使用激光诱导分解光谱仪器手持仪器仪表在黑质中确定的度
Elisa Galli1, Mattia Massa1, Alessandra Zanoletti1
1INSTM & Chemistry for Technologies Laboratory, Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze 38, 25123, Brescia, Italy.
Scientific reports
|May 20, 2025
概括
本研究介绍了一种使用人工神经网络 (ANN) 和激光诱导分解光谱 (LIBS) 的快速而准确的方法,用于确定回收的离子电池中黑质中的含量.
科学领域:
- 材料科学 材料科学 材料科学
- 分析化学 分析化学
- 电化学 电化学 电化学
背景情况:
- 是一种战略性材料,对储能和先进材料至关重要.
- 回收离子电池产生黑质量,这是一个复杂的混合物,需要精确的元素分析.
- 从黑质中有效地回收对于可持续性和资源管理至关重要.
研究的目的:
- 开发一种快速准确的方法来确定黑质中的含量.
- 通过提供必要的组成数据来优化提取过程.
- 为了验证人工神经网络 (ANN) 与激光诱导分解光谱 (LIBS) 结合用于黑质量分析的有效性.
主要方法:
- 使用商用手持式激光诱导分解光谱仪 (LIBS) 仪器进行光谱采集.
- 使用人工神经网络 (ANN) 算法进行自动化数据处理.
- 分析了黑质量等价物质和真实黑质量样本的光谱.
主要成果:
- 通过使用最小的一组光谱特征成功确定了含量.
- 在各种矩阵的黑质量样本中实现了精确的量化.
- 经验证的ANN-LIBS方法与其他元素分析技术的参考值相比.
结论:
- ANN和LIBS的组合提供了一个可行和可靠的方法,用于在黑质中准确地确定.
- 这种方法可以在最小的样本处理下进行高效的分析.
- 这些发现支持改善离子电池的回收过程.
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