forecasts and local station forecasts using the ML model.
Compared Aardvark Weather's forecasts against an operational NWP baseline and a state-of-the-art system.

Main Results:

Aardvark Weather's global forecasts surpassed the operational NWP baseline for multiple variables and lead times.
Local station forecasts demonstrated skill up to ten days, rivaling post-processed NWP and human-assisted systems.
End-to-end tuning further enhanced the accuracy of local forecasts.

Conclusions:

Skilful weather forecasting is achievable without reliance on NWP at deployment.
Data-driven, end-to-end ML models offer significant speed and accuracy benefits.
Aardvark Weather represents a new generation of weather prediction models, reducing costs and enabling customization.