From ashes to answers: decoding acoustically agglomerated soot particle signatures

IF 2.1 4区材料科学 Q3 CHEMISTRY, MULTIDISCIPLINARY

Journal of Nanoparticle Research Pub Date : 2024-08-27 DOI:10.1007/s11051-024-06111-2

Yoon Ko, Yuchuan Li, Hamed Mozaffari, Jamie McAlister, Jae-Young Cho, Kerri Henriques, Aria Khalili, Arash Fellah Jahromi, Benjamin Jones, Olga Naboka, Brendan McCarrick, Zelda Zhao

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引用次数: 0

Abstract

This study investigated the possibility of extending the soot morphology analyses to acoustically agglomerated soot deposited on the surface of smoke alarms and of applying the validity of soot analysis for unique chemical signatures in the field of fire investigations. Through collecting soot samples, including agglomerated soot acquired from smoke alarms, this research presents a pioneering stride in soot morphology data analyses conducted by leveraging advanced deep learning methodologies. Preliminary outcomes underline that the proposed convolutional neural network model has the potential to decode intricate soot characteristics and to distinguish soot particle images between diverse fuel types and burning conditions. In particular, for the acoustically agglomerated soot collected by smoke alarms, it was also found possible to decode their intricate morphology by applying the proposed data-driven approach.

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从灰烬到答案：解码声学凝聚烟尘颗粒特征

本研究探讨了将烟尘形态分析扩展到沉积在烟雾报警器表面的声学烟尘团聚体的可能性，以及在火灾调查领域应用烟尘分析独特化学特征的有效性。通过收集烟尘样本，包括从烟雾报警器中获取的聚集烟尘，本研究利用先进的深度学习方法，在烟尘形态数据分析方面取得了开创性的进展。初步结果表明，所提出的卷积神经网络模型具有解码错综复杂的烟尘特征以及区分不同燃料类型和燃烧条件下的烟尘颗粒图像的潜力。特别是，对于烟雾报警器收集到的声学聚集烟尘，通过应用所提出的数据驱动方法，也可以解码其错综复杂的形态。

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来源期刊

Journal of Nanoparticle Research 工程技术-材料科学：综合

CiteScore

4.40

自引率

4.00%

发文量

198

审稿时长

3.9 months

期刊介绍： The objective of the Journal of Nanoparticle Research is to disseminate knowledge of the physical, chemical and biological phenomena and processes in structures that have at least one lengthscale ranging from molecular to approximately 100 nm (or submicron in some situations), and exhibit improved and novel properties that are a direct result of their small size. Nanoparticle research is a key component of nanoscience, nanoengineering and nanotechnology. The focus of the Journal is on the specific concepts, properties, phenomena, and processes related to particles, tubes, layers, macromolecules, clusters and other finite structures of the nanoscale size range. Synthesis, assembly, transport, reactivity, and stability of such structures are considered. Development of in-situ and ex-situ instrumentation for characterization of nanoparticles and their interfaces should be based on new principles for probing properties and phenomena not well understood at the nanometer scale. Modeling and simulation may include atom-based quantum mechanics; molecular dynamics; single-particle, multi-body and continuum based models; fractals; other methods suitable for modeling particle synthesis, assembling and interaction processes. Realization and application of systems, structures and devices with novel functions obtained via precursor nanoparticles is emphasized. Approaches may include gas-, liquid-, solid-, and vacuum-based processes, size reduction, chemical- and bio-self assembly. Contributions include utilization of nanoparticle systems for enhancing a phenomenon or process and particle assembling into hierarchical structures, as well as formulation and the administration of drugs. Synergistic approaches originating from different disciplines and technologies, and interaction between the research providers and users in this field, are encouraged.