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Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

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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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Precipitation Gravimetry01:03

Precipitation Gravimetry

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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...
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Precipitation Processes01:12

Precipitation Processes

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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...
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What is Weather?01:07

What is Weather?

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Overview
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Precipitation Titration: Overview01:26

Precipitation Titration: Overview

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Precipitation titration involves the reaction of a titrant and an analyte to generate an insoluble precipitate. While precipitation titration uses various precipitating agents, silver nitrate is the most common precipitating reagent; titrations involving Ag+ are called argentometric titrations. Usually, the endpoint in a precipitation titration can be detected by visual indicators.
A precipitation titration curve demonstrates the change in concentration of the titrant or analyte upon adding the...
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Precipitation Titration Curve: Analysis01:21

Precipitation Titration Curve: Analysis

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The precipitation titration curve demonstrates the change in concentration of one reactant with the volume of titrant added. During the titration of chloride ions with silver nitrate, the precipitation titration curve is divided into three regions: before, at, and after the equivalence point. Before the equivalence point, low redissolution of the sparingly soluble silver chloride precipitate gives a low silver ion concentration. However, in the second region, representing the equivalence point,...
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WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks.

Rajat Shinde1, Kumar Ankur2, Christopher E Phillips2

  • 1Earth System Science Center, The University of Alabama in Huntsville, Huntsville, AL, USA. rajat.shinde@uah.edu.

Scientific Data
|March 5, 2026
PubMed
Summary
This summary is machine-generated.

WxC-Bench is a new multi-modal dataset for developing artificial intelligence (AI) models in weather and climate research. It addresses the scarcity of curated, ML-ready datasets for diverse atmospheric scales and applications.

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Area of Science:

  • Meteorology and Climate Science
  • Artificial Intelligence
  • Data Science

Background:

  • High-quality, accessible machine learning (ML)-ready datasets are crucial for advancing AI in scientific applications like weather and climate analysis.
  • A significant gap exists in curated, pre-processed ML-ready datasets, hindering the development of novel deep learning models for atmospheric research.
  • Data modality variations across different spatial and temporal scales pose challenges for creating generalizable AI models.

Purpose of the Study:

  • Introduce WxC-Bench, a multi-modal dataset designed to facilitate the development of generalizable AI models for weather and climate research.
  • Support the creation of AI models capable of analyzing diverse atmospheric processes across various scales.
  • Provide a standardized resource for benchmarking and advancing AI applications in meteorology.

Main Methods:

  • Developed WxC-Bench, a multi-modal dataset encompassing data relevant to atmospheric processes from meso-β to synoptic scales.
  • Included diverse downstream use-cases such as turbulence detection, hurricane monitoring, weather analog identification, gravity wave parameterization, and natural language report generation.
  • Performed technical validation with baseline analyses to demonstrate dataset utility.

Main Results:

  • WxC-Bench offers a comprehensive, multi-modal dataset tailored for AI model development in weather and climate.
  • The dataset covers a wide range of atmospheric phenomena and scales, enabling research on diverse applications.
  • Baseline analyses confirm the dataset's suitability for evaluating and advancing AI models.

Conclusions:

  • WxC-Bench addresses the critical need for curated, ML-ready datasets in weather and climate AI research.
  • The dataset promotes the development of more generalizable and robust AI models for atmospheric science.
  • Publicly available code and dataset on Hugging Face encourage community engagement and further research.