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Related Concept Videos

Extraction: Advanced Methods00:56

Extraction: Advanced Methods

529
Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
529
Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

2.0K
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...
2.0K
Gravimetry: Inorganic And Organic Precipitating Agents00:49

Gravimetry: Inorganic And Organic Precipitating Agents

1.6K
In gravimetry, the precipitant is chosen carefully to obtain a pure solid that can be easily filtered. Common inorganic precipitants can be used to determine several cations and anions. In some cases, the formation of the same precipitate can be used to determine the cation and the anion. For example, the reaction of barium and chromate ions to give barium chromate is used to determine both barium and chromate. However, precipitates such as hydroxides, oxalates, and metal ammonium phosphates...
1.6K
Atomic Emission Spectroscopy: Overview01:20

Atomic Emission Spectroscopy: Overview

2.5K
Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...
2.5K
Atomic Emission Spectroscopy: Lab01:29

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AES is a powerful analytical technique, especially effective when used with plasma sources, producing abundant spectra in characteristic emission lines. The Inductively Coupled Plasma (ICP), in particular, yields superior quantitative analytical data due to its high stability, low noise, low background, and minimal interferences under optimal experimental conditions. However, newer air-operated microwave sources are emerging as promising alternatives that could be more cost-effective than...
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Updated: Sep 12, 2025

Detection and Recovery of Palladium, Gold and Cobalt Metals from the Urban Mine Using Novel Sensors/Adsorbents Designated with Nanoscale Wagon-wheel-shaped Pores
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Explainable artificial intelligence for predicting rare earth elements leaching from secondary resources.

Quang Loc Nguyen1, Huy Nguyen Lai2, Hong T M Nguyen1

  • 1Climate Change Cluster, University of Technology Sydney, Ultimo, NSW 2007, Australia.

Journal of Hazardous Materials
|August 8, 2025
PubMed
Summary
This summary is machine-generated.

This study uses explainable artificial intelligence (AI) to optimize rare earth element (REE) extraction from secondary resources. The AI system predicts leaching efficiency and identifies key factors like silica concentration for improved recovery.

Keywords:
BioleachingElectronic wasteExplainable artificial intelligenceHydrometallurgyMine tailingRare earth elements

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Area of Science:

  • Materials Science
  • Chemical Engineering
  • Artificial Intelligence

Background:

  • Growing global demand for rare earth elements (REEs) necessitates sustainable extraction methods.
  • Secondary resources like e-waste and mine tailings offer a viable alternative to primary mining.
  • Optimizing leaching processes for REE recovery from these complex matrices is challenging.

Purpose of the Study:

  • To develop an explainable AI system for predicting REE leaching efficiency.
  • To identify critical factors influencing REE extraction from secondary resources.
  • To provide real-time recommendations for optimizing leaching conditions and enhancing recovery rates.

Main Methods:

  • An explainable AI model was trained on 572 experimental datasets from the Web of Science database.
  • The system predicts leaching efficiency and provides explanations for key influencing parameters.
  • The AI suggests condition adjustments to improve REE recovery.

Main Results:

  • Silica concentration was identified as the most critical factor affecting REE leaching efficiency.
  • REE classification (light vs. heavy) was the second most influential parameter.
  • Acid strength (pH), aluminum content, and temperature showed moderate impacts on leaching performance (R²=0.81).

Conclusions:

  • Explainable AI effectively bridges the gap between empirical data and process innovation in REE extraction.
  • The developed AI framework enhances decision-making and process efficiency for complex extraction systems.
  • This methodology has broad applicability beyond REEs to other resource-intensive industries.